Latent Embeddings for Collective Activity Recognition

Rather than simply recognizing the action of a person individually, collective activity recognition aims to find out what a group of people is acting in a collective scene. Previ- ous state-of-the-art methods using hand-crafted potentials in conventional graphical model which can only define a limited range of relations. Thus, the complex structural de- pendencies among individuals involved in a collective sce- nario cannot be fully modeled. In this paper, we overcome these limitations by embedding latent variables into feature space and learning the feature mapping functions in a deep learning framework. The embeddings of latent variables build a global relation containing person-group interac- tions and richer contextual information by jointly modeling broader range of individuals. Besides, we assemble atten- tion mechanism during embedding for achieving more com- pact representations. We evaluate our method on three col- lective activity datasets, where we contribute a much larger dataset in this work. The proposed model has achieved clearly better performance as compared to the state-of-the- art methods in our experiments.Comment: 6pages, accepted by IEEE-AVSS201

An Experimental Proposal to Test Dynamic Quantum Non-locality with Single-Atom Interferometry

Quantum non-locality based on the well-known Bell inequality is of kinematic nature. A different type of quantum non-locality, the non-locality of the quantum equation of motion, is recently put forward with connection to the Aharonov-Bohm effect [Nature Phys. 6, 151 (2010)]. Evolution of the displacement operator provides an example to manifest such dynamic quantum non-locality. We propose an experiment using single-atom interferometry to test such dynamic quantum non-locality. We show how to measure evolution of the displacement operator with clod atoms in a spin-dependent optical lattice potential and discuss signature to identify dynamic quantum non-locality under a realistic experimental setting.Comment: 4 page

PABO: Mitigating Congestion via Packet Bounce in Data Center Networks

In today's data center, a diverse mix of throughput-sensitive long flows and delay-sensitive short flows are commonly presented in shallow-buffered switches. Long flows could potentially block the transmission of delay-sensitive short flows, leading to degraded performance. Congestion can also be caused by the synchronization of multiple TCP connections for short flows, as typically seen in the partition/aggregate traffic pattern. While multiple end-to-end transport-layer solutions have been proposed, none of them have tackled the real challenge: reliable transmission in the network. In this paper, we fill this gap by presenting PABO -- a novel link-layer design that can mitigate congestion by temporarily bouncing packets to upstream switches. PABO's design fulfills the following goals: i) providing per-flow based flow control on the link layer, ii) handling transient congestion without the intervention of end devices, and iii) gradually back propagating the congestion signal to the source when the network is not capable to handle the congestion.Experiment results show that PABO can provide prominent advantage of mitigating transient congestions and can achieve significant gain on end-to-end delay