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Alter Heritage: A web app to gather expert knowledge on inclusive cultural heritage metadata
Get PDFIn this demo paper, we present the web application Alter Heritage. Its goal is to support researchers in collecting domain experts' knowledge about inclusive cultural heritage metadata. While stakeholders in the cultural sector have been formulating strategies to mitigate biases in metadata, there is little empirical knowledge on how the metadata can be revised for inclusivity by users. With Alter Heritage, researchers can study what kind of alterations users make in artefacts' metadata to make it inclusive, how these alterations differ per artefacts and users. We develop the app's core metadata editing functionality based on requirements elicited from existing practices and guidelines in the cultural heritage domain. The data gathered via Alter Heritage will contribute to understanding of the specific alterations that make digital artefacts' metadata more inclusive
ChadiHelwe/navigating_the_political_compass
No full textThis project evaluates the political inclinations of various large language models (LLMs) using the Political Compass Test. It runs experiments on different models under various scenarios, including Nationality Scenario and Language Scenario, and saves the results for further analysis
Enhancing decision making through the integration of large language models and operations research optimization
No full textMany critical business and societal decisions in areas such as supply chain and healthcare involve numerous potential actions, complex constraints, and goals that can be modeled as objective functions. Mathematical optimization, a core area in Operations Research (OR), provides robust, mathematically grounded methodologies to address such decisions and has shown tremendous benefits in many applications. However, its application requires the creation of accurate and efficient optimization models, necessitating rare expertise and considerable time, creating a barrier to widespread adoption in decision-making. Thus, it is a long-standing goal to make these capabilities widely accessible.
The advent of Large Language Models (LLMs) has made advanced Artificial Intelligence (AI) capabilities widely accessible through natural language. LLMs can accelerate expert work in creating formal models like computer programs, and emerging research indicates they can also speed up the development of optimization models by OR experts. We, therefore, propose integrating and advancing LLM and optimization modeling to empower organizational decision-makers to model and solve such complex problems without requiring deep expertise in optimization.
In this work, we present our vision for democratizing optimization modeling for organizational decision-making by such a combination of LLMs and optimization modeling. We identify a set of fundamental requirements for the vision’s implementation and describe the state of the art through a literature survey and some experimentation. We show that a) LLMs already provide substantial novel capabilities relevant to realizing this vision, but that b) major research challenges remain to be addressed. We also propose possible research directions to overcome these gaps. We would like this work to serve as a call to action to bring together the LLM and OR optimization modeling communities to pursue this vision, thereby enabling much more widespread improved decision-making and increasing by orders of magnitude the benefits AI and OR can bring to enterprises and society
Forest fires and near-critical percolation
No full textAfter a short introduction on forest fire models, I will focus on a recent joint paper with Pierre Nolin (Hong Kong), where we study a variant of these models in which the forest is an box, and ignitions come from the boundary. In particular we show that, for the case without recoveries, the probability that the center of the box is burnt tends to as tends to . This result is not intuitively obvious, it depends heavily on (bounds for) the values of certain (near-)critical exponents for Bernoulli percolation.
Our paper also presents a (weaker) version of the above result for the case with recoveries, and points out that our methods give some more insight to a remarkable forest fire model in the half-plane studied by Graf around 2015
Open CPS: A Symbolic Model
No full textModeling of the environment presents a challenge in all open systems, particularly in open cyber-physical systems where the environment is nature. In some cases the environment can be considered and modeled as yet another actor or component in the system. However, in cases of complex systems with intricate interactions with nature as their environment, the unpredictability of this environment and the complexity of the physics involved in its modeling make “environment as a component” an untenable approach. Concurrent Rules Machines (CRMs) constitute a model for formal specification and analysis of open, distributed cyber-physical systems [3]. The CRM model makes interaction with the environment explicit and offers an algebra of composition and decomposition for construction and analysis of systems through their constituent components. Systematic and automatic verification of properties of systems modeled as CRMs remains a significant challenge. Symbolic representations have been used to model the inherently continuous properties of physics. In this paper we introduce symbolic execution as a means of reasoning about behaviors of CRM models. A symbolic execution represents possibly infinitely many executions and can focus on environments meeting requirements such as physically reasonable actions. We show that symbolic execution is sound, and is complete for a natural class of CRMs. We illustrate our ideas with a simple cyber-physical system example
Natural and artificial variations of the standard genetic code
Get PDFThe nearly universal standard genetic code is one of the strongest indications for the common ancestry of all
extant life. Yet new code variants representing different combinations of codon reassignments have been
and continue to be discovered with regular frequency. Recently, many man-made codes have been generated
that direct the incorporation of unnatural amino acids, allowing for biocontainment and viral resistance.
However, evolutionary and synthetic biologists often seem to be unaware of the developments in the other
camp’s field. Here we attempt to bridge this gulf and provide an updated overview of different codon reassignments
and genetic code variants reported to occur naturally in organisms and their organelles, both now
containing over 50 examples. We review the highly expanded range of departures from the standard genetic
code, highlighting previously unanticipated code forms and their molecular underpinnings. To account for
cases when a codon has a different meaning depending on its context, such as in variants with no dedicated
termination codons discovered over the past decade in protists, we introduce the concept of codon homonymy.
Considering this new appreciation for the prevalence of genetic code diversity, we also revisit the questions
of how and why genetic codes get altered in evolution. Finally, we summarize the current status of artificially
redesigned genetic codes, which are increasingly deviating from natural code alterations, opening up
completely novel translational possibilities
Description length of canonical and microcanonical models
Get PDFThe (non)equivalence of canonical and microcanonical ensembles is a fundamental question in statistical physics, concerning whether the use of soft and hard constraints in the maximum-entropy construction leads to the same description of a system. Despite the fact that maximum-entropy models are also commonly used in statistical inference, pattern detection, and hypothesis testing, a complete understanding of the effects of ensemble nonequivalence on statistical modeling is still missing. Here, we study this problem from a rigorous model selection perspective by comparing canonical and microcanonical models via the minimum description length principle, which yields a trade-off between likelihood, measuring model accuracy, and complexity, measuring model flexibility and its potential to overfit data. We compute the normalized maximum likelihood (NML) of both formulations and find that (1) microcanonical models always achieve higher likelihood but are always more complex; (2) the optimal model choice depends on the empirical values of the constraints - the canonical model performs best when its fit to the observed data exceeds its uniform average fit across all realizations; (3) in the thermodynamic limit, the difference in description length per node vanishes when ensemble equivalence holds but persists otherwise, showing that nonequivalence implies extensive differences between large canonical and microcanonical models. Finally, we compare the NML approach to Bayesian methods, showing that (4) the choice of priors, practically irrelevant in equivalent models, becomes crucial when an extensive number of constraints are enforced, possibly leading to very different outcomes
Querying templatized document collections with large language models
No full textQuerying and extracting value from unstructured document collection remains a considerable challenge. While Large Language Models (LLMs) have made remarkable progress in document understanding, they fail to give high accuracy results for analytical queries on documents, and additionally incur high costs. While Retrieval-Augmented Generation (RAG) can reduce costs, accuracy degrades further. Our key insight is that documents in a collection often follow similar templates that impart a common semantic structure. We therefore introduce Zendb, a document analytics system that leverages this semantic structure, coupled with LLMs, to answer ad-hoc SQL queries on document collections. Zendb efficiently extracts semantic hierarchical structures from such templatized documents and introduces a novel query engine that leverages these structures for accurate and cost-effective query execution. Extensive experiments on three real-world document collections demonstrate ZENDB's benefits, achieving up to 31× cost savings compared to LLM-based baselines, while maintaining or improving accuracy, and surpassing RAG-based baselines by up to 61% in precision and 81% in recall, at a marginally higher cost
Reconstructing multiple initial pressure and speed of sound distributions simultaneously in photoacoustic tomography
No full textImage reconstruction in photoacoustic tomography relies on an accurate knowledge of the speed of sound in the target. However, the speed of sound distribution is not generally known, which may result in artefacts in the reconstructed distribution of initial pressure. Therefore, reconstructing the speed of sound simultaneously with the initial pressure would be valuable for accurate imaging in photoacoustic tomography. Furthermore, the speed of sound distribution could provide additional valuable information about the imaged target. In this work, simultaneous reconstruction of initial pressure and speed of sound in photoacoustic tomography is studied. This inverse problem is known to be highly ill-posed. To overcome this, we study an approach where the ill-posedness is alleviated by utilising multiple photoacoustic data sets that are generated by different initial pressure distributions within the same imaged target. Then, these initial pressure distributions are reconstructed simultaneously with the speed of sound distribution. A methodology for solving this minimisation problem is formulated using a gradient-based iterative approach equipped with bound constraints and a multigrid approach. The methodology was evaluated with numerical simulations. Different approaches for generating multiple initial pressure distributions and their effect on the solution of the image reconstruction problem were studied. The results show that initial pressure and speed of sound can be simultaneously reconstructed from photoacoustic data. Furthermore, utilising multiple initial pressure distributions improves the reconstructions such that the locations of initial pressure and speed of sound inhomogeneities can be better distinguished and image artifacts are reduced
Quantum Catalytic Space
Get PDFSpace complexity is a key field of study in theoretical computer science. In the quantum setting there are clear motivations to understand the power of space-restricted computation, as qubits are an especially precious and limited resource. Recently, a new branch of space-bounded complexity called catalytic computing has shown that reusing space is a very powerful computational resource, especially for subroutines that incur little to no space overhead. While quantum catalysis in an information theoretic context, and the power of “dirty” qubits for quantum computation, has been studied over the years, these models are generally not suitable for use in quantum space-bounded algorithms, as they either rely on specific catalytic states or destroy the memory being borrowed. We define the notion of catalytic computing in the quantum setting and show a number of initial results about the model. First, we show that quantum catalytic logspace can always be computed quantumly in polynomial time; the classical analogue of this is the largest open question in catalytic computing. This also allows quantum catalytic space to be defined in an equivalent way with respect to circuits instead of Turing machines. We also prove that quantum catalytic logspace can simulate log-depth threshold circuits, a class which is known to contain (and believed to strictly contain) quantum logspace, thus showcasing the power of quantum catalytic space. Finally we show that both unitary quantum catalytic logspace and classical catalytic logspace can be simulated in the one-clean qubit model