Primordial Orbital Alignment of Sednoids

We examined the past history of the three most detached TransNeptunian Objects (TNOs) -- Sedna, 2012 VP113, and Leleakuhonua (2015 TG387) -- the three clearest members of the dynamical class known as sednoids, with high perihelia distances $q$. By integrating backward their nominal (and a set of cloned) orbits for the Solar System's age, we surprisingly find that the only time all their apsidal lines tightly cluster was 4.5 Gyr ago, at perihelion longitude $\varpi$ of 200{\deg}. This "primordial alignment" is independent of the observational biases that contribute to the current on-sky clustering in the large-semimajor axis Kuiper Belt. If future sednoid discoveries confirm these findings, this strongly argues for an initial event during the planet formation epoch which imprinted this particular apsidal orientation on the early detached TNO population and then subsequently modified only by the simple precession from the 4 giant planets. If other sednoids also cluster around the same primordial value, various models suggesting a still present planet in the outer Solar System would be incompatible with this alignment. We inspected two scenarios that could potentially explain the primordial alignment. First, a rogue planet model (where another massive planet raises perihelia near its own longitude until ejection) naturally produces this signature. Alternatively, a close stellar passage early in Solar System history raises perihelia, but it is poor at creating strong apsidal clustering. We show that all other known $35<q<55$ au TNOs are either too perturbed or orbits are still too uncertain to provide evidence for or against this paradigm.Comment: 9 pages, 4 figures, submitted to ApJ

Centaurs potentially in retrograde co-orbit resonance with Saturn

Aims. 2015 BZ509 is the first asteroid confirmed to be in retrograde co-orbit resonance (or 1/-1 resonance) with the giant planets in the solar system. While Saturn is the only giant planet whose Trojans are not discovered until now, we identify some small bodies among Centaurs and Damocloids that are potentially in 1/-1 resonance with Saturn in the present study. Methods. We integrate numerically the motion of the 1000 clones (include the nominal orbit) of each Centaur whose orbit has a semi-major axis between 9.3 au and 9.8 au and an inclination i > 90 deg. To confirm and evaluate the 1/-1 resonant configurations mentioned above, we introduce a useful one-degree integrable approximation for planar 1/-1 resonance. Results. We identify four candidates potentially in 1/-1 resonance with Saturn. The capture in this particular resonant state during the 40000 yr integration timespan is very common for 2006 RJ2 (906/1000 clones), 2006 BZ8 (878/1000 clones), and 2017 SV13 (998/1000 clones), and it is less likely for 2012 YE8 (426/1000 clones). According to our statistical results, 2006 RJ2 is the best candidate to be currently in a 1/-1 mean motion resonance with Saturn, and 2017 SV13 is another important potential candidate. Moreover, 2012 YE8 and 2006 BZ8 are also Centaurs of interest but their current and long-term 1/-1 resonant state with Saturn is less likely. The proportions of the clones captured in the relative long-term stable co-orbit resonance (over 10000 yr) are also given. Conclusions. Small bodies in retrograde co-orbit resonance with giant planets may be more common than previously expected. Identification of these potential mysterious minor bodies encourages the search for such objects on a larger scale in our solar system. The findings of this paper are also useful for understanding the origin and dynamical evolution of the Centaurs and Damocloids on retrograde orbits.Comment: 8 pages, 8 figures. Accepted for publication in A&

Visibility-Aware Pixelwise View Selection for Multi-View Stereo Matching

The performance of PatchMatch-based multi-view stereo algorithms depends heavily on the source views selected for computing matching costs. Instead of modeling the visibility of different views, most existing approaches handle occlusions in an ad-hoc manner. To address this issue, we propose a novel visibility-guided pixelwise view selection scheme in this paper. It progressively refines the set of source views to be used for each pixel in the reference view based on visibility information provided by already validated solutions. In addition, the Artificial Multi-Bee Colony (AMBC) algorithm is employed to search for optimal solutions for different pixels in parallel. Inter-colony communication is performed both within the same image and among different images. Fitness rewards are added to validated and propagated solutions, effectively enforcing the smoothness of neighboring pixels and allowing better handling of textureless areas. Experimental results on the DTU dataset show our method achieves state-of-the-art performance among non-learning-based methods and retrieves more details in occluded and low-textured regions.Comment: 8 page

Spatially Adaptive Computation Time for Residual Networks

This paper proposes a deep learning architecture based on Residual Network that dynamically adjusts the number of executed layers for the regions of the image. This architecture is end-to-end trainable, deterministic and problem-agnostic. It is therefore applicable without any modifications to a wide range of computer vision problems such as image classification, object detection and image segmentation. We present experimental results showing that this model improves the computational efficiency of Residual Networks on the challenging ImageNet classification and COCO object detection datasets. Additionally, we evaluate the computation time maps on the visual saliency dataset cat2000 and find that they correlate surprisingly well with human eye fixation positions.Comment: CVPR 201

What is a good question? Task-oriented asking with fact-level masking

Asking questions is an important element of real-life collaboration on reasoning tasks like question answering. For example, a legal assistant chatbot may be unable to make accurate recommendations without specific information on the user's circumstances. However, large language models are usually deployed to solve reasoning tasks directly without asking follow-up questions to the user or third parties. We term this problem task-oriented asking (TOA). Zero-shot chat models can perform TOA, but their training is primarily based on next-token prediction rather than whether questions contribute to successful collaboration. To enable the training and evaluation of TOA models, we present a definition and framework for natural language task-oriented asking, the problem of generating questions that result in answers useful for a reasoning task. We also present fact-level masking (FLM), a procedure for converting natural language datasets into self-supervised TOA datasets by omitting particular critical facts. Finally, we generate a TOA dataset from the HotpotQA dataset using FLM and evaluate several zero-shot language models on it. Our experiments show that current zero-shot models struggle to ask questions that retrieve useful information, as compared to human annotators. These results demonstrate an opportunity to use FLM datasets and the TOA framework to train and evaluate better TOA models

Whole-Body Lesion Segmentation in 18F-FDG PET/CT

There has been growing research interest in using deep learning based method to achieve fully automated segmentation of lesion in Positron emission tomography computed tomography(PET CT) scans for the prognosis of various cancers. Recent advances in the medical image segmentation shows the nnUNET is feasible for diverse tasks. However, lesion segmentation in the PET images is not straightforward, because lesion and physiological uptake has similar distribution patterns. The Distinction of them requires extra structural information in the CT images. The present paper introduces a nnUNet based method for the lesion segmentation task. The proposed model is designed on the basis of the joint 2D and 3D nnUNET architecture to predict lesions across the whole body. It allows for automated segmentation of potential lesions. We evaluate the proposed method in the context of AutoPet Challenge, which measures the lesion segmentation performance in the metrics of dice score, false-positive volume and false-negative volume

Experimental Study of Fast Sealing Airbag in Simulating Tunnel

AbstractAgainst problems in terms of stability, airtightness and so on of current fast sealing airbag, stability and airtightness of fast sealing airbag in simulating tunnel was studied through combining theoretical analysis and experiment. The ideal viton material is finally found after comparing and analyzing heat resistance, flame resistance, wear resistance, hardness and air permeability of different kinds of rubber. Sealing and wind blocking effects of airbags made from selected material are tested in simulating tunnel. Rate of air leakage and changing rate of wind pressure of each kind of rubber are also determined and further verified, with result that both indexes of viton material are the least, respectively only 4.25% and 4.66%

Quantum Computing Quantum Monte Carlo

Quantum computing and quantum Monte Carlo (QMC) are respectively the state-of-the-art quantum and classical computing methods for understanding many-body quantum systems. Here, we propose a hybrid quantum-classical algorithm that integrates these two methods, inheriting their distinct features in efficient representation and manipulation of quantum states and overcoming their limitations. We first introduce non-stoquasticity indicators (NSIs) and their upper bounds, which measure the sign problem, the most notable limitation of QMC. We show that our algorithm could greatly mitigate the sign problem, which decreases NSIs with the assistance of quantum computing. Meanwhile, the use of quantum Monte Carlo also increases the expressivity of shallow quantum circuits, allowing more accurate computation that is conventionally achievable only with much deeper circuits. We numerically test and verify the method for the N$_2$ molecule (12 qubits) and the Hubbard model (16 qubits). Our work paves the way to solving practical problems with intermediate-scale and early-fault tolerant quantum computers, with potential applications in chemistry, condensed matter physics, materials, high energy physics, etc