A brief comment on the similarities of the IR solutions for the ghost propagator DSE in Landau and Coulomb gauges

This brief note is devoted to reconcile the conclusions from a recent analysis of the IR solutions for the ghost propagator Dyson-Schwinger equations in Coulomb gauge with previous studies in Landau gauge.Comment: 4 pages, 1 figur

TAIP: an anytime algorithm for allocating student teams to internship programs

In scenarios that require teamwork, we usually have at hand a variety of specific tasks, for which we need to form a team in order to carry out each one. Here we target the problem of matching teams with tasks within the context of education, and specifically in the context of forming teams of students and allocating them to internship programs. First we provide a formalization of the Team Allocation for Internship Programs Problem, and show the computational hardness of solving it optimally. Thereafter, we propose TAIP, a heuristic algorithm that generates an initial team allocation which later on attempts to improve in an iterative process. Moreover, we conduct a systematic evaluation to show that TAIP reaches optimality, and outperforms CPLEX in terms of time.Comment: 10 pages, 7 figure

Algorithms for Graph-Constrained Coalition Formation in the Real World

Coalition formation typically involves the coming together of multiple, heterogeneous, agents to achieve both their individual and collective goals. In this paper, we focus on a special case of coalition formation known as Graph-Constrained Coalition Formation (GCCF) whereby a network connecting the agents constrains the formation of coalitions. We focus on this type of problem given that in many real-world applications, agents may be connected by a communication network or only trust certain peers in their social network. We propose a novel representation of this problem based on the concept of edge contraction, which allows us to model the search space induced by the GCCF problem as a rooted tree. Then, we propose an anytime solution algorithm (CFSS), which is particularly efficient when applied to a general class of characteristic functions called $m+a$ functions. Moreover, we show how CFSS can be efficiently parallelised to solve GCCF using a non-redundant partition of the search space. We benchmark CFSS on both synthetic and realistic scenarios, using a real-world dataset consisting of the energy consumption of a large number of households in the UK. Our results show that, in the best case, the serial version of CFSS is 4 orders of magnitude faster than the state of the art, while the parallel version is 9.44 times faster than the serial version on a 12-core machine. Moreover, CFSS is the first approach to provide anytime approximate solutions with quality guarantees for very large systems of agents (i.e., with more than 2700 agents).Comment: Accepted for publication, cite as "in press

Landmark learning in a navigation task is not affected by the female rats' estrus cycle

In two experiments rats were required to escape from a circular pool by swimming to an invisible platform that was located in the same place relative to one configuration of two landmarks (X and Y). The two landmarks were placed relatively far and equidistant from the hidden platform. Training could be either on consecutive days (Experiment 1) or every fourth day (Experiment 2). Subsequent test trials, without the platform, revealed a preference for searching in the correct quadrant of the pool. In Experiment 1 such a test performance was identical in two groups of females, one tested with high hormonal levels (i.e., in the proestrus phase) and the second one tested with low hormonal levels (i.e., either in the estrus, metaestrus or diestrus phase); in addition, these two groups differed from a third group of male rats (i.e., males had a better performance than females). Experiment 2 replicated the females' previous results with a better procedure. The experiment compared the performance of two groups of female rats which were both trained and tested always in the same estrus phase, one group in the proestrus phase, and the second group in the estrus phase. The implication of these results is that the estrus cycle has little impact on the performance of female rats when landmark learning in a navigation task

Genonets server-a web server for the construction, analysis and visualization of genotype networks.

A genotype network is a graph in which vertices represent genotypes that have the same phenotype. Edges connect vertices if their corresponding genotypes differ in a single small mutation. Genotype networks are used to study the organization of genotype spaces. They have shed light on the relationship between robustness and evolvability in biological systems as different as RNA macromolecules and transcriptional regulatory circuits. Despite the importance of genotype networks, no tool exists for their automatic construction, analysis and visualization. Here we fill this gap by presenting the Genonets Server, a tool that provides the following features: (i) the construction of genotype networks for categorical and univariate phenotypes from DNA, RNA, amino acid or binary sequences; (ii) analyses of genotype network topology and how it relates to robustness and evolvability, as well as analyses of genotype network topography and how it relates to the navigability of a genotype network via mutation and natural selection; (iii) multiple interactive visualizations that facilitate exploratory research and education. The Genonets Server is freely available at http://ieu-genonets.uzh.ch

A general approach for computing a consensus in group decision making that integrates multiple ethical principles

[EN] We tackle the problem of computing a consensus according to multiple ethical principles - which can include, for example, the principle of maximum freedom associated with the Benthamite doctrine and the principle of maximum fairness associated with the Rawlsian principles - among the preferences of different individuals in the context of Group Decision-Making (GDM). More formally, we put forward a novel formalisation of the above-mentioned problem based on a multi-lp-norm approximation problem that aims at minimising multiple p-metric distance functions, where each parameter p represents a given ethical principle. Our contribution incurs obvious benefits from a social-choice perspective. Firstly, our approach significantly generalises stateof-the-art approaches that were limited to only two ethical principles (p = 1, for maximum freedom, and p = & INFIN;, for maximum fairness). Secondly, our experimental results considering an established test case demonstrate that our approach is capable, thanks to a novel re-weighting scheme, to compute a multi-norm consensus that takes into account each ethical principle in a balanced way, in contrast with state-of-the-art approaches that were heavily biased towards the p =1 ethical principle.Research supported by projects: CI-SUSTAIN, Spain (PID2019-104156GB-I00) ; TAILOR, Spain (H2020-952215) ; 2021 SGR 00754 funded by Generalitat de Catalunya, Spain; VAE TED2021-131295B-C31, funded by MCIN/AEI, Spain/10.13 039/501100011033 and NextGenerationEU/PRTR, Spain; and VALAWAI, Spain (Horizon Europe #101070930) . Funding for open access charge: CRUE-Universitat Politecnica de Valencia.Salas-Molina, F.; Bistaffa, F.; Rodríguez-Aguilar, JA. (2023). A general approach for computing a consensus in group decision making that integrates multiple ethical principles. Socio-Economic Planning Sciences. 89. https://doi.org/10.1016/j.seps.2023.1016948

Ellagic acid: Biological properties and biotechnological development for production processes

Ellagic acid, 2,3,7,8-tetrahydroxy-chromeno[5,4,3-cde]chromene-5,10-dione, is a powerful bioactive compound with many potential pharmacological and industrial applications. In this review, the chemical aspects, biological properties and diverse potential applications of ellagic acid for different industries were described. This review also discussed the advance in ellagitannin biodegradation, focusing on the process of isolation of microorganisms and strain selection, medium and culture optimization, as well as fermentation systems for commercially viable industrial scale production. The performances of various fermentation techniques that have been applied for the production of ellagic acid from residual by-products were compared, while the advantages and disadvantages of each plant source were also discussed.Key words: Ellagic acid, ellagitannin, biodegradation, fungal physiology, solid-state fermentation, submerged fermentation

Microwave heating processing as alternative of pretreatment in second-generation biorefinery: An overview

The development of a feasible biorefinery is in need of alternative technologies to improve lignocellulosic biomass conversion by the suitable use of energy. Microwave heating processing (MHP) is emerging as promising unconventional pretreatment of lignocellulosic materials (LCMs). MHP applied as pretreatment induces LCMs breakdown through the molecular collision caused by the dielectric polarization. Polar particles movement generates a quick heating consequently the temperatures and times of process are lower. In this way, MHP has positioned as green technology in comparison with other types of heating. Microwave technology represents an excellent option to obtain susceptible substrates to enzymatic saccharification and subsequently in the production of bioethanol and high-added compounds. However, it is still necessary to study the dielectric properties of materials, and conduct economic studies to achieve development in pilot and industrial scale. This work aims to provide an overview of recent progress and alternative configurations for combining the application of microwave technology on the pretreatment of LCMs in terms of biorefinery.Financial support is gratefully acknowledged from the Energy Sustainability Fund 2014-05 (CONACYT-SENER), Mexican Centre for Innovation in Bioenergy (Cemie-Bio), Cluster of Bioalcohols (Ref. 249564). This study was supported by the Secretary of Public Education of Mexico PROMEP project/103.5/13/6595 – UACOAH-PTC-292 and PROMEP project/DSA/103.5/14/10442 – UACOAH-PTC-312. We gratefully acknowledge support for this research by the Mexican Science and Technology Council (CONACYT, Mexico) for the infrastructure project - INFR201601 (Ref. 269461) and CB-2015-01 (Ref. 254808). The author A. Aguilar-Reynosa thanks to Mexican Science and Technology Council (CONACY, Mexico) for master fellowship support