arXiv subjects

Thai Son Mai

Publications and source records attributed to Thai Son Mai.

7 recordsLinked to original sources

arXiv2026

HANCLIP: A Family of Hyperbolic Angular Negation Vision Language Models

Vision-language models (VLMs) achieve strong cross-modal alignment but remain brittle to negation, often relying on shallow word associations rather than compositional reasoning. Fine-tuning on negation-specific data can also compromise their general purpose capabilities through catastrophic forgetting. We introduce HANCLIP (Hyperbolic, Angular, and Negation), a geometry-aware framework that improves negation sensitivity while preserving the structure of the pretrained joint embedding space. HANCLIP combines a hyperbolic contrastive objective, which models hierarchical relations and semantic asymmetries, with an angular triplet loss that separates negated descriptions from their affirmative counterparts. Using only 20,000 image-text quadruplets, HANCLIP consistently improves performance across CLIP, LongCLIP, and SmartCLIP backbones on the NegBench benchmark, while maintaining or improving zero-shot classification and image-text retrieval performance. These results show that lightweight, geometry-guided objectives can enhance negation understanding without large-scale retraining.

cs.CV↗

arXiv2026

Mind the Gap: The Disconnect Between Synthetic and Natural Edge Weights in Parallel Single-Source Shortest Path

Scientific research works often evaluate Parallel Single-Source Shortest Path (SSSP) algorithms using synthetic, uniformly distributed edge weights. However, real-world graphs exhibit very different, often heavy-tailed, weight distributions. This creates a disconnect between how algorithms are evaluated and their real-world performance, since most SSSP implementations inherently rely on the weight distribution for parameter tuning and work efficiency. In this paper, we explore whether current benchmarking methods unintentionally bias the performance results of these algorithms. To this end, we statistically characterize the weight distributions of 17 real-world graphs from a variety of domains and contrast them with six synthetic distributions used in the literature. Through a comprehensive evaluation of seven state-of-the-art parallel SSSP algorithms, we demonstrate severe sensitivity to edge weights, and show that evaluating with synthetic uniform weights alters optimal parameter configurations and can invert the performance hierarchy. These findings challenge existing benchmarking standards and offer practical insights for rigorous SSSP algorithm design.

cs.DC↗

arXiv2026

MALLORN: Many Artificial LSST Lightcurves based on Observations of Real Nuclear transients

The Vera C. Rubin Observatory's 10-Year Legacy Survey of Space and Time (LSST) is expected to produce a hundredfold increase in the number of transients we observe. However, there are insufficient spectroscopic resources to follow up on all of the wealth of targets that LSST will provide. As such it is necessary to be able to prioritise objects for followup observations or inclusion in sample studies based purely on their LSST photometry. We are particularly keen to identify tidal disruption events (TDEs) with LSST. TDEs are immensely useful for determining black hole parameters and probing our understanding of accretion physics. To assist in these efforts, we present the Many Artificial LSST Lightcurves based on the Observations of Real Nuclear transients (MALLORN) data set and the corresponding classifier challenge for identifying TDEs. MALLORN comprises 10178 simulated LSST light curves, constructed from real Zwicky Transient Facility (ZTF) observations of 64 TDEs, 727 nuclear supernovae and 1407 AGN with spectroscopic labels using Gaussian process fitting, empirically-motivated spectral energy distributions from SNCosmo and the baseline from the Rubin Survey Simulator. Our novel approach can be easily adapted to simulate transients for any photometric survey using observations from another, requiring only the limiting magnitudes and an estimate of the cadence of observations. The MALLORN Astronomical Classification Challenge, launched on Kaggle on 15/10/2025, will allow competitors to test their photometric classifiers on simulated LSST data to find TDEs and improve upon their capabilities prior to the start of LSST.

astro-ph.HE↗

arXiv2026

NI-ORCA: A Parallel Algorithm for Counting the Orbits of Non-Induced Graphlets up to K4

Counting the orbits of graphlets in a network is a vital tool for understanding the structural roles of vertices in various graph analytics tasks. While existing algorithms efficiently compute orbits of induced graphlets, many real-world applications require non-induced orbit counts. However, no current method offers exact, scalable, and parallel support for non-induced orbit counting. This paper presents NI-ORCA, a parallel algorithm to efficiently compute the orbits of non-induced graphlets up to size four (4-clique). NI-ORCA extends the ORCA framework for non-induced orbit counting by reformulating a system of linear equations. The algorithm consists of three stages: triangle counting, 4-clique enumeration, and orbit solving. We design and implement stage-specific parallelisation strategies using thread and vertex-local memory models and data structures, minimising contention and balancing workload. We further analyse the impact of scheduling policies, chunk sizes, and affinity strategies on performance. Experimental analysis on eight real-world datasets and a series of synthetic Erddos-Renyi graphs demonstrates that a mixed mode combining stage-specific data structure, with dynamic scheduling with small chunk sizes, delivers consistent speedup and effective load balancing. Our results show that NI-ORCA significantly outperforms state-of-the-art sequential algorithms, achieving up to 30x speedups.

cs.DC↗

arXiv2025

Attention-Based Preprocessing Framework for Improving Rare Transient Classification

With large numbers of transients discovered by current and future imaging surveys, machine learning is increasingly applied to light curve and host galaxy properties to select events for follow-up. However, finding rare types of transients remains difficult due to extreme class imbalances in training sets, and extracting features from host images is complicated by the presence of bright foreground sources, particularly if the true host is faint or distant. Here we present a data augmentation pipeline for images and light curves that mitigates these issues, and apply this to improve classification of Superluminous Supernovae Type I (SLSNe-I) and Tidal Disruption Events (TDEs) with our existing NEEDLE code. The method uses a Similarity Index to remove image artefacts, and a masking procedure that removes unrelated sources while preserving the transient and its host. This focuses classifier attention on the relevant pixels, and enables arbitrary rotations for class upsampling. We also fit observed multi-band light curves with a two-dimensional Gaussian Process and generate data-driven synthetic samples by resampling and redshifting these models, cross-matching with galaxy images in the same class to produce unique but realistic new examples for training. Models trained with the augmented dataset achieve substantially higher purity: for classifications with a confidence of 0.8 or higher, we achieve 75% (43%) purity and 75% (66%) completeness for SLSNe-I (TDEs).

astro-ph.IM↗

arXiv2025

Selective Parallel Loading of Large-Scale Compressed Graphs with ParaGrapher

Comprehensive evaluation is one of the basis of experimental science. In High-Performance Graph Processing, a thorough evaluation of contributions becomes more achievable by supporting common input formats over different frameworks. However, each framework creates its specific format, which may not support reading large-scale real-world graph datasets. This shows a demand for high-performance libraries capable of loading graphs to (i) accelerate designing new graph algorithms, (ii) to evaluate the contributions on a wide range of graph algorithms, and (iii) to facilitate easy and fast comparison over different graph frameworks. To that end, we present ParaGrapher, a high-performance API and library for loading large-scale and compressed graphs. ParaGrapher supports different types of requests for accessing graphs in shared- and distributed-memory and out-of-core graph processing. We explain the design of ParaGrapher and present a performance model of graph decompression, which is used for evaluation of ParaGrapher over three storage types. Our evaluation shows that by decompressing compressed graphs in WebGraph format, ParaGrapher delivers up to 3.2 times speedup in loading and up to 5.2 times speedup in end-to-end execution (i.e., through interleaved loading and execution) in comparison to the binary and textual formats. ParaGrapher is available online on https://blogs.qub.ac.uk/DIPSA/ParaGrapher/.

cs.AR↗

arXiv2022

Detecting Rumours with Latency Guarantees using Massive Streaming Data

Today's social networks continuously generate massive streams of data, which provide a valuable starting point for the detection of rumours as soon as they start to propagate. However, rumour detection faces tight latency bounds, which cannot be met by contemporary algorithms, given the sheer volume of high-velocity streaming data emitted by social networks. Hence, in this paper, we argue for best-effort rumour detection that detects most rumours quickly rather than all rumours with a high delay. To this end, we combine techniques for efficient, graph-based matching of rumour patterns with effective load shedding that discards some of the input data while minimising the loss in accuracy. Experiments with large-scale real-world datasets illustrate the robustness of our approach in terms of runtime performance and detection accuracy under diverse streaming conditions.

cs.SI↗