Algorithms for Constructing Overlay Networks For Live Streaming

We present a polynomial time approximation algorithm for constructing an overlay multicast network for streaming live media events over the Internet. The class of overlay networks constructed by our algorithm include networks used by Akamai Technologies to deliver live media events to a global audience with high fidelity. We construct networks consisting of three stages of nodes. The nodes in the first stage are the entry points that act as sources for the live streams. Each source forwards each of its streams to one or more nodes in the second stage that are called reflectors. A reflector can split an incoming stream into multiple identical outgoing streams, which are then sent on to nodes in the third and final stage that act as sinks and are located in edge networks near end-users. As the packets in a stream travel from one stage to the next, some of them may be lost. A sink combines the packets from multiple instances of the same stream (by reordering packets and discarding duplicates) to form a single instance of the stream with minimal loss. Our primary contribution is an algorithm that constructs an overlay network that provably satisfies capacity and reliability constraints to within a constant factor of optimal, and minimizes cost to within a logarithmic factor of optimal. Further in the common case where only the transmission costs are minimized, we show that our algorithm produces a solution that has cost within a factor of 2 of optimal. We also implement our algorithm and evaluate it on realistic traces derived from Akamai's live streaming network. Our empirical results show that our algorithm can be used to efficiently construct large-scale overlay networks in practice with near-optimal cost

A Constant Factor Approximation for the Single Sink Edge Installation Problem

We present the first constant approximation to the single sink buy-at-bulk network design problem, where we have to design a network by buying pipes of different costs and capacities per unit length to route demands at a set of sources to a single sink. The distances in the underlying network form a metric. This result improves the previous bound of O(log |R|), where R is the set of sources. We also present a better constant approximation to the related Access Network Design problem. Our algorithms are randomized and combinatorial. As a subroutine in our algorithm, we use an interesting variant of facility location with lower bounds on the amount of demand an open facility needs to serve. We call this variant load balanced facility location and present a constant factor approximation for it, while relaxing the lower bounds by a constant factor

Letter to Orrin G. Hatch and Ron Wyden on Donor-Advised Funds

Letter written on the behalf of a number of philanthropic and community foundations to Orrin G. Hatch, chairman of the United States Senate Committee on Finance and Ron Wyden, the ranking member of that committee. The letter was written in response to the July 17 letter by Ray Madoff and Roger Colinvaux, which advocated for changes in the tax code related to donor-advised funds. This letter argues against the changes suggested by Madoff and Colinvaux

Online optimization with switching cost

We consider algorithms for "smoothed online convex optimization (SOCO)" problems. SOCO is a variant of the class of "online convex optimization (OCO)" problems that is strongly related to the class of "metrical task systems", each of which have been studied extensively. Prior literature on these problems has focused on two performance metrics: regret and competitive ratio. There exist known algorithms with sublinear regret and known algorithms with constant competitive ratios; however no known algorithms achieve both. In this paper, we show that this is due to a fundamental incompatibility between regret and the competitive ratio -- no algorithm (deterministic or randomized) can achieve sublinear regret and a constant competitive ratio, even in the case when the objective functions are linear

A Tale of Two Metrics: Simultaneous Bounds on Competitiveness and Regret

We consider algorithms for “smoothed online convex optimization” (SOCO) problems, which are a hybrid between online convex optimization (OCO) and metrical task system (MTS) problems. Historically, the performance metric for OCO was regret and that for MTS was competitive ratio (CR). There are algorithms with either sublinear regret or constant CR, but no known algorithm achieves both simultaneously. We show that this is a fundamental limitation – no algorithm (deterministic or randomized) can achieve sublinear regret and a constant CR, even when the objective functions are linear and the decision space is one dimensional. However, we present an algorithm that, for the important one dimensional case, provides sublinear regret and a CR that grows arbitrarily slowly

The Robertson v. Princeton Case: Too Important to Be Left to the Lawyers

Offers comments from eleven contributors on the Robertson family's donor rights suit against the Woodrow Wilson School of Public and International Affairs for violation of donor intent. Explores its effects on and implications for the nonprofit sector

Language Model Crossover: Variation through Few-Shot Prompting

This paper pursues the insight that language models naturally enable an intelligent variation operator similar in spirit to evolutionary crossover. In particular, language models of sufficient scale demonstrate in-context learning, i.e. they can learn from associations between a small number of input patterns to generate outputs incorporating such associations (also called few-shot prompting). This ability can be leveraged to form a simple but powerful variation operator, i.e. to prompt a language model with a few text-based genotypes (such as code, plain-text sentences, or equations), and to parse its corresponding output as those genotypes' offspring. The promise of such language model crossover (which is simple to implement and can leverage many different open-source language models) is that it enables a simple mechanism to evolve semantically-rich text representations (with few domain-specific tweaks), and naturally benefits from current progress in language models. Experiments in this paper highlight the versatility of language-model crossover, through evolving binary bit-strings, sentences, equations, text-to-image prompts, and Python code. The conclusion is that language model crossover is a promising method for evolving genomes representable as text

Online optimization with switching cost

We consider algorithms for "smoothed online convex optimization (SOCO)" problems. SOCO is a variant of the class of "online convex optimization (OCO)" problems that is strongly related to the class of "metrical task systems", each of which have been studied extensively. Prior literature on these problems has focused on two performance metrics: regret and competitive ratio. There exist known algorithms with sublinear regret and known algorithms with constant competitive ratios; however no known algorithms achieve both. In this paper, we show that this is due to a fundamental incompatibility between regret and the competitive ratio -- no algorithm (deterministic or randomized) can achieve sublinear regret and a constant competitive ratio, even in the case when the objective functions are linear

Multitrophic enemy escape of invasive Phragmites australis and its introduced herbivores in North America

© 2015, Springer International Publishing Switzerland. One explanation for why invasive species are successful is that they escape natural enemies from their native range or experience lower attack from natural enemies in the introduced range relative to native species (i.e., the enemy-release hypothesis). However, little is known about how invasive plants interact with co-introduced herbivores or natural enemies of the introduced herbivores. We focus on Phragmites australis, a wetland grass native to Europe (EU) and North America (NA). Within the past 100–150 years, invasive European genotypes of P. australis and several species of specialist Lipara gall flies have spread within NA. On both continents we surveyed P. australis patches for Lipara infestation (proportion of stems infested) and Lipara mortality from natural enemies. Our objectives were to assess evidence for enemy-release in the invaded (NA) versus native (EU) range and whether Lipara infestation or mortality differed between invasive and native P. australis genotypes in NA. Enemy-release varied regionally; Lipara were absent throughout most of NA, supporting enemy-release of Phragmites. However, where Lipara were present, the proportion of invasive P. australis stems infested with Lipara was higher in the introduced (11 %) than native range (\u3c1 \u3e%). This difference may be explained by the absence of Lipara parasitoids in our NA survey, strongly supporting enemy-release of Lipara. In NA, native P. australis genotypes exhibited higher Lipara infestation (32 %) than invasive genotypes (11 %), largely driven by L. rufitarsis. We attribute genotypic differences in infestation to a combination of Lipara exhibiting 34 % greater performance (gall diameter) and suffering four times less vertebrate predation on native than invasive genotypes. Our study suggests that complex interactions can result from the co-introduction of plants and their herbivores, and that a multitrophic perspective is required for investigating how biotic interactions influence invasion success