Deterministic History-Independent Strategies for Storing Information on Write-Once Memories

Motivated by the challenging task of designing ``secure\u27\u27 vote storage mechanisms, we study information storage mechanisms that operate in extremely hostile environments. In such environments, the majority of existing techniques for information storage and for security are susceptible to powerful adversarial attacks. We propose a mechanism for storing a set of at most $K$ elements from a large universe of size $N$ on write-once memories in a manner that does not reveal the insertion order of the elements. We consider a standard model for write-once memories, in which the memory is initialized to the all $0$\u27s state, and the only operation allowed is flipping bits from $0$ to $1$. Whereas previously known constructions were either inefficient (required $\Theta(K^2)$ memory), randomized, or employed cryptographic techniques which are unlikely to be available in hostile environments, we eliminate each of these undesirable properties. The total amount of memory used by the mechanism is linear in the number of stored elements and poly-logarithmic in the size of the universe of elements. We also demonstrate a connection between secure vote storage mechanisms and one of the classical distributed computing problems: conflict resolution in multiple-access channels. By establishing a tight connection with the basic building block of our mechanism, we construct the first deterministic and non-adaptive conflict resolution algorithm whose running time is optimal up to poly-logarithmic factors

An Optimally Fair Coin Toss

We address one of the foundational problems in cryptography: the bias of coin-flipping protocols. Coin-flipping protocols allow mutually distrustful parties to generate a common unbiased random bit, guaranteeing that even if one of the parties is malicious, it cannot significantly bias the output of the honest party. A classical result by Cleve [STOC \u2786] showed that for any two-party $r$-round coin-flipping protocol there exists an efficient adversary that can bias the output of the honest party by $\Omega(1/r)$. However, the best previously known protocol only guarantees $O(1/\sqrt{r})$ bias, and the question of whether Cleve\u27s bound is tight has remained open for more than twenty years. In this paper we establish the optimal trade-off between the round complexity and the bias of two-party coin-flipping protocols. Under standard assumptions (the existence of oblivious transfer), we show that Cleve\u27s lower bound is tight: we construct an $r$-round protocol with bias $O(1/r)$

Spectral Sparsification and Regret Minimization Beyond Matrix Multiplicative Updates

In this paper, we provide a novel construction of the linear-sized spectral sparsifiers of Batson, Spielman and Srivastava [BSS14]. While previous constructions required $\Omega(n^4)$ running time [BSS14, Zou12], our sparsification routine can be implemented in almost-quadratic running time $O(n^{2+\varepsilon})$. The fundamental conceptual novelty of our work is the leveraging of a strong connection between sparsification and a regret minimization problem over density matrices. This connection was known to provide an interpretation of the randomized sparsifiers of Spielman and Srivastava [SS11] via the application of matrix multiplicative weight updates (MWU) [CHS11, Vis14]. In this paper, we explain how matrix MWU naturally arises as an instance of the Follow-the-Regularized-Leader framework and generalize this approach to yield a larger class of updates. This new class allows us to accelerate the construction of linear-sized spectral sparsifiers, and give novel insights on the motivation behind Batson, Spielman and Srivastava [BSS14]

Native homing endonucleases can target conserved genes in humans and in animal models

In recent years, both homing endonucleases (HEases) and zinc-finger nucleases (ZFNs) have been engineered and selected for the targeting of desired human loci for gene therapy. However, enzyme engineering is lengthy and expensive and the off-target effect of the manufactured endonucleases is difficult to predict. Moreover, enzymes selected to cleave a human DNA locus may not cleave the homologous locus in the genome of animal models because of sequence divergence, thus hampering attempts to assess the in vivo efficacy and safety of any engineered enzyme prior to its application in human trials. Here, we show that naturally occurring HEases can be found, that cleave desirable human targets. Some of these enzymes are also shown to cleave the homologous sequence in the genome of animal models. In addition, the distribution of off-target effects may be more predictable for native HEases. Based on our experimental observations, we present the HomeBase algorithm, database and web server that allow a high-throughput computational search and assignment of HEases for the targeting of specific loci in the human and other genomes. We validate experimentally the predicted target specificity of candidate fungal, bacterial and archaeal HEases using cell free, yeast and archaeal assay

One-Way Functions and (Im)perfect Obfuscation

A program obfuscator takes a program and outputs an scrambled version of it, where the goal is that the obfuscated program will not reveal much about its structure beyond what is apparent from executing it. There are several ways of formalizing this goal. Specifically, in indistinguishability obfuscation, first defined by Barak et al. (CRYPTO 2001), the requirement is that the results of obfuscating any two functionally equivalent programs (circuits) will be computationally indistinguishable. Recently, a fascinating candidate construction for indistinguishability obfuscation was proposed by Garg et al. (FOCS 2013). This has led to a flurry of discovery of intriguing constructions of primitives and protocols whose existence was not previously known (for instance, fully deniable encryption by Sahai and Waters, STOC 2014). Most of them explicitly rely on additional hardness assumptions, such as one-way functions. Our goal is to get rid of this extra assumption. We cannot argue that indistinguishability obfuscation of all polynomial-time circuits implies the existence of one-way functions, since if $P = NP$, then program obfuscation (under the indistinguishability notion) is possible. Instead, the ultimate goal is to argue that if $P \neq NP$ and program obfuscation is possible, then one-way functions exist. Our main result is that if $NP \not\subseteq ioBPP$ and there is an efficient (even imperfect) indistinguishability obfuscator, then there are one-way functions. In addition, we show that the existence of an indistinguishability obfuscator implies (unconditionally) the existence of SZK-arguments for $NP$. This, in turn, provides an alternative version of our main result, based on the assumption of hard-on-the average $NP$ problems. To get some of our results we need obfuscators for simple programs such as 3CNF formulas

DeepSTORM3D: dense three dimensional localization microscopy and point spread function design by deep learning

Localization microscopy is an imaging technique in which the positions of individual nanoscale point emitters (e.g. fluorescent molecules) are determined at high precision from their images. This is the key ingredient in single/multiple-particle-tracking and several super-resolution microscopy approaches. Localization in three-dimensions (3D) can be performed by modifying the image that a point-source creates on the camera, namely, the point-spread function (PSF). The PSF is engineered using additional optical elements to vary distinctively with the depth of the point-source. However, localizing multiple adjacent emitters in 3D poses a significant algorithmic challenge, due to the lateral overlap of their PSFs. Here, we train a neural network to receive an image containing densely overlapping PSFs of multiple emitters over a large axial range and output a list of their 3D positions. Furthermore, we then use the network to design the optimal PSF for the multi-emitter case. We demonstrate our approach numerically as well as experimentally by 3D STORM imaging of mitochondria, and volumetric imaging of dozens of fluorescently-labeled telomeres occupying a mammalian nucleus in a single snapshot.Comment: main text: 9 pages, 5 figures, supplementary information: 29 pages, 20 figure

Native homing endonucleases can target conserved genes in humans and in animal models

In recent years, both homing endonucleases (HEases) and zinc-finger nucleases (ZFNs) have been engineered and selected for the targeting of desired human loci for gene therapy. However, enzyme engineering is lengthy and expensive and the off-target effect of the manufactured endonucleases is difficult to predict. Moreover, enzymes selected to cleave a human DNA locus may not cleave the homologous locus in the genome of animal models because of sequence divergence, thus hampering attempts to assess the in vivo efficacy and safety of any engineered enzyme prior to its application in human trials. Here, we show that naturally occurring HEases can be found, that cleave desirable human targets. Some of these enzymes are also shown to cleave the homologous sequence in the genome of animal models. In addition, the distribution of off-target effects may be more predictable for native HEases. Based on our experimental observations, we present the HomeBase algorithm, database and web server that allow a high-throughput computational search and assignment of HEases for the targeting of specific loci in the human and other genomes. We validate experimentally the predicted target specificity of candidate fungal, bacterial and archaeal HEases using cell free, yeast and archaeal assays

Why did Better Place fail?: Range anxiety, interpretive flexibility, and electric vehicle promotion in Denmark and Israel

With almost $1 billion in funding, Better Place was poised to become one of the most innovative companies in the electric mobility market. The system Better Place proposed had two novel prongs; first, to reduce the cost of batteries, and second, to reduce range anxiety, public infrastructure concerns, and long charging times. Yet, despite this seemingly strong combination, Better Place failed to make any progress in Denmark and Israel, the first two markets it operated in, and subsequently declared bankruptcy, selling off its collective assets for less than$500,000. Drawing from science and technology studies and the notion of “interpretive flexibility,” this paper posits several reasons to explain the failure of Better Place, including that Denmark is not as “green” as it seems nor is the Israeli market as attractive as believed, and that Better Place's solution to charging time and range anxiety resolved a psychological, not a functional, barrier of the general public to adopt electric vehicles. Before investigating these two reasons, the paper presents a short history of Better Place and explores the contours of its operations in Denmark and Israel. It then discusses why Better Place “failed” across both countries before concluding with implications for energy planning, policy, and analysis