Deep Learning for Automatic Detection and Facial Recognition in Japanese Macaques: Illuminating Social Networks

Individual identification plays a pivotal role in ecology and ethology, notably as a tool for complex social structures understanding. However, traditional identification methods often involve invasive physical tags and can prove both disruptive for animals and time-intensive for researchers. In recent years, the integration of deep learning in research offered new methodological perspectives through automatization of complex tasks. Harnessing object detection and recognition technologies is increasingly used by researchers to achieve identification on video footage. This study represents a preliminary exploration into the development of a non-invasive tool for face detection and individual identification of Japanese macaques (Macaca fuscata) through deep learning. The ultimate goal of this research is, using identifications done on the dataset, to automatically generate a social network representation of the studied population. The current main results are promising: (i) the creation of a Japanese macaques' face detector (Faster-RCNN model), reaching a 82.2% accuracy and (ii) the creation of an individual recognizer for K{\=o}jima island macaques population (YOLOv8n model), reaching a 83% accuracy. We also created a K{\=o}jima population social network by traditional methods, based on co-occurrences on videos. Thus, we provide a benchmark against which the automatically generated network will be assessed for reliability. These preliminary results are a testament to the potential of this innovative approach to provide the scientific community with a tool for tracking individuals and social network studies in Japanese macaques

Exploration of the creative processes in animals, robots, and AI: who holds the authorship?

Picture a simple scenario: a worm, in its modest way, traces a trail of paint as it moves across a sheet of paper. Now shift your imagination to a more complex scene, where a chimpanzee paints on another sheet of paper. A simple question arises: Do you perceive an identical creative process in these two animals? Can both of these animals be designated as authors of their creation? If only one, which one? This paper delves into the complexities of authorship, consciousness, and agency, unpacking the nuanced distinctions between such scenarios and exploring the underlying principles that define creative authorship across different forms of life. It becomes evident that attributing authorship to an animal hinges on its intention to create, an aspect intertwined with its agency and awareness of the creative act. These concepts are far from straightforward, as they traverse the complex landscapes of animal ethics and law. But our exploration does not stop there. Now imagine a robot, endowed with artificial intelligence, producing music. This prompts us to question how we should evaluate and perceive such creations. Is the creative process of a machine fundamentally different from that of an animal or a human? As we venture further into this realm of human-made intelligence, we confront an array of ethical, philosophical, and legal quandaries. This paper provides a platform for a reflective discussion: ethologists, neuroscientists, philosophers, and bioinformaticians converge in a multidisciplinary dialogue. Their insights provide valuable perspectives for establishing a foundation upon which to discuss the intricate concepts of authorship and appropriation concerning artistic works generated by non-human entities

Self-similar chain conformations in polymer gels

We use molecular dynamics simulations to study the swelling of randomly end-cross-linked polymer networks in good solvent conditions. We find that the equilibrium degree of swelling saturates at Q_eq = N_e**(3/5) for mean strand lengths N_s exceeding the melt entanglement length N_e. The internal structure of the network strands in the swollen state is characterized by a new exponent nu=0.72. Our findings are in contradiction to de Gennes' c*-theorem, which predicts Q_eq proportional N_s**(4/5) and nu=0.588. We present a simple Flory argument for a self-similar structure of mutually interpenetrating network strands, which yields nu=7/10 and otherwise recovers the classical Flory-Rehner theory. In particular, Q_eq = N_e**(3/5), if N_e is used as effective strand length.Comment: 4 pages, RevTex, 3 Figure

Shrinkage of self-compacting concrete. A comparative analysis

Self-compacting concrete (SCC) is a concrete type that does not require vibration for placing and compacting. SCC possesses special technical features and properties that recommend its application in many jobs. Nevertheless, in some situations, it has been observed an inadequate behaviour of the material at early ages due to shrinkage. The existing shrinkage prediction models were developed for standard concrete. In this paper three SCC mixtures, with different compressive strength, are studied in terms of autogenous and total shrinkage. The results are compared with the Eurocode 2 model. For the studied mixtures it was found that this model underestimates the autogenous shrinkage, while the total shrinkage is generally overestimated.Fundacao para a Ciencia e a Tecnologia (FCT), Portugal [UID/MULTI/00308/2013]info:eu-repo/semantics/publishedVersio

PHF3 regulates neuronal gene expression through the Pol II CTD reader domain SPOC

The C-terminal domain (CTD) of the largest subunit of RNA polymerase II (Pol II) is a regulatory hub for transcription and RNA processing. Here, we identify PHD-finger protein 3 (PHF3) as a regulator of transcription and mRNA stability that docks onto Pol II CTD through its SPOC domain. We characterize SPOC as a CTD reader domain that preferentially binds two phosphorylated Serine-2 marks in adjacent CTD repeats. PHF3 drives liquid-liquid phase separation of phosphorylated Pol II, colocalizes with Pol II clusters and tracks with Pol II across the length of genes. PHF3 knock-out or SPOC deletion in human cells results in increased Pol II stalling, reduced elongation rate and an increase in mRNA stability, with marked derepression of neuronal genes. Key neuronal genes are aberrantly expressed in Phf3 knock-out mouse embryonic stem cells, resulting in impaired neuronal differentiation. Our data suggest that PHF3 acts as a prominent effector of neuronal gene regulation by bridging transcription with mRNA decay

PHF3 regulates neuronal gene expression through the Pol II CTD reader domain SPOC

The C-terminal domain (CTD) of the largest subunit of RNA polymerase II (Pol II) is a regulatory hub for transcription and RNA processing. Here, we identify PHD-finger protein 3 (PHF3) as a regulator of transcription and mRNA stability that docks onto Pol II CTD through its SPOC domain. We characterize SPOC as a CTD reader domain that preferentially binds two phosphorylated Serine-2 marks in adjacent CTD repeats. PHF3 drives liquid-liquid phase separation of phosphorylated Pol II, colocalizes with Pol II clusters and tracks with Pol II across the length of genes. PHF3 knock-out or SPOC deletion in human cells results in increased Pol II stalling, reduced elongation rate and an increase in mRNA stability, with marked derepression of neuronal genes. Key neuronal genes are aberrantly expressed in Phf3 knock-out mouse embryonic stem cells, resulting in impaired neuronal differentiation. Our data suggest that PHF3 acts as a prominent effector of neuronal gene regulation by bridging transcription with mRNA decay

Bayes'sche Sequentielle Verfahren in Klinischen Studien

Abweichender Titel nach Übersetzung der Verfasserin/des VerfassersBayesian techniques allow the design of flexible and adaptive trials. This flexibility is given by accepting the Likelihood principle, which is presented in the first chapter, that shows equivalence to the Conditionality principle. The second chapter introduces the Bayesian foundations and finishes with an introduction into hierarchical Bayesian modeling. Latter permits inference about the efficiency of treatments on rare diseases with many subgroups and/or by including patients from multiple clinics into the study. Additionally, a coherent combination of multiple studies is possible in this framework. The third chapter covers decision theory and the intrinsically linked Bayesian hypothesis testing. It further shows some modeling tools available to the statisticians. The fourth chapter presents Bayesian sequential decision theory. Backward induction a method to find an optimal procedure is used to deduce the widely known sequential probability ratio test. This chapter concludes with the introduction of predictive probabilities and the corresponding clinical trial design. The temporal classification is used in the fifth chapter to introduce the reader into clinical trials. The work completes with exemplary clinical studies. A decision theoretical design optimize the simultaneous run of many phase \RM{2} studies in one center is presented in detail. Furthermore, a lung cancer trial designed with predictive probabilities is described. Lastly, accrual of patients for trials on the treatment of rare diseases like sarcomas is challenging. A design that uses hierarchical Bayes to analyze a treatment for twelve different sarcomas is shown.8

VIENNA UNIVERSITY OF TECHNOLOGY Watson Jeopardy! A Thinking Machine

In 2011 the US-American quiz show ‘Jeopardy! ’ had its first non-human competitor: ‘Watson’, a computer developed by IBM. ‘Watson ’ had been specifically conceived for this ultimate test. The aim was to demonstrate the machinic ability to not only decrypt, but understand natural language. In this seminar paper, this topic will be explored from a philosophical perspective. The focus will be onto whether the artificial intelligence of ‘Watson ’ is comparable to the human capability to understand language. An outlook on the usage of ‘Watson ’ as for today in the medical sector will be followed by In 1997 the research team of IBM had achieved what would have been considered impossible before: their computer ‘Deep Blue ’ won a chess game against the world-champion Gary Kasparov. For the first time a machine had been able to win against someone who had until then been considered as unbeatable. Nonetheless, the Deep Blue project was suspende