25 papers · ranked by Valyu relevance
Mohammad Majid al-Rifaie, Marc Cavazza, Maurizio Ciani
Modern computational techniques offer new perspectives for the personalisation of food properties through the optimisation of their production process. This paper addresses the personalisation of beer properties in the specific case of craft beers where the production process is more flexible. Furthermore, this work…
Pablo Rodríguez-Mier, Nathalie Poupin, Carlo de Blasio, Laurent Le Cam + 1 more
The correct identification of metabolic activity in tissues or cells under different environmental or genetic conditions can be extremely elusive due to mechanisms such as post-transcriptional modification of enzymes or different rates in protein degradation, making difficult to perform predictions on the basis of gene…
Izuwa Ahanor, Hugh Medal, Andrew C. Trapp
While most methods for solving mixed-integer optimization problems compute a single optimal solution, a diverse set of near-optimal solutions can often lead to improved outcomes. We present a new method for finding a set of diverse solutions by emphasizing diversity within the search for near-optimal solutions.…
Anh Viet, Mingyu Guo, Aneta Neumann, Frank Neumann
In this work, we consider the problem of finding a set of tours to a traveling salesperson problem (TSP) instance maximizing diversity, while satisfying a given cost constraint. This study aims to investigate the effectiveness of applying niching to maximize diversity rather than simply maintaining it. To this end, we…
Kokila Kasuni Perera, Frank Neumann, Aneta Neumann
Bayesian optimisation (BO) is a surrogate-based optimisation technique that efficiently solves expensive black-box functions with small evaluation budgets. Recent studies consider trust regions to improve the scalability of BO approaches when the problem space scales to more dimensions. Motivated by this research, we…
Paul E. Smaldino, Cody Moser, Alejandro Pérez Velilla, Mikkel Werling
Humans regularly solve complex problems in cooperative teams. A wide range of mechanisms have been identified that improve the quality of solutions achieved by those teams on reaching consensus. We argue that many of these mechanisms work via increasing the transient diversity of solutions while the group attempts to…
Timo Camillo Merkl, Reinhard Pichler, Sebastian Skritek
Enumeration problems aim at outputting, without repetition, the set of solutions to a given problem instance. However, outputting the entire solution set may be prohibitively expensive if it is too big. In this case, outputting a small, sufficiently diverse subset of the solutions would be preferable. This leads to the…
Tesshu Hanaka, Masashi Kiyomi, Yasuaki Kobayashi, Yusuke Kobayashi + 2 more
'Kazuhiro Kurita' 'Yota Otachi'] Finding a single best solution is the most common objective in combinatorial optimization problems. However, such a single solution may not be applicable to real-world problems as objective functions and constraints are only "approximately" formulated for original real-world problems.…
Florian Mrugalla, Christopher Franz, Yannic Alber, Georg Mogk + 3 more
'Martín Villalba' 'Thomas Mrziglod' 'Kevin Schewior'] Abstract Chemical synthesis planning has considerably benefited from advances in the field of machine learning. Neural networks can reliably and accurately predict reactions leading to a given, possibly complex, molecule. In this work we focus on algorithms for…
Irit Talmor
This work aims to improve an earlier methodology for assigning personnel to diverse three-member teams. Notably, the original algorithm focused only on diversity within teams, to ensure that conflicting interests are represented in each team. While this indeed created diverse teams, in many cases different teams…
Authors not listed
Computer-aided synthesis planning aims to identify viable synthetic routes from a target compound to readily available building blocks by iteratively decomposing molecules into smaller precursors. Self-play search algorithms, trained with simulated experience, reach state-of-the-art performance. However, these methods…
T. Ganesan, I. Elamvazuthi, Ku Zilati Ku Shaari, P. Vasant
Multiobjective (MO) optimization is an emerging field which is increasingly being encountered in many fields globally. Various metaheuristic techniques such as differential evolution (DE), genetic algorithm (GA), gravitational search algorithm (GSA), and particle swarm optimization (PSO) have been used in conjunction…
Max Ruiz Luyten, Mihaela van der Schaar
State-of-the-art large language model (LLM) pipelines rely on bootstrapped reasoning loops—sampling diverse chains of thought and reinforcing the highest-scoring ones—mainly optimizing correctness. We analyze how this design choice is sensitive to the collapse of the model's distribution over reasoning paths, slashing…
Yiming Zhang, Ping Yu, Swarnadeep Saha, Daniel Khashabi
Post-training of Large Language Models (LMs) often prioritizes accuracy and helpfulness at the expense of diversity. This creates a tension: while post-training improves response quality, it also sharpens output distributions and reduces the range of ideas, limiting the usefulness of LMs in creative and exploratory…
Robin Schimmelpfennig, Layla Razek, Eric Schnell, Michael Muthukrishna
'Michael Muthukrishna'] Human societies are collective brains. People within every society have cultural brains-brains that have evolved to selectively seek out adaptive knowledge and socially transmit solutions. Innovations emerge at a population level through the transmission of serendipitous mistakes, incremental…
Francesca Arese Lucini, Flaviano Morone, Maria S. Tomassone, Hernán A. Makse
In 1972, Robert May showed that diversity is detrimental to an ecosystem since, as the number of species increases, the ecosystem is less stable. This is the so-called diversity-stability paradox, which has been derived by considering a mathematical model with linear interactions between the species. Despite being in…
Daniela Dolciami, Robert Ziolek, Daniel Davies, Michael Carter + 2 more
Chemical diversity is challenging to describe objectively. Despite this, various notions of chemical diversity are used throughout the medicinal chemistry optimization process in drug discovery. In this work, we show the usefulness of considering exploited vectors during different phases of the drug design process to…
José J. Naveja-Romero, Fernanda I. Saldívar-González, Diana L. Prado-Romero, Angel J. Ruiz-Moreno + 3 more
The manuscript discusses recent advances on computer-aided drug discovery (CADD) with focus on data-dependent drug discovery. Herein, we do not intend to review the many CADD methodologies comprehensively. Instead, the review discusses progress on selected concepts, methodologies, resources, and applications that are…
Dionisio A. Olmedo, Armando A. Durant-Archibold, José Luis López-Pérez, Jose L. Medina-Franco
Chemical libraries and compound data sets are among the main inputs to start the drug discovery process at universities, research institutes, and the pharmaceutical industry. The approach used in the design of compound libraries, the chemical information they possess, and the representation of structures, play a…
Samuel Genheden, Esben Bjerrum
We introduce a framework for benchmarking multi-step retrosynthesis methods, i.e. route predictions, called PaRoutes. The framework consists of two sets of 10,000 synthetic routes extracted from the patent literature, a list of stock compounds, and a curated set of reactions on which one-step retrosynthesis models can…
Karen K. Huruntz, Lilit E. Ghukasyan, Gor A. Gevorgyan
From a mathematical perspective, randomly assembled species-rich competitive communities exhibit a high degree of instability, as described by May’s stability theorem. This suggests that actual ecological communities should possess a significant level of organization in their competitive network structure, perhaps with…
Andre KY Low, Flore Mekki-Berrada, Aleksandr Ostudin, Jiaxun Xie + 7 more
The development of automated high-throughput experimental platforms has enabled fast sampling of high-dimensional decision spaces. To reach target properties efficiently, these platforms are increasingly paired with intelligent experimental design. When solving optimization problems, Bayesian-based optimizers are often…
Paolo Tommasino, Antonella Maselli, Domenico Campolo, Francesco Lacquaniti + 1 more
In complex real-life motor skills such as unconstrained throwing, performance depends on how accurate is on average the outcome of noisy, high-dimensional, and redundant actions. What characteristics of the action distribution relate to performance and how different individuals select specific action distributions are…
Authors not listed
An efficient and common strategy for the generation of pharmaceutically relevant functionalized molecules based on the diversity- and lead-oriented synthesis (DOS/LOS)-like concepts is presented. The approach is implemented for the case of functionalized sp3-enriched derivatives of isoxazole and isoxazoline. The key…
Kathakali Sarkar, Deepro Bonnerjee, Sangram Bagh
Maze generating and solving are challenging problems in mathematics and computing. Here we generated simple 2X2 maze problems applying four chemicals and created a set of engineered bacteria, which in a mixed population worked as a computational solver for any such problem. The input-output matrices of a mathematical…