Blog
Learning the Spoofability of Limit Order Books With Interpretable Probabilistic Neural Networks
This paper investigates real-time detection of spoofing activity in limit order books, focusing on cryptocurrency centralized exchanges. We first introduce novel order flow variables based on multi-scale Hawkes processes that account both for the size and placement distance from current best prices...
Synergizing RAG and Reasoning: A Systematic Review
Recent breakthroughs in large language models (LLMs), particularly in reasoning capabilities, have propelled Retrieval-Augmented Generation (RAG) to unprecedented levels. By synergizing retrieval mechanisms with advanced reasoning, LLMs can now tackle increasingly complex problems. This paper presents a systematic review of the collaborative...
Consistency between Bulk and Boundary Causalities in Asymptotically Anti-de Sitter Spacetimes
We investigate the consistency between bulk and boundary causalities in static, spherically symmetric, asymptotically anti-de Sitter (AdS) spacetimes. We derive a general formula that provides sufficient conditions for time advance, where bulk propagation arrives earlier than any boundary propagation. As an application,...
Direct and inverse problem for bi-wave equation with time-dependent coefficients from partial data
In this article, we study a direct and an inverse problem for the bi-wave operator $(\Box^2)$ along with second and lower order time-dependent perturbations. In the direct problem, we prove that the operator is well-posed, given initial and boundary data in suitable...
Continuity Conditions for Piecewise Quadratic Functions on Simplicial Conic Partitions are Equivalent
Analysis of continuous-time piecewise linear (PWL) systems based on piecewise quadratic (PWQ) Lyapunov functions typically requires continuity of these functions over a partition of the state space. Several conditions for guaranteeing continuity of PWQ functions over state space partitions can be found...
Lax dynamics
A novel approach is proposed to characterize the dynamics of perturbed many-body integrable systems. Focusing on the paradigmatic case of the Toda chain under non-integrable Hamiltonian perturbations, this study introduces a method based the time evolution of the Lax eigenvalues $\lambda_\alpha$ as...
ScaleGNN: Towards Scalable Graph Neural Networks via Adaptive High-order Neighboring Feature Fusion
Graph Neural Networks (GNNs) have demonstrated strong performance across various graph-based tasks by effectively capturing relational information between nodes. These models rely on iterative message passing to propagate node features, enabling nodes to aggregate information from their neighbors. Recent research has significantly...
Improving robustness and training efficiency of machine-learned potentials by incorporating short-range empirical potentials
Machine learning force fields (MLFFs) are powerful tools for materials modeling, but their performance is often limited by training dataset quality, particularly the lack of rare event configurations. This limitation undermines their accuracy and robustness in long-time and large-scale molecular dynamics simulations....
StreamRL: Scalable, Heterogeneous, and Elastic RL for LLMs with Disaggregated Stream Generation
Reinforcement learning (RL) has become the core post-training technique for large language models (LLMs). RL for LLMs involves two stages: generation and training. The LLM first generates samples online, which are then used to derive rewards for training. The conventional view holds...
Benchmarking the Reproducibility of Brain MRI Segmentation Across Scanners and Time
Accurate and reproducible brain morphometry from structural MRI is critical for monitoring neuroanatomical changes across time and across imaging domains. Although deep learning has accelerated segmentation workflows, scanner-induced variability and reproducibility limitations remain-especially in longitudinal and multi-site settings. In this study, we...