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HenTwin: A Multimodal Digital Twin Framework for Longitudinal Biological State Monitoring in Laying Hens
Yashan Dhaliwal, Shreya Rao, Suresh Neethirajan
https://arxiv.org/abs/2607.28652 https://arxiv.org/pdf/2607.28652 https://arxiv.org/html/2607.28652
arXiv:2607.28652v1 Announce Type: new
Abstract: Early-life monitoring in laying hens remains constrained by fragmented single-modality sensing and the absence of formal system-level state representations. HenTwin, a multimodal digital twin framework implemented as a five-layer IoT architecture, formalizes flock-level multimodal biological state dynamics from hatch through 25 weeks of age. A four-dimensional biological state vector integrating body surface temperature, acoustic energy entropy, band energy ratio, and optical-flow-based motion is defined, with the temperature-humidity index treated as an exogenous environmental input to preserve intervention capability. A discrete-time state transition model is estimated from 25 weeks of longitudinal multimodal data collected from 150 Lohmann LSL-Lite hens across five controlled rooms at the Atlantic Poultry Research Centre, Dalhousie University. The estimated transition matrix exhibits modality-specific persistence while remaining asymptotically stable. Perturbation analysis demonstrates that a sustained 2.0 THI increase produces a stable long-run acoustic entropy elevation of 0.54 nats, approximately one-quarter of the entire 1.87-nat developmental decline observed across the study period. Pettitt change-point detection identifies coordinated multimodal developmental state transitions at Weeks 12-14. Cross-room validation suggests that structural transition parameters are partially transferable across rooms, whereas environmental input sensitivity requires room-specific calibration, supporting a two-tier IoT deployment architecture. Leave-one-out cross-validation demonstrates consistent out-of-sample model performance. HenTwin takes a first step toward formal, state-aware digital twin inference in precision livestock farming.
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I have worked at a few dot-com companies in my time, including one that was aggressively gobbling up other dot-com companies that were in trouble. I know the signs of a company having cash flow problems. I now work for a state agency, and we are exhibiting those same signs. This is disconcerting.

Why Do Prefetchers Fail? Let Agents Answer
Xiangfeng Sun, Ceyu Xu, Ningzhi Ai, Zeyu Zhu, Yiyang Yuan, Yuan Xie
https://arxiv.org/abs/2608.13027 https://arxiv.org/pdf/2608.13027 https://arxiv.org/html/2608.13027
arXiv:2608.13027v1 Announce Type: new
Abstract: Hardware prefetchers are crucial to processor performance, yet their design remains labor-intensive and expert-driven. Architects inspect execution and memory-access traces, identify patterns, translate them into online hardware heuristics, and evaluate them in simulation, often with no guarantee of improvement. Human experts cannot systematically inspect billion-instruction traces across diverse real-world workloads.
We present a performance-anomaly-driven autoresearch flow that repeatedly asks why a deployed prefetcher fails and uses the diagnoses to construct the Mixture of Prefetchers (MoP). Each iteration localizes high-impact unexplained misses to program counters, gives agents hardware logs, source code, and sliced traces, validates diagnoses through runnable minimal cases, and synthesizes specialized sub-prefetchers for recurring pattern families. Measured performance and remaining anomalies feed subsequent iterations, enabling simulator-in-the-loop discovery beyond model priors.
The campaign consumes 1.91 billion DeepSeek V4 Pro tokens. On SPEC CPU2006 and SPEC CPU2017, MoP achieves a 61.1% geomean IPC speedup over no prefetching, outperforming the human-designed Alecto, Berti, and Pythia prefetchers by 14.5%, 21.6%, and 23.6%, respectively. RTL synthesis in a 6nm library reports 110 KB of on-chip storage and 0.0347 mm^2 area. To our knowledge, this is the first empirical demonstration that an agent-driven hardware-design process can produce an RTL-practical prefetcher that outperforms state-of-the-art human designs on unseen workloads.
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FQTree: Fine-grained Quantization and Hardware Generation of Boosted Decision Trees
Zhiqiang Que, Chang Sun, Haiyang Wang, Dinesh Pamunuwa, Roshan Weerasekera, Qijia Tang, Bakhtiar Zadeh, Wayne Luk, Maria Spiropulu
https://arxiv.org/abs/2608.12140 https://arxiv.org/pdf/2608.12140 https://arxiv.org/html/2608.12140
arXiv:2608.12140v1 Announce Type: new
Abstract: Boosted decision trees (BDTs) are widely used in latency-critical applications, but efficient hardware deployment remains challenging. Existing designs often rely on uniform or manually tuned fixed-point formats, which can introduce unnecessary hardware cost or accuracy loss. This work presents the FQTree algorithm{https://github.com/ecs-bristol/FQTree} for fine-grained quantization-aware training of BDTs, together with the QXGB framework for automatic hardware generation. FQTree introduces a hardware-oriented leaf-value quantization scheme that uses a global quantization step together with a tree-wise shift, enabling compact non-negative integer leaf representations, controlled clipping/pruning, and bias folding to reduce datapath cost. This work further applies this quantization during boosting so that later trees adapt to the errors of the already-quantized ensemble, and then lowers the trained model into low-latency hardware implementations through a compiler-based flow. Results on JSC, MNIST, and NID show that our method reduces LUT usage by 26-57\% compared with the state-of-the-art FPGA-based BDT designs while matching or improving accuracy.
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Elastohydrodynamic instability of a spinning elastic disk
Sifan Yin, Paul R. Kaneelil, L. Mahadevan
https://arxiv.org/abs/2607.19561 https://arxiv.org/pdf/2607.19561 https://arxiv.org/html/2607.19561
arXiv:2607.19561v1 Announce Type: new
Abstract: A soft thin elastic disk spinning in a viscous fluid experiences centrifugal tension generated by rotation together with viscous shear generated by the surrounding flow. While the former stabilizes the flat state, the latter can destabilize it. We combine the linearized F\"{o}ppl-von K\'{a}rm\'{a}n equations for a rotating elastic disk with the shear stresses arising from the classical von K\'{a}rm\'{a}n swirling flow to derive an elastohydrodynamic stability problem. Linear stability analysis identifies the onset of buckling in terms of two dimensionless control parameters measuring centrifugal stiffening and fluid-induced shear. Above threshold the disk buckles into azimuthally periodic saddle-like modes whose wavenumber increases with increasing rotational tension. The buckled configuration also supports retrograde traveling waves that rotate more slowly than the material frame. These results identify a simple mechanism whereby fluid shear destabilizes rotating elastic structures.
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