Miracle-dz/UniFAR-FTUs huggingface dataset:
Annotating Situated Actions in Dialogue

Actions are critical for interpreting dialogue: they provide context for demonstratives and definite descriptions in discourse, and they continually update the common ground. This paper describes how Abstract Meaning Representation (AMR) can be used to annotate actions in multimodal human-human and human-object interactions. We conduct initial annotations of shared task and first-person point-of-view videos. We show that AMRs can be interpreted by a proxy language, such as VoxML, as executable annotation structures in order to recreate and simulate a series of annotated events.
Data-Driven Frame-Semantic Parsing with Tree Wrapping Grammar

We describe the first experimental results for data-driven semantic parsing with Tree Rewriting Grammars (TRGs) and semantic frames. While several theoretical papers previously discussed approaches for modeling frame semantics in the context of TRGs, this is the first data-driven implementation of such a parser. We experiment with Tree Wrapping Grammar (TWG), a grammar formalism closely related to Tree Adjoining Grammar (TAG), developed for formalizing the typologically inspired linguistic theory of Role and Reference Grammar (RRG). We use a transformer-based multi-task architecture to predict semantic supertags which are then decoded into RRG trees augmented with semantic feature structures. We present experiments for sentences in different genres for English data. We also discuss our compositional semantic analyses using TWG for several linguistic phenomena.
Critical assessment of methods of protein structure prediction (CASP)—Round XV

Computing protein structure from amino acid sequence information has been a long‐standing grand challenge. Critical assessment of structure prediction (CASP) conducts community experiments aimed at advancing solutions to this and related problems. Experiments are conducted every 2 years. The 2020 experiment (CASP14) saw major progress, with the second generation of deep learning methods delivering accuracy comparable with experiment for many single proteins. There is an expectation that these methods will have much wider application in computational structural biology. Here we summarize results from the most recent experiment, CASP15, in 2022, with an emphasis on new deep learning‐driven progress. Other papers in this special issue of proteins provide more detailed analysis. For single protein structures, the AlphaFold2 deep learning method is still superior to other approaches, but there are two points of note. First, although AlphaFold2 was the core of all the most successful methods, there was a wide variety of implementation and combination with other methods. Second, using the standard AlphaFold2 protocol and default parameters only produces the highest quality result for about two thirds of the targets, and more extensive sampling is required for the others. The major advance in this CASP is the enormous increase in the accuracy of computed protein complexes, achieved by the use of deep learning methods, although overall these do not fully match the performance for single proteins. Here too, AlphaFold2 based method perform best, and again more extensive sampling than the defaults is often required. Also of note are the encouraging early results on the use of deep learning to compute ensembles of macromolecular structures. Critically for the usability of computed structures, for both single proteins and protein complexes, deep learning derived estimates of both local and global accuracy are of high quality, however the estimates in interface regions are slightly less reliable. CASP15 also included computation of RNA structures for the first time. Here, the classical approaches produced better agreement with experiment than the new deep learning ones, and accuracy is limited. Also, for the first time, CASP included the computation of protein–ligand complexes, an area of special interest for drug design. Here too, classical methods were still superior to deep learning ones. Many new approaches were discussed at the CASP conference, and it is clear methods will continue to advance.
Constraint based modeling according to reference design

Reference models in form of best practices are an essential element to ensured knowledge as design for reuse. Popular modeling approaches do not offer mechanisms to embed reference models in a supporting way, let alone a repository of it. Therefore, it is hardly possible to profit from this expertise. The problem is that the reference models are not described formally enough to be helpful in developing solutions. Consequently, the challenge is about the process, how a user can be supported in designing dedicated solutions assisted by reference models. In this paper, we present a generic approach for the formal description of reference models using semantic technologies and their application. Our modeling assistant allows the construction of solution models using different techniques based on reference building blocks. This environment enables the subsequent verification of the developed designs against the reference models for conformity. Therefore, our reference modeling assistant highlights the interdependency. The application of these techniques contributes to the formalization of requirements and finally to quality assurance in context of maturity model. It is possible to use multiple reference models in context of system of system designs. The approach is evaluated in industrial area and it can be integrated into different modeling landscapes.
Comparative taphonomy of bivalves and foraminifera from Holocene tidal flat sediments, Bahia la Choya, Sonora, Mexico (Northern Gulf of California): taphonomic grades and temporal resolution

We compare the preservation (taphonomic grade) and age of Chione (bivalve) and foraminifera from modern siliciclastic tidal flat sediments of Bahia la Choya, Sonora, Mexico (northern Gulf of California). Disarticulated shells of Chione collected from the sediment-water interface of Choya Bay exhibit a substantial range in taphonomic grade and age, several hundred years to ∼80–125 ka based on Accelerator Mass Spectrometer 14C dates and D-Alloisoleucine/L-Isoleucine values. There is not, however, a one-to-one correspondence between age and taphonomic alteration of Chione: old (or young) valves may be highly altered or they may be relatively pristine. In contrast to Chione, most foraminiferal tests at Choya Bay are quite pristine, which suggests a quite young age, but tests are surprisingly old (up to ∼2,000 calendar years based on Accelerator Mass Spectrometer 14C dates). We suggest that following seasonal pulses in reproduction, some foraminiferal tests are rapidly incorporated into a subsurface shell layer by “Conveyor Belt” deposit feeders and preserved there, while the rest of the reproductive pulse rapidly dissolves. Ultimately, some of these buried tests, along with Chione, are transported back to the surface by biological activity and storms. The much greater range of taphonomic grades and ages among Chione shells suggests that they, unlike foraminifera, are sufficiently large and preservable (low surface/volume ratio and chemical reactivity) to undergo many cycles of degradation, burial, and exhumation before complete destruction. The age of foraminiferal tests indicates that time-averaging of microfossil assemblages at Choya Bay is much more insidious than would be expected considering the relatively pristine state of the tests alone. Based on our studies, the lower limit of temporal resolution of shallow shelf microfossil assemblages appears to be ∼1000 years. We caution, however, that each depositional setting (taphofacies) should be evaluated on a case-by-case basis before gross generalizations are made. Indeed, the discrepancy between age and taphonomic grade of fossil assemblages at Choya Bay suggests that neither hardpart size or taphonomic grade are infallible indicators of test preservability or likely temporal resolution of the host assemblage, and that the dynamics of hardpart input and loss must also be evaluated.
A User-centric View on Data Breach Response Expectations

Due to the growing prevalence of data breaches and the associated negative outcomes, data breaches pose a serious problem for companies. Since universal response strategies may not fully address diverse customer expectations, their effectiveness could be limited. As a result, understanding customer expectations serves as the cornerstone of a successful response strategy. By integrating prior data breach research with expectation confirmation theory, we examine individual customer expectations across a wide range of situations and business environments. Therefore, we conducted twelve qualitative interviews. Our findings enrich the body of research on data breaches by highlighting the individualized nature of customer expectations regarding data breach responses, which are shaped by numerous factors. We also discuss our contributions to the literature and the implications for managing data breach responses more effectively.
Gamification for Climate Change Engagement: A User-Centered Design Agenda

Policymakers and environmental organizations are increasingly implementing gamification because of its ability to stimulate playful learning and transform human attitudes and behavior. Although it has become a prominent development for climate change engagement, the literature does not adequately address how the underlying motivational systems depend upon user-centered design mechanisms. If gamification is to realize its potential for climate change engagement, it is crucial for designers to apply solid user-centered design principles, as suboptimal designs may do more harm than good. Therefore, we conducted a systematic literature review on gamification for climate change engagement to investigate the application of user-centered design and find key links between design choices and outcomes in 64 articles. We develop the findings into a user-centered design agenda and framework for game-based climate change engagement. This agenda includes design aspects–team multidisciplinarity, context dependence, user involvement, iterative testing, and design choice explication–and outcome aspects–value to users, game mechanics for learning, game content for learning, affective engagement, social engagement, changing real-world behavior, and using game data for research. The agenda is intended to inform gamification academics and practitioners aiming to design effective games for climate change engagement and its recommendations may apply to other complex issues.
Graph attention-based fusion of pathology images and gene expression for prediction of cancer survival

Multimodal machine learning models are being developed to analyze pathology images and other modalities, such as gene expression, to gain clinical and biological in-sights. However, most frameworks for multimodal data fusion do not fully account for the interactions between different modalities. Here, we present an attention-based fusion architecture that integrates a graph representation of pathology images with gene expression data and concomitantly learns from the fused information to predict patient-specific survival. In our approach, pathology images are represented as undirected graphs, and their embeddings are combined with embeddings of gene expression signatures using an attention mechanism to stratify tumors by patient survival. We show that our framework improves the survival prediction of human non-small cell lung cancers, out-performing existing state-of-the-art approaches that lever-age multimodal data. Our framework can facilitate spatial molecular profiling to identify tumor heterogeneity using pathology images and gene expression data, complementing results obtained from more expensive spatial transcriptomic and proteomic technologies.
Fungal infection alters collective nutritional intake of ant colonies

In many animals, parasitic infections impose significant fitness costs [1–6]. Animals are known to alter their feeding behavior when infected to help combat various parasites [7–12]. For instance, they can adjust nutrient intake to support their immune system [13,14]. However, parasites can also manipulate host foraging behavior to increase their own development, survival and transmission [15–18]. The mechanisms by which nutrition influences host-parasite interactions are still not well understood. Until now, studies that examine the impact of diet on infection have mainly focused on the host, and less on the parasite [12,13, 19–25]. Using Nutritional Geometry [26], we investigated the role of key nutrients: amino acids and carbohydrates, in a host-parasite system: the Argentine ant, Linepithema humile, and the entomopathogenic fungus, Metarhizium brunneum. We first established that the fungus grew and reproduced better on diets comprising four times less amino acids than carbohydrates (1:4 AA:C ratio). Second, when facing food combinations, the fungus exploited the two complementary food resources to reach the same performance as on this optimal diet, revealing the ability of fungal pathogens to solve complex nutritional challenges. Third, when ants were fed on this optimal fungal diet, their lifespan decreased when healthy, yet not when Metarhizium-infected, compared to their favored carbohydrate-rich diet. Interestingly, when the ants were given a binary choice between different diets, the foragers of uninfected colonies avoided intake of the fungal optimum diet, whilst choosing it when infected. Experimental disentanglement of full pathogenic infection and pure immune response to fungal cell wall material, combined with immune measurements, allowed us to conclude that this change of nutritional choice in infected ants did not result from pathogen manipulation but likely represents a compensation of the host to counterbalance the cost of using amino acids during the immune response. The observed change in foraging behavior in infected colonies towards an otherwise harmful diet (self-medication), suggests a collective compensatory mechanism for the individual cost of immunity. In short, we demonstrated that infected ants converge on a diet that is proven to be costly for survival in the long term but that could help them fight infection in the short term. Highlights The insect-pathogenic fungus Metarhizium brunneum performs best on protein-rich diets and is able to solve complex nutritional challenges While harmful to healthy ants, protein-rich diets did not shorten infected ants’ lifespan Contrary to healthy ants, when given a choice, infected and immune-stimulated ants choose a protein-rich diet
Root phosphatase activity is coordinated with the root conservation gradient across a phosphorus gradient in a lowland tropical forest

Soil phosphorus (P) is a growth-limiting nutrient in tropical ecosystems, driving diverse P-acquisition strategies among plants. Particularly, mining for inorganic P through phosphomonoesterase (PME) activity is essential, given the substantial proportion of organic P in soils. Yet the relationship between PME activity and other P-acquisition root traits remains unclear. We measured root PME activity and commonly-measured root traits, including root diameter, specific root length (SRL), root tissue density (RTD), and nitrogen concentration ([N]) in 18 co-occurring trees across soils with varying P availability to better understand trees response to P supply. Root [N] and RTD were inversely related, and that axis was related to soil P supply. Indeed, both traits correlated positively and negatively to PME activity, which responded strongly to P supply. Conversely, root diameter was inversely related to SRL, but this axis was not related to P supply. Suggesting that limiting similarity influenced variation along the diameter-SRL axis, explaining high local trait diversity. Meanwhile, environmental filtering tended to impact trait values along the root [N]-RTD axis. Overall, P availability indicator traits like PME activity and root hairs only tended to be associated with these axes, highlighting limitations of these axes in describing convergent adaptations at local sites.
Criticality of resting-state EEG predicts perturbational complexity and level of consciousness during anesthesia

Consciousness has been proposed to be supported by electrophysiological patterns poised at criticality, a dynamical regime which exhibits adaptive computational properties, maximally complex patterns and divergent sensitivity to perturbation. Here, we investigated dynamical properties of the resting-state electroencephalogram of healthy subjects undergoing general anesthesia with propofol, xenon or ketamine. We then studied the relation of these dynamic properties with the perturbational complexity index (PCI), which has shown remarkably high sensitivity in detecting consciousness independent of behavior. All participants were unresponsive under anesthesia, while consciousness was retained only during ketamine anesthesia (in the form of vivid dreams). , enabling an experimental dissociation between unresponsiveness and unconsciousness. We estimated (i) avalanche criticality, (ii) chaoticity, and (iii) criticality-related measures, and found that states of unconsciousness were characterized by a distancing from both the edge of activity propagation and the edge of chaos. We were then able to predict individual subjects’ PCI (i.e., PCImax) with a mean absolute error below 7%. Our results establish a firm link between the PCI and criticality and provide further evidence for the role of criticality in the emergence of consciousness. 2 Significance Statement Complexity has long been of interest in consciousness science and had a fundamental impact on many of today’s theories of consciousness. The perturbational complexity index (PCI) uses the complexity of the brain’s response to cortical perturbations to quantify the presence of consciousness. We propose criticality as a unifying framework underlying maximal complexity and sensitivity to perturbation in the conscious brain. We demonstrate that criticality measures derived from resting-state electroencephalography can distinguish conscious from unconscious states, using propofol, xenon and ketamine anesthesia, and from these measures we were able to predict the PCI with a mean error below 7%. Our results support the hypothesis that critical brain dynamics are implicated in the emergence of consciousness and may provide new directions for the assessment of consciousness.
Deep-Learning-Enabled Differentiation between Intraprostatic Gold Fiducial Markers and Calcification in Quantitative Susceptibility Mapping

Purpose Interest is growing in MR-only radiotherapy (RT) planning for prostate cancer (PCa) due to the potential reductions in cost and patient exposure to radiation, and a more streamlined work-flow and patient imaging pathway. However, in MRI, the gold fiducial markers (FMs) used for target localization appear as signal voids, complicating differentiation from other void sources such as calcifications and bleeds. This work investigates using Quantitative Susceptibility Mapping (QSM), an MRI phase post-processing technique, to aid in the differentiation task. It also presents deep learning models that capture nuanced information and automate the segmentation task, facilitating a streamlined approach to MR-only RT. Methods CT and MRI, including GRE and T1-weighted imaging, were acquired from 26 PCa patients, each with three implanted gold FMs. GRE data were post-processed into QSM, T 2*, and R2* maps using QSMxT’s body imaging pipeline. Statistical analyses were conducted to investigate the quantitative differentiation of FMs and calcification in each contrast. 3D U-Nets were developed using fastMONAI to automate the segmentation task using various combinations of MR-derived contrasts, with a model trained on CT used as a baseline. Models were evaluated using precision and recall calculated using a leave-one-out cross-validation scheme. Results Significant differences were observed between FM and calcification regions in CT, QSM and T 2*, though overlap was observed in QSM and T 2*. The baseline CT U-Net achieved an FM-level precision of ≈ 98% and perfect recall. The best-performing QSM-based model achieved precision and recall of 80% and 90%, respectively, while conventional MRI had values below 70% and 80%, respectively. The QSM-based model produced segmentations with good agreement with the ground truth, including a challenging FM that coincided with a bleed. Conclusion The model performance highlights the value of using QSM over indirect measures in MRI, such as signal voids in magnitude-based contrasts. The results also indicate that a U-Net can capture more information about the presentation of FMs and other sources than would be possible using susceptibility quantification alone, which may be less reliable due to the diverse presentation of sources across a patient population. In our study, QSM was a reliable discriminator of FMs and other sources in the prostate, facilitating an accurate and streamlined approach to MR-only RT.
Effects of Gene Dosage and Development on Subcortical Nuclei Volumes in Individuals with 22q11.2 Copy Number Variations

The 22q11.2 locus contains genes critical for brain development. Reciprocal Copy Number Variations (CNVs) at this locus impact risk for neurodevelopmental and psychiatric disorders. Both 22q11.2 deletions (22qDel) and duplications (22qDup) are associated with autism, but 22qDel uniquely elevates schizophrenia risk. Understanding brain phenotypes associated with these highly penetrant CNVs can provide insights into genetic pathways underlying neuropsychiatric disorders. Human neuroimaging and animal models indicate subcortical brain alterations in 22qDel, yet little is known about developmental differences across specific nuclei between reciprocal 22q11.2 CNV carriers and typically developing (TD) controls. We conducted a longitudinal MRI study in 22qDel (n=96, 53.1% female), 22qDup (n=37, 45.9% female), and TD controls (n=80, 51.2% female), across a wide age range (5.5-49.5 years). Volumes of the thalamus, hippocampus, amygdala, and anatomical subregions were estimated using FreeSurfer, and the effect of 22q11.2 gene dosage was examined using linear mixed models. Age-related changes were characterized with general additive mixed models (GAMMs). Positive gene dosage effects (22qDel < TD < 22qDup) were observed for total intracranial and whole hippocampus volumes, but not whole thalamus or amygdala volumes. Several amygdala subregions exhibited similar positive effects, with bi-directional effects found across thalamic nuclei. Distinct age- related trajectories were observed across the three groups. Notably, both 22qDel and 22qDup carriers exhibited flattened development of hippocampal CA2/3 subfields relative to TD controls. This study provides novel insights into the impact of 22q11.2 CNVs on subcortical brain structures and their developmental trajectories.
Semantic composition in experimental and naturalistic paradigms

Naturalistic paradigms using movies or audiobooks have become increasingly popular in cognitive neuroscience, but connecting them to findings from controlled experiments remains rare. Here, we aim to bridge this gap in the context of semantic composition in language processing, which is typically examined using a “minimal” two-word paradigm. Using magnetoencephalography (MEG), we investigated whether the neural signatures of semantic composition observed in an auditory two-word paradigm can extend to naturalistic story listening, and vice versa. Our results demonstrate consistent differentiation between phrases and single nouns in the left anterior and middle temporal lobe, regardless of the context. Notably, this distinction emerged later during naturalistic listening. Yet this latency difference disappeared when accounting for various factors in the naturalistic data, such as prosody, word rate, word frequency, surprisal, and emotional content. These findings suggest the presence of a unified compositional process underlying both isolated and connected speech comprehension.