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Optimization of cell formation during lithium -ion battery (LIB) production is needed to reduce time and cost. Operando gas analysis can provide unique insights into the nature, extent, and duration of the formation process. Herein we present the development and application of an Online Electrochemical Mass Spectrometry (OEMS) design capable of monitoring gas evolution and consumption in both model coin-cells (Q = 0.72 mAh) with a graphite/electrolyte weight-ratio of 1:12.5 and large-format Li -ion cells (Q = 72 Ah) with a graphite/electrolyte weight -ratio of 1:0.63 during operation. Although the composition and amounts of gas are highly comparable, even when validated against ex -situ analysis, the gas release rate is lower from the larger cell size and likely limited by gas bubble transport through the electrode stack of the cell during formation. Higher temperatures accelerate the formation process, but also alters the composition and extent of gas released. Apart from providing novel insights into the formation processes of large -format Li-ion cells, our OEMS setup offers an opportunity for the battery manufacturing and automotive industry to explore the impact of battery formation and/or operating conditions on gas evolution in next -generation Li-ion batteries of any size.

STI sind ein weltweites Gesundheitsproblem. Die STI-Lebenszeitprävalenz lag bei den 468 befragten Studierenden (Durchschnittsalter 24,9 Jahre) bei 15,6% (Studentinnen: 14,7%, Studenten: 17,1%). Die häufigsten STI waren Chlamydien und HPV. Mit STI assoziierte Parameter der Studentinnen waren: insges. ≥10 Sexualpartner*innen, Alter >25 Jahre, One-Night-Stand in der Vorgeschichte. Mit STI assoziierte Parameter der Studenten waren: insges. ≥10 Sexualpartner*innen, Männer als sexuell bevorzugtes Geschlecht, jemals Geschlechtsverkehr mit mehr als einer Person zur selben Zeit., STIs remain a global health problem. Lifetime STI prevalence among the 468 students surveyed (mean age 24.9 years) was 15.6% (female: 14.7%, male: 17.1%). The most frequent STIs were chlamydia and HPV. Parameters associated with STIs in female students were: total ≥10 sexual partners, age >25, history of one-night stand. Parameters associated with STIs in male students were: total ≥10 sexual partners, men as sexually preferred gender, ever having sexual intercourse with more than one person at the same time.

Human learning is sensitive to rule-like structure and the curriculum of examples used for training. In tasks governed by succinct rules, learning is more robust when related examples are blocked across trials, but in the absence of such rules, interleaving is more effective. To date, no neural model has simultaneously captured these seemingly contradictory effects. Here we show that this same tradeoff spontaneously emerges with ``in-context learning'' (ICL) both in neural networks trained with metalearning and in large language models (LLMs). ICL is the ability to learn new tasks ``in context'' -- without weight changes -- via an inner-loop algorithm implemented in activation dynamics. Experiments with pretrained LLMs and metalearning transformers show that ICL exhibits the blocking advantage demonstrated in humans on a task involving rule-like structure, and conversely, that concurrent in-weight learning reproduces the interleaving advantage observed in humans on tasks lacking such structure., Comment: 7 pages, 4 figures, accepted as a talk + full paper at CogSci 2024

Models based on the Transformer neural network architecture have seen success on a wide variety of tasks that appear to require complex "cognitive branching" -- or the ability to maintain pursuit of one goal while accomplishing others. In cognitive neuroscience, success on such tasks is thought to rely on sophisticated frontostriatal mechanisms for selective \textit{gating}, which enable role-addressable updating -- and later readout -- of information to and from distinct "addresses" of memory, in the form of clusters of neurons. However, Transformer models have no such mechanisms intentionally built-in. It is thus an open question how Transformers solve such tasks, and whether the mechanisms that emerge to help them to do so bear any resemblance to the gating mechanisms in the human brain. In this work, we analyze the mechanisms that emerge within a vanilla attention-only Transformer trained on a simple sequence modeling task inspired by a task explicitly designed to study working memory gating in computational cognitive neuroscience. We find that, as a result of training, the self-attention mechanism within the Transformer specializes in a way that mirrors the input and output gating mechanisms which were explicitly incorporated into earlier, more biologically-inspired architectures. These results suggest opportunities for future research on computational similarities between modern AI architectures and models of the human brain., Comment: 8 pages, 4 figures

Developmental psychologists have argued about when cognitive capacities such as language understanding or theory of mind emerge. These debates often hinge on the concept of "task demands" -- the auxiliary challenges associated with performing a particular evaluation -- that may mask the child's underlying ability. The same issues arise when measuring the capacities of language models (LMs): performance on a task is a function of the model's underlying competence, combined with the model's ability to interpret and perform the task given its available resources. Here, we show that for analogical reasoning, reflective reasoning, word prediction, and grammaticality judgments, evaluation methods with greater task demands yield lower performance than evaluations with reduced demands. This "demand gap" is most pronounced for models with fewer parameters and less training data. Our results illustrate that LM performance should not be interpreted as a direct indication of intelligence (or lack thereof), but as a reflection of capacities seen through the lens of researchers' design choices.

Human learning and decision making is supported by multiple systems operating in parallel. Recent studies isolating the contributions of reinforcement learning (RL) and working memory (WM) have revealed a trade-off between the two. An interactive WM-RL computational model predicts that while high WM load slows behavioral acquisition, it also induces larger prediction errors in the RL system that enhance robustness and retention of learned behaviors. Here we tested this account by parametrically manipulating WM load during RL in conjunction with EEG, in both male and female participants, and administered two surprise memory tests. We further leveraged single trial decoding of EEG signatures of RL and WM to determine whether their interaction predicted robust retention. Consistent with the model, behavioral learning was slower for associations acquired under higher load but showed parametrically improved future retention. This paradoxical result was mirrored by EEG indices of RL, which were strengthened under higher WM loads and predictive of more robust future behavioral retention of learned stimulus-response contingencies. We further tested whether stress alters the ability to shift between the two systems strategically to maximize immediate learning versus retention of information and found that induced stress had only a limited effect on this trade-off. The present results offer a deeper understanding of the cooperative interaction between WM and RL and show that relying on WM can benefit the rapid acquisition of choice behavior during learning but impairs retention.SIGNIFICANCE STATEMENT:Successful learning is achieved by the joint contribution of the dopaminergic reinforcement learning (RL) system and working memory (WM). The cooperative WMRL model was productive in improving our understanding of the interplay between the two systems during learning, demonstrating that reliance on RL computations is modulated by WM load. However, the role of WM/RL systems in the

Young children acquire a wide range of linguistic and cognitive skills in the first three years of life. Decades of experimental work have established a solid empirical foundation for our understanding of cognitive development. But most experimental studies are limited in statistical power and focus on specific psychological constructs, thus making them unsuitable for describing developmental growth at scale. Here, we turned to meta-analyses of experimental research. We conducted a meta-meta-analysis to consolidate and integrate 23 meta-analyses compiled on MetaLab, a community-augmented meta-analysis platform. We found that most datasets can not meaningfully distinguish different functional forms for developmental change, but in those that could, there is great diversity in the best-fitting functional forms of the age model. We also evaluated the impact of a range of methodological factors. Overall, our work sheds light on the heterogeneous nature of developmental trajectories and the subtle interactions between research methods and experimental outcomes.

Is “cow” more closely related to “grass” or to “chicken”? Speakers of different languages judge similarity in this context differently, but why? One possibility is that cultures covarying with these languages induce differences in conceptualizations of similarity. Specifically, East Asian cultures may promote reasoning about thematic similarity, by which cow and grass are more related, whereasWestern cultures may bias judgments toward taxonomic relations, like cow and chicken. We measure similarity judgments across the US, China, and Vietnam and replicate US-China differences, but do not find that responding in Vietnam patterns with China. Instead, similarity judgments in Vietnam are intermediate between the US and China. We also show that word embedding models (fastText models for each language) are related to judgments within each country, suggesting a possible alternative interpretation of cross cultural differences. Perhaps notions of similarity are similar across contexts, but the statistics of the linguistic environment vary.

From birth, humans make decisions about what to look at and for how long. A classic framework proposes encoding as a key driver of looking behavior in development - in early stages of encoding, infants and young children prefer to engage with familiar stimuli, while at later stages of encoding they prefer novel stimuli. Though this framework is often invoked when interpreting looking time studies, it is rarely validated empirically. Here, we test these predictions by explicitly manipulating exposure durations within-subjects. While we found robust evidence for habituation and novelty preferences, limiting exposure to visual concepts did not result in familiarity preferences in any age group. Our findings suggest that limited exposure does not generically lead to familiarity preferences, and that interpretations of observed familiarity preferences should be made with care. We argue for the development of formal frameworks which link the learning problem faced by participants to their attentional preferences.

Early abstract reasoning follows qualitatively different developmental trajectories in the US and China (Carstensen et al., 2019), but the causal mechanisms for these differences are unknown. Existing accounts implicate several potential factors that differ between the US and China, including language (Hoyos et al., 2016), executive function (Richland et al., 2010), visual attention (Christie et al., 2020), and social reasoning (Jurkat et al., 2022). While there is extensive work documenting both language and executive function in US and Chinese children, much less is known about the development of cross-cultural differences in visual attention and social reasoning. We document abstract reasoning about relations (Ambiguous cRMTS, Carstensen et al., 2019) alongside the potential moderating factors of visual attention (Free Description; Imada et al., 2013), and social reasoning (Causal Attribution, Seiver et al., 2013; Uniqueness Preference, Kim & Markus, 1999) in a cross-sectional sample of 240 3-12-year-olds, and observe both similarities and differences.

In many cultures, adults use simple, slow, and dynamic speech when talking to infants ("parentese," or infant-directed speech) and make expansive, repetitive movements when demonstrating object properties to infants ("motionese," or infant-directed actions). These modifications enhance infants’ attention to and learning about language and goal-directed actions. Adults’ interactions with infants are also full of emotions—do adults also modify their emotional expressions when interacting with infants? Here we showed parents of infants (aged 7 to 14 months; N = 25) emotion-evoking pictures including colorful bubbles, adorable stuffed animals, yummy snacks, broken toys, dangerous fire, and rotten fruits. We asked parents to describe their feelings about these pictures either to their infant or to an adult partner (i.e., an experimenter). While the parents’ use of emotion words did not differ between conditions, their emotional expressions did: Their infant-directed emotional expressions were more positive when they discussed positive pictures and more negative when they discussed negative pictures compared to their adult-directed emotional expressions. These findings suggest that besides "parentese" and "motionese," there is also a unique form of emotional communication in parent-child interaction—"emotionese."

The ability to form novel conventions is a key signature of efficient linguistic communication. Reduction of referring expressions, one measure of convention formation, is found robustly in dyadic repeated reference games when the target images are initially difficult to name. In reference games, participants share the explicit goal of establishing joint reference. However, establishing reference is a key subgoal of many conversations where interlocuters have more complex goals. In the current work, we analyze a dataset where reference was embedded in strategic 3- player negotiation and coordination games. In these more complex games, we found that the patterns of reduction and convergence to conventions still held across two different incentive conditions, with some modest differences between the conditions.

Early language skill is predictive of later life outcomes, and is thus of great interest to developmental psychologists and clinicians. The Communicative Development Inventories (CDIs), parent-reported inventories of early-learned vocabulary items, have proven to be valid and reliable instruments for measuring children's early language skill. CDIs have been painstakingly adapted to dozens of languages, and cross-linguistic comparisons thus far show both consistency and variability in language acquisition trajectories. However, thousands of languages do not yet have CDIs, posing a significant barrier to increasing the diversity of languages that are studied. Here, we propose a method for selecting candidate words to include on new CDIs, leveraging analysis of psychometric properties of translation-equivalent concepts that are frequently included on existing CDIs. Leveraging 26 datasets from existing CDIs, we propose a list of 229 concepts that have low variability in their cross-linguistic learning difficulty. This pool of common concepts---analogous to the "Swadesh" lists used in glottochronology---can be used as a starting point for future CDI adaptations. We test how well the proposed list generalizes to data from 8 additional languages.

Despite the unpredictable and ubiquitous nature of noise in the natural acoustic environment, most children still manage to ex- tract the linguistic, cognitive, and social information needed to engage with their world. This is no small feat. We examined what strategies children use to navigate different acoustic environments. One possibility we test is that children can select acoustic contexts that are consistent with particular goals. In Experiment 1, we presented preschool children with a set of auditory stimuli, meant to approximate various acoustic environments, and activity goals to complete within those environments. Children integrated auditory information with goals to select the best environment. To assess the flexibility of children’s decision-making, Experiment 2 built on this framework by replacing familiar activity goals with relatively less familiar ones. In preliminary data, adults and preschoolers reliably evaluated acoustic environments that best matched these less familiar activities, providing evidence for flexible reasoning about goal-consistent environments.

Dynamic Task Scheduling is an enticing programming model aiming to ease the development of parallel programs with intrinsically irregular or data-dependent parallelism. The performance of such solutions relies on the ability of the Task Scheduling HW/SW stack to efficiently evaluate dependencies at runtime and schedule work to available cores. Traditional SW-only systems implicate scheduling overheads of around 30K processor cycles per task, which severely limit the ( core count , task granularity ) combinations that they might adequately handle. Previous work on HW-accelerated Task Scheduling has shown that such systems might support high performance scheduling on processors with up to eight cores, but questions remained regarding the viability of such solutions to support the greater number of cores now frequently found in high-end SMP systems. The present work presents an FPGA-proven, tightly-integrated, Linux-capable, 30-core RISC-V system with hardware accelerated Task Scheduling. We use this implementation to show that HW Task Scheduling can still offer competitive performance at such high core count, and describe how this organization includes hardware and software optimizations that make it even more scalable than previous solutions. Finally, we outline ways in which this architecture could be augmented to overcome inter-core communication bottlenecks, mitigating the cache-degradation effects usually involved in the parallelization of highly optimized serial code., This work is supported by the TEXTAROSSA project G.A. n.956831, as part of the EuroHPC initiative, by the Spanish Government (grants PCI2021-121964, TEXTAROSSA; PDC2022-133323-I00, Multi-Ka; PID2019-107255GB-C21 MCIN/AEI/10.13039/501100011033; and CEX2021-001148-S), by Generalitat de Catalunya (2021 SGR 01007), and FAPESP (grant 2019/26702-8)., Peer Reviewed, Postprint (published version)

At the close of eighteenth century, we can observe a complex overlap between political philosophy, poetics, and representations of Jacobin state terrorism. When Maximilien Robespierre described terror as a means of achieving virtue, he seemed to echo contemporary theories of tragedy and their notion of "purifying" terror. Conversely, the Reign ofTerror had a significant impact on post-Revolutionary literature: early commentaries on Gothic fiction describe it as an offspring of the French Revolution. According to the American philosopher Robert Solomon, all emotions are by nature political because they occur in a social and interactive context, where they are purposively deployed "to move other people." Solomon adds that "[m]any emotions are about power, persuasion, manipulation, and intimidation." Eighteenth-century discussions of "terror" are very much aware of the political nature of fear. They emphasize the power of pathos to not only move the viewer, but to also steer them in a particular direction, namely towards virtue. This chapter traces the politics of terror from the theory of tragedy through philosophical discussions of tyranny to the Jacobins' justification of state terrorism to demonstrate that in each case, the evocation of terror was conceptualized as a means of deterrence. With the rise of the discourse of sublimity, terror became an end in itself. As such, it no Ionger served a didactic or disciplinary purpose. Significantly, however, "terror" continued to resonate with the political notion of "terror(ism)," as is evidenced by the critical debate surrounding Gothic fiction during the 1790s.

Magnetic skyrmions are topological quasiparticles whose non-volatility, detectability, and mobility make them exciting candidates for low-energy computing. Previous works have demonstrated the feasibility and efficiency of current-driven skyrmions in cascaded logic structures inspired by reversible computing. As skyrmions can be propelled through the voltage-controlled magnetic anisotropy (VCMA) effect with much greater efficiency, this work proposes a VCMA-based skyrmion propagation mechanism that drastically reduces energy dissipation. Additionally, we demonstrate the functionality of skyrmion logic gates enabled by our novel voltage-based propagation and estimate its energy efficiency relative to other logic schemes. The minimum dissipation of this VCMA-driven magnetic skyrmion logic at 0 K is found to be $\sim$6$\times$ the room-temperature Landauer limit, indicating the potential for sub-Landauer dissipation through further engineering., Comment: 4 pages, 6 figures

Although code generation for Convolution Neural Network (CNN) models has been extensively studied, performing efficient data slicing and parallelization for highly-constrai\-ned Multicore Neural Processor Units (NPUs) is still a challenging problem. Given the size of convolutions' input/output tensors and the small footprint of NPU on-chip memories, minimizing memory transactions while maximizing parallelism and MAC utilization are central to any effective solution. This paper proposes a TensorFlow XLA/LLVM compiler optimization pass for Multicore NPUs, called Tensor Slicing Optimization (TSO), which: (a) maximizes convolution parallelism and memory usage across NPU cores; and (b) reduces data transfers between host and NPU on-chip memories by using DRAM memory burst time estimates to guide tensor slicing. To evaluate the proposed approach, a set of experiments was performed using the NeuroMorphic Processor (NMP), a multicore NPU containing 32 RISC-V cores extended with novel CNN instructions. Experimental results show that TSO is capable of identifying the best tensor slicing that minimizes execution time for a set of CNN models. Speed-ups of up to 21.7\% result when comparing the TSO burst-based technique to a no-burst data slicing approach. To validate the generality of the TSO approach, the algorithm was also ported to the Glow Machine Learning framework. The performance of the models were measured on both Glow and TensorFlow XLA/LLVM compilers, revealing similar results.

With the advent of interconnected and sensor-equipped edge devices, Federated Learning (FL) has gained significant attention, enabling decentralized learning while maintaining data privacy. However, FL faces two challenges in real-world tasks: expensive data labeling and domain shift between source and target samples. In this paper, we introduce a privacy-preserving, resource-efficient FL concept for client adaptation in hardware-constrained environments. Our approach includes server model pre-training on source data and subsequent fine-tuning on target data via low-end clients. The local client adaptation process is streamlined by probabilistic mixing of instance-level feature statistics approximated from source and target domain data. The adapted parameters are transferred back to the central server and globally aggregated. Preliminary results indicate that our method reduces computational and transmission costs while maintaining competitive performance on downstream tasks., Comment: Accepted at the Adapting to Change: Reliable Multimodal Learning Across Domains Workshop @ ECML PKKD 2023

Recent breakthroughs by deep neural networks (DNNs) in natural language processing (NLP) and computer vision have been driven by a scale-up of models and data rather than the discovery of novel computing paradigms. Here, we investigate if scale can have a similar impact for models designed to aid small molecule drug discovery. We address this question through a large-scale and systematic analysis of how DNN size, data diet, and learning routines interact to impact accuracy on our Phenotypic Chemistry Arena (Pheno-CA) benchmark: a diverse set of drug development tasks posed on image-based high content screening data. Surprisingly, we find that DNNs explicitly supervised to solve tasks in the Pheno-CA do not continuously improve as their data and model size is scaled-up. To address this issue, we introduce a novel precursor task, the Inverse Biological Process (IBP), which is designed to resemble the causal objective functions that have proven successful for NLP. We indeed find that DNNs first trained with IBP then probed for performance on the Pheno-CA significantly outperform task-supervised DNNs. More importantly, the performance of these IBP-trained DNNs monotonically improves with data and model scale. Our findings reveal that the DNN ingredients needed to accurately solve small molecule drug development tasks are already in our hands, and project how much more experimental data is needed to achieve any desired level of improvement. We release our Pheno-CA benchmark and code to encourage further study of neural scaling laws for small molecule drug discovery.

Humans learn by interacting with their environments and perceiving the outcomes of their actions. A landmark in artificial intelligence has been the development of deep reinforcement learning (dRL) algorithms capable of doing the same in video games, on par with or better than humans. However, it remains unclear whether the successes of dRL models reflect advances in visual representation learning, the effectiveness of reinforcement learning algorithms at discovering better policies, or both. To address this question, we introduce the Learning Challenge Diagnosticator (LCD), a tool that separately measures the perceptual and reinforcement learning demands of a task. We use LCD to discover a novel taxonomy of challenges in the Procgen benchmark, and demonstrate that these predictions are both highly reliable and can instruct algorithmic development. More broadly, the LCD reveals multiple failure cases that can occur when optimizing dRL algorithms over entire video game benchmarks like Procgen, and provides a pathway towards more efficient progress.

Interpreting a seemingly-simple function word like "or", "behind", or "more" can require logical, numerical, and relational reasoning. How are such words learned by children? Prior acquisition theories have often relied on positing a foundation of innate knowledge. Yet recent neural-network based visual question answering models apparently can learn to use function words as part of answering questions about complex visual scenes. In this paper, we study what these models learn about function words, in the hope of better understanding how the meanings of these words can be learnt by both models and children. We show that recurrent models trained on visually grounded language learn gradient semantics for function words requiring spatial and numerical reasoning. Furthermore, we find that these models can learn the meanings of logical connectives and and or without any prior knowledge of logical reasoning, as well as early evidence that they are sensitive to alternative expressions when interpreting language. Finally, we show that word learning difficulty is dependent on frequency in models' input. Our findings offer proof-of-concept evidence that it is possible to learn the nuanced interpretations of function words in visually grounded context by using non-symbolic general statistical learning algorithms, without any prior knowledge of linguistic meaning., Comment: Published in Cognitive Science 2024

El presente trabajo tiene como finalidad analizar desde el enfoque criminológico la otro pandemia que no tiene la suficiente atención y preocupación por parte del estado y la ciudadanía, la violencia de la libertad sexual contra menores, en nuestro contexto de la pandemia por el virus Covid-19 para lo cual acudimos a fuentes estadísticas, conceptuales, doctrinales ,además de un análisis criminológico crítico, lo que nos permitió primero contextualizar cual era la situación en nuestro país  antes de la pandemia y durante la pandemia, analizar el actuar de nuestro Estado a través de instituciones como el Ministerio de la Mujer y Poblaciones Vulnerables , programas como el centro de ayuda mujer y  las ahora implementados instrumentos línea 100 y 1801 , para así poder concluir cuales son las teorías criminológicas de la conducta criminal que establece los factores criminológicos por los que se presentaba este acto delictivo y por los que se incrementó durante el confinamiento.

Psychological scientists have become increasingly concerned with issues related to methodology and replicability, and infancy researchers in particular face specific challenges related to replicability: For example, high-powered studies are difficult to conduct, testing conditions vary across labs, and different labs have access to different infant populations. Addressing these concerns, we report on a large-scale, multisite study aimed at (a) assessing the overall replicability of a single theoretically important phenomenon and (b) examining methodological, cultural, and developmental moderators. We focus on infants’ preference for infant-directed speech (IDS) over adult-directed speech (ADS). Stimuli of mothers speaking to their infants and to an adult in North American English were created using seminaturalistic laboratory-based audio recordings. Infants’ relative preference for IDS and ADS was assessed across 67 laboratories in North America, Europe, Australia, and Asia using the three common methods for measuring infants’ discrimination (head-turn preference, central fixation, and eye tracking). The overall meta-analytic effect size (Cohen’s d) was 0.35, 95% confidence interval = [0.29, 0.42], which was reliably above zero but smaller than the meta-analytic mean computed from previous literature (0.67). The IDS preference was significantly stronger in older children, in those children for whom the stimuli matched their native language and dialect, and in data from labs using the head-turn preference procedure. Together, these findings replicate the IDS preference but suggest that its magnitude is modulated by development, native-language experience, and testing procedure.

Published on 06 Feb 2022, The COVID-19 pandemic, and the resulting closure of daycare centers worldwide, led to unprecedented changes in children’s learning environments. This period of increased time at home with caregivers, with limited access to external sources (e.g., daycares) provides a unique opportunity to examine the associations between the caregiver-child activities and children’s language development. The vocabularies of 1742 children aged 8-36 months across 13 countries and 12 languages were evaluated at the beginning and end of the first lockdown period in their respective countries (from March to September 2020). Children who had less passive screen exposure and whose caregivers read more to them showed larger gains in vocabulary development during lockdown, after controlling for SES and other caregiver-child activities. Children also gained more words than expected (based on normative data) during lockdown; either caregivers were more aware of their child’s development, or vocabulary development benefited from intense caregiver-child interaction during lockdown, or both. We discuss these results in the context of the extraordinary circumstances of the COVID-19 pandemic and highlight limitations of the study.

Prior work suggests children understand how speech conveys information and influences others’ minds. Although these studies have focused on communication under ideal conditions, auditory noise plagues the real world, often corrupting the transmission of information. The current study examines how children reason about the impact of auditory noise on communication. Children (N=72, Age:3;0-5;11) watched scenarios where a teacher tells a learner about two toys, but loud auditory noise masks one of the explanations. When asked which toy the learner wants to hear about again, children were more likely to select the noise-masked toy when the learner knew about neither toy (No Knowledge) than when he already knew about the masked toy (Partial Knowledge). However, their preference for the masked toy also increased with age in both conditions. Overall, these results demonstrate children's developing understanding of when and how communication affects listeners' knowledge and information-seeking behaviors.

From classrooms to dinner parties, many of our everyday conversations take place in larger groups where speakers address multiple listeners at once. Such multiparty settings raise a number of challenges for classical theories of communication, which largely focus on dyadic interactions. In this study, we investigated how speakers adapt their referring expressions over time as a function of the feedback they receive from multiple parties. We collected a large corpus of multiparty repeated reference games (98 games, 390 participants, 116K words) where speakers designed referring expressions for groups of 1 to 5 listeners. Larger groups tended to use more words total and to introduce more new words; nonetheless, most groups were able to converge to more efficient conventions regardless of the number of listeners.

Children's early language skill has been linked to later educational outcomes, making it important to measure early language accurately. Parent-reported instruments such as the Communicative Development Inventories (CDIs) have been shown to provide reliable and valid measures of children's aggregate early language skill. However, CDIs contain hundreds of vocabulary items, some of which may not be heard (and thus learned) equally often by children of varying backgrounds. This study used a database of American English CDIs to identify words demonstrating strong bias for particular demographic groups of children, on dimensions of sex (male vs. female), race (white vs. non-white), and maternal education (high vs. low). For each dimension, we identified dozens of strongly biased items, and showed that eliminating even some of these items can reduce the magnitude of differences between groups. Additionally, we investigated how well the relative frequency of words spoken to young girls vs. boys predicted sex-based word learning bias, and discuss possible sources of demographic differences in early word learning.

In learning about and building models of their social and physical worlds, children exhibit a wide range of complex, spontaneous, intrinsically motivated, curiosity-driven behaviors. To understand how curiosity-driven behavior in social environments differs in diverse learners, we experimentally measure attention allocation in children on the autism spectrum and their typically-developing peers. We designed an augmented reality smartphone activity where children freely explore novel stimuli. Some agents behave “socially” (i.e., animate), while others have more regular (i.e., periodic, static) or irregular (i.e., random) inanimate behavioral patterns. This project is part of a broader program in which we attempt to model curiosity with artificial intelligence. Our augmented reality systems will lead to large and diverse data acquisition, allowing us to model a comprehensive range of learning behaviors and enable more inclusive, personalized pedagogical and diagnostic tools.

Children are navigating a world with massive amounts of auditory input, sometimes relevant while other times purely noise, and must somehow make sense of it all. The early auditory environment is critical for speech perception and recognition, auditory discrimination, and word learning, all of which support language outcomes. What strategies do children use to learn in noisy environments? One potential strategy is environmental selection, which allows children to seek environments that align with particular goals. In the current paper, we examined whether children and adults make decisions about their environments by integrating auditory information and goal-states. While 3- and 4-year olds struggle with discriminating the level of noise in noisy speech streams (and likely do not use this information for environmental selection), 5-year-old children and adults can. Further, we show initial evidence that they can use this information to reason about acoustic environments that are consistent with specific goals.

Is a cow more closely related to grass or to a chicken? Responses vary by culture and age, among other factors. Those from western societies (or independent-leaning regions within interdependent non-western societies) are more likely to endorse the taxonomic match, the chicken, over the thematic match, grass (Chiu, 1972; Talhelm et al., 2014). This preference has been documented -- largely in western cultures -- to increase over development (e.g., Smiley & Brown, 1979). While neither development nor culture occur independently of the other, comparisons across these areas are problematic. We address one potential barrier to comparing cultural and developmental research using this classic paradigm -- stimulus format -- and show that the use of text (versus image) stimuli can bias participants toward taxonomic responding in some contexts. We present stimuli designed for cross-cultural use with children and adults and document country, regional, and demographic variation across the US and Italy.

Children’s early word learning is to a large extent driven by the prevalence of words in their language environment, with words that are spoken more often to children being learned earlier. However, children receive language from a variety of sources, including books, television, and movies meant for children, as well as speech and media that is meant for adults, but over- heard by children. Despite considerable similarity of word frequency distributions from these different input sources, there is also significant and predictable variability between them. For example, function words are far more frequent in books than in everyday speech, while early-learned nouns (e.g., ‘ball’ and ‘mommy’) are more frequent in child-directed speech than in other sources. Children receive a mixture of these different frequency distributions. The goal of this paper is to better understand the shared and unique variance in these input sources – in both English and French – and to evaluate how predictive these distributions are of children’s early word learning.

Previous studies report that children acquire emotion words gradually during ages 3--5 and beyond (e.g., Widen, 2013). Most of this work, however, has used tasks that are demanding for young children (e.g., asking children to produce emotion labels in a free-labeling task), and has asked children to map emotion labels to facial configurations alone. In our study, we tested children's ability to comprehend, rather than produce, emotion words, and used not only facial configurations but also body language. In two pre-registered online experiments, two to four-year-old children (N = 96) were asked to connect emotion words, including happy, sad, angry, and scared, to either facial configurations (Experiment 1) or to combined facial configurations and body postures (Experiment 2). While we found an overall pattern consistent with prior work (i.e., better performance when distractors were from opposite valences than when they were of the same valence), we also found much earlier competence in understanding same-valence emotions. Even 2-year-olds succeeded in differentiating the three negative emotion words and connecting them to facial cues (Experiment 1). Experiment 2 replicated this pattern and further showed that children performed equally well (but not substantially better) given additional body cues. These results suggest that before children can produce emotion words in an adult-like manner, they have some systematic understanding of those words and can map them to emotion cues within valence domains. (Preprint: psyarxiv.com/ka3ed)

From birth, humans constantly make decisions about what to look at and for how long. Yet the mechanism behind such decision-making remains poorly understood. Here we present the rational action, noisy choice for habituation (RANCH) model. RANCH is a rational learning model that takes noisy perceptual samples from stimuli and makes sampling decisions based on Expected Information Gain (EIG). The model captures key patterns of looking time documented in developmental research: habituation and dishabituation. We evaluated the model with adult looking time collected from a paradigm analogous to the infant habituation paradigm. We compared RANCH with baseline models (no learning model, no perceptual noise model) and models with alternative linking hypotheses (Surprisal, KL divergence). We showed that 1) learning and perceptual noise are critical assumptions of the model, and 2) Surprisal and KL are good proxies for EIG under the current learning context.

Yarkoni's analysis clearly articulates a number of concerns limiting the generalizability and explanatory power of psychological findings, many of which are compounded in infancy research. ManyBabies addresses these concerns via a radically collaborative, large-scale and open approach to research that is grounded in theory-building, committed to diversification, and focused on understanding sources of variation.

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In figure 1 the schematic approach of this thesis is summarized. The outcome can be categorized in four sections with similar extent. In the first section, "Service", the digital twin concept is elaborated on the basis of investigated requirements which are defined via the identification of the system stakeholder for the realization of MoL applications. The focus on service provision is enabled via the system-protagonist interaction analysis which is represented by the aggregation of use cases. The development procedure is accompanied by SysML diagrams which are part of the section "Representation". For the modeling of the interactions between the protagonists and the digital twin, sequence diagrams are created. The static system structure is represented by block diagrams which are generated in the software "Enterprise Architect". The third section, "Analysis", includes the related gearbox investigation which is summarized in a simulationmodel that is comprising besides a diagnostics part with load-based (damage accumulation) and vibration-based (IAS signal analysis) approaches, a prognostics part, which is presenting a similarity and degradation estimation. The data input to the simulationmodel is established by HCP-Sense bearings and magnetoresistive sensors which are applied for monitoring the gearbox components, namely the bearings and the gear wheels with their toothing. Based on the data input from the sensors the subsequent information transmission within the system structure is described in the fourth section, "Communication". In this section, interfaces which are categorized by OSI-layers and the specification of objects, transferred between the system elements, are defined. The modularity of the system structure is achieved by the definition of adaption nodes.

Demonstratoren sind äußerst hilfreiche Werkzeuge, welche der Veranschaulichung neuer Technologien dienen. Besonders im Kontext von Living Labs, in denen es um Bildung, Forschung, gemeinsame Entwicklung und Experimente geht, können sie von großer Bedeutung sein. Sie unterliegen in ihrer Umsetzung besonderen Anforderungen, die sich stark von denen an Produkte für den Markt unterscheiden. Diese Arbeit stellt das Konzept und die Umsetzung eines Smart Mirror Demonstrators vor, der für das Living Lab des KI-Kompetenzzentrums ScaDS.AI in Leipzig entwickelt wurde. Der Demonstrator veranschaulicht einen Anwendungsfall von Objekt- und Ähnlichkeitserkennung, die auf künstlichen neuronalen Netzen basieren. Er besteht aus einem Smart Mirror, der Kleidungsstücke erkennen und visuell ähnliche Kleidungsstücke vorschlagen kann. Ein Nutzer kann mit dem Demonstrator ein Foto von sich aufnehmen, auf dem die erkannten Kleidungsstücke mit sogenannten Bounding Boxes markiert werden. Anschließend werden visuell ähnliche Kleidungsstücke aus einer Datenbank herausgefiltert und angezeigt. In der Arbeit geht es zum einen um die verwendeten Technologien, als auch um die Architektur des Demonstrators selbst. Die Softwarearchitektur des Demonstrators orientiert sich an dem Aufbau von Webanwendungen und besteht aus einem Backend und einem Frontend. Das Backend wurde als REST-API umgesetzt, die in Python unter Verwendung des Frameworks Flask geschrieben wurde. Das Frontend besteht aus einer Single Page Application, umgesetzt mit dem Javascript Framework Vue. Beide Anwendungen wurden mit Hilfe der Software Docker in Containern umgesetzt, um die Laufzeitumgebung zu virtualisieren und die Ausführung auf verschiedenen Rechnern zu erleichtern. Der Technologie-Stack bestehend aus Flask, Vue und Docker und die Umsetzung unter Beachtung der REST Prinzipien sind positiv zu bewerten und wurden im ScaDS.AI in Anlehnung an den be- schriebenen Demonstrator zum Standard für die Entwicklung weiterer Demonstratore

BACKGROUND: Psychiatric diagnosis and treatment have historically taken a symptom-based approach, with less attention on identifying underlying symptom-producing mechanisms. Recent efforts have illuminated the extent to which different underlying circuitry can produce phenotypically similar symptomatology (e.g., psychosis in bipolar disorder vs. schizophrenia). Computational modeling makes it possible to identify and mathematically differentiate behaviorally unobservable, specific reinforcement learning differences in patients with schizophrenia versus other disorders, likely owing to a higher reliance on prediction error-driven learning associated with basal ganglia and underreliance on explicit value representations associated with orbitofrontal cortex. METHODS: We used a well-established probabilistic reinforcement learning task to replicate those findings in individuals with schizophrenia both on (n = 120) and off (n = 44) antipsychotic medications and included a patient comparison group of bipolar patients with psychosis (n = 60) and healthy control subjects (n = 72). RESULTS: Using accuracy, there was a main effect of group (F3,279 = 7.87, p < .001), such that all patient groups were less accurate than control subjects. Using computationally derived parameters, both medicated and unmediated individuals with schizophrenia, but not patients with bipolar disorder, demonstrated a reduced mixing parameter (F3,295 = 13.91, p < .001), indicating less dependence on learning explicit value representations as well as greater learning decay between training and test (F1,289 = 12.81, p < .001). Unmedicated patients with schizophrenia also showed greater decision noise (F3,295 = 2.67, p = .04). CONCLUSIONS: Both medicated and unmedicated patients showed overreliance on prediction error-driven learning as well as significantly higher noise and value-related memory decay, compared with the healthy control subjects and the

Lucas Pope’s 2013 video game Papers, Please places the player-character in the position of an immigration inspector in a fictional Eastern Bloc nation. Since its release, the game has garnered enthusiastic responses, being celebrated by critics as an essential dramatization of the ethical dilemmas associated with the migratory flows and fraught border politics of the post-9/11 world. However, this essay argues that this reputation must be scrutinized: for while Papers, Please indeed illustrates some of the contradictions at the heart of contemporary border policy – such as immigrant-receiving states’ pragmatic application of exclusionary immigration practices that run contrary to their self-professed liberal-democratic ideals – it also engages in a ‘universalizing’ abstraction of the border space, which limits its feasibility as a lens through which to view 2010s border discourses. Drawing from current research in Border Studies, a close reading of Pope’s game illustrates how its geo-historical setting and its omission of crucial phenomena associated with the twenty-first-century border, such as the interactional processes of bordering and the existence of ‘bi-cultural’ borderscapes, ultimately render it a liberal-democratic fantasy, according to which the conflicts engendered by the enforced geopolitical border are abstract questions of morality and ethics rather than practical socio-political.

Dieser 2007 erschienene Roman wählt den Schauplatz New York, um die Auswirkungen der Terroranschläge des 11. September 2001 in Szene zu setzen. Es handelt sich um die Ich-Erzählung eines pakistanischen Einwanderers, was eine neue Sichtweise auf die Ereignisse ermöglicht, jenseits der amerikanischen Opferperspektive. Der kurze und präzise komponierte Roman besteht aus einer Rahmen- und einer Binnenerzählung. Letztere umfasst viereinhalb Jahre und handelt davon, wie der Erzähler Changez nach erfolgreichem Studium an der Elite-Universität Princeton in die Finanzelite Manhattans aufsteigt. Bei der renommierten Unternehmensberatung Underwood Samson lebt er zunächst den „amerikanischen Traum“. Wie die Initialen der Firma andeuten (U.S.), steht sie sinnbildlich für die...