Deterministic Approximation of Random Walks in Small Space

We give a deterministic, nearly logarithmic-space algorithm that given an undirected graph G, a positive integer r, and a set S of vertices, approximates the conductance of S in the r-step random walk on G to within a factor of 1+epsilon, where epsilon>0 is an arbitrarily small constant. More generally, our algorithm computes an epsilon-spectral approximation to the normalized Laplacian of the r-step walk. Our algorithm combines the derandomized square graph operation [Eyal Rozenman and Salil Vadhan, 2005], which we recently used for solving Laplacian systems in nearly logarithmic space [Murtagh et al., 2017], with ideas from [Cheng et al., 2015], which gave an algorithm that is time-efficient (while ours is space-efficient) and randomized (while ours is deterministic) for the case of even r (while ours works for all r). Along the way, we provide some new results that generalize technical machinery and yield improvements over previous work. First, we obtain a nearly linear-time randomized algorithm for computing a spectral approximation to the normalized Laplacian for odd r. Second, we define and analyze a generalization of the derandomized square for irregular graphs and for sparsifying the product of two distinct graphs. As part of this generalization, we also give a strongly explicit construction of expander graphs of every size

Spectral Sparsification via Bounded-Independence Sampling

We give a deterministic, nearly logarithmic-space algorithm for mild spectral sparsification of undirected graphs. Given a weighted, undirected graph $G$ on $n$ vertices described by a binary string of length $N$, an integer $k\leq \log n$, and an error parameter $\epsilon > 0$, our algorithm runs in space $\tilde{O}(k\log (N\cdot w_{\mathrm{max}}/w_{\mathrm{min}}))$ where $w_{\mathrm{max}}$ and $w_{\mathrm{min}}$ are the maximum and minimum edge weights in $G$, and produces a weighted graph $H$ with $\tilde{O}(n^{1+2/k}/\epsilon^2)$ edges that spectrally approximates $G$, in the sense of Spielmen and Teng [ST04], up to an error of $\epsilon$. Our algorithm is based on a new bounded-independence analysis of Spielman and Srivastava's effective resistance based edge sampling algorithm [SS08] and uses results from recent work on space-bounded Laplacian solvers [MRSV17]. In particular, we demonstrate an inherent tradeoff (via upper and lower bounds) between the amount of (bounded) independence used in the edge sampling algorithm, denoted by $k$ above, and the resulting sparsity that can be achieved.Comment: 37 page

Concentrated Differential Privacy: Simplifications, Extensions, and Lower Bounds

"Concentrated differential privacy" was recently introduced by Dwork and Rothblum as a relaxation of differential privacy, which permits sharper analyses of many privacy-preserving computations. We present an alternative formulation of the concept of concentrated differential privacy in terms of the Renyi divergence between the distributions obtained by running an algorithm on neighboring inputs. With this reformulation in hand, we prove sharper quantitative results, establish lower bounds, and raise a few new questions. We also unify this approach with approximate differential privacy by giving an appropriate definition of "approximate concentrated differential privacy.

The Complexity of Computing the Optimal Composition of Differential Privacy

In the study of differential privacy, composition theorems (starting with the original paper of Dwork, McSherry, Nissim, and Smith (TCC’06)) bound the degradation of privacy when composing several differentially private algorithms. Kairouz, Oh, and Viswanath (ICML’15) showed how to compute the optimal bound for composing k arbitrary ( , δ)- differentially private algorithms. We characterize the optimal composition for the more general case of k arbitrary ( 1, δ1), . . . ,( k, δk)-differentially private algorithms where the privacy parameters may differ for each algorithm in the composition. We show that computing the optimal composition in general is #P-complete. Since computing optimal composition exactly is infeasible (unless FP=#P), we give an approximation algorithm that computes the composition to arbitrary accuracy in polynomial time. The algorithm is a modification of Dyer’s dynamic programming approach to approximately counting solutions to knapsack problems (STOC’03).Engineering and Applied Science

Derandomization Beyond Connectivity: Undirected Laplacian Systems in Nearly Logarithmic Space

We give a deterministic O˜(log n)-space algorithm for approximately solving linear systems given by Laplacians of undirected graphs, and consequently also approximating hitting times, commute times, and escape probabilities for undirected graphs. Previously, such systems were known to be solvable by randomized algorithms using O(log n) space (Doron, Le Gall, and Ta-Shma, 2017) and hence by deterministic algorithms using O(log3/2 n) space (Saks and Zhou, FOCS 1995 and JCSS 1999). Our algorithm combines ideas from time-efficient Laplacian solvers (Spielman and Teng, STOC ‘04; Peng and Spielman, STOC ‘14) with ideas used to show that UNDIRECTED S-T CONNECTIVITY is in deterministic logspace (Reingold, STOC ‘05 and JACM ‘08; Rozenman and Vadhan, RANDOM ‘05).Engineering and Applied Science

The Complexity of Computing the Optimal Composition of Differential Privacy

In the study of differential privacy, composition theorems (starting with the original paper of Dwork, McSherry, Nissim, and Smith (TCC’06)) bound the degradation of privacy when composing several differentially private algorithms. Kairouz, Oh, and Viswanath (ICML’15) showed how to compute the optimal bound for composing k arbitrary ( , δ)- differentially private algorithms. We characterize the optimal composition for the more general case of k arbitrary ( 1, δ1), . . . ,( k, δk)-differentially private algorithms where the privacy parameters may differ for each algorithm in the composition. We show that computing the optimal composition in general is #P-complete. Since computing optimal composition exactly is infeasible (unless FP=#P), we give an approximation algorithm that computes the composition to arbitrary accuracy in polynomial time. The algorithm is a modification of Dyer’s dynamic programming approach to approximately counting solutions to knapsack problems (STOC’03).Engineering and Applied Science

Validation of the Aura Microwave Limb Sounder HNOmeasurements

We assess the quality of the version 2.2 (v2.2) HNO3 measurements from the Microwave Limb Sounder (MLS) on the Earth Observing System Aura satellite. The MLS HNO3 product has been greatly improved over that in the previous version (v1.5), with smoother profiles, much more realistic behavior at the lowest retrieval levels, and correction of a high bias caused by an error in one of the spectroscopy files used in v1.5 processing. The v2.2 HNO3 data are scientifically useful over the range 215 to 3.2 hPa, with single-profile precision of ∼0.7 ppbv throughout. Vertical resolution is 3–4 km in the upper troposphere and lower stratosphere, degrading to ∼5 km in the middle and upper stratosphere. The impact of various sources of systematic uncertainty has been quantified through a comprehensive set of retrieval simulations. In aggregate, systematic uncertainties are estimated to induce in the v2.2 HNO3 measurements biases that vary with altitude between ±0.5 and ±2 ppbv and multiplicative errors of ±5–15% throughout the stratosphere, rising to ∼±30% at 215 hPa. Consistent with this uncertainty analysis, comparisons with correlative data sets show that relative to HNO3 measurements from ground-based, balloon-borne, and satellite instruments operating in both the infrared and microwave regions of the spectrum, MLS v2.2 HNO3 mixing ratios are uniformly low by 10–30% throughout most of the stratosphere. Comparisons with in situ measurements made from the DC-8 and WB-57 aircraft in the upper troposphere and lowermost stratosphere indicate that the MLS HNO3 values are low in this region as well, but are useful for scientific studies (with appropriate averaging)

Physical environmental factors that invite older adults to walk for transportation

Knowledge on the physical environmental factors that invite older adults to walk for transportation is limited. The current study aimed to investigate the relationships between environmental factors and invitingness to walk for transportation and the potential moderating effects of gender, functional limitations and current walking for transportation behavior. Sixty older participants evaluated 40 panoramic photographs on their invitingness in two ways: a forced choice (first impressions) and a rating task (more deliberate evaluation). Presence of vegetation, benches, and surveillance significantly positively related to both invitingness-measures. Upkeep and presence of historic elements significantly positively related to the assigned invitingness-ratings. For the forced choice task, significant positive relationships emerged for land use and separation between sidewalk and cycling path, but only in functionally limited participants. Environments offering comfort, safety from crime, and pleasantness may attract older adults to walk for transportation. Experimental and on-site studies are needed to elaborate on current findings