Identifying cognitive capacities underlying the human evolutionary transition is challenging, and many hypotheses exist for what makes humans capable of, for example, producing and understanding ...language, preparing meals, and having culture on a grand scale. Instead of describing processes whereby information is processed, recent studies have suggested that there are key differences between humans and other animals in how information is recognized and remembered. Such constraints may act as a bottleneck for subsequent information processing and behavior, proving important for understanding differences between humans and other animals. We briefly discuss different sequential aspects of cognition and behavior and the importance of distinguishing between simultaneous and sequential input, and conclude that explicit tests on non-human great apes have been lacking. Here, we test the memory for stimulus sequences-hypothesis by carrying out three tests on bonobos and one test on humans. Our results show that bonobos’ general working memory decays rapidly and that they fail to learn the difference between the order of two stimuli even after more than 2,000 trials, corroborating earlier findings in other animals. However, as expected, humans solve the same sequence discrimination almost immediately. The explicit test on whether bonobos represent stimulus sequences as an unstructured collection of memory traces was not informative as no differences were found between responses to the different probe tests. However, overall, this first empirical study of sequence discrimination on non-human great apes supports the idea that non-human animals, including the closest relatives to humans, lack a memory for stimulus sequences. This may be an ability that sets humans apart from other animals and could be one reason behind the origin of human culture.
Human language is unique in its compositional, open-ended, and sequential form, and its evolution is often solely explained by advantages of communication. However, it has proven challenging to ...identify an evolutionary trajectory from a world without language to a world with language, especially while at the same time explaining why such an advantageous phenomenon has not evolved in other animals. Decoding sequential information is necessary for language, making domain-general sequence representation a tentative basic requirement for the evolution of language and other uniquely human phenomena. Here, using formal evolutionary analyses of the utility of sequence representation we show that sequence representation is exceedingly costly and that current memory systems found in animals may prevent abilities necessary for language to emerge. For sequence representation to evolve, flexibility allowing for ignoring irrelevant information is necessary. Furthermore, an abundance of useful sequential information and extensive learning opportunities are required, two conditions that were likely fulfilled early in human evolution. Our results provide a novel, logically plausible trajectory for the evolution of uniquely human cognition and language, and support the hypothesis that human culture is rooted in sequential representational and processing abilities.
There is a growing need for systems that efficiently support the work of medical teams at the precision-oncology point of care. Here, we present the implementation of the Molecular Tumor Board Portal ...(MTBP), an academic clinical decision support system developed under the umbrella of Cancer Core Europe that creates a unified legal, scientific and technological platform to share and harness next-generation sequencing data. Automating the interpretation and reporting of sequencing results decrease the need for time-consuming manual procedures that are prone to errors. The adoption of an expert-agreed process to systematically link tumor molecular profiles with clinical actions promotes consistent decision-making and structured data capture across the connected centers. The use of information-rich patient reports with interactive content facilitates collaborative discussion of complex cases during virtual molecular tumor board meetings. Overall, streamlined digital systems like the MTBP are crucial to better address the challenges brought by precision oncology and accelerate the use of emerging biomarkers.
Correctly assessing the amount of blood loss is crucial in order to adequately treat postpartum haemorrhage (PPH) at an early stage and diminish any related symptoms and/or complications.
The aim of ...our study is to analyse correctness in visually estimated blood loss during labour and to measure the differences between subjectively measured and weighted blood losses (ml).
Cross-sectional study
A Swedish maternity unit with 6000 annual births
Midwives employed at a big maternity unit at a hospital in northern Stockholm, Sweden.
Midwives assisting 192 vaginal births were asked to visually estimate the blood loss from the assisted delivery. Coasters and sanitary pads were weighed following the birth. We analysed if there were any differences between subjective measured blood loss (ml) and weighted blood loss. These two methods were also compared to quantify concordance between estimated blood volume and the actual volume.
The number of overestimates of blood loss was 45.3 % (n=87) with an average of 72.9 ml; the number of underestimates was 49.4 % (n=95) with an average of 73.8 ml. Exact correct estimations of blood loss were done in 5.2 % of the cases (n=10).
The largest overestimation of a postpartum bleeding was by 520 ml; the largest underestimation was by 745 ml.
There was both underestimation and overestimation of blood loss. We found small but significant overestimates in PPH < 300 ml (16 ml). In PPH > 300 ml, there was a small but not significant underestimates (34 ml). Based upon our findings, we conclude that it is reasonable to start weighing blood loss when it exceeds 300 ml.
•Both underestimating and overestimating blood loss was done.•Exact correct estimates of PPH were done in 5.2 % of the cases (n=10).•In 83 % of the cases (n=160), the absolute estimation error is less than 120 ml.
We introduce \emph{$p_n$-random $q_n$-proportion Bulgarian solitaire}
($0<p_n,q_n\le 1$), played on $n$ cards distributed in piles. In each pile, a
number of cards equal to the proportion $q_n$ of ...the pile size rounded upward
to the nearest integer are candidates to be picked. Each candidate card is
picked with probability $p_n$, independently of other candidate cards. This
generalizes Popov's random Bulgarian solitaire, in which there is a single
candidate card in each pile. Popov showed that a triangular limit shape is
obtained for a fixed $p$ as $n$ tends to infinity. Here we let both $p_n$ and
$q_n$ vary with $n$. We show that under the conditions $q_n^2 p_n n/{\log
n}\rightarrow \infty$ and $p_n q_n \rightarrow 0$ as $n\to\infty$, the
$p_n$-random $q_n$-proportion Bulgarian solitaire has an exponential limit
shape.
This thesis explores adapting self-supervised representation learning to visual domains beyond natural scenes, focusing on medical imaging. The research addresses the central question: "How can ...self-supervised representation learning be specifically adapted for detecting liver cancer in histopathology images?" The study utilizes the PAIP 2019 dataset for liver cancer segmentation and employs a self-supervised approach based on the VICReg method. The evaluation results demonstrated that the ImageNet-pretrained model achieved superior performance on the test set, with a clipped Jaccard index of 0.7747 at a threshold of 0.65. The VICReg-pretrained model followed closely with a score of 0.7461, while the model initialized with random weights trailed behind at 0.5420. These findings indicate that while ImageNet-pretrained models outperformed VICReg-pretrained models, the latter still captured essential data characteristics, suggesting the potential of self-supervised learning in diverse visual domains. The research attempts to contribute to advancing self-supervised learning in non-natural scenes, provides insights into model pretraining strategies, and introduces novel non-linear data augmentation techniques.
Abnormal behaviours are common in captive animals, and despite a lot of research, the development, maintenance and alleviation of these behaviours are not fully understood. Here, we suggest that ...conditioned reinforcement can induce sequential dependencies in behaviour that are difficult to infer from direct observation. We develop this hypothesis using recent models of associative learning that include conditioned reinforcement and inborn facets of behaviour, such as predisposed responses and motivational systems. We explore three scenarios in which abnormal behaviour emerges from a combination of associative learning and a mismatch between the captive environment and inborn predispositions. The first model considers how abnormal behaviours, such as locomotor stereotypies, may arise from certain spatial locations acquiring conditioned reinforcement value. The second model shows that conditioned reinforcement can give rise to abnormal behaviour in response to stimuli that regularly precede food or other reinforcers. The third model shows that abnormal behaviour can result from motivational systems being adapted to natural environments that have different temporal structures than the captive environment. We conclude that models including conditioned reinforcement offer an important theoretical insight regarding the complex relationships between captive environments, inborn predispositions, and learning. In the future, this general framework could allow us to further understand and possibly alleviate abnormal behaviours.
•Associative learning models help us understand abnormal behaviour in captive animals.•Conditioned reinforcement causes sequential dependencies in behaviour.•Combined with inborn predispositions and motivation this can cause abnormal behaviour.•These processes explain both the acquisition and persistence of abnormal behaviour.
Markov Chains on Graded Posets Eriksson, Kimmo; Jonsson, Markus; Sjöstrand, Jonas
Order,
01/2018, Letnik:
35, Številka:
1
Journal Article
Recenzirano
Odprti dostop
We consider two types of discrete-time Markov chains where the state space is a graded poset and the transitions are taken along the covering relations in the poset. The first type of Markov chain ...goes only in one direction, either up or down in the poset (an up chain or down chain). The second type toggles between two adjacent rank levels (an up-and-down chain). We introduce two compatibility concepts between the up-directed transition probabilities (an up rule) and the down-directed (a down rule), and we relate these to compatibility between up-and-down chains. This framework is used to prove a conjecture about a limit shape for a process on Young’s lattice. Finally, we settle the questions whether the reverse of an up chain is a down chain for some down rule and whether there exists an up or down chain at all if the rank function is not bounded.
Markov Chains on Graded Posets Eriksson, Kimmo; Jonsson, Markus; Sjöstrand, Jonas
Order (Dordrecht),
03/2018, Letnik:
35, Številka:
1
Journal Article
Recenzirano
Odprti dostop
We consider two types of discrete-time Markov chains where the state space is a graded poset and the transitions are taken along the covering relations in the poset. The first type of Markov chain ...goes only in one direction, either up or down in the poset (an
up chain
or
down chain
). The second type toggles between two adjacent rank levels (an
up-and-down chain
). We introduce two compatibility concepts between the up-directed transition probabilities (an
up rule
) and the down-directed (a
down rule
), and we relate these to compatibility between up-and-down chains. This framework is used to prove a conjecture about a limit shape for a process on Young’s lattice. Finally, we settle the questions whether the reverse of an up chain is a down chain for some down rule and whether there exists an up or down chain at all if the rank function is not bounded.
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