From Spaghetti Bowl to Jigsaw Puzzle? Addressing the Disarray in the World Trade System

The rise of mega-regionals such as the Regional Comprehensive Economic Partnership (RCEP) and the Trans-Pacific Partnership (TPP) suggests that the world trade system is fragmenting to the point it appears more like a jigsaw puzzle than a spaghetti bowl. There are both regional and global jigsaw puzzles to be solved—in that order—to clean up the world trade system. But is this even likely? The difficulties of free trade agreement (FTA) consolidation at the regional level are well known, while piecing together the blocs around the world to form a coherent whole is even more challenging. In this context, a way forward is to return to the most widely used modality of trade liberalization—unilateral actions—but this time involving the multilateralization of preferences rather than unreciprocated reductions in tariff rates. As more and more FTAs are negotiated, preference erosion sets in, reducing the resistance of FTA partners to multilateralization. Multilateralization of preferences may then present a practical way forward in addressing the disarray in the world trade system

Why are images smooth?

It is a well observed phenomenon that natural images are smooth, in the sense that nearby pixels tend to have similar values. We describe a mathematical model of images that makes no assumptions on the nature of the environment that images depict. It only assumes that images can be taken at different scales (zoom levels). We provide quantitative bounds on the smoothness of a typical image in our model, as a function of the number of available scales. These bounds can serve as a baseline against which to compare the observed smoothness of natural images

Nucleation scaling in jigsaw percolation

Jigsaw percolation is a nonlocal process that iteratively merges connected clusters in a deterministic "puzzle graph" by using connectivity properties of a random "people graph" on the same set of vertices. We presume the Erdos--Renyi people graph with edge probability p and investigate the probability that the puzzle is solved, that is, that the process eventually produces a single cluster. In some generality, for puzzle graphs with N vertices of degrees about D (in the appropriate sense), this probability is close to 1 or small depending on whether pD(log N) is large or small. The one dimensional ring and two dimensional torus puzzles are studied in more detail and in many cases the exact scaling of the critical probability is obtained. The paper settles several conjectures posed by Brummitt, Chatterjee, Dey, and Sivakoff who introduced this model.Comment: 39 pages, 3 figures. Moved main results to the introduction and improved exposition of section

Rational Verification in Iterated Electric Boolean Games

Electric boolean games are compact representations of games where the players have qualitative objectives described by LTL formulae and have limited resources. We study the complexity of several decision problems related to the analysis of rationality in electric boolean games with LTL objectives. In particular, we report that the problem of deciding whether a profile is a Nash equilibrium in an iterated electric boolean game is no harder than in iterated boolean games without resource bounds. We show that it is a PSPACE-complete problem. As a corollary, we obtain that both rational elimination and rational construction of Nash equilibria by a supervising authority are PSPACE-complete problems.Comment: In Proceedings SR 2016, arXiv:1607.0269

Generative Adversarial Perturbations

In this paper, we propose novel generative models for creating adversarial examples, slightly perturbed images resembling natural images but maliciously crafted to fool pre-trained models. We present trainable deep neural networks for transforming images to adversarial perturbations. Our proposed models can produce image-agnostic and image-dependent perturbations for both targeted and non-targeted attacks. We also demonstrate that similar architectures can achieve impressive results in fooling classification and semantic segmentation models, obviating the need for hand-crafting attack methods for each task. Using extensive experiments on challenging high-resolution datasets such as ImageNet and Cityscapes, we show that our perturbations achieve high fooling rates with small perturbation norms. Moreover, our attacks are considerably faster than current iterative methods at inference time.Comment: CVPR 2018, camera-ready versio

Jigsaw: Scalable Software-Defined Caches (Extended Version)

Shared last-level caches, widely used in chip-multiprocessors (CMPs), face two fundamental limitations. First, the latency and energy of shared caches degrade as the system scales up. Second, when multiple workloads share the CMP, they suffer from interference in shared cache accesses. Unfortunately, prior research addressing one issue either ignores or worsens the other: NUCA techniques reduce access latency but are prone to hotspots and interference, and cache partitioning techniques only provide isolation but do not reduce access latency. We present Jigsaw, a technique that jointly addresses the scalability and interference problems of shared caches. Hardware lets software define shares, collections of cache bank partitions that act as virtual caches, and map data to shares. Shares give software full control over both data placement and capacity allocation. Jigsaw implements efficient hardware support for share management, monitoring, and adaptation. We propose novel resource-management algorithms and use them to develop a system-level runtime that leverages Jigsaw to both maximize cache utilization and place data close to where it is used. We evaluate Jigsaw using extensive simulations of 16- and 64-core tiled CMPs. Jigsaw improves performance by up to 2.2x (18% avg) over a conventional shared cache, and significantly outperforms state-of-the-art NUCA and partitioning techniques.This work was supported in part by DARPA PERFECT contract HR0011-13-2-0005 and Quanta Computer