Forecasting Long-Term Government Bond Yields: An Application of Statistical and AI Models

This paper evaluates several artificial intelligence and classical algorithms on their ability of forecasting the monthly yield of the US 10-year Treasury bonds from a set of four economic indicators. Due to the complexity of the prediction problem, the task represents a challenging test for the algorithms under evaluation. At the same time, the study is of particular significance for the important and paradigmatic role played by the US market in the world economy. Four data-driven artificial intelligence approaches are considered, namely, a manually built fuzzy logic model, a machine learned fuzzy logic model, a self-organising map model and a multi-layer perceptron model. Their performance is compared with the performance of two classical approaches, namely, a statistical ARIMA model and an econometric error correction model. The algorithms are evaluated on a complete series of end-month US 10-year Treasury bonds yields and economic indicators from 1986:1 to 2004:12. In terms of prediction accuracy and reliability of the modelling procedure, the best results are obtained by the three parametric regression algorithms, namely the econometric, the statistical and the multi-layer perceptron model. Due to the sparseness of the learning data samples, the manual and the automatic fuzzy logic approaches fail to follow with adequate precision the range of variations of the US 10-year Treasury bonds. For similar reasons, the self-organising map model gives an unsatisfactory performance. Analysis of the results indicates that the econometric model has a slight edge over the statistical and the multi-layer perceptron models. This suggests that pure data-driven induction may not fully capture the complicated mechanisms ruling the changes in interest rates. Overall, the prediction accuracy of the best models is only marginally better than the prediction accuracy of a basic one-step lag predictor. This result highlights the difficulty of the modelling task and, in general, the difficulty of building reliable predictors for financial markets.interest rates; forecasting; neural networks; fuzzy logic.

A Ky Fan minimax inequality for quasiequilibria on finite dimensional spaces

Several results concerning existence of solutions of a quasiequilibrium problem defined on a finite dimensional space are established. The proof of the first result is based on a Michael selection theorem for lower semicontinuous set-valued maps which holds in finite dimensional spaces. Furthermore this result allows one to locate the position of a solution. Sufficient conditions, which are easier to verify, may be obtained by imposing restrictions either on the domain or on the bifunction. These facts make it possible to yield various existence results which reduce to the well known Ky Fan minimax inequality when the constraint map is constant and the quasiequilibrium problem coincides with an equilibrium problem. Lastly, a comparison with other results from the literature is discussed

The bees algorithm: Modelling nature to solve complex optimisation problems

The Bees Algorithm models the foraging behaviour of honey bees in order to solve optimisation problems. The algorithm performs a kind of exploitative neighbourhood search combined with random explorative search. This paper describes the Bees Algorithm and presents two application examples: the training of neural networks to predict the energy efficiency of buildings, and the solution of the protein folding problem. The Bees Algorithm proved its effectiveness and speed, and obtained very competitive modelling accuracies compared with other state-of-the-art methods

Holocentric plants of the genus Rhynchospora as a new model to study meiotic adaptations to chromosomal structural rearrangements

Climate change, world hunger and overpopulation are some of the biggest challenges the world is currently facing. Moreover, they are part of a multidimensional single scenario: as climate change continues to modify our planet, we might see a decrease of arable land and increase in extreme weather patterns, posing a threat to food security. This has a direct impact on regions with high population growth, where food security is already scarce. Considering additionally the unsustainability of intensive global food production and its contribution to greenhouse emissions and biodiversity loss, it´s clear that all these factors are interconnected (Cardinale et al., 2012; Prosekov & Ivanova, 2018; Wiebe et al., 2019). Plants are the main source of staple food in the world and are also the main actors in carbon fixation, they are therefore key protagonists in controlling climate change. Plants are also an essential habitat-defining element balancing our ecosystem. Thus, how we grow plants and crops will, aside from the obvious implications for food security, also have a profound impact on the climate and biodiversity. The natural variability of species is considered an immense pool of genes and traits, and their understanding is key to generate new useful knowledge. For instance, natural populations can be more tolerant to abiotic and biotic stresses, or carry traits that combined together in hybrids, might achieve a higher seed number, or a faster growth. Classical breeding has exploited unrelated varieties to achieve traits of interest like dwarfism and higher grain production. However, only a limited number of crop species have been the focus of recent scientific and technological approaches, and they do not represent the extremely vast natural diversity of species that could generate useful knowledge for future applications (Castle et al., 2006; Pingali, 2012). The key to this natural variability is a process called meiotic recombination, the exchange of genomic material between homologous parental chromosomes. Meiotic recombination takes place during meiosis, a specialized cell division in which sexually reproducing organisms reduce the genomic complement of their gametes by half in preparation for fertilization. Meiotic recombination takes place at the beginning of meiosis, in a stage called prophase I. To exchange DNA sequences, the strands of two homologous chromosomes must be fragmented. This specific process of physiologically induced DNA fragmentation is conserved in the vast majority of eukaryotes (Keeney et al., 1997). After the formation of double-strand breaks, the 3’ ends that are left are targeted by recombinases that help the strands search and invade templates for repair. After invasion, the 3’ end is extended by DNA synthesis, exposing sequences on the opposite strand that can anneal to the other 3’ end of the original double strand break. DNA synthesis at both ends generates a new structure called a double Holliday Junction (dHJ), forming a physical link between homologous chromosomes, named chiasma (Wyatt & West, 2014). The resolutions of these structures are called crossovers (COs), which is the molecular event representing the outcome of meiotic recombination. Other outcomes are possible, like noncrossovers (NCOs). In this case, the invading strand is ejected and anneals to the single-strand 3´end of the original double-strand break (Allers & Lichten, 2001). Crossovers can be divided into two main groups, called class I and class II. COs of the first group are considered to be sensitive to interference, which means that there are mechanisms that prevent two class I COs from happening in proximity of each other. Class II is insensitive to interference. Class I COs are the result of a pathway called ZMM, which involves a group of specialised proteins that are highly conserved among eukaryotes (Lambing et al., 2017; Mercier et al., 2015). Class I COs are the most common, studied and important type of COs. Centromeres are structures, located on regions of the chromosomes, that allow proper chromosome segregation during mitosis and meiosis. Centromeres have a profound effect on plant breeding and crop improvement, as it is known that meiotic recombination is suppressed at centromeres in most eukaryotes. This represents a great limitation for crop improvement, as many possibly useful traits might be in regions not subject to recombination and thus might not be available for breeding purposes. Additionally, the mechanisms behind how recombination is regulated and prevented from happening at centromeres are still unclear. In most model organisms centromeres are single entities localized on specific regions on the chromosomes. This configuration is called monocentric. However, another type of configuration can be found in nature, but is less studied. In fact, some organisms harbour multiple centromeric determinants distributed over their whole chromosomal length. This configuration is called holocentric. The Cyperaceae comprise a vast, diverse family of plants, with a cosmopolitan distribution in all habitats (Spalink et al., 2016). Despite the presence of this family worldwide, knowledge about it is limited. Few genomes are available and molecular insights are scarce. This family is also known to be mainly formed by holocentric species (Melters et al., 2012). Understanding if and how meiotic recombination is achieved in holocentric plants will generate new knowledge that in the future might unlock new traits in elite crops, previously unavailable to breeding, that could help humanity face global climatic, economic and social challenges. Recent studies have reported new knowledge about important meiotic, chromosome and genome adaptions found in species of the Cyperaceae family and in particular the genus Rhynchospora (Marques et al., 2015, 2016a). With the recent publication of the first reference genomes for several Rhynchospora species, we could already perform a comprehensive analysis of their unique genome features and trace the evolutionary history of their karyotypes and how these have been determined by chromosome fusions (Hofstatter et al., 2021, 2022). This new resource paves the way for future research utilising Rhynchospora as a model genus to study adaptations to holocentricity in plants. With this work, my intention is to shed light on the underexplored topic of holocentricity in plants. Using cutting edge techniques, I examine the conservation of meiotic recombination together with other species-specific adaptations like achiasmy and polyploidy in holocentrics. My results reveal new insights into how plant meiotic recombination is regulated when small centromere units are found distributed chromosome-wide, challenging the classic dogma of suppression of recombination at centromeres

Metal-superconductor transition in low-dimensional superconducting clusters embedded in two-dimensional electron systems

Motivated by recent experimental data on thin film superconductors and oxide interfaces, we propose a random-resistor network apt to describe the occurrence of a metal-superconductor transition in a two-dimensional electron system with disorder on the mesoscopic scale. We consider low-dimensional (e.g. filamentary) structures of a superconducting cluster embedded in the two-dimensional network and we explore the separate effects and the interplay of the superconducting structure and of the statistical distribution of local critical temperatures. The thermal evolution of the resistivity is determined by a numerical calculation of the random-resistor network and, for comparison, a mean-field approach called effective medium theory (EMT). Our calculations reveal the relevance of the distribution of critical temperatures for clusters with low connectivity. In addition, we show that the presence of spatial correlations requires a modification of standard EMT to give qualitative agreement with the numerical results. Applying the present approach to an LaTiO3/SrTiO3 oxide interface, we find that the measured resistivity curves are compatible with a network of spatially dense but loosely connected superconducting islands

Micro Behavioural Attitudes and Macro Technological Adaptation in Industrial Districts. An Agent-Based Prototype

Industrial Districts (IDs) are complex productive systems based on an evolutionary network of heterogeneous, functionally integrated and complementary firms, which are within the same market and geographical space. Setting up a prototype, able to reproduce an idealised ID, we model cognitive processes underlying the behaviour of ID firms. ID firms are bounded rationality agents, able to process information coming from technology and market environment and from their relational contexts. They are able to evaluate such information and to transform it into courses of action, routinising their choices, monitoring the environment, categorising, typifying and comparing information. But they have bounded cognitive resources: attention, time and memory. We test two different settings: the first one shows ID firms behaving according to a self-centred attitude, while the second one shows ID firms behaving according to a social centred attitude. We study how such a strong difference at micro-level can affect at macro-level the technological adaptation of IDs

A note on quantum structure constants

The Cartan-Maurer equations for any $q$-group of the $A_{n-1}, B_n, C_n, D_n$ series are given in a convenient form, which allows their direct computation and clarifies their connection with the $q=1$ case. These equations, defining the field strengths, are essential in the construction of $q$-deformed gauge theories. An explicit expression \om ^i\we \om^j= -\Z {ij}{kl}\om ^k\we \om^l for the $q$-commutations of left-invariant one-forms is found, with \Z{ij}{kl} \om^k \we \om^l \qonelim \om^j\we\om^i.Comment: 9 pp., LaTe