Architectures and Ensemble Learning
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TeamHerald@CHIPSAL 2026: Hate Speech Detection and Sentiment Analysis of Nepali Memes using Transformer-based Architectures and Ensemble Learning
arXiv:2606.08770v1 Announce Type: new Abstract: The analysis of internet memes in the Nepali language is complicated by frequent code-mixing and a lack of established baseline resources. While memes inherently combine visual and textual elements, this study focuses on a text-centric approach by extracting embedded text using an OCR layer and modeling it with Transformer-based architectures. We evaluate six distinct models and investigate the comparative effectiveness of Hard and Soft Voting...
WISE-HAR: A Generalizable Ensemble Deep Learning Framework for WiFi-Based Human Activity Recognition
Announce Type: new Abstract: Human Activity Recognition (HAR) using WiFi signals has emerged as a transformative technology for smart homes, healthcare monitoring, security systems, and ambient assisted living. Unlike traditional camera-based systems that raise significant privacy concerns and fail in low-light conditions, or wearable sensors that require user compliance, WiFi-based HAR is non-intrusive, privacy-preserving, cost-effective, and works seamlessly in any lighting condition. This...
Equivalent volitional learning emerges through circuit-specific population dynamics in motor cortex and hippocampus
Learning operates across different brain circuits to associate population activity patterns with desired outcomes, and to enable volitional reactivation of those patterns to control behavior. These circuits differ profoundly in their architecture and dynamical regimes, yet which features of learning are shared across them and which arise from circuit-specific implementations remains unknown. Here, we use a brain-computer interface (BCI) to train mice to modulate the activity of selected...
Latent-Conditioned Parameterized Quantum Circuits as Universal Approximators for Distributions over Quantum States
arXiv:2605.28690v2 Announce Type: replace-cross Abstract: Many applications in quantum simulation, quantum chemistry, and quantum machine learning require not a single quantum state but an ensemble of states characterizing the heterogeneity of a target system. Preparing such ensembles state-by-state is prohibitive in both variational and fault-tolerant settings, motivating a generative-modeling approach. We introduce latent-conditioned parameterized quantum circuits (LPQCs), a hybrid...
Human-Like Neural Nets by Catapulting
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A Pan-Cancer Multi-Omic SuperLearner for Regulated Cell Death Survival Topologies
Introduction: Regulated cell death (RCD) pathways profoundly influence tumor progression and immune modulation. In prior work, we constructed a comprehensive database mapping 25 forms of RCD across seven multi-omic layers encompassing 33 tumor types (CancerRCDShiny). Despite their robust ability to identify risk populations, translating these prognostic signatures into personalized clinical workflows requires a shift from generalized cohort stratification to individualized risk mapping.
When Entropy Is Not Enough: Multi-Modal Classification of Encrypted and Compressed Data Fragments
arXiv:2605.31337v1 Announce Type: new Abstract: Reliable identification of encrypted data fragments is essential in cybersecurity, with applications to ransomware detection, digital forensics, and large-scale data analysis. Distinguishing encrypted from compressed fragments is particularly challenging, as short fragments lack structural data and exhibit low statistical redundancy. Traditional statistical methods based on byte-level distributions show limited effectiveness on this task.
Inverse Depth Scaling From Most Layers Being Similar
arXiv:2602.05970v2 Announce Type: replace Abstract: Neural scaling laws relate loss to model size in large language models (LLMs), yet depth and width may contribute to performance differently, requiring more detailed studies. Here, we quantify how depth affects loss via analysis of LLMs and toy residual networks. We find loss scales inversely proportional to depth in LLMs, probably due to functionally similar layers reducing error through ensemble averaging rather than compositional...
Kalimati Vegetable Price Index Forecasting with a Momentum Corrected Online Stacking Ensemble
arXiv:2605.30720v1 Announce Type: new Abstract: Forecasting agricultural commodity prices in emerging economies is difficult due to high volatility, frequent supply disruptions, and strong cultural influences on demand. This study introduces the Kalimati Vegetable Price Index (KVPI), a new inverse-volatility weighted composite index that aggregates 135 daily wholesale commodities from Kathmandu over ten years (2013-2023). By creating a stable macro-level signal, the KVPI reduces the noise...
SPAMoE: Spectrum-Aware Hybrid Operator Framework for Full-Waveform Inversion
arXiv:2604.07421v3 Announce Type: replace Abstract: Full-waveform inversion (FWI) is pivotal for reconstructing high-resolution subsurface velocity models but remains computationally intensive and ill-posed. While deep learning approaches promise efficiency, existing Convolutional Neural Networks (CNNs) and single-paradigm Neural Operators (NOs) struggle with one fundamental issue: frequency entanglement of multi-scale geological features. To address this challenge, we propose...