Preferred Networks (Japan)

As AI writing support becomes ubiquitous, the question of how disclosing its use affects reader perception remains critical and underexplored. We conducted a controlled study with 261 participants to examine how disclosing varying levels of AI involvement shifts perceptions of the author across six distinct communicative acts. Our analysis of 990 evaluations reveals that disclosure generally erodes perceived trustworthiness, caring, competence, and likability, with the most precipitous declines observed in social and interpersonal writing. A thematic analysis of participant feedback attributes these negative shifts to a perceived loss of human sincerity, diminished authorial effort, and the contextual inappropriateness of AI. Notably, however, we find that higher AI literacy mitigates these negative perceptions, leading to greater tolerance or even appreciation for AI assistance. Our results highlight the nuanced social dynamics of AI-mediated authorship and inform design implications for transparent, context-sensitive writing systems that better preserve trust and authenticity.

Artificial intelligence (AI) is reshaping computational science, but AI-driven workflows routinely span heterogeneous tasks executed across diverse high-performance computing (HPC) systems. We introduce DPDispatcher, an open-source Python framework for scalable, fault-tolerant task scheduling in such environments with an emphasis on lightweight submission, automatic retries, and robust resumption. DPDispatcher separates connection and file-staging concerns from scheduler control, supports multiple HPC job managers, and provides both local and secure shell (SSH) backends. DPDispatcher has been adopted by more than ten scientific packages. Representative use cases include active learning for machine-learning potentials, free-energy and thermodynamic integration workflows, large-scale materials screening, and large language model (LLM)-driven agents that launch HPC computations. Across these settings, DPDispatcher reduces operational overhead and error rates while improving portability and automation for reliable, high-throughput scientific computing.

Vision Transformers (ViTs) have revolutionized image classification, but their application in dense prediction tasks such as segmentation, particularly for 3D medical imaging, faces scalability challenges. To address these issues, there has been growing interest in utilizing foundation models like the Segment Anything Model (SAM) for medical image analysis. The recent introduction of SAM 2 has generated optimism regarding enhancements for tasks such as 3D MRI segmentation. However, SAM 2’s memory bank mechanism, designed for object tracking and segmentation, introduces computational complexity and memory overhead, limiting training efficiency. In this paper, we address these limitations by proposing a bootstrap and memory annealing approach. The bootstrap mechanism replaces the memory bank with ground truth data, significantly enhancing training speed without compromising performance, achieving a 6x improvement on 3D medical image benchmarks such as KiTS and ACDC. To counteract potential overfitting caused by the bootstrap approach, we introduce memory annealing, which adaptively adjusts the selection of ground truth frames based on validation loss, resulting in a 5.46% improvement on the KiTS dataset. Our approach accelerates model training while maintaining or improving segmentation performance, offering a robust solution for efficient 3D medical image segmentation.

Large models have shown unprecedented capabilities in natural language processing, image generation, and most recently, time series forecasting. This leads us to ask the question: treating market prices as a time series, can large models be used to predict the market? In this paper, we answer this by evaluating the performance of the latest time series foundation model TimesFM on price prediction. We find that due to the irregular nature of price data, directly applying TimesFM gives unsatisfactory results and propose to fine-tune TimeFM on financial data for the task of price prediction. This is done by continual pre-training of the latest time series foundation model TimesFM on price data containing <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathbf{1 0 0}$</tex> million time points, spanning a range of financial instruments spanning hourly and daily granularities. The fine-tuned model demonstrates higher price prediction accuracy than the baseline model. We conduct mock trading for our model in various financial markets and show that it outperforms various benchmarks in terms of returns, sharpe ratio, max drawdown and trading cost.

A computer algorithm to search the symmetries of crystal structures, as implemented in the spglib code, is described. An iterative algorithm is employed to robustly identify space group types, tolerating a certain amount of distortion in the crystal structures. The source code is distributed under the 3-Clause BSD License, a permissive open-source software license. This paper focuses on the algorithm for identifying the space group symmetry of crystal structures.

Micro well-plates are an apparatus commonly used in chemical and biological experiments that are a few centimeters thick and contain wells or divets. In this paper, we aim to solve the task of placing the well-plate onto a well-plate holder (referred to as holder). This task is challenging due to the holder's raised grooves being a few millimeters in height, with a clearance of less than 1 mm between the well-plate and holder, thus requiring precise control during placing. Our placing task has the following challenges: 1) The holder's detected pose is uncertain; 2) the required accuracy is at the millimeter to submillimeter level due to the raised groove's shallow height and small clearance; 3) the holder is not fixed to a desk and is susceptible to movement from external forces. To address these challenges, we developed methods including a) using tactile sensors for accurate pose estimation of the grasped well-plate to handle issue (1); b) sliding the well-plate onto the target holder while maintaining contact with the holder's groove and estimating its orientation for accurate alignment. This allows for high precision control (addressing issue (2)) and prevents displacement of the holder during placement (addressing issue (3)). We demonstrate a high success rate for the well-plate placing task, even under noisy observation of the holder's pose.

Ensuring stable object placement is crucial to prevent objects from toppling over, breaking, or causing spills. When an object makes initial contact to a surface, and some force is exerted, the moment of rotation caused by the instability of the object's placing can cause the object to rotate in a certain direction (henceforth referred to as direction of corrective rotation). Existing methods often employ a Force/Torque (F/T) sensor to estimate the direction of corrective rotation by detecting the moment of rotation as a torque. However, its effectiveness may be hampered by sensor noise and the tension of the external wiring of robot cables. To address these issues, we propose a method for stable object placing using GelSights, vision-based tactile sensors, as an alternative to F/T sensors. Our method estimates the direction of corrective rotation of objects using the displacement of the black dot pattern on the elastomeric surface of GelSight. We calculate the Curl from vector analysis, indicative of the rotational field magnitude and direction of the displacement of the black dots pattern. Simultaneously, we calculate the difference (Diff ) of displacement between the left and right fingers' GelSight's black dots. Then, the robot can manipulate the objects' pose using Curl and Diff features, facilitating stable placing. Across experiments, handling 18 differently characterized objects, our method achieves precise placing accuracy (less than 1-degree error) in nearly 100% of cases. 3 1 K.Takahashi and S. Masuda are with Preferred Networks, Inc. {takahashi, masuda}@preferred.jp 2 T.Taniguchi is with Ritsumeikan University, College of Information Science and Engineering.