Miracle-dz/UniFAR-FTUs huggingface dataset:
Sexualhormone und Stimmlage des Mannes*

Zusammenhänge zwischen Sexualhormonen im Blute und der männlichen Stimmlage wurden bei 102 Sängern untersucht. Im Vergleich zu Tenören wurden bei den Stimmlagen Baß und Bariton höhere Testosteronund niedrigere Östradiol-Konzentrationen im Plasma gemessen. Daraus ergaben sich zugunsten der Androgene verschobene Testosteron/ÖstradiolQuotienten bei tiefen Stimmlagen. Eine tiefere Stimmlage war mit größerem und schwererem Körperbau assoziiert. In der jüngeren Altersgruppe wiesen Bässe, in der mittleren und älteren wiesen Tenöre die höchste sexuelle Aktivität auf. Aus anamnestisch erhobenen Daten ergaben sich keine stimmiagenspezifischen Unterschiede im Hinblick auf Reihenfolge und Eintritt der verschiedenen Pubertätsmerkmale. Lediglich wiesen spätere Bässe eine höhere Inzidenz von Akne auf. Aus den Ergebnissen wird geschlossen, daß Unterschiede sowohl in den Konzentrationen der zirkulierenden Sexualhormone als auch in der Androgensensibilität der Zielorgane für die verschiedenen männlichen Stimmlagen ausschlaggebend sind. Alle Manuskripte sind direkt an die Schriftleitung zu richten. Grundsätzlich werden nur solche Arbeiten angenommen, die vorher weder im inland noch im Ausland veröffentlicht worden sind. Die Manuskripte dürfen auch nicht gleichzeitig anderen Blättern zum Abdruck angeboten werden. Mit der Annahme des Manuskriptes erwirbt der Verlag für die Dauer der gesetelichen Schutzfrist (564 UrhRG) die ausschließliche Befugnis zur Wahrnehmung der Verwertungsrechte im Sinne der 5515ff. des Urheberrechtsgesetzes. Übersetzung, Nachdruck-auch von Abbildungen Vervielfältigung auf fotomechanischem oder ähnlichem Wege oder im Magnerton-Verfahren, Vortrag, Funkund Fernsehsendung sowie Speicherung in Datenverarbeitungsanlagen auch auszugsweise sind nur mit
Space time geometry in the atomic hydrogenoid system. Approach to a dust relativistic model from causal quantum mechanics

We intend to use the description of the electron orbital trajectory in the de Broglie - Bohm ( dBB ) theory to assimilate to a geodesic corresponding to the General Relativity (GR) and get from it physical conclusions . The dBB approach indicates us the existence of a non-local quantum field ( corresponding with the quantum potential ), an electromagnetic field and a comparatively very weak gravitatory field , together with a translation kinetic energy of electron . If we admit that those fields and kinetic energy can deform the space time, according to Einstein's field equations (and to avoid the violation of the equivalence principle as well ), we can made the hypothesis that the geodesics of this space -time deformation coincide with the orbits belonging to the dBB approach ( hypothesis that is coherent with the stability of matter ). From it , we deduce a general equation that relates the components of the metric tensor. Then we find an appropriate metric for it , by modification of an exact solution of Einstein's field equations, which corresponds to dust in cylindrical symmetry . The found model proofs to be in agreement with the basic physical features of the hydrogen quantum system, particularly with the independence of the electron kinetic momentum in relation with the orbit radius. Moreover , the model can be done Minkowski - like for a macroscopic short distance with a convenient election of a constant . According to this approach , the guiding function of the wave on the particle could be identified with the deformations of the space -time and the stability of matter would be easily justified by the null acceleration corresponding to a geodesic orbit .
Variable neighborhood search for solving the k-domination problem

In this paper we are concerned with solving a generalized version of the well-known minimum dominating set problem, the so-called k-domination problem, k ∈ ℕ. This problem is about finding a minimal cardinality subset D of vertices of a graph G = (V, E) such that every υ ∈ V belongs to D or has at least k neighbors from D. The k-domination problem has applications in distributed systems, biological networks etc. We propose a variable neighborhood search (VNS) metaheuristic for solving the k-domination problem. The Vns is equipped with an efficient fitness function that allows it to consider both feasible and infeasible solutions, while appropriately penalizing infeasible solutions. The control parameters of the Vns are tuned using a grid search approach. The method is compared to the best known heuristic approaches from the literature: the beam search and several greedy approaches. Experimental evaluations are performed on a real-world benchmark set whose instances represent the road networks of different cities. The Vns provided new state-of-the-art results for all considered problem instances with k ∈ {1, 2, 4}.
Kheperax: a Lightweight JAX-based Robot Control Environment for Benchmarking Quality-Diversity Algorithms

This work introduces a new lightweight and massively parallelizable implementation of a Quality-Diversity (QD) task: the libfastsim maze. This QD task involves finding a collection of neural network controllers navigating a robot to diverse positions in a maze. The proposed implementation, called Kheperax, can be used as a benchmark task for standard QD algorithms, but also for Model-based, Unsupervised and Uncertain QD algorithms. It can automatically run and parallelize parameter evaluations on hardware accelerators, such as Graphical Processing Units (GPUs). When evaluating large batches of parameters, Kheperax is at least 4 times faster than the original libfastsim implementation. The source code is available online1.
Traffic offloading based on content sharing in D2D for Smart Grid

The Edge-Device architecture is an excellent companion of mobile communication systems, to alleviate load of IoT (internet of things) and to bring user improved experience. We consider a content sharing network (CSN) where smart devices functioned as contributory offloading-devices (CODs) can perform content sharing on demand via Edge-Device links in order to offload the cellular traffic. In this paper we study the problem of maximizing traffic offloading via content sharing among smart devices, by applying a contribution-based incentive mechanism, and by selectively caching popular contents locally. Specifically, the incentive mechanism formulated as a utility maximization problem can converge to Nash equilibrium while selective caching scheme formulated as a 0-1 Knapsack problem can improve performance of CSN. Our simulation results identify the important characteristics of proposed policies in the CSN.
Reducing high-dimensional feature set of hyperspectral measurements for plant phenotype classification

Quantifying the drought resistance of potato cultivars plays a key role in precision agriculture, and it may lead to the development of new varieties of plants that are more resistant to harsh environmental conditions. In this work, we tackle the issue of extracting such information in a non-invasive way by acquiring in-field hyperspectral measurements of the potato leaves. Then, we exploit an array of machine learning models to classify plants into three wilting classes based on such data, with those classes corresponding to their drought resistance. We show that evolutionary band selection can dramatically reduce the dimensionality of hyperspectral data while improving classification accuracy. Our experimental study revealed that the evolutionarily-optimized models offer high-quality performance with the impartial rϕ reaching 0.784, accuracy: 0.867, and a 30% improvement over the baseline models which do not benefit from band selection.
Laser-Induced Fano Resonance in Condensed Matter Physics

Recent development of laser technology toward the realization of high-power laser has opened up a new research area exploring various fascinating phenomena governed by strongly photoexcited electronic states in diverse fields of science. In this chapter, we review the laser-induced Fano resonance (FR) in condensed matter systems, which is one of the representative resonance effects successfully exposed by strong laser field. The FR of concern sharply differs from FR effects commonly observed in conventional quantum systems where FR is caused by a weak external perturbation in a stationary system in the following two aspects. One is that the present FR is a transient phenomenon caused by nonequilibrium photoexcited states. The other is that this is induced by an optically nonlinear process. Here, we introduce two physical processes causing such transient and optically nonlinear FR in condensed matter, followed by highlighting anomalous effects inherent in it. The first is a Floquet exciton realized in semiconductor superlattices driven by a strong continuous-wave laser, and the second is the coherent phonon induced by an ultrashort pulse laser in bulk crystals.
Optimization of MFCCs for Time Signature Detection Using Genetic Algorithm

In the Music Information Retrieval domain, the concept of time signature detection has been ongoing for some time now. Time signature is being considered as a feature in broader classification problems such as music genre classification. To develop a realistic model that is capable of accurately detecting any time signature from any music track, different techniques are adopted. Based on previous research, two models have been highlighted (Beat and Audio Similarity Matrix) in this study for their potential but not without their limitations. Therefore, a new model was developed with the idea from the previous models. We called this model Mel-Frequency Cepstral Coefficient Similarity Matrix (MFCCSM). It uses Mel-coefficients extracted from audio signals which produce mel-frequency cepstrum coefficients that are eventually optimized. Two optimization techniques, Bayesian optimization and Genetic Algorithm, were considered for optimization, with the latter being the major technique used in the model. In comparison with the previous two models, MFCCSM showed a significant increase in accuracy of about 8% after optimization based on two datasets that were used for the study. Overall, the GA optimization also gives insight into the set of frequencies in each coefficients of the MFCC which can be further explored in future research.
Overcoming Deceptive Rewards with Quality-Diversity

Quality-Diversity offers powerful ideas to create diverse, high-performing populations. Here, we investigate the capabilities these ideas hold to solve exploration-hard single-objective problems, in addition to creating diverse high-performing populations. We find that MAP-Elites is well suited to overcome deceptive reward structures, while an Elites-type approach with an unstructured, distance based container and extinction events can even outperform it. Furthermore, we analyse how the QD score, the standard evaluation of MAP-Elites type algorithms, is not well suited to predict the success of a configuration in solving a maze. This shows that the exploration capacity is an entirely different dimension in which QD algorithms can be utilized, evaluated, and improved on. It is a dimension that does not currently seem to be covered, implicitly or explicitly, by the current advances in the field.
Trust evaluation model based on improved Bayesian theory for the Internet of Things

At present, the Internet of Things (IoT) is applied to all aspects of people's lives. The traditional cloud-based Internet of Things has been unable to support the development needs of the Internet of Things at this stage. The emergence of edge computing provides new opportunities for the development of the Internet of Things, but also brings new security threats.In view of the security requirements for nodes in the IoT edge computing scenario, this paper proposes a comprehensive trust evaluation model for terminal nodes based on improved Bayesian theory. Aiming at the time-effectiveness of trust, a trust decay strategy is designed, and a direct trust evaluation of IoT terminals is carried out based on improved Bayesian theory.At the same time, aiming at the problem of dishonest recommendation behavior in indirect trust evaluation, a consistent optimization strategy of node recommendation behavior is designed. Finally, the comprehensive trust evaluation value of the terminal node is obtained according to the direct trust evaluation and the indirect trust evaluation. The effectiveness of this scheme is verified by simulation experiments.
Embedded Real-time System Trusted Protection Technology for Supply Chain Scenarios

Embedded real-time operating systems are widely used in communication, industrial control and weapon intelligent control and other fields. However, due to the complex personnel and environment involved in the development of embedded systems, the development software is difficult to control independently, so it is extremely vulnerable to malicious code implantation attacks. Based on this, this paper focuses on the UCOS real-time operating system, analyzes the security threats faced by the UCOS development environment GCC in the process of precompiling, compiling, assembling and linking, and designs a plug-in-based GCC attack mode, which can implement malicious code implantation attacks on UCOS. At the same time, a trusted protection framework for embedded real-time systems is proposed. By implementing active control and integrity measurement of firmware, operating systems and applications in embedded real-time systems, the trusted startup of embedded real-time systems is guaranteed and the supply chain stage is independently resisted. malicious code implantation attack.
A mutual authentication and key establishment scheme for peer to peer network scenario

Due to the lack of capability of solving computationally hard problems and the lack of high processing power by one single entity. Swarm robotics emerge, a group of individual robotics shares their processing resources to solve a complicated problem. Authentication schemes within the peer robotics are necessary, especially when the swarm robotics is sent out for different tasks. In this study, an authentication and key establishment scheme specially designed for peer-to-peer networks is proposed. The proposed scheme enables authentication between two registered peer entities directly without the help of a trusted server, after authentication, two peer robotics can build a secure two-way communication channel in a public environment. Security analysis shows that this scheme is secure, compared to related works the proposed scheme has more security features. In addition, our experiment results show the proposed scheme is efficient compared to related works.
When to Elicit Preferences in Multi-Objective Bayesian Optimization

We consider the case of interactive multi-objective Bayesian optimization where decision maker (DM) preferences can be elicited by asking the DM to select the more preferred among pairs of observations. Assuming that there is a cost to evaluating a solution as well as to eliciting preferences, and given a total budget, we propose an acquisition function that, in each iteration, decides whether to evaluate another solution or to query the DM. Thus, the approach automatically chooses how often and when to interact with the DM. It furthermore decides which pair of observations is likely to be most informative when shown to the DM. We show empirically that the proposed criterion is not only able to pick suitable pairs of observations, but also automatically results in a sensible balance between optimization and querying the DM.
Deep Learning for Physics Simulation

Numerical simulation of physical systems has become an increasingly important scientific tool supporting various research fields. Despite its remarkable success, simulating intricate physical systems typically requires advanced domain-specific knowledge, meticulous implementation, and enormous computational resources. With the surge of deep learning in the last decade, there has been a growing interest in the machine-learning and graphics communities to address these limitations of numerical simulation with deep learning. This course provides a gentle introduction to this topic for audiences interested in exploring this trend but with little to modest machine-learning or physics-simulation backgrounds. We begin with a brief overview of the numerical simulation framework on which we ground our discussion of deep-learning methods. Next, the course provides a possible classification of several hybrid simulation strategies based on the roles of learning and physics insights incorporated. We then review the implications of such deep-learning strategies and discuss some practical considerations in combining deep learning and physics simulation. Finally, we briefly mention several advanced machine-learning techniques for further exploration. The full course information can be found in https://people.iiis. tsinghua.edu. cn/~taodu/dl4sim/.
A Novel Fitness Function for Automated Software Test Case Generation Based on Nested Constraint Hardness

Search-Based Software Testing (SBST) has drawn a lot of interests as a powerful approach for automated test data generation. One major limitation of search-based methods is that they may get stuck in local optima and become inefficient if the fitness function does not provide any direction toward a test target, particularly when dealing with hard branch targets (e.g., nested predicates). Recent research has focused on enhancing the fitness function by taking branch hardness into account in order to direct the evolutionary process. However, either the characteristics of constraints (e.g., their involved variables' domain) are not taken into account or the difficulty level of a branch is separately studied. In this paper, we aim to address the test data generation with a focus on hard branches by proposing a novel fitness function based on nested constraints (i.e., including the target constraint and those that impact its coverage) and the domain sizes of their variables. The empirical study promises efficiency and effectiveness for the new fitness function. Our proposed approach outperforms its counterparts significantly, particularly for the branches that are difficult to be covered.
Multi-Objective Policy Evolution for a Same-Day Delivery Problem with Soft Deadlines

Same-day delivery problems (SDDPs) deal with efficient near-term satisfaction of dynamic and stochastic customer demand. During the day, a sequence of decisions has to be performed related to delivery route construction and driver dispatching. Formulated as Markov decision process, the goal is to find a policy that maximizes the expected reward over a specified class of instances. In the literature, scalar reward functions have dominated so far, by either considering only one objective or several objectives transformed into one using a weighted sum or lexicographic order. In this work, we consider a vector-valued reward with two dimensions, tardiness and costs, for a real-world inspired SDDP with tight soft deadlines within hours. The goal is to evolve non-dominated sets of policies for typical days with a certain stochastic load pattern and geographical order distribution in advance and let the decision maker select which one to deploy. We achieve this utilizing classical multi-objective genetic algorithms, NSGA-II and SPEA2, and by parallelization of the computationally intensive policy evaluations. Based on the experimental results, we observe that accepting a small amount of additional tardiness initially leads to a substantial return in terms of reduced mean delivery times per order.
AbCD: A Component-wise Adjustable Framework for Dynamic Optimization Problems

Dynamic Optimization Problems (DOPs) are characterized by changes in the fitness landscape that can occur at any time and are common in real world applications. The main issues to be considered include detecting the change in the fitness landscape and reacting in accord. Over the years, several evolutionary algorithms have been proposed to take into account this characteristic during the optimization process. However, the number of available tools or open source codebases for these approaches is limited, making reproducibility and extensive experimentation difficult. To solve this, we developed a component-oriented framework for DOPs called Adjustable Components for Dynamic Problems (AbCD), inspired by similar works in the Multiobjective static domain. Using this framework, we investigate components that were proposed in several popular DOP algorithms. Our experiments show that the performance of these components depends on the problem and the selected components used in a configuration, which differs from the results reported in the literature. Using irace, we demonstrate how this framework can automatically generate DOP algorithm configurations that take into account the characteristics of the problem to be solved. Our results highlight existing problems in the DOP field that need to be addressed in the future development of algorithms and components.
MLStar: A System for Synthesis of Machine-Learning Programs

This paper introduces our auto-ML system, MLStar, which uses genetic programming to create scikit-learn and Keras-based Python programs to perform supervised learning. MLStar leverages our own genetic programming system (GPStar4) and provides a greater search space compared to traditional genetic programming frameworks. Key elements that enable MLStar's performance include representing individuals as Directed Acyclic Graphs (DAGs), a rich type system to shape the kinds of graphs generated, novel genetic operators which work on the DAG structure, and advanced hyperparameter tuning via the Optuna hyperparameter optimization framework. MLStar also offers multiobjective fitnesses and a variety of complex population types. We show that MLStar performs favorably to several other auto-ML frameworks on benchmark tests. We also demonstrate that MLStar is capable of competitive solutions even when running with computationally expensive features disabled.
Representations for Evolutionary Algorithms

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