Miracle-dz/UniFAR-FTUs huggingface dataset:
Hyperparameter Optimization for Portfolio Selection

Portfolio selection involves a trade-off between maximizing expected return and minimizing risk. In practice, useful formulations also include various costs and constraints that regularize the problem and reduce the risk due to estimation errors, resulting in solutions that depend on a number of hyperparameters. As the number of hyperparameters grows, selecting their value becomes increasingly important and difficult. In this article, the authors propose a systematic approach to hyperparameter optimization by leveraging recent advances in automated machine learning and multiobjective optimization. They optimize hyperparameters on a train set to yield the best result subject to market-determined realized costs. In applications to single- and multiperiod portfolio selection, they show that sequential hyperparameter optimization finds solutions with better risk–return trade-offs than manual, grid, and random search over hyperparameters using fewer function evaluations. At the same time, the solutions found are more stable from in-sample training to out-of-sample testing, suggesting they are less likely to be extremities that randomly happened to yield good performance in training. TOPICS: Portfolio theory, portfolio construction, big data/machine learning Key Findings • The growing number of applications of machine-learning approaches to portfolio selection means that hyperparameter optimization becomes increasingly important. We propose a systematic approach to hyperparameter optimization by leveraging recent advances in automated machine learning and multiobjective optimization. • We establish a connection between forecast uncertainty and holding- and trading-cost parameters in portfolio selection. We argue that they should be considered regularization parameters that can be adjusted in training to achieve optimal performance when tested subject to realized costs. • We show that multiobjective optimization can find solutions with better risk–return trade-offs than manual, grid, and random search over hyperparameters for portfolio selection. At the same time, the solutions are more stable across in-sample training and out-of-sample testing.
Growth and reproduction of vascular plants in polluted environments: a synthesis of existing knowledge

Identification of factors explaining diversity in plant responses to industrial pollution is crucial for predicting fates of polluted ecosystems. Meta-analysis based on 203 publications demonstrated that plants growing near point pollut- ers showed similar decreases in characters reflecting growth (-13.1%) and reproduction processes (-8.5%). In herbaceous plants, root growth was reduced, while aboveground biomass did not change, because the decrease in leaf size was com- pensated by an increase in leaf number. In contrast, woody plants demonstrated no changes in allometry and their growth was reduced to a greater extent than growth of herbaceous plants. Raunkiaer's classification of life forms appeared the best predictor of species' responses to pollution. Within woody plants, trees and shrubs, but not dwarf shrubs, showed strong decreases in growth and reproduction. Within herbaceous plants, significant growth reduction was observed only in annuals. Longevity of foliage or plant phylogeny did not explain variation in species' responses. Adverse effects of pollu- tion were stronger in regions with higher temperature and precipitation, hinting that existing pollution loads may become more harmful for plants as climate changes. Relatively minor explanatory value of the characteristics of individual pollut- ers removes one of the principal obstacles to accounting for the effects of pollution in vegetation models and allows ex- trapolation of the effects observed near point polluters to both regional and global scales. We conclude that losses in productivity of plant communities due to aerial pollution can be approximately estimated on the basis of the life form spectra and climate.
Long-term fitness effects of fledging date in great tits

Reproductive decisions, such as timing of reproduction and the number of offspring to product, affect the conditions for the offspring at the time of independence These conditions can refer to the state of an individual, such as mass, or of the environment, such as time of the season, and will affect the reproductive value of the offspring. Knowledge of these fitness consequences is important when assessing the adaptive value of reproductive decisions. However it is often unclear how long- term the effect of these conditions is on an individual's reproductive success. Previous work has shown that the probability thar great tit fledglings recruit into the breeding population is strongly affected by their fledging date and mass. Using a long-term field study of a great tit population, we studied whether fledging conditions (fledging date, fledging mass and tarsus length) also affected reproductive success after an individual had recruited into the breeding population. For female recruits. this was not the case. For males, however, there was an effect of fledging date on lifetime reproductive success (LRS), calculated for individuals that had recruited. Males that fledged late produced fer er recruiting offspring in their first year of breeding partly because they had a twice as high probability that their breeding attempt failed to produce fledglings. Due to this decrease in LRS of recruited sons with increase in their hedging date, the fitness cost of breeding late is underestimated when counting the number of recruits produced from a breeding attempt. [KEYWORDS: Parus-major; clutch size; establishment; reproduction; dispersal; dominance; palustris]
RESPIRATION DURING PHOTOSYNTHESIS

The respiratory activity of plants in the light, measured as C02 release from the TCA cycle or 02 consumptio n by the respiratory chain, varies between 25 and 100% of the dark respiratory activity. This has been interpreted as evi­ dence for an inhibition of respiration during photosynthesis. However, studies with specific respiratory inhibitiors have shown that oxidative phosp horylatio n
ADULT SURVIVAL PATTERN OF THE SEXUALLY DIMORPHIC ALPINE IBEX (CAPRA IBEX IBEX)

Using recent developments in capture – recapture modeling, we analyzed the adult survival pattern of an Alpine ibex (Capra ibex ibex) population recently established in the northern Alps of France. Survival rates varied little over time and did not differ between the sexes. The survival rate was 0.97 (SE = 0.011). Recapture probabilities were stable over time but differed according to sex: 0.71 (SE = 0.035) for males versus 0.88 (SE = 0.033) for females. The absence of a between-sex difference in survival was not expected for a highly sexually dimorphic species. Environmental conditions, such as a low population density and a low pressure of limiting factors on population dynamics, could explain this absence of a survival cost of sexual selection for males. The high and rather constant survival rate of ibex over time is consistent with the idea that, in large mammals, individuals should be selected for maximizing their own adult survivorship.
Overexpression of Key Sterol Pathway Enzymes in Two Model Marine Diatoms Alters Sterol Profiles in Phaeodactylum tricornutum

Sterols are a class of triterpenoid molecules with diverse functional roles in eukaryotic cells, including intracellular signaling and regulation of cell membrane fluidity. Diatoms are a dominant eukaryotic phytoplankton group that produce a wide diversity of sterol compounds. The enzymes 3-hydroxy-3-methyl glutaryl CoA reductase (HMGR) and squalene epoxidase (SQE) have been reported to be rate-limiting steps in sterol biosynthesis in other model eukaryotes; however, the extent to which these enzymes regulate triterpenoid production in diatoms is not known. To probe the role of these two metabolic nodes in the regulation of sterol metabolic flux in diatoms, we independently over-expressed two versions of the native HMGR and a conventional, heterologous SQE gene in the diatoms Thalassiosira pseudonana and Phaeodactylum tricornutum. Overexpression of these key enzymes resulted in significant differential accumulation of downstream sterol pathway intermediates in P. tricornutum. HMGR-mVenus overexpression resulted in the accumulation of squalene, cycloartenol, and obtusifoliol, while cycloartenol and obtusifoliol accumulated in response to heterologous NoSQE-mVenus overexpression. In addition, accumulation of the end-point sterol 24-methylenecholesta-5,24(24’)-dien-3β-ol was observed in all P. tricornutum overexpression lines, and campesterol increased 3-fold in P. tricornutum lines expressing NoSQE-mVenus. Minor differences in end-point sterol composition were also found in T. pseudonana, but no accumulation of sterol pathway intermediates was observed. Despite the successful manipulation of pathway intermediates and individual sterols in P. tricornutum, total sterol levels did not change significantly in transformed lines, suggesting the existence of tight pathway regulation to maintain total sterol content.
Solving Higher-Order Boundary and Initial Value Problems via Chebyshev-Spectral Method: Application in Elastic Foundation

In this work, we introduce an efficient scheme for the numerical solution of some Boundary and Initial Value Problems (BVPs-IVPs). By using an operational matrix, which was obtained from the first kind of Chebyshev polynomials, we construct the algebraic equivalent representation of the problem. We will show that this representation of BVPs and IVPs can be represented by a sparse matrix with sufficient precision. Sparse matrices that store data containing a large number of zero-valued elements have several advantages, such as saving a significant amount of memory and speeding up the processing of that data. In addition, we provide the convergence analysis and the error estimation of the suggested scheme. Finally, some numerical results are utilized to demonstrate the validity and applicability of the proposed technique, and also the presented algorithm is applied to solve an engineering problem which is used in a beam on elastic foundation.
Inference of population genetic parameters from the continuously serial-sampled sequences of human seasonal influenza A/H3N2

Basic summary statistics that quantify the population genetic structure of influenza virus are important for understanding and inferring the evolutionary and epidemiological processes. However, global virus sequences were sampled continuously over several decades, scattered semi-randomly over time. This temporal structure of samples and the small effective size of viral population make it difficult to use conventional methods to calculate summary statistics. Here we define statistics that overcome this problem by correcting for sampling time difference in quantifying a pairwise sequence difference. A simple method of linear regression jointly estimates the mutation rate and the level of sequence polymorphism, thus providing the estimate of the effective population size. It also leads to the definition of Wright’s FST for arbitrary time-series data. In addition, as an alternative to Tajima’s D statistic or site frequency spectrum, mismatch distribution corrected for sampling time differences can be obtained and compared between actual and simulated data. Application of these methods to seasonal influenza A/H3N2 viruses sampled between 1980 and 2017 and sequences simulated under the model of recurrent positive selection with meta-population dynamics allowed us to estimate the synonymous mutation rate and find parameter values of selection and demographic structure that fit the observation. We found that the mutation rates of HA and PB1 segments before 2007 were particularly high, and that adding recurrent positive selection in our model was essential for the genealogical structure of the HA segment. Methods developed here can be generally applied to population genetic inferences using serially sampled genetic data.
Performance Evaluation of Machine Learning Methods for Forest Fire Modeling and Prediction

Predicting and mapping fire susceptibility is a top research priority in fire-prone forests worldwide. This study evaluates the abilities of the Bayes Network (BN), Naïve Bayes (NB), Decision Tree (DT), and Multivariate Logistic Regression (MLP) machine learning methods for the prediction and mapping fire susceptibility across the Pu Mat National Park, Nghe An Province, Vietnam. The modeling methodology was formulated based on processing the information from the 57 historical fires and a set of nine spatially explicit explanatory variables, namely elevation, slope degree, aspect, average annual temperate, drought index, river density, land cover, and distance from roads and residential areas. Using the area under the receiver operating characteristic curve (AUC) and seven other performance metrics, the models were validated in terms of their abilities to elucidate the general fire behaviors in the Pu Mat National Park and to predict future fires. Despite a few differences between the AUC values, the BN model with an AUC value of 0.96 was dominant over the other models in predicting future fires. The second best was the DT model (AUC = 0.94), followed by the NB (AUC = 0.939), and MLR (AUC = 0.937) models. Our robust analysis demonstrated that these models are sufficiently robust in response to the training and validation datasets change. Further, the results revealed that moderate to high levels of fire susceptibilities are associated with ~19% of the Pu Mat National Park where human activities are numerous. This study and the resultant susceptibility maps provide a basis for developing more efficient fire-fighting strategies and reorganizing policies in favor of sustainable management of forest resources.
A Confront between Amati and Combo Correlations at Intermediate and Early Redshifts

I consider two gamma-ray burst (GRB) correlations: Amati and Combo. After calibrating them in a cosmology-independent way by employing Beziér polynomials to approximate the Observational Hubble Dataset (OHD), I perform Markov Chain Monte Carlo (MCMC) simulations within the Λ CDM and the wCDM models. The results from the Amati GRB dataset do not agree with the standard Λ CDM model at a confidence level ≥ 3 – σ . For the Combo correlation, all MCMC simulations give best-fit parameters which are consistent within 1– σ with the Λ CDM model. Pending the clarification of whether the diversity of these results is statistical, due to the difference in the dataset sizes, or astrophysical, implying the search for the most suited correlation for cosmological analyses, future investigations require larger datasets to increase the predictive power of both correlations and enable more refined analyses on the possible non-zero curvature of the Universe and the dark energy equation of state and evolution.
Characterisation of the benzimidazole-binding site on the cytoskeletal protein tubulin

The binding kinetics of several benzimidazole compounds were determined with recombinant tubulin monomers and heterodimers from benzimidazole-sensitive and -insensitive organisms. This study utilised the naturally occurring high efficacy of the benzimdazoles for the parasitic protozoa Giardia duodenalis and Encephalitozoon intestinalis. The benzimidazoles are not active against the protozoan Cryptosporidium parvum or mammalian hosts, including humans. The affinity of several benzimidazole derivatives for monomeric and heterodimeric beta-tubulin was clearly demonstrated, thus supporting previous studies of drug-resistant nematode and fungal populations. A homology model of protozoan alpha beta-tubulin, produced using the three-dimensionalstructure of mammalian alpha beta-tubulin, identified a strongly hydrophobic domain only on the beta-tubulin protein of sensitive protozoa. This domain is proposed to be the benzimidazole-binding domain and the amino acid residues within it include three key residues which are substituted between benzimidazole-sensitive and -insensitive organisms. These residues are Ile-189, Val-199, and Phe-200 that all have non-polar, hydrophobic side groups and are proposed to bind with the R5 side chain of several benzimidazole derivatives. In addition to this, the benzimidazole derivatives were able to bind irreversibly with assembling microtubules from sensitive parasites. The incorporation of benzimidazole-bound alpha beta-heterodimers into assembling microtubules was shown to arrest polymerisation in vitro although the addition of benzimidazole compounds to assembled microtubules did not result in depolymerisation. Taken together, these results suggest that the mechanism of action of these compounds is through disruption of the dynamic equilibrium that balances the cycle of microtubule polymerisation and disintegration within these protozoa. Further, this effect is brought about by preferential binding of the benzimidazoles to a hydrophobic region on the beta-tubulin protein.
Effect of a change in physical structure and cover on fish and fish habitat in freshwater ecosystems – a review and meta-analysis

Aquatic resource managers are continually faced with construction or site development proposals which, if allowed to proceed, would ultimately alter the physical structure and cover of fish habitat. In the absence of clear quantitative guidelines linking the change in habitat to fish, resource managers often use the change in habitat area as a basis for decisions. To assess the weight of scientific evidence in support of management decisions, we summarized both the observational and experimental freshwater fish-habitat literature. We then extracted data from experimental studies (where possible) for inclusion in a meta-analysis, to provide a more rigorous assessment of the published results of experimental habitat manipulations. We found relatively strong and consistent correlational evidence linking fish and physical habitat features, yet inconsistent evidence when narratively reviewing the experimental literature. On the whole, decreases in structural habitat complexity are detrimental to fish diversity.
Fruit Fly, Drosophila melanogaster, as an In Vivo Tool to Study the Biological Effects of Proton Irradiation

The clinical superiority of proton therapy over photon therapy has recently gained recognition; however, the biological effects of proton therapy remain poorly understood. The lack of in vivo evidence is especially important. Therefore, the goal of this study was to validate the usefulness of Drosophila melanogaster as an alternative tool in proton radiobiology. To determine whether the comparative biological effects of protons and X rays are detectable in Drosophila, we assessed their influence on survival and mRNA expression. Postirradiation observation revealed that protons inhibited their development and reduced the overall survival rates more effectively than X rays. The relative biological effectiveness of the proton beams compared to the X rays estimated from the 50% lethal doses was 1.31. At 2 or 24 h postirradiation, mRNA expression analysis demonstrated that the expression patterns of several genes (such as DNA-repair-, apoptosis- and angiogenesis-related genes) followed different time courses depending on radiation type. Moreover, our trials suggested that the knockdown of individual genes by the GAL4/UAS system changes the radiosensitivity in a radiation type-specific manner. We confirmed this Drosophila model to be considerably useful to evaluate the findings from in vitro studies in an in vivo system. Furthermore, this model has a potential to elucidate more complex biological mechanisms underlying proton irradiation.
Olive Cultivars in Spain

The diversity and antiquity of the cultivars, their restricted distribution and the limited use of rootstocks characterize cultivated olive (Olea europaea L.) plant material in Spain. An exploration survey identified 262 different cultivars, which have been introduced in the Olive Germplasm Bank in Cordoba, Spain. Evaluation of this germplasm, field trials and a breeding program are in progress.
Conserved regions of budding yeast Tim22 have a role in structural organization of the carrier translocase

ABSTRACT Mitochondrial biogenesis requires efficient sorting of various proteins into different mitochondrial sub-compartments, mediated by dedicated protein machinery present in the outer and inner membrane. Among them, the TIM22 complex enables the integration of complex membrane proteins with internal targeting signals into the inner membrane. Although the Tim22 protein forms the core of the complex, the dynamic recruitment of subunits to the channel is still enigmatic. In this study, we highlight that the intermembrane space (IMS) and transmembrane 4 (TM4) regions of Tim22 are critically required for interactions with the membrane-embedded subunits, including Tim54, Tim18, and Sdh3, and thereby maintain the functional architecture of the TIM22 translocase. Furthermore, we find that the TM1 and TM2 regions of Tim22 are important for association with Tim18, whereas TM3 is exclusively required for the interaction with Sdh3. Moreover, impairment of TIM22 complex assembly influences its translocase activity, the mitochondrial network, and the viability of cells lacking mitochondrial DNA. Overall, our findings provide compelling evidence highlighting the significance of conserved regions of Tim22 that are important for the maintenance of the TIM22 complex and mitochondrial integrity. Summary: The functional significance of different regions of Tim22 remains elusive. We find that conserved regions of Tim22 play distinct roles in associations with partner subunits, thereby maintaining carrier translocase integrity.
A Combinatorial Solution to Causal Compatibility

Abstract Within the field of causal inference, it is desirable to learn the structure of causal relationships holding between a system of variables from the correlations that these variables exhibit; a sub-problem of which is to certify whether or not a given causal hypothesis is compatible with the observed correlations. A particularly challenging setting for assessing causal compatibility is in the presence of partial information; i.e. when some of the variables are hidden/latent. This paper introduces the possible worlds framework as a method for deciding causal compatibility in this difficult setting. We define a graphical object called a possible worlds diagram, which compactly depicts the set of all possible observations. From this construction, we demonstrate explicitly, using several examples, how to prove causal incompatibility. In fact, we use these constructions to prove causal incompatibility where no other techniques have been able to. Moreover, we prove that the possible worlds framework can be adapted to provide a complete solution to the possibilistic causal compatibility problem. Even more, we also discuss how to exploit graphical symmetries and cross-world consistency constraints in order to implement a hierarchy of necessary compatibility tests that we prove converges to sufficiency.