Yikun Zhao | Searcharxiv

arXiv2026

Development of a xenon triple point apparatus suitable for calibrating long-stem SPRTs and preliminary measurements of the temperature

Xenon is of high chemical-physical stability and health compatibility. The xenon triple point (Xe TP) is accounted for a promising candidate replacing the mercury triple point (Hg TP) from the set of the defining fixed points of the international temperature scale ITS-90. The success of the alternative highly depends on the level of the realization of the Xe TP using long-stem standard platinum resistance thermometers (LSPRTs). We report in this article our study on the development of an immersion-type Xe TP apparatus, which is suitable for calibration of both LSPRTs and capsule standard platinum resistance thermometers (CSPRT). We realize the melting plateaus of the Xe TP using the continuous heating method on the apparatus. The effective melting plateaus extend for 8-12 hours long with temperature flatness range of 0.37 mK-1.0 mK over the melted fractions from 0.2 to 0.75. We find the axial heat leak contributing a principal effect influencing measurements of the Xe TP. We investigate the effect by varying the offset temperatures on the outer wall of the Xe TP cell. We measure the Xe TP using two LSPRTs upon correction of the axial heat leak. The new measurement, giving the Xe TP of 161.405 71 (55) K (k=1) at the melted fraction F=1.0, agrees well with those previously obtained by the adiabatic apparatuses. Their differences fall within 0.11 mK to 0.42 mK. by. Those differences are well covered by the estimated measurement uncertainty.

physics.ins-det↗

arXiv2025

Orientation Matters: Making 3D Generative Models Orientation-Aligned

Humans intuitively perceive object shape and orientation from a single image, guided by strong priors about canonical poses. However, existing 3D generative models often produce misaligned results due to inconsistent training data, limiting their usability in downstream tasks. To address this gap, we introduce the task of orientation-aligned 3D object generation: producing 3D objects from single images with consistent orientations across categories. To facilitate this, we construct Objaverse-OA, a dataset of 14,832 orientation-aligned 3D models spanning 1,008 categories. Leveraging Objaverse-OA, we fine-tune two representative 3D generative models based on multi-view diffusion and 3D variational autoencoder frameworks to produce aligned objects that generalize well to unseen objects across various categories. Experimental results demonstrate the superiority of our method over post-hoc alignment approaches. Furthermore, we showcase downstream applications enabled by our aligned object generation, including zero-shot object orientation estimation via analysis-by-synthesis and efficient arrow-based object rotation manipulation.

cs.CV↗