Miracle-dz/UniFAR-FTUs huggingface dataset:
Derivations and 2-local derivations on the mirror Heisenberg-Virasoro algebra

Using the first cohomology from the mirror Heisenberg-Virasoro algebra to the twisted Heisenberg algebra (as the mirror Heisenberg-Virasoro algebra-module), in this paper we determined the derivations on the mirror Heisenberg-Virasoro algebra. Based on this result, we proved that any 2-local derivation on the mirror Heisenberg-Virasoro algebra is a derivation.
A highly efficient algorithm for solving exclusive lasso problems

The exclusive lasso (also known as elitist lasso) regularizer has become popular recently due to its superior performance on intra-group feature selection. Its complex nature poses difficulties for the computation of high-dimensional machine learning models involving such a regularizer. In this paper, we propose a highly efficient dual Newton method based proximal point algorithm (PPDNA) for solving large-scale exclusive lasso models. As important ingredients, we systematically study the proximal mapping of the weighted exclusive lasso regularizer and the corresponding generalized Jacobian. These results also make popular first-order algorithms for solving exclusive lasso models more practical. Extensive numerical results are presented to demonstrate the superior performance of the PPDNA against other popular numerical algorithms for solving the exclusive lasso problems.
Interpretable Neural Embeddings with Sparse Self-Representation

Interpretability benefits the theoretical understanding of representations. Existing word embeddings are generally dense representations. Hence, the meaning of latent dimensions is difficult to interpret. This makes word embeddings like a black-box and prevents them from being human-readable and further manipulation. Many methods employ sparse representation to learn interpretable word embeddings for better interpretability. However, they also suffer from the unstable issue of grouped selection in $\ell1$ and online dictionary learning. Therefore, they tend to yield different results each time. To alleviate this challenge, we propose a novel method to associate data self-representation with a shallow neural network to learn expressive, interpretable word embeddings. In experiments, we report that the resulting word embeddings achieve comparable and even slightly better interpretability than baseline embeddings. Besides, we also evaluate that our approach performs competitively well on all downstream tasks and outperforms benchmark embeddings on a majority of them.
KLASIFIKASI BERBASIS GRAVITASI DATA DANPROBABILITAS POSTERIOR

Klasifikasi berbasis gravitasi data atau data gravitation-based classification (DGC) merupakan salah satu teknik klasifikasi baru yang menggunakan gravitasi data sebagai kriteria klasifikasi. Dalam DGC, sebuah obyek akan diklasifikasikan sebagai suatu kelas yang menghasilkan gravitasi data terbesar pada obyek tersebut. Metode DGC memberikan hasil yang buruk jika data pelatihan yang digunakan mengalami permasalahan ketidakseimbangan kelas. Hal ini dikarenakan, jika terdapat sebuah kelas pada data pelatihan yang massanya sangat besar, maka sebuah obyek akan cenderung diklasifikasikan sebagai anggota kelas tersebut karena gravitasi datanya sangat besar, dan demikian pula sebaliknya. Dalam penelitian ini diajukan metode DGC yang dimodifikasi, yaitu metode klasifikasi berbasis gravitasi data dan probabilitas posterior (DGCPP). Dalam metode DGCPP, konsep massa dalam metode DGC yang diinterpretasikan sebagai probabilitas prior. diganti dengan probabilitas posterior. Dengan cara ini, proses perhitungan gravitasi data diharapkan akan mampu memberikan hasil yang lebih akurat dibandingkan dengan metode DGC. Dengan meningkatnya akurasi perhitungan gravitasi data, akurasi hasil klasifikasi dengan metode DGCPP diharapkan akan lebih baik dibandingkan dengan metode DGC, baik untuk set data normal maupun untuk set data yang mengalami permasalahan ketidakseimbangan kelas. Hasil uji coba menggunakan metode ten-fold crossvValidation pada empat set data normal dan empat set data yang mengalami permasalahan ketidakseimbangan kelas menunjukkan bahwa metode DGCPP memiliki rata-rata selisih akurasi positif dibandingkan dengan metode DGC. Untuk seluruh set data normal yang diujicobakan, selisih akurasi yang dihasilkan signifikan secara statistik pada tingkat kepercayaan 95%. Dari empat set data uji coba yang mengalami permasalahan ketidakseimbangan kelas, dua di antaranya menghasilkan rata-rata selisih akurasi yang signifikan secara statistik pada tingkat kepercayaan 95%. Hasil uji coba juga menunjukkan bahwa walaupun kecepatan proses klasifikasi yang dibutuhkan oleh metode DGCPP lebih lambat dibandingkan dengan metode DGC untuk semua data yang diujicobakan, tetapi perbedaannya sangat kecil dan sangat tidak signifikan.
Hierarchical Matching and Reasoning for Multi-Query Image Retrieval

As a promising field, Multi-Query Image Retrieval (MQIR) aims at searching for the semantically relevant image given multiple region-specific text queries. Existing works mainly focus on a single-level similarity between image regions and text queries, which neglect the hierarchical guidance of multi-level similarities and result in incomplete alignments. Besides, the high-level semantic correlations that intrinsically connect different region-query pairs are rarely considered. To address above limitations, we propose a novel Hierarchical Matching and Reasoning Network (HMRN) for MQIR. It disentangles MQIR into three hierarchical semantic representations, which is responsible to capture fine-grained local details, contextual global scopes, and high-level inherent correlations. HMRN consists of two modules: Scalar-based Matching (SM) module and Vector-based Reasoning (VR) module. Specifically, the SM module characterizes the multi-level alignment similarity, which consists of a fine-grained local-level similarity and a context-aware global-level similarity. Afterwards, the VR module is developed to excavate the potential semantic correlations among multiple region-query pairs, which further explores the high-level reasoning similarity. Finally, these three-level similarities are aggregated into a joint similarity space to form the ultimate similarity. Extensive experiments on the benchmark dataset demonstrate that our HMRN substantially surpasses the current state-of-the-art methods. For instance, compared with the existing best method Drill-down, the metric R@1 in the last round is improved by 23.4%. Our source codes will be released at https://github.com/LZH-053/HMRN.
Black Hole Menagerie, Charged/Dyonic BHs and Radiation from Interacting Dyonic BH Pairs

We describe charged BHs, Penrose process for energy extraction from Kerr BHs and Wald's proposal concerning a Kerr BH slowly becoming a Kerr-Newman BH in the presence of a uniform magnetic field. In the context of BHs bearing magnetic charge, we discuss both magnetic monopoles as well as dyons, and their emergence from various models like string theory, GUTs and electroweak theories, etc. In the later portions, we concentrate on our recent research work pertaining to the non-relativistic dynamics of dyon-dyon interaction that includes mutual gravitational attraction. From the derived classical equations of motion, we obtain not only the well known Schwinger-Zwanziger quantization condition for dyons using Saha's argument based on quantized angular momentum of electromagnetic field but also a scalar virial theorem for an astrophysical system consisting of point particles, some of which carry both electric and magnetic charges. In the final sections, we obtain expressions for the generated electromagnetic wave as well as gravitational wave amplitudes, and the corresponding luminosities due to dyon-dyon interactions. Lastly, we discuss the results after computing these quantities using a range of values for the mass, electric and magnetic charges, etc. of the dyonic BHs.
Boundedness of certain linear operators on twisted Fock spaces

On the twisted Fock spaces $ \mathcal{F}^\lambda(\mathbb{C}^{2n}) $ we consider two types of convolution operators $ S_\varphi $ and $ \widetilde{S}_\varphi $ associated to an element $ \varphi \in \mathcal{F}^\lambda(\mathbb{C}^{2n}) . $ We find a necessary and sufficient condition on $ \varphi $ so that $ S_\varphi $ (resp. $ \widetilde{S}_\varphi $ ) is bounded on $ \mathcal{F}^\lambda(\mathbb{C}^{2n}). $ We show that for any given non constant $ \varphi $ at least one of these two operators is unbounded.
What drives the acceptance of AI technology?: the role of expectations and experiences

In recent years, Artificial intelligence products and services have been offered potential users as pilots. The acceptance intention towards artificial intelligence is greatly influenced by the experience with current AI products and services, expectations for AI, and past experiences with ICT technology. This study aims to explore the factors that impact AI acceptance intention and understand the process of its formation. The analysis results of this study reveal that AI experience and past ICT experience affect AI acceptance intention in two ways. Through the direct path, higher AI experience and ICT experience are associated with a greater intention to accept AI. Additionally, there is an indirect path where AI experience and ICT experience contribute to increased expectations for AI, and these expectations, in turn, elevate acceptance intention. Based on the findings, several recommendations are suggested for companies and public organizations planning to implement artificial intelligence in the future. It is crucial to manage the user experience of ICT services and pilot AI products and services to deliver positive experiences. It is essential to provide potential AI users with specific information about the features and benefits of AI products and services. This will enable them to develop realistic expectations regarding AI technology.
Multi-task multi-station earthquake monitoring: An all-in-one seismic Phase picking, Location, and Association Network (PLAN)

Earthquake monitoring is vital for understanding the physics of earthquakes and assessing seismic hazards. A standard monitoring workflow includes the interrelated and interdependent tasks of phase picking, association, and location. Although deep learning methods have been successfully applied to earthquake monitoring, they mostly address the tasks separately and ignore the geographic relationships among stations. Here, we propose a graph neural network that operates directly on multi-station seismic data and achieves simultaneous phase picking, association, and location. Particularly, the inter-station and inter-task physical relationships are informed in the network architecture to promote accuracy, interpretability, and physical consistency among cross-station and cross-task predictions. When applied to data from the Ridgecrest region and Japan regions, this method showed superior performance over previous deep learning-based phase-picking and localization methods. Overall, our study provides for the first time a prototype self-consistent all-in-one system of simultaneous seismic phase picking, association, and location, which has the potential for next-generation autonomous earthquake monitoring.
Segmentation of Industrial Burner Flames: A Comparative Study from Traditional Image Processing to Machine and Deep Learning

Abstract. In many industrial processes, such as power generation, chemical production, and waste management, accurately monitoring industrial burner flame characteristics is crucial for safe and efficient operation. A key step involves separating the flames from the background through binary segmentation. Decades of machine vision research have produced a wide range of possible solutions, from traditional image processing to traditional machine learning and modern deep learning methods. In this work, we present a comparative study of multiple segmentation approaches, namely Global Thresholding, Region Growing, Support Vector Machines, Random Forest, Multilayer Perceptron, U-Net, and DeepLabV3+, that are evaluated on a public benchmark dataset of industrial burner flames. We provide helpful insights and guidance for researchers and practitioners aiming to select an appropriate approach for the binary segmentation of industrial burner flames and beyond. For the highest accuracy, deep learning is the leading approach, while for fast and simple solutions, traditional image processing techniques remain a viable option.
A new approach to generalisation error of machine learning algorithms: Estimates and convergence

In this work we consider a model problem of deep neural learning, namely the learning of a given function when it is assumed that we have access to its point values on a finite set of points. The deep neural network interpolant is the the resulting approximation of f, which is obtained by a typical machine learning algorithm involving a given DNN architecture and an optimisation step, which is assumed to be solved exactly. These are among the simplest regression algorithms based on neural networks. In this work we introduce a new approach to the estimation of the (generalisation) error and to convergence. Our results include (i) estimates of the error without any structural assumption on the neural networks and under mild regularity assumptions on the learning function f (ii) convergence of the approximations to the target function f by only requiring that the neural network spaces have appropriate approximation capability.
BatchGNN: Efficient CPU-Based Distributed GNN Training on Very Large Graphs

We present BatchGNN, a distributed CPU system that showcases techniques that can be used to efficiently train GNNs on terabyte-sized graphs. It reduces communication overhead with macrobatching in which multiple minibatches' subgraph sampling and feature fetching are batched into one communication relay to reduce redundant feature fetches when input features are static. BatchGNN provides integrated graph partitioning and native GNN layer implementations to improve runtime, and it can cache aggregated input features to further reduce sampling overhead. BatchGNN achieves an average $3\times$ speedup over DistDGL on three GNN models trained on OGBN graphs, outperforms the runtimes reported by distributed GPU systems $P^3$ and DistDGLv2, and scales to a terabyte-sized graph.
Group cohesion under asymmetric voting behaviors

Cohesion plays a crucial role in achieving collective goals, promoting cooperation and trust, and improving efficiency within social groups. To gain deeper insights into the dynamics of group cohesion, we have extended our previous model of noisy group formation by incorporating asymmetric voting behaviors. Through a combination of theoretical analysis and numerical simulations, we have explored the impact of asymmetric voting noise, the attention decay rate, voter selection methods, and group sizes on group cohesion. For a single voter, we discovered that as the group size approaches infinity, group cohesion converges to $1/(R+1)$, where $R$ represents the ratio of asymmetric voting noise. Remarkably, even in scenarios with extreme voting asymmetry ($R \to \infty$), a significant level of group cohesion can be maintained. Furthermore, when the positive or negative voter's voting noise surpasses or falls below the phase transition point of $R_c=1$, a higher rate of attention decay can lead to increased group cohesion. In the case of multiple voters, a similar phenomenon arises when the attention decay rate reaches a critical point. These insights provide practical implications for fostering effective collaboration and teamwork within growing groups striving to achieve shared objectives.
When SAM Meets Sonar Images

Segment Anything Model (SAM) has revolutionized the way of segmentation. However, SAM's performance may decline when applied to tasks involving domains that differ from natural images. Nonetheless, by employing fine-tuning techniques, SAM exhibits promising capabilities in specific domains, such as medicine and planetary science. Notably, there is a lack of research on the application of SAM to sonar imaging. In this paper, we aim to address this gap by conducting a comprehensive investigation of SAM's performance on sonar images. Specifically, we evaluate SAM using various settings on sonar images. Additionally, we fine-tune SAM using effective methods both with prompts and for semantic segmentation, thereby expanding its applicability to tasks requiring automated segmentation. Experimental results demonstrate a significant improvement in the performance of the fine-tuned SAM.
About the Cost of Global Privacy in Density Estimation

We study non-parametric density estimation for densities in Lipschitz and Sobolev spaces, and under global privacy. In particular, we investigate regimes where the privacy budget is not supposed to be constant. We consider the classical deﬁnition of global diﬀerential privacy, but also the more recent notion of global concentrated diﬀerential privacy. We recover the result of Barber & Duchi (2014) stating that histogram estimators are optimal against Lipschitz distributions for the L 2 risk and, under regular diﬀerential privacy, we extend it to other norms and notions of privacy. Then, we investigate higher degrees of smoothness, drawing two conclusions: First, and contrary to what happens with constant privacy budget (Wasserman & Zhou, 2010), there are regimes where imposing privacy degrades the regular minimax risk of estimation on Sobolev densities. Second, so-called projection estimators are near-optimal against the same classes of densities in this new setup with pure diﬀerential privacy, but contrary to the constant privacy budget case, it comes at the cost of relaxation. With zero concentrated diﬀerential privacy, there is no need for relaxation, and we prove that the estimation is optimal.
Feature Adversarial Distillation for Point Cloud Classification

Due to the point cloud’s irregular and unordered geometry structure, conventional knowledge distillation technology lost a lot of information when directly used on point cloud tasks. In this paper, we propose Feature Adversarial Distillation (FAD) method, a generic adversarial loss function in point cloud distillation, to reduce loss during knowledge transfer. In the feature extraction stage, the features extracted by the teacher are used as the discriminator, and the students continuously generate new features in the training stage. The feature of the student is obtained by attacking the feedback from the teacher and getting a score to judge whether the student has learned the knowledge well or not. In experiments on standard point cloud classification on ModelNet40 and ScanObjectNN datasets, our method reduced the information loss of knowledge transfer in distillation in 40x model compression while maintaining competitive performance.
How About Kind of Generating Hedges using End-to-End Neural Models?

Hedging is a strategy for softening the impact of a statement in conversation. In reducing the strength of an expression, it may help to avoid embarrassment (more technically, “face threat”) to one’s listener. For this reason, it is often found in contexts of instruction, such as tutoring. In this work, we develop a model of hedge generation based on i) fine-tuning state-of-the-art language models trained on human-human tutoring data, followed by ii) reranking to select the candidate that best matches the expected hedging strategy within a candidate pool using a hedge classifier. We apply this method to a natural peer-tutoring corpus containing a significant number of disfluencies, repetitions, and repairs. The results show that generation in this noisy environment is feasible with reranking. By conducting an error analysis for both approaches, we reveal the challenges faced by systems attempting to accomplish both social and task-oriented goals in conversation.
Interpretable Sparsification of Brain Graphs: Better Practices and Effective Designs for Graph Neural Networks

Brain graphs, which model the structural and functional relationships between brain regions, are crucial in neuroscientific and clinical applications that can be formulated as graph classification tasks. However, dense brain graphs pose computational challenges such as large time and memory consumption and poor model interpretability. In this paper, we investigate effective designs in Graph Neural Networks (GNNs) to sparsify brain graphs by eliminating noisy edges. Many prior works select noisy edges based on explainability or task-irrelevant properties, but this does not guarantee performance improvement when using the sparsified graphs. Additionally, the selection of noisy edges is often tailored to each individual graph, making it challenging to sparsify multiple graphs collectively using the same approach. To address the issues above, we first introduce an iterative framework to analyze the effectiveness of different sparsification models. By utilizing this framework, we find that (i) methods that prioritize interpretability may not be suitable for graph sparsification, as the sparsified graphs may degenerate the performance of GNN models; (ii) it is beneficial to learn the edge selection during the training of the GNN, rather than after the GNN has converged; (iii) learning a joint edge selection shared across all graphs achieves higher performance than generating separate edge selection for each graph; and (iv) gradient information, which is task-relevant, helps with edge selection. Based on these insights, we propose a new model, Interpretable Graph Sparsification (IGS), which improves the graph classification performance by up to 5.1% with 55.0% fewer edges than the original graphs. The retained edges identified by IGS provide neuroscientific interpretations and are supported by well-established literature.
FastBCSD: Fast and Efficient Neural Network for Binary Code Similarity Detection

Binary code similarity detection (BCSD) has various applications, including but not limited to vulnerability detection, plagiarism detection, and malware detection. Previous research efforts mainly focus on transforming binary code to assembly code strings using reverse compilation and then using pre-trained deep learning models with large parameters to obtain feature representation vector of binary code. While these models have proven to be effective in representing binary code, their large parameter size leads to considerable computational expenses during both training and inference. In this paper, we present a lightweight neural network, called FastBCSD, that employs a dynamic instruction vector encoding method and takes only assembly code as input feature to achieve comparable accuracy to the pre-training models while reducing the computational resources and time cost. On the BinaryCorp dataset, our method achieves a similar average MRR score to the state-of-the-art pre-training-based method (jTrans), while on the BinaryCorp 3M dataset, our method even outperforms the latest technology by 0.01. Notably, FastBCSD has a much smaller parameter size (13.4M) compared to jTrans (87.88M), and its latency time is 1/5 of jTrans on NVIDIA GTX 1080Ti.