arXiv subjects

Dimitris Dimitropoulos

Publications and source records attributed to Dimitris Dimitropoulos.

5 recordsLinked to original sources

arXiv2026

ANN Search: Recall What Matters

Approximate nearest neighbor (ANN) search has become a core primitive in information retrieval and modern machine learning tasks, from classification to retrieval-augmented generation. The community evaluates and tunes ANN algorithms primarily on their throughput at a given Recall@k, the fraction of true exact neighbors retrieved. We argue that what really matters in ANN search is the quality of the retrieved results and not their overlap with the true kNN set. We show that using Recall@k to assess retrieval quality forces unnecessary computational overhead and investigate replacing it by 1/Ratio@k, the inverse approximation ratio. 1/Ratio@k evaluates the differences between the distances of the retrieved and true neighbors. It is judge-free, hyperparameter-free, and computable from standard ANN benchmark inputs alone. We benchmark state-of-the-art ANN algorithms across diverse datasets spanning a wide range of intrinsic dimensionalities, evaluating the two metrics comprehensively across efficiency, downstream classification, and retrieval-augmented generation. On the efficiency axis, optimizing for 1/Ratio@k reaches operational quality thresholds at a substantially lower computational cost than Recall@k. In downstream tasks, performance indicators (label precision, semantic similarity, BERTScore, and LLM-graded quality) remain highly stable even when Recall@k drops significantly. The inverse approximation ratio, on the other hand, closely mirrors this stability, tracking true utility much better than Recall@k. Ultimately, while Recall@k overstates the true cost of approximation, 1/Ratio@k offers a more accurate, deployable proxy for actual ANN quality.

cs.IR↗

arXiv2026

CRISP: Correlation-Resilient Indexing via Subspace Partitioning

As the dimensionality of modern learned representations increases to thousands of dimensions, the state-of-the-art Approximate Nearest Neighbor (ANN) indices exhibit severe limitations. Graph-based methods (e.g., HNSW) suffer from prohibitive memory consumption and routing degradation, while recent randomized quantization and learned rotation approaches (e.g., RaBitQ, OPQ) impose significant preprocessing overheads. We introduce CRISP, a novel framework designed for ANN search in very-high-dimensional spaces. Unlike rigid pipelines that apply expensive orthogonal rotations indiscriminately, CRISP employs a lightweight, correlation- aware adaptive strategy that redistributes variance only when necessary, effectively reducing the preprocessing complexity. We couple this adaptive mechanism with a cache-coherent Compressed Sparse Row (CSR) index structure. Furthermore, CRISP incorporates a multi-stage dual-mode query engine: a Guaranteed Mode that preserves rigorous theoretical lower bounds on recall, and an Optimized Mode that leverages rank-based weighted scoring and early termination to reduce query latency. Extensive evaluation on datasets of very high dimensionality (up to 4096) demonstrates that CRISP achieves state-of-the-art query throughput, low construction costs, and peak memory efficiency.

cs.DB↗

arXiv2014

Optical information capacity of silicon

Modern computing and data storage systems increasingly rely on parallel architectures where processing and storage load is distributed within a cluster of nodes. The necessity for high-bandwidth data links has made optical communication a critical constituent of modern information systems and silicon the leading platform for creating the necessary optical components. While silicon is arguably the most extensively studied material in history, one of its most important attributes, an analysis of its capacity to carry optical information, has not been reported. The calculation of the information capacity of silicon is complicated by nonlinear losses, phenomena that emerge in optical nanowires as a result of the concentration of optical power in a small geometry. Nonlinear losses are absent in silica glass optical fiber and other common communication channels. While nonlinear loss in silicon is well known, noise and fluctuations that arise from it have never been considered. Here we report sources of fluctuations that arise from two-photon absorption and free-carrier plasma effects and use these results to investigate the theoretical limit of the information capacity of silicon. Our results show that noise and fluctuations due to nonlinear absorption become significant and limit the information capacity well before nonlinear loss itself becomes dominant. We present closed-form analytical expressions that quantify the capacity and provide an intuitive understanding of the underlying interactions. Our results provide the capacity limit and its origin, and suggest solutions for extending it via coding and coherent signaling. The amount of information that can be transmitted by light through silicon is the key element in future information systems. Results presented here are not only applicable to silicon but also to other semiconductor optical channels.

physics.optics↗

arXiv2008

Proposal for a Photonic Remote Active Heat Sink Technology (PHRAHST)

We propose a new method to effect heat removal from an object by using a laser beam. The proposed method is based on inelastic scattering of a laser beam from the object and in particular by making the anti-Stokes emission more efficient than the Stokes emission. In that manner more energy is removed from the body per unit time than deposited. Various ways are outlined in order to achieve this result ranging from careful selection of the laser frequency with respect to the resonant frequencies of the medium, use of the frequency dependence of the density of electromagnetic modes in a three-dimensional system, use of photonic crystals and the polarization dependence of electromagnetic modes in cavities. The proposed methods could find use for example in the cooling of devices of nanoscale dimensions.

physics.optics↗

arXiv2008

A new stochastic differential equation approach for waves in a random medium

We present a mathematical approach that simplifies the theoretical treatment of electromagnetic localization in random media and leads to closed form analytical solutions. Starting with the assumption that the dielectric permittivity of the medium has delta-correlated spatial fluctuations, and using the Ito lemma, we derive a linear stochastic differential equation for a one dimensional random medium. The equation leads to localized wave solutions. The localized wave solutions have a localization length that scales inversely with the square of the frequency of the wave in the low frequency regime, whereas in the high frequency regime, this length varies inversely with the frequency to the power of two thirds.

physics.optics↗