In the era of foundation models, fine-tuning pre-trained models for specific downstream tasks has become crucial. This drives the need for robust fine-tuning methods to address challenges such as model overfitting and sparse labeling. Moleculargraph foundation models (MGFMs) face unique difficulties that complicate fine-tuning. These models are limited by smaller pre-training datasets and more severedata scarcity for downstream tasks, both of which require enhanced model generalization. Moreover, MGFMs must accommodate diverse objectives, including bothregression and classification tasks. To better understand and improve fine-tuningtechniques under these conditions, we classify eight fine-tuning methods into threemechanisms: weight-based, representation-based, and partial fine-tuning. Webenchmark these methods on downstream regression and classification tasks acrosssupervised and self-supervised pre-trained models in diverse labeling settings. Thisextensive evaluation provides valuable insights and informs the design of a refinedrobust fine-tuning method, ROFT-MOL. This approach combines the strengths ofsimple post-hoc weight interpolation with more complex weight ensemble fine-tuning methods, delivering improved performance across both task types whilemaintaining the ease of use inherent in post-hoc weight interpolation.

Foundation models have been transformational in machine learning fields such as natural language processing and computer vision. Similar success in atomic property prediction has been limited due to the challenges of training effective models across multiple chemical domains. To address this, we introduce Joint Multi-domain Pre-training (JMP), a supervised pre-training strategy that simultaneously trains on multiple datasets from different chemical domains, treating each dataset as a unique pre-training task within a multi-task framework. Our combined training dataset consists of $\sim$120M systems from OC20, OC22, ANI-1x, and Transition-1x. We evaluate performance and generalization by fine-tuning over a diverse set of downstream tasks and datasets including: QM9, rMD17, MatBench, QMOF, SPICE, and MD22. JMP demonstrates an average improvement of 59% over training from scratch and matches or sets state-of-the-art on 34 out of 40 tasks. Our work highlights the potential of pre-training strategies that utilize diverse data to advance property prediction across chemical domains, especially for low-data tasks.

With fully autonomous flight capabilities coupled with user-specific applications, drones, in particular quadcopter drones, are becoming prevalent solutions in myriad commercial and research contexts. However, autonomous drones must operate within constraints and design considerations that are quite different from any other compute-based agent. At any given time, a drone must arbitrate among its limited compute, energy, and electromechanical resources. Despite huge technological advances in this area, each of these problems has been approached in isolation and drone systems design-space tradeoffs are largely unknown. To address this knowledge gap, we formalize the fundamental drone subsystems and find how computations impact this design space. We present a design-space exploration of autonomous drone systems and quantify how we can provide productive solutions. As an example, we study widely used simultaneous localization and mapping (SLAM) on various platforms and demonstrate that optimizing SLAM on FPGA is more fruitful for the drones. Finally, to address the lack of publicly available experimental drones, we release our open-source drone that is customizable across the hardware-software stack.

A key real-time task in autonomous systems is simultaneous localization and mapping (SLAM). Although prior work has proposed hardware accelerators to process SLAM in real time, they paid less attention to power consumption. To be more power-efficient, we propose Pisces, which co-optimizes power consumption and latency by exploiting sparsity, a key characteristic of SLAM missed in prior work. By orchestrating sparse data, Pisces aligns correlated data and enables deterministic, one-time, and parallel accesses to the on-chip memory. Therefore, Pisces (i) eliminates unnecessary memory accesses and (ii) enables pipelined and parallel processing. Our FPGA implementation shows that Pisces consumes 2.5× less power and executes SLAM 7.4× faster than the state of the art.

Fully autonomous drones have a plethora of applications in the real world, from agriculture and communication to public services. With increasing attention, a new market segment has opened up for highly efficient drones. However, the deployment of efficient drones requires an in-depth analysis of several components spanning from hardware sensors to software stack. Specifically, to achieve high reliability, safety, and performance, the top concerns in the professional drone industry are characterizing underlying architecture and flight stack. In this paper, we characterize a widely-used open source flight stack, ArduCopter, to understand the performance requirements as a research community. Additionally, we study how area-specific applications affect flight stack. Our characterizations and benchmarks indicate that the drone flying range can be dramatically increased by optimizing the underlying flight controller software.