Reviewerly/exHarmony huggingface dataset:
Regional Geochemical Characteristics of Lithium in the Mufushan Area, South China

Authors:
raw_author_name: Peiyu Li
raw_affiliation_string: School of Earth Sciences and Resources, China University of Geosciences, Beijing 100083, China
raw_author_name: Qingjie Gong
raw_affiliation_string: School of Earth Sciences and Resources, China University of Geosciences, Beijing 100083, China
raw_author_name: Shaoyu Chen
raw_affiliation_string: School of Earth Sciences and Resources, China University of Geosciences, Beijing 100083, China
raw_author_name: Jiankang Li
raw_affiliation_string: MNR Key Laboratory of Metallogeny and Mineral Resource Assessment, Institute of Mineral Resources, Chinese Academy of Geological Sciences, Beijing 100037, China
raw_author_name: Xuan Wu
raw_affiliation_string: Center for Development and Research, China Geological Survey, Beijing 100037, China
raw_author_name: Xiaolei Li
raw_affiliation_string: Center for Development and Research, China Geological Survey, Beijing 100037, China
raw_author_name: Xuefeng Wang
raw_affiliation_string: School of Gemology, China University of Geosciences, Beijing 100083, China
raw_author_name: Ningqiang Liu
raw_affiliation_string: School of Earth Sciences and Resources, China University of Geosciences, Beijing 100083, China

Abstract: With the explosive growth in demand for lithium (Li) resources, the Mufushan area has been a hotspot for Li deposit exploration in China in recent years. Geochemical maps and geochemical anomaly maps are basic maps in the geochemical exploration of mineral resources. A fixed-value method to contour a Li geochemical map is presented here, in which Li concentrations are divided into 19 levels on 18 fixed values, ranging from 5 μg/g (corresponding to the detection limit) to 1858 μg/g (corresponding to the cut-off grade of Li deposit in hard-rock type) and illustrated in six color tones corresponding to Li areas of low background, high background, low anomaly, high anomaly, mineralization in clay-type, and mineralization in hard-rock type. The geochemical map of Li in the Mufushan area using the new fixed-value method indicates that the study area belongs to the high background area, and the known Li deposits are located in the high anomaly areas. In addition, the geochemical anomaly map of the Mufushan area is drawn using the method of seven levels of classification, and indicates that the known Li deposits are all in the anomaly areas, with anomaly levels not lower than the second level. Furthermore, four other areas are recognized for Li resource potential based on the geochemical map and geochemical anomaly map in the Mufushan area.
Toxic Element Contamination Sources in the Surface and Groundwater of the Elbrus Region: Geochemistry and Health Risks

Authors:
raw_author_name: Qinghai Guo
raw_affiliation_string: State Key Laboratory of Biogeology and Environmental Geology & School of Environmental Studies, China University of Geosciences, Wuhan 430074, China

Abstract: The region surrounding the Elbrus volcano in the Great Caucasus is a popular tourist destination, home to native people and an area of the richest ore mineral resources. Based on the chemical composition of surface water, mineral, and groundwater in the Elbrus area, as well as statistical and thermodynamic calculations, waters with high concentrations of Mn, Cu, Zn, Mo, Ba, W, Pb, and As were identified, possible mineral phases that may lead to these metal concentrations in water were estimated, and risks to human health were assessed. Two main sources of surface water pollution have been identified: (1) water from tailings that are enriched due to oxidation of sulfide minerals is considered a source of As (181 µg/L) and Mo (2070 µg/L) pollution in the area of the Tyrnyauz W-Mo deposit; and (2) CO2-rich waters of deep horizons discharge on the earth surface with high concentrations of As (828 µg/L) and W (504 µg/L). It has been shown that compared to all toxic elements studied, only Mo and As have adverse non-carcinogenic effects on adult health. The estimated carcinogenic risk of As in the mine area was 1.7 × 10−3 and exceeded the generally accepted range of 10−4 to 10−6. In order to minimize the health risks, regular monitoring of water quality is required when mining activities on the Tyrnyauz W-Mo deposit are renewed.
Deciphering the Efficacy of No-Attention Architectures in Computed Tomography Image Classification: A Paradigm Shift

Authors:
raw_author_name: Salha M. Alzahrani
raw_affiliation_string: Department of Computer Science, College of Computers and Information Technology, Taif University, Taif 21944, Saudi Arabia

Abstract: The burgeoning domain of medical imaging has witnessed a paradigm shift with the integration of AI, particularly deep learning, enhancing diagnostic precision and expediting the analysis of Computed Tomography (CT) images. This study introduces an innovative Multilayer Perceptron-driven model, DiagnosticMLP, which sidesteps the computational intensity of attention-based mechanisms, favoring a no-attention architecture that leverages Fourier Transforms for global information capture and spatial gating units for local feature emphasis. This study’s methodology encompasses a sophisticated augmentation and patching strategy at the input level, followed by a series of MLP blocks designed to extract hierarchical features and spatial relationships, culminating in a global average pooling layer before classification. Evaluated against state-of-the-art MLP-based models including MLP-Mixer, FNet, gMLP, and ResMLP across diverse and extensive CT datasets, including abdominal, and chest scans, DiagnosticMLP demonstrated a remarkable ability to converge efficiently, with competitive accuracy, F1 scores, and AUC metrics. Notably, in datasets featuring kidney and abdomen disorders, the model showcased superior generalization capabilities, underpinned by its unique design that addresses the complexity inherent in CT imaging. The findings in terms of accuracy and precision-recall balance posit DiagnosticMLP as an exceptional outperforming alternative to attention-reliant models, paving the way for streamlined, efficient, and scalable AI tools in medical diagnostics, reinforcing the potential for AI-augmented precision medicine without the dependency on attention-based architectures.
The modal behaviour of a violin corpus

Authors:
raw_author_name: Özge Akar
raw_affiliation_string: Institute of Applied Mechanics, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany
raw_author_name: Kai Willner
raw_affiliation_string: Institute of Applied Mechanics, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany

Abstract: Violins incorporate numerous interfaces and anisotropic behaviour due to natural materials. Hence, the modelling of these instruments is extremely complex. The present work investigates the structural dynamics of a specific violin corpus (violin resonant body) with the aim of using numerical modelling to accurately understand the modal behaviour of the violin using a novel stepwise approach. In the first step, an inverse material parameter identification via experimental and numerical modal analysis of the raw woods is performed to obtain the material parameters of the individual woods. In the second step, an experimental modal analysis of the violin substructures is performed using 2D scanning laser Doppler vibrometry and building finite element models based on accurate 3D scans of the parts manufactured by a luthier. The experimentally obtained modal parameters of the individual parts are compared with the numerical results. In the third step, the whole violin corpus, i.e. the resonant body of the instrument, is assessed. The good agreement between the numerical and the experimental results demonstrates that the application of standard techniques of modal analysis on a very complex mechanical system, the violin body, is possible. Our methodology offers a novel approach to analysing individual components of bowed string instruments, facilitating a comprehensive vibrational analysis with implications for broader musical research, including luthier optimisation tools, digital reconstruction of historical violins, and advancements in instrument design for improved playability and tonal quality.
Causal Conditionals, Tendency Causal Claims and Statistical Relevance

Authors:
raw_author_name: Jan Sprenger
raw_affiliation_string: Center for Logic, Language and Cognition (LLC), Department of Philosophy and Education, University of Turin, Turin, Italy

Abstract: Abstract Indicative conditionals and tendency causal claims are closely related (e.g., Frosch and Byrne, 2012), but despite these connections, they are usually studied separately. A unifying framework could consist in their dependence on probabilistic factors such as high conditional probability and statistical relevance (e.g., Adams, 1975; Eells, 1991; Douven, 2008, 2015). This paper presents a comparative empirical study on differences between judgments on tendency causal claims and indicative conditionals, how these judgments are driven by probabilistic factors, and how these factors differ in their predictive power for both causal and conditional claims.
Invariant Feature Learning Based on Causal Inference from Heterogeneous Environments

Authors:
raw_author_name: Hang Su
raw_affiliation_string: School of Mathematics, Renmin University of China, Beijing 100872, China
raw_author_name: Wei Wang
raw_affiliation_string: School of Mathematics, Renmin University of China, Beijing 100872, China

Abstract: Causality has become a powerful tool for addressing the out-of-distribution (OOD) generalization problem, with the idea of invariant causal features across domains of interest. Most existing methods for learning invariant features are based on optimization, which typically fails to converge to the optimal solution. Therefore, obtaining the variables that cause the target outcome through a causal inference method is a more direct and effective method. This paper presents a new approach for invariant feature learning based on causal inference (IFCI). IFCI detects causal variables unaffected by the environment through the causal inference method. IFCI focuses on partial causal relationships to work efficiently even in the face of high-dimensional data. Our proposed causal inference method can accurately infer causal effects even when the treatment variable has more complex values. Our method can be viewed as a pretreatment of data to filter out variables whose distributions change between different environments, and it can then be combined with any learning method for classification and regression. The result of empirical studies shows that IFCI can detect and filter out environmental variables affected by the environment. After filtering out environmental variables, even a model with a simple structure and common loss function can have strong OOD generalization capability. Furthermore, we provide evidence to show that classifiers utilizing IFCI achieve higher accuracy in classification compared to existing OOD generalization algorithms.
Joint Distortion Restoration and Quality Feature Learning for No-Reference Image Quality Assessment

Authors:
raw_author_name: Jifan Yang
raw_affiliation_string: School of Computer science, Wuhan University, Wuhan, China
raw_author_name: Zhongyuan Wang
raw_affiliation_string: School of Computer science, Wuhan University, Wuhan, China
raw_author_name: Baojin Huang
raw_affiliation_string: School of Computer science, Wuhan University, Wuhan, China
raw_author_name: Jiaxin Ai
raw_affiliation_string: School of Computer science, Wuhan University, Wuhan, China
raw_author_name: Yuhong Yang
raw_affiliation_string: School of Computer science, Wuhan University, Wuhan, China
raw_author_name: Zixiang Xiong
raw_affiliation_string: Department of Electrical and Computer Engineering, Texas A&amp;M University, College Station, USA

Abstract: No-reference image quality assessment (NR-IQA) methods inspired by the free energy principle, improve the accuracy of image quality prediction by simulating the human brain’s repair process for distorted images. However, existing methods use separate optimisation schemes for distortion restoration and quality prediction, which undermines the accurate mapping of feature representations to quality scores. To address this issue, we propose a joint restoration and quality feature learning NR-IQA (RQFL-IQA) method to jointly tackle distortion image restoration and quality prediction within a unified framework. To accurately establish the quality reconstruction relationship between distorted and restored images, a hybrid loss function based on pixel-wise and structure-wise representations is used to improve the restoration capability of the image restoration network. The proposed RQFL-IQA exploits rich labels, including restored images and quality scores, to enable the model to learn more discriminative features and establish a more accurate mapping from feature representation to quality scores. In addition, to avoid the impact of poor restoration on quality prediction, we propose a module with a cleaning function to reweight the fusion of restored and primitive features to achieve more perceptual consistency in feature fusion. Experimental results on public IQA datasets show that the proposed RQFL-IQA is superior over existing methods.
The Complexity of LTL Rational Synthesis

Authors:
raw_author_name: Orna Kupferman
raw_affiliation_string: The Hebrew University, Jerusalem School of Computer Science and Engineering, Israel

Abstract: In rational synthesis , we automatically construct a reactive system that satisfies its specification in all rational environments, namely environments that have objectives and act to fulfill them. We complete the study of the complexity of LTL rational synthesis, when the objectives are given by formulas in Linear Temporal Logic. Our contribution is threefold. First, we tighten the known upper bounds for settings that were left open in earlier work. Second, our complexity analysis is parametric, and we describe tight upper and lower bounds in each of the problem parameters: the game graph, the objectives of the system components, and the objectives of the environment components. Third, we generalize the definition of rational synthesis by adding hostile players to the setting and by combining the cooperative and non-cooperative approaches studied in earlier work.
Emission Reduction Decisions in Blockchain-Enabled Low-Carbon Supply Chains under Different Power Structures

Authors:
raw_author_name: Liping Qin
raw_affiliation_string: Zhejiang College, Shanghai University of Finance and Economics, Jinhua 321013, China
raw_author_name: Qiang Hu
raw_affiliation_string: College of Business, Shanghai University of Finance and Economics, Shanghai 200433, China; Zhejiang College, Shanghai University of Finance and Economics, Jinhua 321013, China

Abstract: With the global climate problem becoming increasingly severe, governments have adopted policies to encourage enterprises to invest in low-carbon technologies. However, the opacity of the carbon emission reduction process leads to incomplete consumer trust in low-carbon products as well as higher supply chain transaction costs. Based on this, this paper constructs Stackelberg game models with and without blockchain under different power structures and compares the impact of these models on low-carbon emission reduction decisions. The results show that: (1) blockchain does not necessarily improve enterprise profits and can only help enterprises maintain optimal profits within a certain range when the carbon emission cost is low; (2) when consumers’ environmental awareness is high, the blockchain can incentivize manufacturers to enhance carbon emission reduction, and it has an obvious promotional effect on retailers’ profits; and (3) the profit gap between enterprises in the supply chain is larger under different power structures, and the implementation of blockchain can coordinate profit distribution and narrow the gap between enterprises. Compared with the manufacturer-dominated model, the emission reduction in products is maximized under the retailer-dominated model. Our study provides theoretical support for the government to regulate greenhouse gas emissions as well as for the optimization of enterprises’ decision-making supported by blockchain.
Real-Time Multi-Robot Mission Planning in Cluttered Environment

Authors:
raw_author_name: Zehui Lu
raw_affiliation_string: School of Aeronautics and Astronautics, Purdue University, West Lafayette, IN 47907, USA
raw_author_name: Tianyu Zhou
raw_affiliation_string: School of Aeronautics and Astronautics, Purdue University, West Lafayette, IN 47907, USA
raw_author_name: Shaoshuai Mou
raw_affiliation_string: School of Aeronautics and Astronautics, Purdue University, West Lafayette, IN 47907, USA

Abstract: Addressing a collision-aware multi-robot mission planning problem, which involves task allocation and path-finding, poses a significant difficulty due to the necessity for real-time computational efficiency, scalability, and the ability to manage both static and dynamic obstacles and tasks within a complex environment. This paper introduces a parallel real-time algorithm aimed at overcoming these challenges. The proposed algorithm employs an approximation-based partitioning mechanism to partition the entire unassigned task set into several subsets. This approach decomposes the original problem into a series of single-robot mission planning problems. To validate the effectiveness of the proposed method, both numerical and hardware experiments are conducted, involving dynamic obstacles and tasks. Additionally, comparisons in terms of optimality and scalability against an existing method are provided, showcasing its superior performance across both metrics. Furthermore, a computational burden analysis is conducted to demonstrate the consistency of our method with the observations derived from these comparisons. Finally, the optimality gap between the proposed method and the global optima in small-size problems is demonstrated.
Attribute Graph Embedding Based on Multi-Order Adjacency Views and Attention Mechanisms

Authors:
raw_author_name: Jinfang Sheng
raw_affiliation_string: School of Computer Science and Engineering, Central South University, Changsha 410083, China
raw_author_name: Bin Wang
raw_affiliation_string: School of Computer Science and Engineering, Central South University, Changsha 410083, China
raw_author_name: Yu Chen
raw_affiliation_string: School of Computer Science and Engineering, Central South University, Changsha 410083, China

Abstract: Graph embedding plays an important role in the analysis and study of typical non-Euclidean data, such as graphs. Graph embedding aims to transform complex graph structures into vector representations for further machine learning or data mining tasks. It helps capture relationships and similarities between nodes, providing better representations for various tasks on graphs. Different orders of neighbors have different impacts on the generation of node embedding vectors. Therefore, this paper proposes a multi-order adjacency view encoder to fuse the feature information of neighbors at different orders. We generate different node views for different orders of neighbor information, consider different orders of neighbor information through different views, and then use attention mechanisms to integrate node embeddings from different views. Finally, we evaluate the effectiveness of our model through downstream tasks on the graph. Experimental results demonstrate that our model achieves improvements in attributed graph clustering and link prediction tasks compared to existing methods, indicating that the generated embedding representations have higher expressiveness.
Differential Fault and Algebraic Equation Combined Analysis on PICO

Authors:
raw_author_name: Hongxin Zhang
raw_affiliation_string: Beijing Key Laboratory of Work Safety Intelligent Monitoring, Beijing University of Posts and Telecommunications, Beijing 100876, China; School of Electronic Engineering, Beijing University of Posts and Telecommunications, Beijing 100876, China
raw_author_name: Jun Xu
raw_affiliation_string: Beijing Institute of Spacecraft System Engineering, Beijing 100094, China
raw_author_name: Xing Fang
raw_affiliation_string: Beijing Institute of Computer Technology and Applications, Beijing 100039, China

Abstract: In modern information technology, research on block cipher security is imperative. Concerning the ultra lightweight block cipher PICO, there has been only one study focused on recovering its complete master key, with a large search space of 264, and no fault analysis yet. This paper proposes a new fault analysis approach, combining differential fault and algebraic equation techniques. It achieved the recovery of PICO’s entire master key with 40 faults in an average time of 0.57 h. S-box decomposition was utilized to optimize our approach, reducing the time by a remarkable 75.83% under the identical 40-fault condition. Furthermore, PICO’s complete master key could be recovered with 28 faults in an average time of 0.78 h, indicating a significant 237 reduction in its search space compared to the previous study. This marks the first fault analysis on PICO. Compared to conventional fault analysis methods DFA (differential fault analysis) and AFA (algebraic fault analysis), our approach outperforms in recovering PICO’s entire master key, highlighting the cruciality of key expansion complexity in block cipher security. Therefore, our approach could serve to recover master keys of block ciphers with comparably complicated key expansions, and production of more secure block ciphers could result.
DP-GCN: Node Classification by Connectivity and Local Topology Structure on Real-World Network

Authors:
raw_author_name: Zhe Chen
raw_affiliation_string: Innovation Lab, PayPal, Singapore, Singapore
raw_author_name: Aixin Sun
raw_affiliation_string: Nanyang Technological University, Singapore, Singapore

Abstract: Node classification is to predict the class label of a node by analyzing its properties and interactions in a network. We note that many existing solutions for graph-based node classification only consider node connectivity but not node’s local topology structure. However, nodes residing in different parts of a real-world network may share similar local topology structures. For example, local topology structures in a payment network may reveal sellers’ business roles ( e.g., supplier or retailer). To model both connectivity and local topology structure for better node classification performance, we present DP-GCN, a dual-path graph convolution network. DP-GCN consists of three main modules: (i) a C-GCN module to capture the connectivity relationships between nodes, (ii) a T-GCN module to capture the topology structure similarity among nodes, and (iii) a multi-head self-attention module to align both properties. We evaluate DP-GCN on seven benchmark datasets against diverse baselines to demonstrate its effectiveness. We also provide a case study of running DP-GCN on three large-scale payment networks from PayPal, a leading payment service provider, for risky seller detection. Experimental results show DP-GCN’s effectiveness and practicability in large-scale settings. PayPal’s internal testing also show DP-GCN’s effectiveness in defending real risks from transaction networks.