Apple (United States)

Acute toxicity of pesticides to wild bee species is inferred by using the LD50 values of the western honey bee, Apis mellifera, as a standard baseline. However, substantial discrepancies are often reported between the LD50 values for wild bees and those for A. mellifera. Here, we conducted tests to compare acute toxicity between Japanese mason bees (Osmia spp.) and the western honey bee. For nearly all tested insecticides, the mortality rates of mason bees were equivalent to, or slightly lower than, those of the western honey bee. However, the LD50 of acetamiprid in male mason bees was approximately five-fold lower than that in workers of the western honey bee. These results suggest that, although the current pesticide risk assessment is generally conservative for Japanese mason bees, continued attention is warranted to ensure that their sensitivity—particularly to acetamiprid—remains within the presumed safety factor.

This paper introduces a novel Model Predictive Control (MPC) implementation for legged robot locomotion that leverages GPU parallelization. Our approach enables both temporal and state-space parallelization by incorporating a parallel associative scan to solve the primal-dual Karush-Kuhn-Tucker (KKT) system. In this way, the optimal control problem is solved in <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\mathcal {O}(\log ^{2}(n)\log {N} + \log ^{2}(m))$</tex-math></inline-formula> complexity, instead of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\mathcal {O}(N(n + m)^{3})$</tex-math></inline-formula>, where <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$n$</tex-math></inline-formula>, <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$m$</tex-math></inline-formula>, and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$N$</tex-math></inline-formula> are the dimension of the system state, control vector, and the length of the prediction horizon. We demonstrate the advantages of this implementation over two state-of-the-art solvers (acados and crocoddyl), achieving up to a 60% improvement in runtime for Whole Body Dynamics (WB)-MPC and a 700% improvement for Single Rigid Body Dynamics (SRBD)-MPC when varying the prediction horizon length. The presented formulation scales efficiently with the problem state dimensions as well, enabling the definition of a centralized controller for up to 16 legged robots that can be computed in less than 25 ms. Furthermore, thanks to the JAX implementation, the solver supports large-scale parallelization across multiple environments, allowing the possibility of performing learning with the MPC in the loop directly in GPU. The code associated with this work can be found at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/iit-DLSLab/mpx</uri>.

Sonocrystallization, a process that uses sound waves to control the formation and growth of crystals, is gaining attention in the chemical mechanical planarization (CMP) process for semiconductor manufacturing. CMP is a polishing technique used to smooth and flatten the surfaces of wafers during chip production. One of the main challenges in CMP is the need for high-performance polishing slurries—liquid mixtures that contain abrasive particles and chemicals. The size and uniformity of these particles are critical, as they directly impact how evenly the wafer is polished and how well it performs. Sonocrystallization offers a unique solution by using ultrasound to produce particles with highly uniform sizes. This improves the polishing efficiency and reduces defects on the wafer surface, leading to more reliable chips. Additionally, the technique allows for better control over slurry composition, making it easier to tailor slurries for advanced semiconductor materials, such as those used in 3D chips or next-generation transistors. As semiconductor devices become smaller and more complex, sonocrystallization in CMP slurries is emerging as a promising innovation to meet the industry's high standards for precision and performance.

Aging biomarkers play a vital role in understanding longevity, with the potential to improve clinical decisions and interventions. Existing aging clocks typically use blood, vitals, or imaging collected in a clinical setting. Wearables, in contrast, can make frequent and inexpensive measurements throughout daily living. Here we develop PpgAge, an aging clock using photoplethysmography at the wrist from a consumer wearable. Using the Apple Heart & Movement Study (n = 213,593 participants; >149 million participant-days), our observational analysis shows that this non-invasive and passively collected aging clock accurately predicts chronological age and captures signs of healthy aging. Participants with an elevated PpgAge gap (i.e., predicted age greater than chronological age) have significantly higher diagnosis rates of heart disease, heart failure, and diabetes. Elevated PpgAge gap is also a significant predictor of incident heart disease events (and new diagnoses) when controlling for relevant risk factors. PpgAge also associates with behavior, including smoking, exercise, and sleep. Longitudinally, PpgAge exhibits a sharp increase during pregnancy and concurrent with certain types of cardiac events.

Embedded systems do not benefit from strong memory protection, because they are designed to minimize cost. At the same time, there is increasing pressure to connect embedded devices to the internet, where their vulnerable nature makes them routinely subject to compromise. This fundamental tension leads to the current status-quo where exploitable devices put individuals and critical infrastructure at risk.

This paper aims at improving the lowest usable frequency (LUF) of a mode-stirred chamber. To characterize a standard-sized chamber, a 5:1 scaled-down chamber is designed to simplify testing of modifications to the chamber. In the proposed method, varactor tuned resonators are used in the mode-stirred chamber to generate additional modes at frequencies between the first resonant mode and the LUF, where the chamber has a low mode density. This method can be used for emission measurements of a device under test (DUT). The field homogeneity in the working volume of the scaled-down chamber is measured mainly from 400 MHz to 1000 MHz (actual chamber: 80 MHz to 200 MHz).

This paper introduces a framework, called <monospace xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">EMOTION</monospace>, for generating expressive motion sequences in humanoid robots, enhancing their ability to engage in human-like non-verbal communication. Non-verbal cues such as facial expressions, gestures, and body movements play a crucial role in effective interpersonal interactions. Despite the advancements in robotic behaviors, existing methods often fall short in mimicking the diversity and subtlety of human non-verbal communication. To address this gap, our approach leverages the in-context learning capability of large language models (LLMs) to dynamically generate socially appropriate gesture motion sequences for human-robot interaction. We use this framework to generate 10 different expressive gestures and conduct online user studies comparing the naturalness and understandability of the motions generated by <monospace xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">EMOTION</monospace> and its human-feedback version, <monospace xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">EMOTION++</monospace>, against those by human operators. The results demonstrate that our approach either matches or surpasses human performance in generating understandable and natural robot motions under certain scenarios. We also provide design implications for future research to consider a set of variables when generating expressive robotic gestures.

Low-cost retrieval is crucial for document search on resource-limited computing platforms. This paper presents a staged sparse-to-dense retrieval framework that substitutes expensive dense query encoding with a dense pseudo-query (DPQ), an approximation derived solely from sparse retrieval results. DPQ scheme employs a simple, rank-aware weighting to combine corresponding dense representations of top sparse results, providing an opportunity to efficiently leverage an expensive but expressive LLM or BERT-based dense model without requiring GPUs. The evaluation demonstrates that DPQ-based retrieval runs fast on an affordable platform and outperforms several low-cost baselines in zero-shot retrieval.