Point Contrastive Prediction with Semantic Clustering for Self-Supervised Learning on Point Cloud Videos

We propose a unified point cloud video self-supervised learning framework for object-centric and scene-centric data. Previous methods commonly conduct representation learning at the clip or frame level and cannot well capture fine-grained semantics. Instead of contrasting the representations of clips or frames, in this paper, we propose a unified self-supervised framework by conducting contrastive learning at the point level. Moreover, we introduce a new pretext task by achieving semantic alignment of superpoints, which further facilitates the representations to capture semantic cues at multiple scales. In addition, due to the high redundancy in the temporal dimension of dynamic point clouds, directly conducting contrastive learning at the point level usually leads to massive undesired negatives and insufficient modeling of positive representations. To remedy this, we propose a selection strategy to retain proper negatives and make use of high-similarity samples from other instances as positive supplements. Extensive experiments show that our method outperforms supervised counterparts on a wide range of downstream tasks and demonstrates the superior transferability of the learned representations.Comment: Accepted by ICCV 202

A Generalized Packing Server for Scheduling Task Graphs on Multiple Resources

This paper presents the generalized packing server. It reduces the problem of scheduling tasks with precedence constraints on multiple processing units to the problem of scheduling independent tasks. The work generalizes our previous contribution made in the specific context of scheduling Map/Reduce workflows. The results apply to the generalized parallel task model, introduced in recent literature to denote tasks described by workflow graphs, where some subtasks may be executed in parallel subject to precedence constraints. Recent literature developed schedulability bounds for the generalized parallel tasks on multiprocessors. The generalized packing server, described in this paper, is a run-time mechanism that packs tasks into server budgets (in a manner that respects precedence constraints) allowing the budgets to be viewed as independent tasks by the underlying scheduler. Consequently, any schedulability results derived for the independent task model on multiprocessors become applicable to generalized parallel tasks. The catch is that the sum of capacities of server budgets exceeds by a certain ratio the sum of execution times of the original generalized parallel tasks. Hence, a scaling factor is derived that converts bounds for independent tasks into corresponding bounds for generalized parallel tasks. The factor applies to any work-conserving scheduling policy in both the global and partitioned multiprocessor scheduling models. We show that the new schedulability bounds obtained for the generalized parallel task model, using the aforementioned conversion, improve in several cases upon the best known bounds in current literature. Hence, the packing server is shown to improve the schedulability of generalized parallel tasks. Evaluation results confirm this observation.Ope

Masked Spatio-Temporal Structure Prediction for Self-supervised Learning on Point Cloud Videos

Recently, the community has made tremendous progress in developing effective methods for point cloud video understanding that learn from massive amounts of labeled data. However, annotating point cloud videos is usually notoriously expensive. Moreover, training via one or only a few traditional tasks (e.g., classification) may be insufficient to learn subtle details of the spatio-temporal structure existing in point cloud videos. In this paper, we propose a Masked Spatio-Temporal Structure Prediction (MaST-Pre) method to capture the structure of point cloud videos without human annotations. MaST-Pre is based on spatio-temporal point-tube masking and consists of two self-supervised learning tasks. First, by reconstructing masked point tubes, our method is able to capture the appearance information of point cloud videos. Second, to learn motion, we propose a temporal cardinality difference prediction task that estimates the change in the number of points within a point tube. In this way, MaST-Pre is forced to model the spatial and temporal structure in point cloud videos. Extensive experiments on MSRAction-3D, NTU-RGBD, NvGesture, and SHREC'17 demonstrate the effectiveness of the proposed method.Comment: Accepted by ICCV 202

Sharp kinetic acceleration potentials during mediated redox catalysis of insulators

Redox mediators could catalyse otherwise slow and energy-inefficient cycling of Li-S and Li-O 2 batteries by shuttling electrons/holes between the electrode and the solid insulating storage materials. For mediators to work efficiently they need to oxidize the solid with fast kinetics yet the lowest possible overpotential. Here, we found that when the redox potentials of mediators are tuned via, e.g., Li + concentration in the electrolyte, they exhibit distinct threshold potentials, where the kinetics accelerate several-fold within a range as small as 10 mV. This phenomenon is independent of types of mediators and electrolyte. The acceleration originates from the overpotentials required to activate fast Li + /e – extraction and the following chemical step at specific abundant surface facets. Efficient redox catalysis at insulating solids requires therefore carefully considering the surface conditions of the storage materials and electrolyte-dependent redox potentials, which may be tuned by salt concentrations or solvents

Single-photon emission from isolated monolayer islands of InGaN

We identify and characterize a novel type of quantum emitter formed from InGaN monolayer islands grown using molecular beam epitaxy and further isolated via the fabrication of an array of nanopillar structures. Detailed optical analysis of the characteristic emission spectrum from the monolayer islands is performed, and the main transmission is shown to act as a bright, stable, and fast single-photon emitter with a wavelength of ~400 nm. © 2020, The Author(s)

Acid-Base Clusters during Atmospheric New Particle Formation in Urban Beijing

Molecular clustering is the initial step of atmospheric new particle formation (NPF) that generates numerous secondary particles. Using two online mass spectrometers with and without a chemical ionization inlet, we characterized the neutral clusters and the naturally charged ion clusters during NPF periods in urban Beijing. In ion clusters, we observed pure sulfuric acid (SA) clusters, SA-amine clusters, SA-ammonia (NH3) clusters, and SA-amine-NH3 clusters. However, only SA clusters and SA-amine clusters were observed in the neutral form. Meanwhile, oxygenated organic molecule (OOM) clusters charged by a nitrate ion and a bisulfate ion were observed in ion clusters. Acid-base clusters correlate well with the occurrence of sub-3 nm particles, whereas OOM clusters do not. Moreover, with the increasing cluster size, amine fractions in ion acid-base clusters decrease, while NH3 fractions increase. This variation results from the reduced stability differences between SA-amine clusters and SA-NH3 clusters, which is supported by both quantum chemistry calculations and chamber experiments. The lower average number of dimethylamine (DMA) molecules in atmospheric ion clusters than the saturated value from controlled SA-DMA nucleation experiments suggests that there is insufficient DMA in urban Beijing to fully stabilize large SA clusters, and therefore, other basic molecules such as NH3 play an important role.Peer reviewe

Volatile compounds isolated and identified from Kohia (Passiflora tetrandra)

Plants biosynthesise and release a large diversity of volatile compounds. The dominant types of these volatiles can be divided into three categories: carbohydrate-derived compounds, fatty acid derived compounds and amino acid derivatives. In this research, the volatile organic compounds were investigated in three different parts of the New Zealand native plant – Kohia (P. tetrandra), which is also known as New Zealand passion fruit. The parts included leaves, flowers and fruit. Solid phase microextraction (SPME) was chosen as the analytical method. The identification of volatiles was by gas chromatography – mass spectrometry. It was found that the compositions of different parts of Kohia were distinctly different. The volatiles in the leaves were mostly alcohols and carbonyl compounds, whereas the major components in flowers were terpenes, and esters were the principal components of the fruit. In addition, the volatiles in female and male of Kohia leaves were also different. The main components in female leaves were cyclopentenone (31.49%), cyclopentanone (13.10%) and 2-hexenol (8.38%), while the predominant compounds in male leaves were cyclopentanone (17.97%), cis-hexenyl acetate (14.06%) and 2-hexenol (14.02%). Attached (still on the plant) and excised (cut off) flowers of female Kohia were mainly contained (Z)-ocimene and α-farnesene. For the fruit part, two different passion fruit species were studied in this study: Kohia fruit and purple passion fruit (P. edulis Sims). The results showed that hexyl hexanoate (17.72%) and hexyl butanoate (16.99%) followed by 1-methylhexyl hexanoate (10.68%) and 1-methylhexyl butyrate (10.35%) composed the majority of purple passion fruit flavour. Methyl decanoate (16.97%), cyclopentenone (14.21%) and methyl dec-4-enoate (10.38%) were important compounds in Kohia fruit