On type-II singularities in Ricci flow on RN\mathbb{R}^{N}

In each dimension $N\geq 3$ and for each real number $\lambda\geq 1$, we construct a family of complete rotationally symmetric solutions to Ricci flow on $\mathbb{R}^{N}$ which encounter a global singularity at a finite time $T$. The singularity forms arbitrarily slowly with the curvature blowing up arbitrarily fast at the rate $(T-t)^{-(\lambda+1)}$. Near the origin, blow-ups of such a solution converge uniformly to the Bryant soliton. Near spatial infinity, blow-ups of such a solution converge uniformly to the shrinking cylinder soliton. As an application of this result, we prove that there exist standard solutions of Ricci flow on $\mathbb{R}^N$ whose blow-ups near the origin converge uniformly to the Bryant soliton.Comment: Revised version, typos correcte

Robustness of Information Diffusion Algorithms to Locally Bounded Adversaries

We consider the problem of diffusing information in networks that contain malicious nodes. We assume that each normal node in the network has no knowledge of the network topology other than an upper bound on the number of malicious nodes in its neighborhood. We introduce a topological property known as r-robustness of a graph, and show that this property provides improved bounds on tolerating malicious behavior, in comparison to traditional concepts such as connectivity and minimum degree. We use this topological property to analyze the canonical problems of distributed consensus and broadcasting, and provide sufficient conditions for these operations to succeed. Finally, we provide a construction for r-robust graphs and show that the common preferential-attachment model for scale-free networks produces a robust graph.Comment: Preprint of results to appear at 2012 American Control Conferenc

Two-stage Deep Reinforcement Learning for Inverter-based Volt-VAR Control in Active Distribution Networks

Model-based Vol/VAR optimization method is widely used to eliminate voltage violations and reduce network losses. However, the parameters of active distribution networks(ADNs) are not onsite identified, so significant errors may be involved in the model and make the model-based method infeasible. To cope with this critical issue, we propose a novel two-stage deep reinforcement learning (DRL) method to improve the voltage profile by regulating inverter-based energy resources, which consists of offline stage and online stage. In the offline stage, a highly efficient adversarial reinforcement learning algorithm is developed to train an offline agent robust to the model mismatch. In the sequential online stage, we transfer the offline agent safely as the online agent to perform continuous learning and controlling online with significantly improved safety and efficiency. Numerical simulations on IEEE test cases not only demonstrate that the proposed adversarial reinforcement learning algorithm outperforms the state-of-art algorithm, but also show that our proposed two-stage method achieves much better performance than the existing DRL based methods in the online application.Comment: 8 page

Joint Stochastic Approximation learning of Helmholtz Machines

Though with progress, model learning and performing posterior inference still remains a common challenge for using deep generative models, especially for handling discrete hidden variables. This paper is mainly concerned with algorithms for learning Helmholz machines, which is characterized by pairing the generative model with an auxiliary inference model. A common drawback of previous learning algorithms is that they indirectly optimize some bounds of the targeted marginal log-likelihood. In contrast, we successfully develop a new class of algorithms, based on stochastic approximation (SA) theory of the Robbins-Monro type, to directly optimize the marginal log-likelihood and simultaneously minimize the inclusive KL-divergence. The resulting learning algorithm is thus called joint SA (JSA). Moreover, we construct an effective MCMC operator for JSA. Our results on the MNIST datasets demonstrate that the JSA's performance is consistently superior to that of competing algorithms like RWS, for learning a range of difficult models.Comment: Fixing typos. Published at ICLR-2016 Workshop Trac

A Trend-following Trading Indicator on Homomorphically Encrypted Data

Algorithmic trading has proliferated the area of quantitative finance for already over a decade. The decisions are made without human intervention using the data provided by brokerage firms and exchanges. There is an emerging intermediate layer of financial players that are placed in between a broker and algorithmic traders. The role of these players is to aggregate market decisions from the algorithmic traders and send a final market order to a broker. In return, the quantitative analysts receive incentives proportional to the correctness of their predictions. In such a setup, the intermediate player - an aggregator - does not provide the market data in plaintext but encrypts it. Encrypting market data prevents quantitative analysts from trading on their own, as well as keeps valuable financial data private. This paper proposes an implementation of a popular trend-following indicator with two different homomorphic encryption libraries - SEAL and HEAAN - and compares it to the trading indicator implemented for plaintext. Then an attempt to implement a trading strategy is presented and analysed. The trading indicator implemented with SEAL and HEAAN is almost identical to that implemented on the plaintext, the percentage error is of 0.14916% and 0.00020% respectively. Despite many limitations that homomorphic encryption imposes on this algorithm's implementation, quantitative finance has a high potential of benefiting from the methods of homomorphic encryption.Comment: Accepted as a short paper by the 17th International Conference on Security and Cryptography (SECRYPT 2020

Dynamical stability of algebraic Ricci solitons

We consider dynamical stability for a modified Ricci flow equation whose stationary solutions include Einstein and Ricci soliton metrics. Our focus is on homogeneous metrics on non-compact manifolds. Following the program of Guenther, Isenberg, and Knopf, we define a class of weighted little H\"older spaces with certain interpolation properties that allow the use of maximal regularity theory and the application of a stability theorem of Simonett. With this, we derive two stability theorems, one for a class of Einstein metrics and one for a class of non-Einstein Ricci solitons. Using linear stability results of Jablonski, Petersen, and the first author, we obtain dynamical stability for many specific Einstein and Ricci soliton metrics on simply connected solvable Lie groups.Comment: 18 pages, updated reference and statement of Theorems 1.3 and 2.1, revised introduction and fixed typos, final version to appear in Journal f\"ur die reine und angewandte Mathemati

Simple Applications of BERT for Ad Hoc Document Retrieval

Following recent successes in applying BERT to question answering, we explore simple applications to ad hoc document retrieval. This required confronting the challenge posed by documents that are typically longer than the length of input BERT was designed to handle. We address this issue by applying inference on sentences individually, and then aggregating sentence scores to produce document scores. Experiments on TREC microblog and newswire test collections show that our approach is simple yet effective, as we report the highest average precision on these datasets by neural approaches that we are aware of

An efficient model reduction method for solving viscous G-equations in incompressible cellular flows

The G-equation is a well-known model for studying front propagation in turbulent combustion. In this paper, we shall develop an efficient model reduction method for solving viscous G-equations in incompressible steady and time-periodic cellular flows. Our method is based on the Galerkin proper orthogonal decomposition (POD) methods. To facilitate the algorithm design and convergence analysis, we decompose the solution of the viscous G-equation into a mean-free part and a mean part, where their evolution equations can be derived accordingly. We construct the POD basis from the solution snapshots of the mean-free part. With the POD basis, we can efficiently solve the evolution equation for the mean-free part of the solution to the viscous G-equation. After we get the mean-free part of the solution, the mean of the solution can be recovered. We also provide rigorous convergence analysis for our numerical method. Numerical results are presented to demonstrate the accuracy and efficiency of the proposed method. Specifically, we study the turbulent flame speeds of the viscous G-equations in incompressible cellular flows based on the POD method and fully resolved computations.Comment: 25 page

Practical Algorithms for Best-K Identification in Multi-Armed Bandits

In the Best-$K$ identification problem (Best-$K$-Arm), we are given $N$ stochastic bandit arms with unknown reward distributions. Our goal is to identify the $K$ arms with the largest means with high confidence, by drawing samples from the arms adaptively. This problem is motivated by various practical applications and has attracted considerable attention in the past decade. In this paper, we propose new practical algorithms for the Best-$K$-Arm problem, which have nearly optimal sample complexity bounds (matching the lower bound up to logarithmic factors) and outperform the state-of-the-art algorithms for the Best-$K$-Arm problem (even for $K=1$) in practice

Robustness of Complex Networks with Implications for Consensus and Contagion

We study a graph-theoretic property known as robustness, which plays a key role in certain classes of dynamics on networks (such as resilient consensus, contagion and bootstrap percolation). This property is stronger than other graph properties such as connectivity and minimum degree in that one can construct graphs with high connectivity and minimum degree but low robustness. However, we show that the notions of connectivity and robustness coincide on common random graph models for complex networks (Erdos-Renyi, geometric random, and preferential attachment graphs). More specifically, the properties share the same threshold function in the Erdos-Renyi model, and have the same values in one-dimensional geometric graphs and preferential attachment networks. This indicates that a variety of purely local diffusion dynamics will be effective at spreading information in such networks. Although graphs generated according to the above constructions are inherently robust, we also show that it is coNP-complete to determine whether any given graph is robust to a specified extent.Comment: Extended version of paper appearing at the 2012 Conference on Decision and Contro