arXiv:2509.25519v2 Announce Type: replace-cross Abstract: Flow models parameterized as time-dependent velocity fields can generate data from noise by integrating an ODE. These models are often trained using flow matching, i.e. by sampling random pairs of noise and target points $(mathbf{x}_0,mathbf{x}_1)$ and ensuring that the velocity field is aligned, on average, with $mathbf{x}_1-mathbf{x}_0$ when evaluated along a segment linking $mathbf{x}_0$ to $mathbf{x}_1$. While these pairs are sampled independently by default, they can also be selected more carefully by matching […]

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arXiv:2408.12739v2 Announce Type: replace-cross Abstract: Quantum Convolutional Neural Networks (QCNNs) are widely regarded as a promising model for Quantum Machine Learning (QML). In this work we tie their heuristic success to two facts. First, that when randomly initialized, they can only operate on the information encoded in low-bodyness measurements of their input states. And second, that they are commonly benchmarked on “locally-easy” datasets whose states are precisely classifiable by the information encoded in these low-bodyness observables subspace. We […]

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arXiv:2601.17093v1 Announce Type: new Abstract: Comparing neural network representations is essential for understanding and validating models in scientific applications. Existing methods, however, often provide a limited view. We propose the Triangle of Similarity, a framework that combines three complementary perspectives: static representational similarity (CKA/Procrustes), functional similarity (Linear Mode Connectivity or Predictive Similarity), and sparsity similarity (robustness under pruning). Analyzing a range of CNNs, Vision Transformers, and Vision-Language Models using both in-distribution (ImageNetV2) and out-of-distribution (CIFAR-10) testbeds, our initial […]

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Hi everyone, I honestly feel like my mental health is not in a good place right now, and I just want to share this to see if anyone else has gone through something similar. If you’ve noticed, I’ve been posting quite a lot recently about my PhD thesis situation. I submitted my thesis a little over two months ago. Since that day, I’ve been in a constant state of anxiety waiting for the result. Every morning, the very […]

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Sentiment classification struggles with complex semantic relationships using static text graphs. We introduce the Quantum-Enhanced Adaptive Graph Convolutional Network (QAGCN), a hybrid quantum-classical architecture for robust sentiment representation. QAGCN’s core is a Quantum-Enhanced Graph Construction Module employing a Parameterized Quantum Circuit (PQC) to dynamically learn emotional association strengths between word pairs. This generates a task-adaptive adjacency matrix, which then feeds into classical GNN layers. Evaluations on benchmark datasets (Yelp, IMDB, Amazon, MC, RP) demonstrate QAGCN’s superior or competitive […]

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I’m a 2nd year PhD student and looking to broaden my collaboration circle and what better than this community. I primarily work on developing frameworks for fairness (imaging models, LM) (evaluation/mitigation for clinical deployment) but really open for boarder topics. If there’s a possibility we can connect and work on something exciting (for a publication in conf or a workshop), would be great. If you have hold of a dataset which will be useful we can make it […]

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arXiv:2602.23464v1 Announce Type: new Abstract: Multi-scalar multiplication (MSM), defined as MSM(P, x) = sum_{i=1}^n x_i P_i, is a dominant computational kernel in discrete-logarithm-based cryptography and often becomes a bottleneck for verifiers and other resource-constrained clients. We present 2G2T, a simple protocol for verifiably outsourcing MSM to an untrusted server. After a one-time keyed setup for fixed bases P = (P1, …, Pn) that produces a public merged-bases vector T and client secret state, the server answers each query […]

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In this paper, we derive explicit exponential series representations for the sine function involving even values of the Riemann zeta function. The result is obtained via logarithmic differentiation and integration of the Weierstrass product. We further demonstrate that the method extends naturally to the cosine function, yielding an analogous representation. These results highlight a structural connection between trigonometric entire functions and the distribution of their zeros.

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arXiv:2601.21090v1 Announce Type: new Abstract: The increasing density of many-core architectures necessitates interconnection networks that are both high-performance and fault-resilient. Eisenstein-Jacobi (EJ) networks, with their symmetric 6-regular topology, offer superior topological properties but challenge traditional routing heuristics under fault conditions. This paper evaluates three routing paradigms in faulty EJ environments: deterministic Greedy Adaptive Routing, theoretically optimal Dijkstra’s algorithm, and a reinforcement learning (RL)-based approach. Using a multi-objective reward function to penalize fault proximity and reward path efficiency, the […]

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arXiv:2602.11159v1 Announce Type: new Abstract: The increasing use of Machine Learning (ML) in sensitive domains such as healthcare, finance, and public policy has raised concerns about the transparency of automated decisions. Explainable AI (XAI) addresses this by clarifying how models generate predictions, yet most methods demand technical expertise, limiting their value for novices. This gap is especially critical in no-code ML platforms, which seek to democratize AI but rarely include explainability. We present a human-centered XAI module in […]

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