SCAR: Strong Consistency using Asynchronous Replication with Minimal Coordination

Data replication is crucial in modern distributed systems as a means to provide high availability. Many techniques have been proposed to utilize replicas to improve a system's performance, often requiring expensive coordination or sacrificing consistency. In this paper, we present SCAR, a new distributed and replicated in-memory database that allows serializable transactions to read from backup replicas with minimal coordination. SCAR works by assigning logical timestamps to database records so that a transaction can safely read from a backup replica without coordinating with the primary replica, because the records cannot be changed up to a certain logical time. In addition, we propose two optimization techniques, timestamp synchronization and parallel locking and validation, to further reduce coordination. We show that SCAR outperforms systems with conventional concurrency control algorithms and replication strategies by up to a factor of 2 on three popular benchmarks. We also demonstrate that SCAR achieves higher throughput by running under reduced isolation levels and detects concurrency anomalies in real time

STAR: Scaling Transactions through Asymmetric Replication

In this paper, we present STAR, a new distributed in-memory database with asymmetric replication. By employing a single-node non-partitioned architecture for some replicas and a partitioned architecture for other replicas, STAR is able to efficiently run both highly partitionable workloads and workloads that involve cross-partition transactions. The key idea is a new phase-switching algorithm where the execution of single-partition and cross-partition transactions is separated. In the partitioned phase, single-partition transactions are run on multiple machines in parallel to exploit more concurrency. In the single-master phase, mastership for the entire database is switched to a single designated master node, which can execute these transactions without the use of expensive coordination protocols like two-phase commit. Because the master node has a full copy of the database, this phase-switching can be done at negligible cost. Our experiments on two popular benchmarks (YCSB and TPC-C) show that high availability via replication can coexist with fast serializable transaction execution in distributed in-memory databases, with STAR outperforming systems that employ conventional concurrency control and replication algorithms by up to one order of magnitude

Revisiting EmbodiedQA: A Simple Baseline and Beyond

In Embodied Question Answering (EmbodiedQA), an agent interacts with an environment to gather necessary information for answering user questions. Existing works have laid a solid foundation towards solving this interesting problem. But the current performance, especially in navigation, suggests that EmbodiedQA might be too challenging for the contemporary approaches. In this paper, we empirically study this problem and introduce 1) a simple yet effective baseline that achieves promising performance; 2) an easier and practical setting for EmbodiedQA where an agent has a chance to adapt the trained model to a new environment before it actually answers users questions. In this new setting, we randomly place a few objects in new environments, and upgrade the agent policy by a distillation network to retain the generalization ability from the trained model. On the EmbodiedQA v1 benchmark, under the standard setting, our simple baseline achieves very competitive results to the-state-of-the-art; in the new setting, we found the introduced small change in settings yields a notable gain in navigation.Comment: Accepted to IEEE Transactions on Image Processing (TIP

On The Utility of Conditional Generation Based Mutual Information for Characterizing Adversarial Subspaces

Recent studies have found that deep learning systems are vulnerable to adversarial examples; e.g., visually unrecognizable adversarial images can easily be crafted to result in misclassification. The robustness of neural networks has been studied extensively in the context of adversary detection, which compares a metric that exhibits strong discriminate power between natural and adversarial examples. In this paper, we propose to characterize the adversarial subspaces through the lens of mutual information (MI) approximated by conditional generation methods. We use MI as an information-theoretic metric to strengthen existing defenses and improve the performance of adversary detection. Experimental results on MagNet defense demonstrate that our proposed MI detector can strengthen its robustness against powerful adversarial attacks.Comment: Accepted to IEEE GlobalSIP 201

Approximability of the Eight-vertex Model

We initiate a study of the classification of approximation complexity of the eight-vertex model defined over 4-regular graphs. The eight-vertex model, together with its special case the six-vertex model, is one of the most extensively studied models in statistical physics, and can be stated as a problem of counting weighted orientations in graph theory. Our result concerns the approximability of the partition function on all 4-regular graphs, classified according to the parameters of the model. Our complexity results conform to the phase transition phenomenon from physics. We introduce a quantum decomposition of the eight-vertex model and prove a set of closure properties in various regions of the parameter space. Furthermore, we show that there are extra closure properties on 4-regular planar graphs. These regions of the parameter space are concordant with the phase transition threshold. Using these closure properties, we derive polynomial time approximation algorithms via Markov chain Monte Carlo. We also show that the eight-vertex model is NP-hard to approximate on the other side of the phase transition threshold

Online Reinforcement Learning in Stochastic Games

We study online reinforcement learning in average-reward stochastic games (SGs). An SG models a two-player zero-sum game in a Markov environment, where state transitions and one-step payoffs are determined simultaneously by a learner and an adversary. We propose the UCSG algorithm that achieves a sublinear regret compared to the game value when competing with an arbitrary opponent. This result improves previous ones under the same setting. The regret bound has a dependency on the diameter, which is an intrinsic value related to the mixing property of SGs. If we let the opponent play an optimistic best response to the learner, UCSG finds an $\varepsilon$-maximin stationary policy with a sample complexity of $\tilde{\mathcal{O}}\left(\text{poly}(1/\varepsilon)\right)$, where $\varepsilon$ is the gap to the best policy

Pyramidal Neurons in Different Cortical Layers Exhibit Distinct Dynamics and Plasticity of Apical Dendritic Spines.

The mammalian cerebral cortex is typically organized in six layers containing multiple types of neurons, with pyramidal neurons (PNs) being the most abundant. PNs in different cortical layers have distinct morphology, physiology and functional roles in neural circuits. Therefore, their development and synaptic plasticity may also differ. Using in vivo transcranial two-photon microscopy, we followed the structural dynamics of dendritic spines on apical dendrites of layer (L) 2/3 and L5 PNs at different developmental stages. We show that the density and dynamics of spines are significantly higher in L2/3 PNs than L5 PNs in both adolescent (1 month old) and adult (4 months old) mice. While spine density of L5 PNs decreases during adolescent development due to a higher rate of spine elimination than formation, there is no net change in the spine density along apical dendrites of L2/3 PNs over this period. In addition, experiences exert differential impact on the dynamics of apical dendritic spines of PNs resided in different cortical layers. While motor skill learning promotes spine turnover on L5 PNs in the motor cortex, it does not change the spine dynamics on L2/3 PNs. In addition, neonatal sensory deprivation decreases the spine density of both L2/3 and L5 PNs, but leads to opposite changes in spine dynamics among these two populations of neurons in adolescence. In summary, our data reveal distinct dynamics and plasticity of apical dendritic spines on PNs in different layers in the living mouse cortex, which may arise from their distinct functional roles in cortical circuits

On Reinforcement Learning for Full-length Game of StarCraft

StarCraft II poses a grand challenge for reinforcement learning. The main difficulties of it include huge state and action space and a long-time horizon. In this paper, we investigate a hierarchical reinforcement learning approach for StarCraft II. The hierarchy involves two levels of abstraction. One is the macro-action automatically extracted from expert's trajectories, which reduces the action space in an order of magnitude yet remains effective. The other is a two-layer hierarchical architecture which is modular and easy to scale, enabling a curriculum transferring from simpler tasks to more complex tasks. The reinforcement training algorithm for this architecture is also investigated. On a 64x64 map and using restrictive units, we achieve a winning rate of more than 99\% against the difficulty level-1 built-in AI. Through the curriculum transfer learning algorithm and a mixture of combat model, we can achieve over 93\% winning rate of Protoss against the most difficult non-cheating built-in AI (level-7) of Terran, training within two days using a single machine with only 48 CPU cores and 8 K40 GPUs. It also shows strong generalization performance, when tested against never seen opponents including cheating levels built-in AI and all levels of Zerg and Protoss built-in AI. We hope this study could shed some light on the future research of large-scale reinforcement learning.Comment: Appeared in AAAI 201

t anti-t Production Rates at the Tevatron and the LHC in Topcolor- Assisted Multiscale Technicolor Models

We study the contributions of the neutral pseudo Goldstone bosons (technipions and top-pions) to the t anti-t production cross sections at the Tevatron and the LHC in topcolor-assisted multiscale technicolor (TOPCMTC) models via the gluon-gluon fusion process from the loop-level couplings between the pseudo Goldstone bosons and the gluons. The MRS set A' parton distributions are used in the calculation. It is shown that the new CDF datum on the t anti-t production cross section gives constraints on the parameters in the TOPCMTC models. With reasonable values of the parameters in TOPCMTC models, the cross section at the Tevatron is larger than that predicted by the standard model, and is consistent with the new CDF data. The enhancement of the cross section and the resonance peaks at the LHC are more significant, so that it is testable in future experiments.Comment: 16 pages in RevTex, 9 postscript figure

A Secure RFID Deactivation/Activation Mechanism for Supporting Customer Service and Consumer Shopping

RFID has been regarded as a time and money-saving solution for a wide variety of applications, such as manufacturing, supply chain management, and inventory control, etc. However, there are some security problems on RFID in the product managements. The most concerned issues are the tracking and the location privacy. Numerous scholars tried to solve these problems, but their proposals do not include the after-sales service. In this paper, we propose a purchase and after-sales service RFID scheme for shopping mall. The location privacy, confidentiality, data integrity, and some security protection are hold in this propose mechanism.Comment: submitting to computer communication (COMCOM) at 2010.12.2