Large Trajectory Models are Scalable Motion Predictors and Planners

Motion prediction and planning are vital tasks in autonomous driving, and recent efforts have shifted to machine learning-based approaches. The challenges include understanding diverse road topologies, reasoning traffic dynamics over a long time horizon, interpreting heterogeneous behaviors, and generating policies in a large continuous state space. Inspired by the success of large language models in addressing similar complexities through model scaling, we introduce a scalable trajectory model called State Transformer (STR). STR reformulates the motion prediction and motion planning problems by arranging observations, states, and actions into one unified sequence modeling task. With a simple model design, STR consistently outperforms baseline approaches in both problems. Remarkably, experimental results reveal that large trajectory models (LTMs), such as STR, adhere to the scaling laws by presenting outstanding adaptability and learning efficiency. Qualitative results further demonstrate that LTMs are capable of making plausible predictions in scenarios that diverge significantly from the training data distribution. LTMs also learn to make complex reasonings for long-term planning, without explicit loss designs or costly high-level annotations

Preliminary Test Results for the MICE Spectrometer Superconducting Solenoids

This report describes the MICE spectrometer solenoids as built. Each magnet consists of five superconducting coils. Two coils are used to tune the beam going from or to the MICE spectrometer from the rest of the MICE cooling channel. Three spectrometer coils (two end coils and a long center coil) are used to create a uniform 4 T field (to {+-}0.3 percent) over a length of 1.0 m within a diameter of 0.3 m. The three-coil spectrometer set is connected in series. The two end coils use small power supplies to tune the uniform field region where the scintillating fiber tracker is located. This paper will present the results of the preliminary testing of the first spectrometer solenoid

Status of Muon Collider Research and Development and Future Plans

The status of the research on muon colliders is discussed and plans are outlined for future theoretical and experimental studies. Besides continued work on the parameters of a 3-4 and 0.5 TeV center-of-mass (CoM) energy collider, many studies are now concentrating on a machine near 0.1 TeV (CoM) that could be a factory for the s-channel production of Higgs particles. We discuss the research on the various components in such muon colliders, starting from the proton accelerator needed to generate pions from a heavy-Z target and proceeding through the phase rotation and decay ($\pi \to \mu \nu_{\mu}$) channel, muon cooling, acceleration, storage in a collider ring and the collider detector. We also present theoretical and experimental R & D plans for the next several years that should lead to a better understanding of the design and feasibility issues for all of the components. This report is an update of the progress on the R & D since the Feasibility Study of Muon Colliders presented at the Snowmass'96 Workshop [R. B. Palmer, A. Sessler and A. Tollestrup, Proceedings of the 1996 DPF/DPB Summer Study on High-Energy Physics (Stanford Linear Accelerator Center, Menlo Park, CA, 1997)].Comment: 95 pages, 75 figures. Submitted to Physical Review Special Topics, Accelerators and Beam

Stabilization of the 81-channel coherent beam combination using machine learning.

We develop a rapidly converging algorithm for stabilizing a large channel-count diffractive optical coherent beam combination. An 81-beam combiner is controlled by a novel, machine-learning based, iterative method to correct the optical phases, operating on an experimentally calibrated numerical model. A neural-network is trained to detect phase errors based on interference pattern recognition of uncombined beams adjacent to the combined one. Due to the non-uniqueness of solutions in the full space of possible phases, the network is trained within a limited phase perturbation/error range. This also reduces the number of samples needed for training. Simulations have proven that the network can converge in one step for small phase perturbations. When the trained neural-network is applied to a realistic case of 360 degree full range, an iterative scheme exploits random walking at the beginning, with the accuracy of prediction on phase feedback direction, to allow the neural-network to step into the training range for fast convergence. This neural-network-based iterative method of phase detection works tens of times faster than the commonly used stochastic parallel gradient descent approach (SPGD) using a single-detector and random dither when both are tested with random phase perturbations

81-beam coherent combination using a programmable array generator.

We have generated 81 independently controllable beams using a spatial light modulator and combined them on a diffractive combiner, to characterize the combiner and develop a fast phase error detection scheme. A key parameter of the diffractive combiner is measured in a new way, enabling an efficient combination when programming calibrated phases of each beam. This testbed provides a platform for development of advanced feedback phase control of high channel-count beam combination

81-beam coherent combination using a programmable array generator.

We have generated 81 independently controllable beams using a spatial light modulator and combined them on a diffractive combiner, to characterize the combiner and develop a fast phase error detection scheme. A key parameter of the diffractive combiner is measured in a new way, enabling an efficient combination when programming calibrated phases of each beam. This testbed provides a platform for development of advanced feedback phase control of high channel-count beam combination

Optimizing the Aromatic Product Distribution from Catalytic Fast Pyrolysis of Biomass Using Hydrothermally Synthesized Ga-MFI Zeolites

A series of gallium-containing MFI (Ga-MFI) zeolites with varying Ga2O3/Al2O3 ratios were synthesized using hydrothermal synthesis and tested as catalyst in catalytic fast pyrolysis (CFP) of beech wood for aromatic production. The results show that the incorporation of Ga slightly reduced the effective pore size of Ga-MFI zeolites compared to conventional HZSM-5 zeolites. Therefore, the Ga-MFI zeolites increased the aromatic selectivity for smaller aromatics such as benzene, toluene, and p-xylene and decreased the aromatic selectivity for bulkier ones such as m-xylene, o-xylene, and polyaromatics in CFP of beech wood relative to HSZM-5. In particular, the yield and selectivity of p-xylene, the most desired product from CFP of biomass, increased considerably from 1.64 C% and 33.3% for conventional HZSM-5 to 2.98–3.34 C% and 72.1–79.6% for the synthesized Ga-MFI zeolites. These results suggest that slightly reducing the pore size of MFI zeolite by Ga incorporation has a beneficial effect on optimizing the aromatic selectivity toward more valuable monoaromatic products, especially p-xylene, during CFP of biomass