Arooba Farhan, Afsah Syed, Muhammad Fahd Ibrahim Bhatti, Muhammad Areeb Khalid Kidwai, Muhammad Ahsan Salar Khan, Vincenzo Romano, Muhammad Ali Siddiqi
Distinguishing Purkinje cell (PC) simple spikes (SSs) from complex spikes (CSs) is key to studying cerebellar function. PC recordings commonly rely on wired headstages, whose tethers restrict natural movement in freely behaving animals. Untethered headstages remove this constraint, but cannot continuously transmit raw recordings within their power budgets, making on-headstage spike classification an attractive form of compression, provided it runs accurately in real time on constrained hardware. This work presents two compact spiking neural networks (SNNs) matched to their target hardware: ConvSNN, which maps to the Matrix Vector Processor (MVP) of an EFM32PG28 microcontroller, and StreamSNN, which processes one waveform value per timestep using learned time constants and fixed synaptic delays for a dedicated SKY130 circuit. Evaluated on cells unseen during training, ConvSNN achieves a CS F1 of 0.873 with 1,018 parameters and StreamSNN 0.837 with 527 parameters, both with eight-bit weights. On the microcontroller, MVP acceleration reduces ConvSNN latency by 62.3\% and extends estimated battery life to 33.73~hours on a 50~mAh battery. In SKY130, StreamSNN occupies 0.173~mm$^2$ and consumes 0.46~$μ$J per event, with 0.2~ms of computation within an 8.3~ms end-to-end latency.
Israel Abebe Azime, Jesujoba O. Alabi, Iffat Maab, Alexander D. Cai, Tadesse Destaw Belay, Atnafu Lambebo Tonja, Jonas Kgomo, Folasade Alabi 等 23 位作者
Large language models (LLMs) designed to interact with users are typically trained to respond appropriately to harmful or toxic content. However, ensuring appropriate responses becomes more challenging in diverse linguistic and cultural contexts, where safety risks may be expressed or interpreted differently. Building safe AI systems therefore requires detecting safety risks, understanding their nature, and responding appropriately, while accounting for linguistic and cultural variations that add complexity to each stage. In this work, we introduce AfriGuard, a culturally grounded multilingual dataset for studying harmful intent, safety-category understanding, and refusal behavior across 10 African languages and English. Our findings reveal that guardrail models underperform both open- and closed-source LLMs on safety-intent and safety-category classification, while models show limited understanding of appropriate safety behavior, scoring below 60% F1. We further find that models often fail to respond appropriately to unsafe prompts not because they explicitly refuse, but because they produce linguistically inadequate responses that result in incidental non-assistance rather than proper refusals. Finally, using AfriGuard, we develop efficient guardrail models that achieve performance competitive with models up to 30x larger, introduce refusal detectors that improve average performance by 43.9% over existing detectors, and demonstrate that supervised fine-tuning can substantially improve overall safety performance.
Suparno Roy Chowdhury, Tejas Anvekar Arizona State University, Manan Roy Choudhury Arizona State University, Muhammad Ali Khan Mayo Clinic, Kaneez Zahra Rubab Khakwani Mayo Clinic, M Bassam Sonbol Mayo Clinic, Irbaz Bin Riaz Mayo Clinic, Vivek Gupta Arizona State University
Clinicians exploring oncology trial repositories often need ad-hoc, multi-constraint queries over biomarkers, endpoints, interventions, and time, yet writing SQL requires schema expertise. We demo FD-NL2SQL, a feedback-driven clinical NL2SQL assistant for SQLite-based oncology databases. Given a natural-language question, a schema-aware LLM decomposes it into predicate-level sub-questions, retrieves semantically similar expert-verified NL2SQL exemplars via sentence embeddings, and synthesizes executable SQL conditioned on the decomposition, retrieved exemplars, and schema, with post-processing validity checks. To improve with use, FD-NL2SQL incorporates two update signals: (i) clinician edits of generated SQL are approved and added to the exemplar bank; and (ii) lightweight logic-based SQL augmentation applies a single atomic mutation (e.g., operator or column change), retaining variants only if they return non-empty results. A second LLM generates the corresponding natural-language question and predicate decomposition for accepted variants, automatically expanding the exemplar bank without additional annotation. The demo interface exposes decomposition, retrieval, synthesis, and execution results to support interactive refinement and continuous improvement.
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Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 3: System Demonstrations) · 2026 · 正式发表