arXiv subjects

Attila Szabo

Publications and source records attributed to Attila Szabo.

9 recordsLinked to original sources

arXiv2025

Perfectly hidden order and Z2 confinement transition in a fully packed monopole liquid

We investigate a variant of spin ice whose degenerate ground states are densely packed monopole configurations. An applied field drives this model through a Z2 confinement transition. This phase change is a variant of the U(1) Kasteleyn transition, but instead of saturated order the system has fluctuations in the confined phase, and shows critical scaling on both sides of the transition. Remarkably, the magnetic response scales with the critical exponent of the specific heat in the 3D Ising universality class. We prove this universality using a Kramers--Wannier duality to map to an Ising model. The dual order parameter maps back to a non-local string order parameter in the original model, invisible to any local probe. Further, we describe the transition in terms of a bosonic field theory including a pairing term.

cond-mat.str-el↗

arXiv2025

BEMTrace: Visualization-driven approach for deriving Building Energy Models from BIM

Building Information Modeling (BIM) describes a central data pool covering the entire life cycle of a construction project. Similarly, Building Energy Modeling (BEM) describes the process of using a 3D representation of a building as a basis for thermal simulations to assess the building's energy performance. This paper explores the intersection of BIM and BEM, focusing on the challenges and methodologies in converting BIM data into BEM representations for energy performance analysis. BEMTrace integrates 3D data wrangling techniques with visualization methodologies to enhance the accuracy and traceability of the BIM-to-BEM conversion process. Through parsing, error detection, and algorithmic correction of BIM data, our methods generate valid BEM models suitable for energy simulation. Visualization techniques provide transparent insights into the conversion process, aiding error identification, validation, and user comprehension. We introduce context-adaptive selections to facilitate user interaction and to show that the BEMTrace workflow helps users understand complex 3D data wrangling processes.

cs.HC↗

arXiv2023

Feature-assisted interactive geometry reconstruction in 3D point clouds using incremental region growing

Reconstructing geometric shapes from point clouds is a common task that is often accomplished by experts manually modeling geometries in CAD-capable software. State-of-the-art workflows based on fully automatic geometry extraction are limited by point cloud density and memory constraints, and require pre- and post-processing by the user. In this work, we present a framework for interactive, user-driven, feature-assisted geometry reconstruction from arbitrarily sized point clouds. Based on seeded region-growing point cloud segmentation, the user interactively extracts planar pieces of geometry and utilizes contextual suggestions to point out plane surfaces, normal and tangential directions, and edges and corners. We implement a set of feature-assisted tools for high-precision modeling tasks in architecture and urban surveying scenarios, enabling instant-feedback interactive point cloud manipulation on large-scale data collected from real-world building interiors and facades. We evaluate our results through systematic measurement of the reconstruction accuracy, and interviews with domain experts who deploy our framework in a commercial setting and give both structured and subjective feedback.

cs.GR↗

arXiv2021

Adopting Automated Bug Assignment in Practice -- A Registered Report of an Industrial Case Study

[Background/Context] The continuous inflow of bug reports is a considerable challenge in large development projects. Inspired by contemporary work on mining software repositories, we designed a prototype bug assignment solution based on machine learning in 2011-2016. The prototype evolved into an internal Ericsson product, TRR, in 2017-2018. TRR's first bug assignment without human intervention happened in 2019. [Objective/Aim] Our exploratory study will evaluate the adoption of TRR within its industrial context at Ericsson. We seek to understand 1) how TRR performs in the field, 2) what value TRR provides to Ericsson, and 3) how TRR has influenced the ways of working. Secondly, we will provide lessons learned related to productization of a research prototype within a company. [Method] We design an industrial case study combining interviews with TRR developers and users with analysis of data extracted from the bug tracking system at Ericsson. Furthermore, we will analyze sprint planning meetings recorded during the productization. Our data analysis will include thematic analysis, descriptive statistics, and Bayesian causal analysis.

cs.SE↗

arXiv2021

Tilted Cross Entropy (TCE): Promoting Fairness in Semantic Segmentation

Traditional empirical risk minimization (ERM) for semantic segmentation can disproportionately advantage or disadvantage certain target classes in favor of an (unfair but) improved overall performance. Inspired by the recently introduced tilted ERM (TERM), we propose tilted cross-entropy (TCE) loss and adapt it to the semantic segmentation setting to minimize performance disparity among target classes and promote fairness. Through quantitative and qualitative performance analyses, we demonstrate that the proposed Stochastic TCE for semantic segmentation can efficiently improve the low-performing classes of Cityscapes and ADE20k datasets trained with multi-class cross-entropy (MCCE), and also results in improved overall fairness.

cs.CV↗

arXiv2021

Lookahead Adversarial Learning for Near Real-Time Semantic Segmentation

Semantic segmentation is one of the most fundamental problems in computer vision with significant impact on a wide variety of applications. Adversarial learning is shown to be an effective approach for improving semantic segmentation quality by enforcing higher-level pixel correlations and structural information. However, state-of-the-art semantic segmentation models cannot be easily plugged into an adversarial setting because they are not designed to accommodate convergence and stability issues in adversarial networks. We bridge this gap by building a conditional adversarial network with a state-of-the-art segmentation model (DeepLabv3+) at its core. To battle the stability issues, we introduce a novel lookahead adversarial learning (LoAd) approach with an embedded label map aggregation module. We focus on semantic segmentation models that run fast at inference for near real-time field applications. Through extensive experimentation, we demonstrate that the proposed solution can alleviate divergence issues in an adversarial semantic segmentation setting and results in considerable performance improvements (+5% in some classes) on the baseline for three standard datasets.

cs.CV↗

arXiv2019

Molecular free energy profiles from force spectroscopy experiments by inversion of observed committors

In single-molecule force spectroscopy experiments, a biomolecule is attached to a force probe via polymer linkers, and the total extension -- of molecule plus apparatus -- is monitored as a function of time. In a typical unfolding experiment at constant force, the total extension jumps between two values that correspond to the folded and unfolded states of the molecule. For several biomolecular systems the committor, which is the probability to fold starting from a given extension, has been used to extract the molecular activation barrier (a technique known as "committor inversion"). In this work, we study the influence of the force probe, which is much larger than the molecule being measured, on the activation barrier obtained by committor inversion. We use a two-dimensional framework in which the diffusion coefficient of the molecule and of the pulling device can differ. We systematically study the free energy profile along the total extension obtained from the committor, by numerically solving the Onsager equation and using Brownian dynamics simulations. We analyze the dependence of the extracted barrier on the linker stiffness, molecular barrier height, and diffusion anisotropy, and thus, establish the range of validity of committor inversion. Along the way, we showcase the committor of 2-dimensional diffusive models and illustrate how it is affected by barrier asymmetry and diffusion anisotropy.

physics.chem-ph↗

arXiv2005

What can one learn from two-state single molecule trajectories?

A time trajectory of an observable that fluctuates between two values (say, on and off), stemming from some unknown multi-substate kinetic scheme, is the output of many single molecule experiments. Here we show that when all successive waiting times along the trajectory are uncorrelated the on and the off waiting time probability density functions (PDFs) contain all the information. By relating the lack of correlation in the trajectory to the topology of kinetic schemes, we can immediately specify those kinetic schemes that are equally consistent with experiment, which means that it is impossible to differentiate between them by any sophisticated analyses of the trajectory. Correlated trajectories, however, contain additional information about the underlying kinetic scheme, and we consider the strategy that one should use to extract it. An example is given on correlations in the activity of individual lipase molecules.

q-bio.SC↗

arXiv2000

Irreversibble Bimolecular Reactions of Langevin Particles

The reaction A+B --> B is studied when the reactants diffuse in phase space, i.e. their dynamics is described by the Langevin equation. The steady-state rate constants are calculated for both the target (static A and mobile B's) and trapping (mobile A and static B's) problems when the reaction is assumed to occur at the first contact. For Brownian dynamics (i.e., ordinary diffusion), the rate constant for both problems is a monotonically decreasing function of the friction coefficient $γ$. For Langevin dynamics, however, we find that the steady-state rate constant exhibits a turnover behavior as a function of $γ$ for the trapping problem but not for the target problem. This turnover is different from the familiar Kramers' turnover of the rate constant for escape from a deep potential well because the reaction considered here is an activationless process.

cond-mat.stat-mech↗