The discovery of new materials is essential for enabling technological advancements. Computational approaches for predicting novel materials must effectively learn the manifold of stable crystal structures within an infinite design space. We introduce Open Materials Generation (OMatG), a unifying framework for the generative design and discovery of inorganic crystalline materials. OMatG employs stochastic interpolants (SI) to bridge an arbitrary base distribution to the target distribution of inorganic crystals via a broad class of tunable stochastic processes, encompassing both diffusion models and flow matching as special cases. In this work, we adapt the SI framework by integrating an equivariant graph representation of crystal structures and extending it to account for periodic boundary conditions in unit cell representations. Additionally, we couple the SI flow over spatial coordinates and lattice vectors with discrete flow matching for atomic species. We benchmark OMatG’s performance on two tasks: Crystal Structure Prediction (CSP) for specified compositions, and de novo generation (DNG) aimed at discovering stable, novel, and unique structures. In our ground-up implementation of OMatG, we refine and extend both CSP and DNG metrics compared to previous works. OMatG establishes a new state of the art in generative modeling for materials discovery, outperforming purely flow-based and diffusion-based implementations. These results underscore the importance of designing flexible deep learning frameworks to accelerate progress in materials science. The OMatG code is available at https://github.com/FERMat-ML/OMatG.

Modern data center networks are commonly organized in multi-rooted tree topologies. They typically rely on equal-cost multipath to split flows across multiple paths, which can lead to significant load imbalance. Splitting individual flows can provide better load balance, but is not preferred because of potential packet reordering that conventional wisdom suggests may negatively interact with TCP congestion control. In this paper, we revisit this “myth” in the context of data center networks which have regular topologies such as multi-rooted trees. We argue that due to symmetry, the multiple equal-cost paths between two hosts are composed of links that exhibit similar queuing properties. As a result, TCP is able to tolerate the induced packet reordering and maintain a single estimate of RTT. We validate the efficacy of random packet spraying (RPS) using a data center testbed comprising real hardware switches. We also reveal the adverse impact on the performance of RPS when the symmetry is disturbed (e.g., during link failures) and suggest solutions to mitigate this effect.

Current multipath routing techniques split traffic at a per-flow level because, according to conventional wisdom, forwarding packets of a TCP flow along different paths leads to packet reordering which is detrimental to TCP. In this paper, we revisit this "myth" in the context of cloud data center networks which have regular topologies such as multi-rooted trees. We argue that due to the symmetry in the multiple equal-cost paths in such networks, simply spraying packets of a given flow among all equal-cost paths, leads to balanced queues across multiple paths, and consequently little packet reordering. Using a testbed comprising of NetFPGA switches, we show how cloud applications benefit from better network utilization in data centers.

Multi-rooted tree topologies are commonly used to construct high-bandwidth data center network fabrics. In these networks, switches typically rely on equal-cost multipath (ECMP) routing techniques to split traffic across multiple paths, such that packets within a flow traverse the same end-to-end path. Unfortunately, since ECMP splits traffic based on flow-granularity, it can cause load imbalance across paths resulting in poor utilization of network resources. More fine-grained traffic splitting techniques are typically not preferred because they can cause packet reordering that can, according to conventional wisdom, lead to severe TCP throughput degradation. In this work, we revisit this fact in the context of regular data center topologies such as fat-tree architectures. We argue that packet-level traffic splitting, where packets of a flow are sprayed through all available paths, would lead to a better load-balanced network, which in turn leads to significantly more balanced queues and much higher throughput compared to ECMP.

Phishing has been easy and effective way for trickery and deception on the Internet. While solutions such as URL blacklisting have been effective to some degree, their reliance on exact match with the blacklisted entries makes it easy for attackers to evade. We start with the observation that attackers often employ simple modifications (e.g., changing top level domain) to URLs. Our system, PhishNet, exploits this observation using two components. In the first component, we propose five heuristics to enumerate simple combinations of known phishing sites to discover new phishing URLs. The second component consists of an approximate matching algorithm that dissects a URL into multiple components that are matched individually against entries in the blacklist. In our evaluation with real-time blacklist feeds, we discovered around 18,000 new phishing URLs from a set of 6,000 new blacklist entries. We also show that our approximate matching algorithm leads to very few false positives (3%) and negatives (5%).

Fault localization in enterprise networks is extremely challenging. A recent approach called Sherlock makes some headway into this problem by using an inference algorithm over a multi-tier probabilistic dependency graph that relates fault symptoms with possible root causes (e.g., routers, servers). A key limitation of Sherlock is its scalability because of the use of complicated inference algorithms based on Bayesian networks. We present a fault localization system called Spotlight that essentially uses two basic ideas. First, it compresses a multi-tier dependency graph into a bipartite graph with direct probabilistic edges between root causes and symptoms. Second, it runs a novel weighted greedy minimum set cover algorithm to provide fast inference. Through extensive simulations with real service dependency graphs and enterprise network topologies reported previously in literature, we show that Spotlight is about 100× faster than Sherlock in typical settings, with comparable accuracy in diagnosis.