Miracle-dz/UniFAR-FTUs huggingface dataset:
Portfolio Composition Based on High-Ranking Hedge Funds Using Dynamic Genetic Algorithms

— The work presented proposes a system that optimizes a portfolio of stocks based on the hedge funds’ assets, strategies and by tracking their past performances using Dynamic strategies in Genetic Algorithms. Most works that developed systems for the stock market use technical and fundamental indicators as features, but in this work, the hedge fund public data was used to model the behaviour of these all-star teams of financial experts. Being the stock market search space time-variant, the system developed is going to exploit a set of Dynamic strategies based on state-of-the-art approaches, such as Temporal Seeding , Direct Memory , Immigrant manipulation and a custom Hyper-state . The proposed architecture used information from 12 th of February of 2014 until 12 th of February of 2016. The proposed system outperformed the benchmark indexes for an entire quarter, obtaining an average Return On Investment of 5.7% and a Sharpe Ratio of 3.26.
The Interplay of Ageing Population and ICT To what extent can progress in information technology compensate the expected decrease in total output due to an ageing population, within the German manufacturing sector?

On a global scale, life expectancy has increased, and birth rates have reduced. This has both decreased the size of the employed workforce and has negatively affected total factor productivity (TFP) and total output. This research looks at whether technology, and ICT in particular, can counter the effects of an aging population on the German manufacturing sector. Using a Cobb-Douglas production function, we calculate and compare the magnitude of such effects. While we conclude that an ageing population has a negative impact on employment, output, and productivity, findings suggest that the effect of ICT on total output is not statistically significant, and that its relative productivity does not differ from that of non-ICT capital. Our findings add to the existing literature by providing an up to date view of the phenomena and a more comprehensive understanding of its linkage with information technology in German manufacturing sector.
The general induction functors for the category of entwined Hom-modules

We find a sufficient condition for the category of entwined Hom-modules to be monoidal. Moreover, we introduce morphisms between the underlying monoidal Hom-algebras and monoidal Homcoalgebras, which give rise to functors between the category of entwined Hom-modules, and we study tensor identities for monodial categories of entwined Hom-modules. Finally, we give necessary and sufficient conditions for the general induction functor from ~H (Mk)(ψ)CA to ~H (Mk)(ψ')C'A' to be separable.
WHO CAN PREDICT THEIR OWN DEMISE? HETEROGENEITY

Inaccurate longevity expectations will lead to suboptimal life cycle planning with negative consequences for wellbeing in old age. We evaluate the accuracy of expectations by comparing probabilities of living to 75 reported in the Health and Retirement Study with the actual survival of the respondents to that age. On average, survival predictions are poor and downwardly biased. Men predict less accurately, but that is because they face greater uncertainty due to a higher mortality rate. Women underestimate their survival chances more than men. Predictions are least accurate and most noisy at the lowest levels of education and cognitive functioning. A simple model suggests that welfare would be higher if everyone made decisions on the basis of the base survival rate rather than relying on the individual-specific survival probabilities reported. Despite the predictions of the least educated being the least accurate, they are not unambiguously the least valuable for decisions.
Classification of clinical time series: A case study in patients with Multiple Sclerosis

The constant drive towards a more personalized medicine in the last years led to the arrival of temporal gene expression analyses. Due to the consideration of a temporal aspect, this kind of analyses represents a great advantage to better understand disease progression and treatment results at a molecular level. Nevertheless, several problems accompany studies of this kind, with the sample size limitation being one of the most relevant. This limitation is patent in two ways: in the number of objects (in this case, patients), and in the number of measured time points. In this work, the data used were multiple gene expression time series, used to classify the response of multiple sclerosis patients to the standard treatment with Interferon-β, to which nearly half of the patients reveal a negative response. Therefore, obtaining a highly predictive model of a patients’ response would definitely improve the quality of life, avoiding useless and possibly harmful therapies for the non-responder group. In this context, several new strategies for time series classification are proposed, based on a biclustering technique. These are applied to the classification of the Interferon-β response by multiple sclerosis patients from a dataset analyzed over the last decade. Although our classification methods do not outperform the ones proposed for this same dataset, it is worth noting that some of the developed strategies reveal important potentialities that should be further explored, either with other clinical time series data, or even in other classification problems, in general.
Instituto Superior Técnico Ground-truth for Traffic Affected by Security Attacks

Security is a main topic on network communications, where new threats or mutations of existing ones appear at frightening rates. Therefore, to prevent those security attacks several techniques based on traffic analysis have been developed. However, these methods should be evaluated in order to test their effectiveness. For that, researchers need to resort to traffic traces to obtain the ground-truth, which is very difficult to acquire in real networks. This Dissertation is focused on emulating network topologies based on real networks to acquire high quality ground-truth. In different scenarios were generated licit and illicit traffic according to real patterns. The captured traffic was processed and analyzed to produce some final results. This Dissertation intends also to provide all the results obtained to the research community in order to be used to evaluate statistical methods of detecting attacks and applications.
Sensorimotor and Neural Networks for Visual Stimulus Prediction

This work focuses on a recently developed special type of biologically inspired architecture, which here denoted as a Sensorimotor Network, able to co-develop sensorimotor structures directly from the data acquired by a robot interacting with its environment. Such networks learn efficient internal models of the sensorimotor system, developing simultaneously sensor and motor representations (visual and movement receptive fields) adapted to the robot and its environment. Here Sensorimotor Network is compared with a Multilayer Perceptron in the ability to create efficient predictors of visuomotor relationships. It is confirmed that the Sensorimotor Network is significantly more efficient in terms of required computations and is more precise (less prediction error) than a simple feedforward neural network in predicting self-induced visual stimuli. The sensorimotor approach is proved to be replicable in a modified neural network and capable of organizing the same meaningful structures. In addition, all sensorimotor structures are studied regarding their complexity, connectivity and visual perception influence. Finally, a sensorimotor model is trained using real data recorded during a quadricopter drone flight.
What Explains The High Rate of Infant Mortality in Rural Nigeria: Biodemographic or Socioeconomic Factors?

Background: Children in the rural Nigeria are faced with higher mortality than their urban counterparts. And a number of factors are responsible for this. This study was aimed at determining which of bio-demographic or socio-economic factors contribute more to high rate of infant mortality in rural Nigeria. Methods: Data from the 2013 Nigerian Demographic and Health Survey were re-analysed using multivariate Weibull proportional hazard model to determine the effect of bio-demographic and socioeconomic factors on infant mortality in rural Nigeria. Results: After controlling for other factors, child’s sex, maternal age at child birth, birth interval, maternal age at first birth, type of toilet facilities, place of residence and maternal education are factors associated with infant mortality in rural Nigeria. Hazard of infant mortality was lowest among female children (HR = 0.83, CI= 0.75-0.93). Infant whose mother’s age at child birth were 35 yr or more have higher risk of death (HR=1.40, CI=1.131.74). Hazard of death was 64% (CI= 0.540.76) higher at infancy among birth interval of 20-35 and 46% (CI= 0.38, 0.56) higher at infancy among birth interval more than 35 months. The risk of death was significantly highest in North-east (HR=1.26, CI=1.05-1.51) and North-west (HR=1.40, CI=1.16-1.67). The use of improved toilet facility (HR=0.83, CI=0.74, 0.94) lower risk of infant death. Also, infants whose mothers had secondary or higher education had lower risk of infant death (HR= 0.91, CI= 0.75-1.10). Conclusions: Both bio-demographic and socioeconomic factors contribute significantly to infant mortality. Hence, in reducing infant mortality in rural Nigeria attention should be directed towards bio-demographic and socioeconomic factors.
Autism Screening Instrument for Educational Planning, Second Edition (ASIEP-2) The Autism Screening Instrument for Educational Planning, Second Edition (Krug,

The Autism Screening Instrument for Educational Planning, Second Edition (Krug, Arick, & Almond, 1993) is an assessment consisting of five separate subtests. Each subtest is designed to assist in one or more of the following areas: screening, diagnosis, placement, program planning, and progress monitoring. The Autism Behavior Checklist, Sample of Vocal Behavior, and Interaction Assessment are listed as screening and diagnostic scales; Educational Assessment and Prognosis of Learning Rate may be used in conjunction with the diagnostic tests as part of a complete intervention-planning battery. The Autism Behavior Checklist is designed for use with any individual who may have autism, whereas the other subtests are appropriate for individuals functioning at a language and social age of between 3 and 49 months.
Fast uncertainty quantification of tracer distribution in the brain interstitial fluid with multilevel and quasi Monte Carlo

Efficient uncertainty quantification algorithms are key to understand the propagation of uncertainty—from uncertain input parameters to uncertain output quantities—in high resolution mathematical models of brain physiology. Advanced Monte Carlo methods such as quasi Monte Carlo (QMC) and multilevel Monte Carlo (MLMC) have the potential to dramatically improve upon standard Monte Carlo (MC) methods, but their applicability and performance in biomedical applications is underexplored. In this paper, we design and apply QMC and MLMC methods to quantify uncertainty in a convection‐diffusion model of tracer transport within the brain. We show that QMC outperforms standard MC simulations when the number of random inputs is small. MLMC considerably outperforms both QMC and standard MC methods and should therefore be preferred for brain transport models.
Book Section: Essay and Review: A Clinician's Guide to Controversial Illnesses: Chronic Fatigue Syndrome, Fibromyalgia, and Multiple Chemical Sensitivities

Some illnesses get no respect. Often that oversight has a simple explanation: The condition in question is rare. Economic and human capital is instead expended on common medical disorders-hence the social preoccupation with cancer, heart disease, and substance abuse. These three problems alone claim legions of victims. When a publicity-fed clamor ignites interest in less common problems, attention is diverted from managing the "mundane. "
UAV Coverage Path Planning under Varying Power Constraints using Deep Reinforcement Learning

Coverage path planning (CPP) is the task of designing a trajectory that enables a mobile agent to travel over every point of an area of interest. We propose a new method to control an unmanned aerial vehicle (UAV) carrying a camera on a CPP mission with random start positions and multiple options for landing positions in an environment containing no-fly zones. While numerous approaches have been proposed to solve similar CPP problems, we leverage end-to-end reinforcement learning (RL) to learn a control policy that generalizes over varying power constraints for the UAV. Despite recent improvements in battery technology, the maximum flying range of small UAVs is still a severe constraint, which is exacerbated by variations in the UAV’s power consumption that are hard to predict. By using map-like input channels to feed spatial information through convolutional network layers to the agent, we are able to train a double deep Q-network (DDQN) to make control decisions for the UAV, balancing limited power budget and coverage goal. The proposed method can be applied to a wide variety of environments and harmonizes complex goal structures with system constraints.
Bow-tie structure and community identification of global supply chain network

We study on topological properties of global supply chain network in terms of degree distribution, hierarchical structure, and degree-degree correlation in the global supply chain network. The global supply chain data is constructed by collecting various company data from the web site of Standard & Poor's Capital IQ platform in 2018. The in- and out-degree distributions are characterized by a power law with in-degree exponent = 2.42 and out-degree exponent = 2.11. The clustering coefficient decays as power law with an exponent = 0.46. The nodal degree-degree correlation indicates the absence of assortativity. The Bow-tie structure of GWCC reveals that the OUT component is the largest and it consists 41.1% of total firms. The GSCC component comprises 16.4% of total firms. We observe that the firms in the upstream or downstream sides are mostly located a few steps away from the GSCC. Furthermore, we uncover the community structure of the network and characterize them according to their location and industry classification. We observe that the largest community consists of consumer discretionary sector mainly based in the US. These firms belong to the OUT component in the bow-tie structure of the global supply chain network. Finally, we confirm the validity for propositions S1 (short path length), S2 (power-law degree distribution), S3 (high clustering coefficient), S4 ("fit-gets-richer" growth mechanism), S5 (truncation of power-law degree distribution), and S7 (community structure with overlapping boundaries) in the global supply chain network.
Harnessing Multi-View Perspective of Light Fields for Low-Light Imaging

Light Field (LF) offers unique advantages such as post-capture refocusing and depth estimation, but low-light conditions severely limit these capabilities. To restore low-light LFs we should harness the geometric cues present in different LF views, which is not possible using single-frame low-light enhancement techniques. We propose a deep neural network L3Fnet for Low-Light Light Field (L3F) restoration, which not only performs visual enhancement of each LF view but also preserves the epipolar geometry across views. We achieve this by adopting a two-stage architecture for L3Fnet. Stage-I looks at all the LF views to encode the LF geometry. This encoded information is then used in Stage-II to reconstruct each LF view. To facilitate learning-based techniques for low-light LF imaging, we collected a comprehensive LF dataset of various scenes. For each scene, we captured four LFs, one with near-optimal exposure and ISO settings and the others at different levels of low-light conditions varying from low to extreme low-light settings. The effectiveness of the proposed L3Fnet is supported by both visual and numerical comparisons on this dataset. To further analyze the performance of low-light restoration methods, we also propose the L3F-wild dataset that contains LF captured late at night with almost zero lux values. No ground truth is available in this dataset. To perform well on the L3F-wild dataset, any method must adapt to the light level of the captured scene. To do this we use a pre-processing block that makes L3Fnet robust to various degrees of low-light conditions. Lastly, we show that L3Fnet can also be used for low-light enhancement of single-frame images, despite it being engineered for LF data. We do so by converting the single-frame DSLR image into a form suitable to L3Fnet, which we call as pseudo-LF. Our code and dataset is available for download at https://mohitlamba94.github. io/L3Fnet/
Sorting Cayley permutations with pattern-avoiding machines

Pattern avoiding machines were recently introduced by Claesson, Ferrari and the current author to gain a better understanding of the classical $2$-stacksort problem. In this paper we generalize these devices by allowing permutations with repeated elements, also known as Cayley permutations. The main result is a description of those patterns such that the corresponding set of sortable permutations is a class. We also show a new involution on the set of Cayley permutations, obtained by regarding a pattern-avoiding stack as an operator. Finally, we analyze two generalizations of pop-stack sorting on Cayley permutations. In both cases we describe sortable permutations in terms of pattern avoidance.
Spectrum of 
p
-wave hidden-charm exotic mesons in the diquark model

We study the fine structure in the spectrum of known and predicted negative-parity hidden-charm exotic meson states, which comprise the lowest $P$-wave multiplet in the dynamical diquark model. Starting with a form previously shown to successfully describe the $S$-wave states, we develop a 5-parameter Hamiltonian that includes spin-orbit and tensor terms. After discussing the experimental status of the observed ${J}^{PC}={1}^{\ensuremath{-}\ensuremath{-}}$ states $Y$ with respect to masses and decay modes (classified by eigenvalues of heavy-quark spin), we note a number of inconsistencies between measurements from different experiments that complicate a unique determination of the spectrum. Outlining a variety of scenarios for interpreting the $Y$ data, we perform fits to each one, obtaining results that demonstrate differing possibilities for the $P$-wave spectra. Choosing one of these fits for illustration, we predict masses for all 28 isomultiplets in this $1P$ multiplet, compare the results to tantalizing hints in the data, and discuss the rich discovery potential for new states.
Watch and learn—a generalized approach for transferrable learning in deep neural networks via physical principles

Transfer learning refers to the use of knowledge gained while solving a machine learning task and applying it to the solution of a closely related problem. Such an approach has enabled scientific breakthroughs in computer vision and natural language processing where the weights learned in state-of-the-art models can be used to initialize models for other tasks which dramatically improve their performance and save computational time. Here we demonstrate an unsupervised learning approach augmented with basic physical principles that achieves fully transferrable learning for problems in statistical physics across different physical regimes. By coupling a sequence model based on a recurrent neural network to an extensive deep neural network, we are able to learn the equilibrium probability distributions and inter-particle interaction models of classical statistical mechanical systems. Our approach, distribution-consistent learning, DCL, is a general strategy that works for a variety of canonical statistical mechanical models (Ising and Potts) as well as disordered interaction potentials. Using data collected from a single set of observation conditions, DCL successfully extrapolates across all temperatures, thermodynamic phases, and can be applied to different length-scales. This constitutes a fully transferrable physics-based learning in a generalizable approach.
Bandits with adversarial scaling

We study adversarial scaling, a multi-armed bandit model where rewards have a stochastic and an adversarial component. Our model captures display advertising where the click-through rate can be decomposed to a (fixed across time) arm-quality component and a non-stochastic user-relevance component (fixed across arms). Despite the relative stochasticity of our model, we demonstrate two settings where most bandit algorithms suffer. On the positive side, we show that two algorithms, one from the action elimination and one from the mirror descent family are adaptive enough to be robust to adversarial scaling. Our results shed light on the robustness of adaptive parameter selection in stochastic bandits, which may be of independent interest.
Massive core/star formation triggered by cloud–cloud collision: Effect of magnetic field

We study effect of magnetic field on massive dense core formation in colliding unequal molecular clouds by performing magnetohydrodynamic simulations with sub-parsec resolution (0.015 pc) that can resolve the molecular cores. Initial clouds with the typical gas density of the molecular clouds are immersed in various uniform magnetic fields. The turbulent magnetic fields in the clouds consistent with the observation by Crutcher et al. (2010) are generated by the internal turbulent gas motion before the collision, if the uniform magnetic field strength is 4.0 $\mu$G. The collision speed of 10 km s$^{-1}$ is adopted, which is much larger than the sound speeds and the Alfven speeds of the clouds. We identify gas clumps with gas densities greater than 5 $\times$ 10$^{-20}$ g cm$^{-3}$ as the dense cores and trace them throughout the simulations to investigate their mass evolution and gravitational boundness. We show that a greater number of massive, gravitationally bound cores are formed in the strong magnetic field (4.0 $\mu$G) models than the weak magnetic field (0.1 $\mu$G) models. This is partly because the strong magnetic field suppresses the spatial shifts of the shocked layer that should be caused by the nonlinear thin shell instability. The spatial shifts promote formation of low-mass dense cores in the weak magnetic field models. The strong magnetic fields also support low-mass dense cores against gravitational collapse. We show that the numbers of massive, gravitationally bound cores formed in the strong magnetic field models are much larger than the isolated, non-colliding cloud models, which are simulated for comparison. We discuss the implications of our numerical results on massive star formation.