Google (United States)

As urban waste issues become increasingly severe, traditional waste classification methods can no longer meet the demands for efficiency and accuracy. Deep learning technology has gradually become a key solution to this problem, yet improving classification accuracy, processing efficiency, and model adaptability in complex scenarios remains a challenge. This paper proposes a lightweight waste classification model, ResSeMo, based on multi-module collaborative optimization. The model combines ResNeXt's multi-scale feature extraction, SENet's channel attention mechanism, and MobileNetV3's lightweight design to effectively improve classification accuracy, reduce computational overhead, and enhance robustness in complex backgrounds. Experimental results show that ResSeMo achieved 91.3% accuracy on the TrashNet dataset and 86.9% on the TACO dataset, maintaining high stability in disturbance experiments. Specifically, with Gaussian noise and random blur, ResSeMo's accuracy dropped by only 4.1% on TrashNet and 6.4% on TACO. Compared to traditional models like VGG16, it demonstrates a clear advantage in anti-disturbance performance. This model not only performs excellently on standard datasets but also adapts to waste classification needs in complex environments, providing strong support for smart waste classification systems.

Insulin resistance (IR), a primary precursor to type 2 diabetes, is characterized by impaired insulin action in tissues1. However, diagnostic methods remain expensive and inaccessible, which hinders early intervention2,3. Here we present the WEAR-ME study, a large, remotely conducted study of IR (n = 1,165 participants; median body mass index (BMI) = 28 kg m-2, medianage = 45 years, medianhaemoglobin A1c (HbA1c) = 5.4%) that uses time-series data from wearable devices and routine blood biomarkers to train deep neural networks against a ground-truth measure of IR (homeostatic model assessment of IR; HOMA-IR). Using a HOMA-IR cut-off of 2.9, our multimodal model achieved robust performance (area under the receiver operating characteristic curve (AUROC) = 0.80, sensitivity = 76%, specificity = 84%) with data from wearable devices, together with demographic and routine blood biomarker data. To enhance the use of time-series data from wearables, we fine-tuned a wearable foundation model (WFM) pretrained on 40 million hours of sensor data. In an independent validation cohort (n = 72), a model integrating WFM-derived representations with demographic data surpassed a demographics-only baseline (AUROC = 0.75 versus 0.66). Moreover, adding WFM-derived representations to a model with demographics, fasting glucose and a lipid panel substantially improved performance, compared with an identical model without data from wearables (AUROC = 0.88 versus 0.76). We integrate IR prediction into a large language model to contextualize the results and facilitate personalized recommendations. This work establishes a scalable, accessible framework for the early detection of metabolic risk, which could enable timely lifestyle interventions to prevent progression to type 2 diabetes.

Rank aggregation is a task of combining the rankings of items from multiple users into a single ranking that best represents the users' rankings. Alabi et al. (AAAI'22) presents differentially-private (DP) polynomial-time approximation schemes (PTASes) and 5-approximation algorithms with certain additive errors for the Kemeny rank aggregation problem in both central and local models. In this paper, we present improved DP PTASes with smaller additive error in the central model. Furthermore, we are first to study the footrule rank aggregation problem under DP. We give a near-optimal algorithm for this problem; as a corollary, this leads to 2-approximation algorithms with the same additive error as the 5-approximation algorithms of Alabi et al. for the Kemeny rank aggregation problem in both central and local models.

Coarse-Grained Reconfigurable Arrays (CGRAs) are a promising solution for energy-efficient acceleration across multiple application domains. Yet, CGRAs face significant scalability challenges that hinder their widespread adoption, stemming from three main concerns: (1) Mapping Scalability — existing mapping algorithms struggle to find feasible and optimal solutions as the design complexity grows; (2) Architectural Limitations — rigid mapping granularity and memory access restrict flexibility and performance; and (3) Dynamic Multi-Kernel Support — dynamic and simultaneous execution of multiple kernels are not thoroughly explored, limiting the applicability of CGRAs in complex multi-kernel scenarios.

Optical health sensing algorithms, such as SpO2, sleep monitoring, and metabolic health sensing, critically depend on the accurate measurement of optical emission from Light Emitting Diodes (LEDs) transmitted through user tissue and detected by a photodiode (PD). A significant challenge to the reliability of these measurements is the inherent degradation of LED optical emission intensity over time due to device aging. This degradation can confound the physiological changes being monitored. Our work quantifies the impact of LED aging on sensor signal integrity, specifically examining the Current Transfer Ratio (CTR), which is a key metric defining the ratio of received photocurrent to the LED drive current used for transmission in various health sensing algorithms. We investigate the degradation characteristics across LEDs of different wavelengths. Our findings indicate a relative CTR change due to degradation ranging from 1% to 8% within 100 hours of continuous operation which translates to approximately 3.5 to 7 years of device lifetime. Furthermore, we explore the non-linearity of this degradation and the observed initial ”overshoot” phenomenon in the CTR during aging. We discuss how understanding these dynamics could inform the development of robust specifications for different physiological sensing algorithms. Finally, we present several potential solutions to mitigate the effects of LED aging. During the product design phase, integrating a calibrating photodiode or compensating circuitry around the LED can help preemptively address degradation. In the application space, run-time calibration strategies employing two differently degraded optical paths offer a promising approach to maintain measurement accuracy.

The large volume of abdominal computed tomography (CT) scans1,2 coupled with the shortage of radiologists3-6 have intensified the need for automated medical image analysis tools. Previous state-of-the-art approaches for automated analysis leverage vision-language models (VLMs) that jointly model images and radiology reports7-12. However, current medical VLMs are generally limited to 2D images and short reports. Here to overcome these shortcomings for abdominal CT interpretation, we introduce Merlin, a 3D VLM that learns from volumetric CT scans, electronic health record dataand radiology reports. This approach is enabled by a multistage pretraining framework that does not require additional manual annotations. We trained Merlin using a high-quality clinical dataset of paired CT scans (>6 million images from 15,331CT scans), diagnosis codes (>1.8 million codes) and radiology reports (>6 million tokens). We comprehensively evaluated Merlin on 6 task types and 752 individual tasks that covered diagnostic, prognostic and quality-related tasks. The non-adapted (off-the-shelf) tasks included zero-shot classification of findings (30findings), phenotype classification (692 phenotypes) and zero-shot cross-modal retrieval (image-to-findings and image-to-impression). The model-adapted tasks included 5-year chronic disease prediction (6 diseases), radiology report generation and 3D semantic segmentation (20 organs). We validated Merlin at scale, with internal testing on 5,137 CT scans and external testing on 44,098 CT scans from 3 independent sites and 2 public datasets. The results demonstrated high generalization across institutions and anatomies. Merlin outperformed 2D VLMs, CT foundation models and off-the-shelf radiology models. We also computed scaling laws and conducted ablation studies to identify optimal training strategies. We release our trained models, code and dataset for 25,494 pairs of abdominal CT scans and radiology reports. Ourresults demonstrate how Merlin may assist in the interpretation of abdominal CT scans and mitigate the burden on radiologists while simultaneously adding value for future biomarker discovery and disease risk stratification.

Artificial intelligence (AI) promises to enhance breast cancer screening. Here we evaluated Google's mammography AI system (version 1.2) across two phases: a retrospective study using 115,973 mammograms from five National Health Service screening services with 39-month follow-up and prospective noninterventional feasibility deployment at 12 sites (9,266 cases). The primary endpoint was AI sensitivity and specificity versus first reader using a 5% noninferiority margin. The secondary endpoints were performance versus second or consensus readers and breast-level analyses. Retrospectively, AI achieved superior sensitivity (0.541 versus 0.437 for first reader, P < 0.001) and noninferior specificity (0.943 versus 0.952, P < 0.001). Cancer detection rate increased from 7.54 to 9.33 per 1,000 women, with AI detecting 25.0% of interval cancers. Performance was particularly strong for first screens (39.3% fewer recalls, 8.8% higher detection) and invasive cancers. No systematic demographic disparities were observed. Simulated second-reader replacement reduced reading time by 32% while increasing detection by 17.7%. Prospective deployment confirmed technical feasibility but revealed a distribution shift requiring threshold recalibration. Implementation requires adaptive calibration and continuous monitoring to ensure safety and equity.

Abstract This paper presents the dynamical core of the Climate Modeling Alliance (CliMA) atmosphere model, designed for efficient simulation of a wide range of atmospheric flows across scales. The core uses the nonhydrostatic equations of motion for a deep atmosphere, discretized with a hybrid approach that combines a spectral element method (SEM) in the horizontal and a staggered finite‐difference method in a height‐based, terrain‐following coordinate in the vertical. This approach leverages the high‐order accuracy and scalability of the SEM, while maintaining the computational efficiency and stability of finite differences on a staggered grid. The model's coordinate‐independent equation set allows for simulations in a variety of geometries and planetary configurations. The use of the specific total energy of moist air as a prognostic variable, along with a consistent thermodynamic formulation, ensures the conservation of energy, air mass, and water mass, even in moist atmospheres and in the presence of subgrid‐scale parameterizations, without ad hoc fixers. A horizontally explicit, vertically implicit (HEVI) timestepping strategy treats fast vertical processes implicitly and further enhances computational efficiency by allowing larger timesteps. The model demonstrates excellent strong and weak scaling on CPUs and GPUs, making it well‐suited for high‐resolution simulations on modern supercomputing architectures, including those on the cloud, which widens access to climate models.

The complexity and dynamism of business management system pose higher requirements for the accuracy and timeliness of fault diagnosis. This paper proposes a compensation distance evaluation technique-kernel principal component analysis-convolutional neural network-bidirectional long short-term memory network (CDETKPCA-CNN-BiLSTM) that integrates attention mechanism to address the limitations of traditional diagnostic methods in nonlinear and high-dimensional data scenarios. The bidirectional long short-term memory (BiLSTM) layer and attention mechanism layer further improve the accuracy and reliability of fault diagnosis. Feature extraction is performed in business management system data from both time and frequency domains, effectively utilizing temporal information to form an initial feature set. To address the issue of data redundancy in business management system, a compensation distance evaluation technique and kernel principal component analysis (CDETKPCA) feature fusion method is proposed. Through CDET, the initial feature set is screened and weighted to guide KPCA feature fusion processing, generating a fused feature set for subsequent fault diagnosis research. The experimental results show that CDETKPCA-CNN-BiLSTM can extract effective information more efficiently and significantly improve analysis accuracy. And this provides a new technical method for fault diagnosis in business management system.