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Using unmanned aerial vehicles (UAVs) to track multiple individuals simultaneously in their natural environment is a powerful approach for better understanding the collective behavior of primates. Previous studies have demonstrated the feasibility of automating primate behavior classification from video data, but these studies have been carried out in captivity or from ground-based cameras. However, to understand group behavior and the self-organization of a collective, the whole troop needs to be seen at a scale where behavior can be seen in relation to the natural environment in which ecological decisions are made. To tackle this challenge, this study presents a novel dataset for baboon detection, tracking, and behavior recognition from drone videos where troops are observed on-the-move in their natural environment as they move to and from their sleeping sites. Videos were captured from drones at Mpala Research Centre, a research station located in Laikipia County, in central Kenya. The baboon detection dataset was created by manually annotating all baboons in drone videos with bounding boxes. A tiling method was subsequently applied to create a pyramid of images at various scales from the original 5.3K resolution images, resulting in approximately 30K images used for baboon detection. The baboon tracking dataset is derived from the baboon detection dataset, where bounding boxes are consistently assigned the same ID throughout the video. This process resulted in half an hour of dense tracking data. The baboon behavior recognition dataset was generated by converting tracks into mini-scenes, a video subregion centered on each animal. These mini-scenes were annotated with 12 distinct behavior types and one additional category for occlusion, resulting in over 20 hours of data. Benchmark results show mean average precision (mAP) of 92.62% for the YOLOv8-X detection model, multiple object tracking precision (MOTP) of 87.22% for the DeepSORT tracking algorithm, and micro top-1 accuracy of 64.89% for the X3D behavior recognition model. Using deep learning to rapidly and accurately classify wildlife behavior from drone footage facilitates non-invasive data collection on behavior enabling the behavior of a whole group to be systematically and accurately recorded. The dataset can be accessed at https://baboonland.xyz. 

The lack of a detailed mechanistic understanding for plasmon-mediated charge transfer at metal-semiconductor interfaces severely limits the design of efficient photovoltaic and photocatalytic devices. A major remaining question is the relative contribution from indirect transfer of hot electrons generated by plasmon decay in the metal to the semiconductor compared to direct metal-to-semiconductor interfacial charge transfer. Here, we demonstrate an overall electron transfer efficiency of 44 ± 3% from gold nanorods to titanium oxide shells when excited on resonance. We prove that half of it originates from direct interfacial charge transfer mediated specifically by exciting the plasmon. We are able to distinguish between direct and indirect pathways through multimodal frequency-resolved approach measuring the homogeneous plasmon linewidth by single-particle scattering spectroscopy and time-resolved transient absorption spectroscopy with variable pump wavelengths. Our results signify that the direct plasmon-induced charge transfer pathway is a promising way to improve hot carrier extraction efficiency by circumventing metal intrinsic decay that results mainly in nonspecific heating. 

Continued advances in technology have led to falling costs and a dramatic increase in the aggregate amount of solar capacity installed across the world. A drawback of increased solar penetration is the potential for supply-demand mismatches in the grid due to the intermittent nature of solar generation. While energy storage can be used to mask such problems, we argue that there is also a need to explicitly control the rate of solar generation of each solar array in order to achieve high penetration while also handling supply-demand mismatches. To address this issue, we present the notion of smart solar arrays that can actively modulate their solar output based on the notion of proportional fairness. We present a decentralized algorithm based on Lagrangian optimization that enables each smart solar array to make local decisions on its fair share of solar power it can inject into the grid and then present a sense-broadcast-respond protocol to implement our decentralized algorithm into smart solar arrays. We also study the benefits of using energy storage when we rate control solar. To do so, we present a decentralized algorithm to charge and discharge batteries for each smart solar. Our evaluation on a city-scale dataset shows that our approach enables 2.6× more solar penetration while causing smart arrays to reduce their output by as little as 12.4%. By employing an adaptive gradient approach, our decentralized algorithm has 3 to 30× faster convergence. Finally, we demonstrate energy storage can help netmeter more solar energy while ensuring fairness and grid constraints are met. 

Programmed death-ligand 1 (PD-L1) drives inhibition of antigen-specific T cell responses through engage- ment of its receptor programmed death-1 (PD-1) on activated T cells. Overexpression of these immune checkpoint proteins in the tumor microenvironment has motivated the design of targeted antibodies that disrupt this interaction. Despite clinical success of these antibodies, response rates remain low, necessi- tating novel approaches to enhance performance. Here, we report the development of antibody fusion pro- teins that block immune checkpoint pathways through a distinct mechanism targeting molecular trafficking. By engaging multiple receptor epitopes on PD-L1, our engineered multiparatopic antibodies induce rapid clustering, internalization, and degradation in an epitope- and topology-dependent manner. The comple- mentary mechanisms of ligand blockade and receptor downregulation led to more durable immune cell acti- vation and dramatically reduced PD-L1 availability in mouse tumors. Collectively, these multiparatopic anti- bodies offer mechanistic insight into immune checkpoint protein trafficking and how it may be manipulated to reprogram immune outcomes. 

Plasmonic photocatalysis uses the light-induced resonant oscillation of free electrons in a metal nanoparticle to concentrate optical energy for driving chemical reactions. By altering the joint electronic structure of the catalyst and reactants, plasmonic catalysis enables reaction pathways with improved selectivity, activity, and catalyst stability. However, designing an optimal catalyst still requires a fundamental understanding of the underlying plasmonic mechanisms at the spatial scales of single particles, at the temporal scales of electron transfer, and in conditions analogous to those under which real reactions will operate. Thus, in this review, we provide an overview of several of the available and developing nanoscale and ultrafast experimental approaches, emphasizing those that can be performed in situ. Specifically, we discuss high spatial resolution optical, tip-based, and electron microscopy techniques; high temporal resolution optical and x-ray techniques; and emerging ultrafast optical, x-ray, tip-based, and electron microscopy techniques that simultaneously achieve high spatial and temporal resolution. Ab initio and classical continuum theoretical models play an essential role in guiding and interpreting experimental exploration, and thus, these are also reviewed and several notable theoretical insights are discussed. 

Although photothermal imaging was originally designed to detect individual molecules that do not emit or small nanoparticles that do not scatter, the technique is now being applied to image and spectroscopically characterize larger and more sophisticated nanoparticle structures that scatter light strongly. Extending photothermal measurements into this regime, however, requires revisiting fundamental assumptions made in the interpretation of the signal. Herein, we present a theoretical analysis of the wavelength-resolved photothermal image and its extension to the large particle scattering regime, where we find the photothermal signal to inherit a nonlinear dependence upon pump intensity, together with a contraction of the full-width-at-half-maximum of its point spread function. We further analyze theoretically the extent to which photothermal spectra can be interpreted as an absorption spectrum measure, with deviations between the two becoming more prominent with increasing pump intensities. Companion experiments on individual 10, 20, and 100 nm radius gold nanoparticles evidence the predicted nonlinear pump power dependence and image contraction, verifying the theory and demonstrating new aspects of photothermal imaging relevant to a broader class of targets.