Tootfinder

We all perform tasks in our day-to-day work that are considered 'non-promotable' – these are crucial for project success, but they won't get you promoted. This is commonly known as 'glue work', a term coined by Tanya Reilly. Join Fatima Taj at this year's Berlin Buzzwords as she shares her personal experience of narrowly avoiding the trap of being permanently stuck with glue work, and explains how to handle similar situations.
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From IOCs to Group Profiles: On the Specificity of Threat Group Behaviors in CTI Knowledge Bases
Aakanksha Saha, Martina Lindorfer, Juan Caballero
https://arxiv.org/abs/2506.10645

From IOCs to Group Profiles: On the Specificity of Threat Group Behaviors in CTI Knowledge Bases
Indicators of Compromise (IOCs) such as IP addresses, file hashes, and domain names are commonly used for threat detection and attribution. However, IOCs tend to be short-lived as they are easy to change. As a result, the cybersecurity community is shifting focus towards more persistent behavioral profiles, such as the Tactics, Techniques, and Procedures (TTPs) and the software used by a threat group. However, the distinctiveness and completeness of such behavioral profiles remain largely unexp…

SpeechRefiner: Towards Perceptual Quality Refinement for Front-End Algorithms
Sirui Li, Shuai Wang, Zhijun Liu, Zhongjie Jiang, Yannan Wang, Haizhou Li
https://arxiv.org/abs/2506.13709

SpeechRefiner: Towards Perceptual Quality Refinement for Front-End Algorithms
Speech pre-processing techniques such as denoising, de-reverberation, and separation, are commonly employed as front-ends for various downstream speech processing tasks. However, these methods can sometimes be inadequate, resulting in residual noise or the introduction of new artifacts. Such deficiencies are typically not captured by metrics like SI-SNR but are noticeable to human listeners. To address this, we introduce SpeechRefiner, a post-processing tool that utilizes Conditional Flow Match…

New preprint!
User-space library rootkits revisited: Are user-space detection mechanisms futile?
https://arxiv.org/abs/2506.07827

Viral Dark Matter: Illuminating Protein Function, Ecology, and Biotechnological Promises
James C. Kosmopoulos, Karthik Anantharaman
https://arxiv.org/abs/2506.11942

Viral Dark Matter: Illuminating Protein Function, Ecology, and Biotechnological Promises
Viruses are the most abundant biological entities on Earth and play central roles in shaping microbiomes and influencing ecosystem functions. Yet, most viral genes remain uncharacterized, comprising what is commonly referred to as "viral dark matter." Metagenomic studies across diverse environments consistently show that 40-90% of viral genes lack known homologs or annotated functions. This persistent knowledge gap limits our ability to interpret viral sequence data, understand virus-host inter…

Optimal quantum metrology under energy constraints
Longyun Chen, Yuxiang Yang
https://arxiv.org/abs/2506.09436 https://arxiv.org/pdf/…

Optimal quantum metrology under energy constraints
Quantum metrology promises enhanced precision in parameter estimation by exploiting quantum effects. The traditional framework of metrology commonly assumes unlimited access to resources, overlooking resource constraints in realistic scenarios. As such, the optimal strategies therein are often infeasible in practice. Here, we investigate quantum metrology where the total energy consumption of the probe state preparation, intermediate control operations, and the final measurement is subject to a…

Don't be Afraid of Cell Complexes! An Introduction from an Applied Perspective
Josef Hoppe, Vincent P. Grande, Michael T. Schaub
https://arxiv.org/abs/2506.09726

Don't be Afraid of Cell Complexes! An Introduction from an Applied Perspective
Cell complexes (CCs) are a higher-order network model deeply rooted in algebraic topology that has gained interest in signal processing and network science recently. However, while the processing of signals supported on CCs can be described in terms of easily-accessible algebraic or combinatorial notions, the commonly presented definition of CCs is grounded in abstract concepts from topology and remains disconnected from the signal processing methods developed for CCs. In this paper, we aim to …

Feasibility Study of CNNs and MLPs for Radiation Heat Transfer in 2-D Furnaces with Spectrally Participative Gases
Axel TahmasebiMoradi, Vincent Ren, Benjamin Le-Creurer, Chetra Mang
https://arxiv.org/abs/2506.08033

Feasibility Study of CNNs and MLPs for Radiation Heat Transfer in 2-D Furnaces with Spectrally Participative Gases
Aiming to reduce the computational cost of numerical simulations, a convolutional neural network (CNN) and a multi-layer perceptron (MLP) are introduced to build a surrogate model to approximate radiative heat transfer solutions in a 2-D walled domain with participative gases. The originality of this work lays in the adaptation of the inputs of the problem (gas and wall properties) in order to fit with the CNN architecture, more commonly used for image processing. Two precision datasets have be…

Detecting State Manipulation Vulnerabilities in Smart Contracts Using LLM and Static Analysis
Hao Wu, Haijun Wang, Shangwang Li, Yin Wu, Ming Fan, Yitao Zhao, Ting Liu
https://arxiv.org/abs/2506.08561

Detecting State Manipulation Vulnerabilities in Smart Contracts Using LLM and Static Analysis
An increasing number of DeFi protocols are gaining popularity, facilitating transactions among multiple anonymous users. State Manipulation is one of the notorious attacks in DeFi smart contracts, with price variable being the most commonly exploited state variable-attackers manipulate token prices to gain illicit profits. In this paper, we propose PriceSleuth, a novel method that leverages the Large Language Model (LLM) and static analysis to detect Price Manipulation (PM) attacks proactively.…

Heavy Lasso: sparse penalized regression under heavy-tailed noise via data-augmented soft-thresholding
The Tien Mai
https://arxiv.org/abs/2506.07790 https:…

Heavy Lasso: sparse penalized regression under heavy-tailed noise via data-augmented soft-thresholding
High-dimensional linear regression is a fundamental tool in modern statistics, particularly when the number of predictors exceeds the sample size. The classical Lasso, which relies on the squared loss, performs well under Gaussian noise assumptions but often deteriorates in the presence of heavy-tailed errors or outliers commonly encountered in real data applications such as genomics, finance, and signal processing. To address these challenges, we propose a novel robust regression method, terme…