Graph-based data structures have become powerful and ubiquitous tools for scalable approximate nearest-neighbor (ANN) search over the past decade. In spite of their apparent practical performance, there has only recently been progress on the **worst-case** performance of these data structures. Indeed, the influential work of Indyx and Xu (2023) introduced the key concept of $\alpha$-reachable graphs, showing that graphs constructed by the DiskANN algorithm (Subramanya, et. al. 2023) produce an $\left(\frac{\alpha+1}{\alpha-1}\right)$-approximate solution with a simple best-first search that runs in poly-logarithmic query time. In our work, we improve and generalize this analysis as follows: - We introduce **sorted** $\alpha$-reachable graphs, and use this notion to obtain a stronger approximation factor of $\frac{\alpha}{\alpha-1}$ for the DiskANN algorithm on Euclidean metrics. - We present the **first** worst-case theoretical analysis for the popular **beam-search** algorithm, which is used in practice to search these graphs for $k > 1$ candidate nearest neighbors. We also present empirical results validating the significance of sorted $\alpha$-reachable graphs, which aligns with our theoretical findings.

Given a directed graph $G = (V, E)$ with $n$ vertices, $m$ edges and a designated source vertex $s\in V$, we consider the question of finding a sparse subgraph $H$ of $G$ that preserves the flow from $s$ up to a given threshold $λ$ even after failure of $k$ edges. We refer to such subgraphs as $(λ,k)$-fault-tolerant bounded-flow-preserver ($(λ,k)$-FT-BFP). Formally, for any $F \subseteq E$ of at most $k$ edges and any $v\in V$, the $(s, v)$-max-flow in $H \setminus F$ is equal to $(s, v)$-max-flow in $G \setminus F$, if the latter is bounded by $λ$, and at least $λ$ otherwise. Our contributions are summarized as follows: 1. We provide a polynomial time algorithm that given any graph $G$ constructs a $(λ,k)$-FT-BFP of $G$ with at most $λ2^kn$ edges. 2. We also prove a matching lower bound of $Ω(λ2^kn)$ on the size of $(λ,k)$-FT-BFP. In particular, we show that for every $λ,k,n\geq 1$, there exists an $n$-vertex directed graph whose optimal $(λ,k)$-FT-BFP contains $Ω(\min\{2^kλn,n^2\})$ edges. 3. Furthermore, we show that the problem of computing approximate $(λ,k)$-FT-BFP is NP-hard for any approximation ratio that is better than $O(\log(λ^{-1} n))$.

The laboratory is an indispensable component of learning in engineering education. In this paper, we examine the impact of Advanced VLSI Virtual Lab, which is a part of the Government of India's suite of Virtual Labs, in improving the understanding and learning of students at a small sized university in India. The Advanced VLSI Virtual Lab includes ten simulated interactive experiments in the area of design and application development. Over a hundred Virtual Labs have been proposed and built, but, so far, few have been subject to systematic investigation of their effectiveness in helping the student learn. Our work is one of the first efforts to statistically study the effectiveness of the Virtual Lab. To this end, we designed and conducted pre- and post-tests, and feedback surveys on the lab. The tests and the survey, on analysis, reveal that the lab is effective in enhancing student learning. Our results are encouraging to several teachers in India who are in the midst of using Virtual Labs at their colleges.