Data61

• End-to-end estrus detection from ear-tag accelerometry for on-device use • Compact deep-learning behavior model fits Cortex-M4F MCU for on-tag inference • Causal 14-day baselines normalize behavior deviations per animal to reduce drift • Regularized logistic regression accurately detects estrus from behavior deviations • Suites digital-ag decision support: automated alerts and workflow integration Timely and accurate estrus detection is essential for improving cattle reproductive efficiency and herd management. We present a lightweight deployable pipeline that infers estrus from daily behavior patterns estimated from ear-tag triaxial accelerometer data. As the key enabler, we propose a compact deep-learning model that classifies core cattle behaviors from raw accelerometer readings and supports construction of per-animal daily behavior profiles. To accommodate individual differences and temporal variability, we compute individualized baseline profiles using causal dynamic trimmed means over preceding days and define features as deviations of daily behavior durations from these baselines. We use a regularized logistic regression model to map the behavior-deviation features to the daily probability of estrus. Evaluated on data from 33 Angus cows fitted with smart ear-tags during a four-month grazing trial in temperate Australia, using five-fold stratified non-shuffled cross-validation, the proposed approach achieves mean precision 0.967, recall 0.917, F1 score 0.941, specificity 0.998, and MCC 0.938. The results show that subtle yet systematic behavioral shifts measured by low-cost accelerometers are reliable predictors of estrus, enabling scalable, infrastructure-light, and autonomous reproductive monitoring in extensive grazing systems. The pipeline is compatible with on-tag or edge deployment and integrates readily with digital-agriculture workflows for alerting and decision support.

Understanding the interaction between polymers and proteins is of interest for researchers in medicine, biology, food science, and water treatment, among other fields. The goal may be to create strong interactions with enzymes to improve their catalytic stability, while in nanomedicine and biomedical engineering, the focus is often on reducing protein adsorption on polymer surfaces. Researchers have developed libraries of polymers with various monomer combinations and tested their binding to different proteins to better understand these interactions. In this work, we aimed to identify the polymer with the highest or lowest binding affinity to all proteins, respectively, using Gaussian Process Regression (GPR). However, incorporating categorical features such as the type of monomer has not been widely applied in GPR. Here we compare a range of process models, which were coined Multiplicative kernel, Additive kernel, Easy to interpret Gaussian Process model (EzGP), Latent Variable Gaussian Processes (LVGP), and the Latent Map Gaussian Processes (LMGP) by their developers. The LVGP model was found to perform best on the polymer-protein data set, where the output for binding strength was given by Förster resonance energy transfer (FRET), which can be used to help generate large data sets for machine learning (ML). The polymer that had the highest affinity to glucose oxidase (GOx), uricase (Uri), casein (Cas), trypsin (Trp), carbonic anhydrase (CAn) and bovine serum albumin (BSA) carried positive charges as well as hydrophobic benzyl groups. Negatively charged monomers dominated the polymer that rejected the most proteins intermixed with some cationic units, reminiscent of zwitterionic polymers.

Introduction:Total hip arthroplasty (THA) is a widely performed procedure. Despite its overall success, adverse outcomes have been associated with defined comorbidities. Hypertension (HTN), which affects a notable portion of the adult population, may be one such factor but has not been independently studied in this regard.Methods:THA patients were abstracted from the 2010 to 2022 M165 Ortho PearlDiver Mariner Database. Adult patients with versus without HTN were matched 1:1 based on age, sex, and Elixhauser Comorbidity Index. Ninety-day adverse outcomes and 5-year implant survival rates were compared for the matched cohorts with multivariable analysis, adjusting for multiple comparisons.Results:Of 851,605 THA patients identified, HTN was noted for 601,475 (70.6%). After matching, there were 148,946 patients with and the same number without HTN. Patients with HTN were at independently greater odds of most individual adverse outcomes assessed, any adverse events (odds ratio [OR] 2.18), serious adverse events (OR 2.19), minor adverse events (OR 2.26), and readmissions (OR 1.52) (P < 0.001 for each). Furthermore, the 5-year implant survival rate was mildly lower in HTN group (96.5% versus 97.5%, P < 0.001).Discussion:The current, large-cohort study identified HTN to be diagnosed for more than half of patients undergoing THA. The clear correlation of HTN with many adverse outcomes and mildly (but statistically) lower 5-year implant survival highlights the need for further consideration of this variable. Although there could be other confounding factors that may not have been fully controlled, those with HTN are clearly an “at-risk” group.

Abstract Quantum machine learning (QML) is an emerging field that promises advantages such as faster training, improved reliability and superior feature extraction over classical counterparts. However, its implementation on quantum hardware is challenging due to the noise inherent in these systems, necessitating the use of quantum error correction (QEC) codes. Current QML research remains primarily theoretical, often assuming noise-free environments and offering little insight into the integration of QEC with QML implementations. To address this, we investigate the performance of a simple, parity-classifying Variational Quantum Classifier (VQC) implemented with the [[4,2,2]] error-detecting stabiliser code in a simulated noisy environment. To our knowledge, this is the first implementation of a stabiliser-based error detection code to a QML algorithm. We invoke ancilla qubits to logically encode rotation gates, and classically simulate the logically-encoded VQC under two simple noise models representing gate noise and environmental noise. We demonstrate that the stabiliser code improves the training accuracy at convergence compared to noisy implementations without error detection. However, we find that the effectiveness and reliability of error detection is contingent upon keeping the ancilla qubit error rates below a specific threshold, due to the propagation of ancilla errors to the physical qubits. Our results provide an important insight that extends to QML implemented with full error-correction: when the QEC code requires ancilla qubits for logical rotations but cannot fully correct errors propagated between ancilla and physical qubits, the maximum achievable accuracy of the QML model is constrained. This highlights the need for additional error correction or mitigation strategies to support the practical implementation of QML algorithms with QEC on quantum devices.

• ML predicts tillage and stubble practices from 22 years of soil data. • Random forest outperforms spatial baseline in predicting farm practices. • Year and latitude are key drivers of no-till and stubble grazing adoption. • Gridded maps reveal national trends in tillage and residue management. • Framework supports GHG accounting modelling and soil health policy development. Tillage and stubble management are key drivers of soil health, crop productivity, and greenhouse gas (GHG) emissions, yet long-term spatially explicit data on these practices remain scarce. Using data from two Australian national soil monitoring programs Soil Carbon Research Project (SCaRP) and Soil Organic Carbon Monitoring (SOC-M) we analyzed over two decades (2001–2023) of tillage and stubble management records collated across 300 Australian farmer paddock scale datasets. We developed machine learning models based on random forest to predict spatial and temporal trends in these practices, incorporating crop type, soil classification, climate variables, and spatial coordinates as predictors. Our models were benchmarked against multinomial logistic regression and a nearest-neighbour baseline, demonstrating that random forest consistently achieved higher accuracy, particularly for dominant practices such as no-tillage and stubble grazing. Variable importance analyses identified “Year” as the most influential predictors, capturing widespread no-tillage adoption post-2010, while latitude and crop type further explained regional variations. Partial dependence plots revealed sharp inflection points in adoption trajectories around 2010, coinciding with major policy and technological shifts. We applied the models to generate gridded predictions (0.5° resolution) across the Australian cropping zone, providing insights into the evolving distribution of tillage and stubble practices. These spatial products not only improve GHG accounting frameworks and Earth system models but also offer actionable intelligence for soil health policy and targeted extension programs. Our approach demonstrates how combining ground-based monitoring with machine learning can fill critical data gaps and provide a scalable framework for agricultural management assessment and climate mitigation strategies.

This paper provides a comprehensive exploration of network resilience in next-generation (xG) cellular communication infrastructures. It begins with an introduction to the network science framework and its relevance to modern communication systems, and then delves into the theoretical foundations of network resilience, including key concepts from graph theory, complex network analysis, and cascading failure models. A detailed examination of network failures in xG networks follows, employing graph-theoretic approaches to analyze failure propagation, identify critical nodes, and assess network vulnerabilities. The paper also outlines practical methodologies for enhancing resilience, such as adaptive topology design, failure prediction, and decentralized architectural frameworks. The paper further discusses future research directions, emphasizing emerging challenges and opportunities in network resilience. It also reviews ongoing standardization efforts aimed at integrating network science principles into communication infrastructure design. Lessons learned and open challenges are summarized that require further investigation, making it a valuable resource for researchers, engineers, and practitioners seeking to advance the resilience and adaptability of xG networks.

Recent years have witnessed a surge in deep learning research, marked by the introduction of expensive generative models like OpenAI’s SORA and GPT, Meta AI’s LLAMA series, and Google’s FLAN, BART, and Gemini models. However, the rapid advancement of large models (LM) has intensified the demand for computing resources, particularly GPUs, which are crucial for their parallel processing capabilities. This demand is exacerbated by limited GPU availability due to supply chain delays and monopolistic acquisition by major tech firms. Distributed Machine Learning (DML) methods, such as Federated Learning (FL), mitigate these challenges by partitioning data and models across multiple servers, though implementing optimizations like tensor and pipeline parallelism remains complex. Blockchain technology emerges as a promising solution, ensuring data integrity, scalability, and trust in distributed computing environments, but still lacks guidance on building practical DML systems. In this paper, we propose a trustworthy distributed machine learning (TDML) framework that leverages blockchain to coordinate remote trainers and validate workloads, achieving privacy, transparency, and efficient model training across public remote computing resources. Experimental validation demonstrates TDML’s efficacy in overcoming performance limitations and malicious node detection, positioning it as a robust solution for scalable and secure distributed machine learning. • Efficient Distributed, Trustworthy, Secure Computing Framework: (i) Proposed a new framework that utilizes blockchain technology to coordinate and verify the workloads of remote trainers in distributed ML to achieve privacy, transparency, and data traceability. • Byzantine Attack Detection and Defense: (i) To overcome the Byzantine attacks from malicious nodes, we proposed malicious detection and incentive-driven mechanisms, respectively. (ii) Conducted experiments verified that our gradient-based malicious detection effectively identifies and isolates malicious trainers from benign nodes. • Comprehensive Performance Evaluation: (i) Conducted comprehensive experiments showcasing the effectiveness of the proposed framework TDML against three baseline techniques. (ii) Experimental results show that our TDML framework surpasses the performance limitations of FedAvg and matches the baseline performance of single-node training.

The potential synergy between quantum communications and future wireless communication systems is explored. By proposing a quantum-native or quantum-by-design philosophy, the survey examines technologies such as quantumdomain (QD) multi-input multi-output, QD non-orthogonal multiple access, quantum secure direct communication, QD resource allocation, QD routing, and QD artificial intelligence. The recent research advances in these areas are summarized. Given the behavior of photonic and particle-like Terahertz (THz) systems, a comprehensive system-oriented perspective is adopted to assess the feasibility of using quantum communications in future systems. This survey also reviews quantum optimization algorithms and quantum neural networks to explore the potential integration of quantum communication and quantum computing in future systems. Additionally, the current status of quantum sensing, quantum radar, and quantum timing is briefly reviewed in support of future applications. The associated research gaps and future directions are identified, including extending the entanglement coherence time, developing THz quantum communications devices, addressing challenges in channel estimation and tracking, and establishing the theoretical bounds and performance tradeoffs of quantum communication, computing, and sensing. This survey offers a unique perspective on the potential for quantum communications to revolutionize future systems and pave the way for even more advanced technologies.