Case Report Forms (CRFs) are largely used in medical research as they ensure accuracy, reliability, and validity of results in clinical studies. However, publicly available, wellannotated CRF datasets are scarce, limiting the development of CRF slot filling systems able to fill in a CRF from clinical notes. To mitigate the scarcity of CRF datasets, we propose to take advantage of available datasets annotated for information extraction tasks and to convert them into structured CRFs. We present a semi-automatic conversion methodology, which has been applied to the E3C dataset in two languages (English and Italian), resulting in a new, high-quality dataset for CRF slot filling. Through several experiments on the created dataset, we report that slot filling achieves 59.7% for Italian and 67.3% for English on a closed Large Language Models (zero-shot) and worse performances on three families of open-source models, showing that filling CRFs is challenging even for recent state-of-the-art LLMs. We release the datest at https://huggingface.co/collections/NLP-FBK/e3c-to-crf-67b9844065460cbe42f80166

Physical AI systems need to perceive, understand, and perform complex actions in the physical world. In this paper, we present the Cosmos-Reason1 models that can understand the physical world and generate appropriate embodied decisions (e.g., next step action) in natural language through long chain-of-thought reasoning processes. We begin by defining key capabilities for Physical AI reasoning, with a focus on physical common sense and embodied reasoning. To represent physical common sense, we use a hierarchical ontology that captures fundamental knowledge about space, time, and physics. For embodied reasoning, we rely on a two-dimensional ontology that generalizes across different physical embodiments. Building on these capabilities, we develop two multimodal large language models, Cosmos-Reason1-8B and Cosmos-Reason1-56B. We curate data and train our models in four stages: vision pre-training, general supervised fine-tuning (SFT), Physical AI SFT, and Physical AI reinforcement learning (RL) as the post-training. To evaluate our models, we build comprehensive benchmarks for physical common sense and embodied reasoning according to our ontologies. Evaluation results show that Physical AI SFT and reinforcement learning bring significant improvements.

Event-based object detection has recently garnered attention in the computer vision community due to the exceptional properties of event cameras, such as high dynamic range and no motion blur. However, feature asynchronism and sparsity cause invisible objects due to no relative motion to the camera, posing a significant challenge in the task. Prior works have studied various memory mechanisms to preserve as many features as possible at the current time, guided by temporal clues. While these implicit-learned memories retain some short-term information, they still struggle to preserve long-term features effectively. In this paper, we consider those invisible objects as pseudo-occluded objects and aim to reveal their features. Firstly, we introduce visibility attribute of objects and contribute an auto-labeling algorithm to append additional visibility labels on an existing event camera dataset. Secondly, we exploit tracking strategies for pseudo-occluded objects to maintain their permanence and retain their bounding boxes, even when features have not been available for a very long time. These strategies can be treated as an explicit-learned memory guided by the tracking objective to record the displacements of objects across frames. Lastly, we propose a spatio-temporal feature aggregation module to enrich the latent features and a consistency loss to increase the robustness of the overall pipeline. We conduct comprehensive experiments to verify our method's effectiveness where still objects are retained but real occluded objects are discarded. The results demonstrate that (1) the additional visibility labels can assist in supervised training, and (2) our method outperforms state-of-the-art approaches with a significant improvement of 7.9% absolute mAP.

Object permanence, which refers to the concept that objects continue to exist even when they are no longer perceivable through the senses, is a crucial aspect of human cognitive development. In this work, we seek to incorporate this understanding into interactive robots by proposing a set of assumptions and rules to represent object permanence in multi-object, multi-agent interactive scenarios. We integrate these rules into the particle filter, resulting in the Object Permanence Filter (OPF). For multi-object scenarios, we propose an ensemble of K interconnected OPFs, where each filter predicts plausible object tracks that are resilient to missing, noisy, and kinematically or dynamically infeasible measurements, thus bringing perceptional robustness. Through several interactive scenarios, we demonstrate that the proposed OPF approach provides robust tracking in human-robot interactive tasks agnostic to measurement type, even in the presence of prolonged and complete occlusion. Webpage: https://opfilter.github.io/.

As machine learning models become more general, we need to characterise them in richer, more meaningful ways. We describe a method to infer the cognitive profile of a system from diverse experimental data. To do so, we introduce measurement layouts that model how task-instance features interact with system capabilities to affect performance. These features must be triangulated in complex ways to be able to infer capabilities from non-populational data -- a challenge for traditional psychometric and inferential tools. Using the Bayesian probabilistic programming library PyMC, we infer different cognitive profiles for agents in two scenarios: 68 actual contestants in the AnimalAI Olympics and 30 synthetic agents for O-PIAAGETS, an object permanence battery. We showcase the potential for capability-oriented evaluation.

Tracking objects with persistence in cluttered and dynamic environments remains a difficult challenge for computer vision systems. In this paper, we introduce $\textbf{TCOW}$, a new benchmark and model for visual tracking through heavy occlusion and containment. We set up a task where the goal is to, given a video sequence, segment both the projected extent of the target object, as well as the surrounding container or occluder whenever one exists. To study this task, we create a mixture of synthetic and annotated real datasets to support both supervised learning and structured evaluation of model performance under various forms of task variation, such as moving or nested containment. We evaluate two recent transformer-based video models and find that while they can be surprisingly capable of tracking targets under certain settings of task variation, there remains a considerable performance gap before we can claim a tracking model to have acquired a true notion of object permanence.

Existing research on training-time attacks for deep neural networks (DNNs), such as backdoors, largely assume that models are static once trained, and hidden backdoors trained into models remain active indefinitely. In practice, models are rarely static but evolve continuously to address distribution drifts in the underlying data. This paper explores the behavior of backdoor attacks in time-varying models, whose model weights are continually updated via fine-tuning to adapt to data drifts. Our theoretical analysis shows how fine-tuning with fresh data progressively "erases" the injected backdoors, and our empirical study illustrates how quickly a time-varying model "forgets" backdoors under a variety of training and attack settings. We also show that novel fine-tuning strategies using smart learning rates can significantly accelerate backdoor forgetting. Finally, we discuss the need for new backdoor defenses that target time-varying models specifically.

This study evaluates the discriminating capacity (uniqueness) of the EEG data from the WAY EEG GAL public dataset to authenticate individuals against one another as well as its permanence. In addition to the EEG data, Luciw et al. provide EMG (Electromyography), and kinematics data for engineers and researchers to utilize WAY EEG GAL for further studies. However, evaluating the EMG and kinematics data is outside the scope of this study. The goal of the state-of-the-art is to determine whether EEG data can be utilized to control prosthetic devices. On the other hand, this study aims to evaluate the separability of individuals through EEG data to perform user authentication. A feature importance algorithm is utilized to select the best features for each user to authenticate them against all others. The authentication platform implemented for this study is based on Machine Learning models/classifiers. As an initial test, two pilot studies are performed using Linear Discriminant Analysis (LDA) and Support Vector Machine (SVM) to observe the learning trends of the models by multi-labeling the EEG dataset. Utilizing kNN first as the classifier for user authentication, accuracy around 75% is observed. Thereafter to improve the performance both linear and non-linear SVMs are used to perform classification. The overall average accuracies of 85.18% and 86.92% are achieved using linear and non-linear SVMs respectively. In addition to accuracy, F1 scores are also calculated. The overall average F1 score of 87.51% and 88.94% are achieved for linear and non-linear SVMs respectively. Beyond the overall performance, high performing individuals with 95.3% accuracy (95.3% F1 score) using linear SVM and 97.4% accuracy (97.3% F1 score) using non-linear SVM are also observed.

Biological infants are naturally curious and try to comprehend their physical surroundings by interacting, in myriad multisensory ways, with different objects - primarily macroscopic solid objects - around them. Through their various interactions, they build hypotheses and predictions, and eventually learn, infer and understand the nature of the physical characteristics and behavior of these objects. Inspired thus, we propose a model for curiosity-driven learning and inference for real-world AI agents. This model is based on the arousal of curiosity, deriving from observations along discontinuities in the fundamental macroscopic solid-body physics parameters, i.e., shape constancy, spatial-temporal continuity, and object permanence. We use the term body-budget to represent the perceived fundamental properties of solid objects. The model aims to support the emulation of learning from scratch followed by substantiation through experience, irrespective of domain, in real-world AI agents.

In the past decade, the study of magic effects has started to gain attention from the scientific community, particularly psychologists. This interest stems from what magic effects might reveal about the blind spots in our perception and roadblocks in our thinking. The study of magic effects may offer researchers opportunities for new lines of inquiry about perception and attention. Moreover, because magic effects capitalize on our ability to remember what happened and our ability to anticipate what will happen next, using magical frameworks elicits ways to investigate complex cognitive abilities such as mental time travel (i.e., remembering the past and anticipating the future). Moving beyond the intersection between magic and the human mind, the application of magic effects to investigate the animal mind can prompt the comparison of behavioral reactions among diverse species, in which magic effects might exploit similar perceptive blind spots and cognitive roadblocks. The internet is filled with videos of magicians performing magic effects to animals (mostly captive primates and domesticated pets), in which the attentive animal spectators appear to react with awe and exultation when objects or food magically vanish. Without further investigation, it cannot be assumed that the animal audiences in the videos are amazed and surprised by the magic effect, akin to a human spectator. However, these encounters prompt investigation about the extent to which animals are susceptible to the same techniques of deception commonly used by magicians. Over the past several decades, comparative psychologists, perhaps unintentionally, have been using magic effects as a methodological tool to explore a diverse range of cognitive abilities in animals. For instance, when investigating how dogs and great apes mentally represent different kinds of objects, experimenters have used devices inspired by props commonly used in magic effects, such as boxes with false bottoms ([ 1 ][1]). Researchers have also investigated causal cognition in New Caledonian crows using invisible string, a see-through thread frequently used for levitation effects, to determine how crows respond to objects moving “without” human interaction ([ 2 ][2]). Moreover, violation of expectation paradigms, in which a subject is presented with a series of expected and unexpected outcomes, has been extensively used in comparative cognition (the investigation of cognitive mechanisms in diverse species and their origins). Such a premise is directly comparable to magic effects, given that the result of both magic and violation of expectation paradigms aim to elicit the same reaction from the observer, namely being surprised by witnessing the unexpected. Although animal subjects do not typically verbalize their surprise at unexpected events, surprise can be measured by using looking time. For example, if the subject finds an event surprising, they spend significantly longer looking at the event compared with an event that is deemed ordinary. Although magical effects have permeated the field of comparative cognition, the scientific community has yet to study whether animals can be deceived by the same magic methodologies that would deceive a human observer. This is an interesting query because the use of magic effects to deceive animals could only be feasible if both human and animal spectators shared some analogous cognitive processes that capitalize on perceptive blind spots and cognitive roadblocks. Investigating the psychology behind magic effects in humans offers comparative psychologists an accessible pathway to formulate initial hypotheses to test in animal audiences. For example, the vanishing ball—an effect in which the magician seemingly vanishes a ball in thin air—could be used to investigate whether past experiences and current expectations alter the animal's perception. In humans, the illusion's success appears to be reliant on the spectator's expectation of the ball's movement and the social cues elicited by the magician ([ 3 ][3]). Using a similar design with animals could be insightful, regarding both the animal's expectations (i.e., throwing a ball toward the ceiling will make the ball go upward) and whether human body language offers an animal audience social cues when priming such illusions. A popular magic technique is misdirection, the manipulation of the spectator by the magician to prevent the discovery of the cause of a magic effect. Controlling the audience's attention is an important skill for magicians, otherwise spectators might discover the mechanics behind the effect. Some species have been observed using behavioral tactics that can be considered analogous to misdirection. For example, chimpanzees sometimes divert their gaze from a desired object to detract a competitor's attention from it ([ 4 ][4]). Jays (i.e., corvids) will protect their food caches from possible pilferers by moving them several times or discretely hiding the food while performing several bluff caching events, thereby making it difficult for the observer to trace the genuine cache location ([ 5 ][5]). The use of analogous methodologies by a diverse range of animal taxa to deceive conspecifics suggests that some misdirection techniques could exploit similar blind spots in attention. It also prompts the question of whether misdirection techniques used by magicians can also effectively fool animal minds. However, when doing so, experimenters must engage the attentional mechanisms of their spectators, because misdirection techniques are contingent on this. This might be challenging with animal subjects who might not pay sufficient attention to humans. Engaging the undivided attention of our closest relative, the chimpanzee, is one of the major challenges of implementing experimental designs on apes ([ 6 ][6]). Offering them long periods of intensive training, during which the ape must pay close attention to human movement, might ameliorate the challenge. By contrast, corvids possess sophisticated attentional mechanisms and are a suitable candidate for this line of research because they follow human gaze around particular objects and monitor human attentional states ([ 7 ][7], [ 8 ][8]). ![Figure][9] Hand gestures influence choice A priming experiment to observe whether a magpie's choice can be influenced by human hand gestures is shown. Magpies are first trained to discriminate between three differently shaped objects and exchange any shaped object for a food reward. GRAPHIC: A. KITTERMAN/ SCIENCE In addition to misdirection, magicians often rely on our cognitive abilities to create a magical illusion. One such ability is object permanence—the ability to represent objects in the mind's eye when the object is out of sight. This ability appears to be adaptive for diverse taxa. For example, object permanence is harnessed by corvids during caching to successfully cache and recover because individuals must understand and remember that hidden items continue to exist even when they are out of sight ([ 9 ][10]). The ability to form a mental representation of an object when it is out of sight and to maintain it in memory is also vital for conjuring magic effects, because most effects tend to involve the appearance and disappearance of objects. Thus, object permanence paradigms grant a suitable starting point for comparative psychologists to investigate the analogous mechanisms of both human and animal observers of magic. Interesting insights into object permanence have been made when adopting magic as a framework of study. When using a fake transfer technique (i.e., where the magician pretends to place an object in one hand while keeping it in the initial hand instead), human observers appear to retain the erroneous belief that a coin is placed inside the hand only for a limited period of time. Elongated reveal times seem to decrease the strength of this belief significantly ([ 10 ][11]), suggesting that inducing a false belief of object permanence might be contingent on not allowing enough time for the spectator to replay the events in their mind. Given the current research on object permanence in diverse taxa, translating the fake transfer technique to a suitable animal and paradigm (e.g., corvid caching) might elucidate the degree of commonality with object permanence abilities in humans and highlight whether perception of object permanence and memory of the hidden location in animal minds can be manipulated in analogous ways. Although the science of magic has mainly focused on the exploitation of simpler mechanisms such as attention and perception, magic effects also use techniques that affect complex cognitive abilities such as memory and mental time travel. For example, magicians often alter the spectator's recollection of an event and induce fake memories through suggestions. When researchers suggested to human subjects that a “magic” key, which had been previously bent, would continue to bend once the effect finished, the spectators were more likely to report that they had observed the bending process during and after the magic effect ([ 11 ][12]). Other effects such as the “one ahead principle” exploit the spectator's inability to effectively deconstruct memories to make them think that the magician can read their mind. This is done by the magician forcing the outcome of one of the predictions while altering the order of events that the spectator is experiencing. Given the reconstructive nature of human memory, the spectator will recall the sequences in the order they occurred, instead of dissecting it into the events that were key for the experience ([ 12 ][13]). Such effects could only be investigated with species that possess mental time travel abilities, given that, evidently, one cannot exploit the faults of a nonexistent mechanism. Current research suggests that corvids exhibit sophisticated mental time travel abilities ([ 13 ][14], [ 14 ][15]) and therefore are ideal subjects for experiments with such magic effects. The application of similar techniques adapted to an animal audience might reveal whether animals that possess complex memory abilities also encounter comparable constraints. The imperative use of language in this kind of research is a strong barrier if one is to transpose it to an animal audience. However, recent research on humans raises the possibility that simple choices can be influenced by using hand gestures ([ 15 ][16]), thus offering a more relevant way to test for analogous roadblocks in animal memories. Magical frameworks ought to be the subject of in-depth methodological inspection and theorization. A good starting point might be the use of hand gestures depicting simple primes to observe if humans can influence choice in corvids. For example, subjects could be trained to discriminate between three differently shaped objects and asked, by the experimenter, to retrieve any object in exchange for a reward. Experimental conditions could include whether making heart-shape gestures, when asking, primes the subject to retrieve the heart object instead of the circular or rectangular object (see the figure). The psychology of magic offers the scientific community a powerful methodological tool for testing the perceptive blind spots and cognitive roadblocks in diverse taxa. Studying whether animals can be deceived by the same magic effects that deceive humans can offer a window into the cognitive parallels and variances in attention, perception, and mental time travel, especially those species thought to possess the necessary prerequisites to be deceived by magic effects. 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{openurl}?query=rft.jtitle%253DJournal%2Bof%2Bcomparative%2Bpsychology%2B%2528Washington%252C%2BD.C.%2B%253A%2B%2B1983%2529%26rft.stitle%253DJ%2BComp%2BPsychol%26rft.aulast%253DSalwiczek%26rft.auinit1%253DL.%2BH.%26rft.volume%253D123%26rft.issue%253D3%26rft.spage%253D295%26rft.epage%253D303%26rft.atitle%253DThe%2Bdevelopment%2Bof%2Bcaching%2Band%2Bobject%2Bpermanence%2Bin%2BWestern%2Bscrub-jays%2B%2528Aphelocoma%2Bcalifornica%2529%253A%2Bwhich%2Bemerges%2Bfirst%253F%26rft_id%253Dinfo%253Adoi%252F10.1037%252Fa0016303%26rft_id%253Dinfo%253Apmid%252F19685971%26rft.genre%253Darticle%26rft_val_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Ajournal%26ctx_ver%253DZ39.88-2004%26url_ver%253DZ39.88-2004%26url_ctx_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Actx [46]: /lookup/external-ref?access_num=10.1037/a0016303&link_type=DOI [47]: /lookup/external-ref?access_num=19685971&link_type=MED&atom=%2Fsci%2F369%2F6510%2F1424.atom [48]: /lookup/external-ref?access_num=000268963900008&link_type=ISI [49]: #xref-ref-10-1 "View reference 10 in text" [50]: {openurl}?query=rft.jtitle%253DPsychol.%2BRes.%26rft.volume%253D79%26rft.spage%253D513%26rft.genre%253Darticle%26rft_val_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Ajournal%26ctx_ver%253DZ39.88-2004%26url_ver%253DZ39.88-2004%26url_ctx_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Actx [51]: #xref-ref-11-1 "View reference 11 in text" [52]: {openurl}?query=rft.jtitle%253DThe%2BBritish%2Bjournal%2Bof%2Bpsychology%26rft.stitle%253DBr%2BJ%2BPsychol%26rft.aulast%253DWiseman%26rft.auinit1%253DR.%26rft.volume%253D96%26rft.issue%253DPt%2B1%26rft.spage%253D115%26rft.epage%253D127%26rft.atitle%253D%2527It%2527s%2Bstill%2Bbending%2527%253A%2Bverbal%2Bsuggestion%2Band%2Balleged%2Bpsychokinetic%2Bability.%26rft_id%253Dinfo%253Adoi%252F10.1348%252F000712604X15428%26rft_id%253Dinfo%253Apmid%252F15826327%26rft.genre%253Darticle%26rft_val_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Ajournal%26ctx_ver%253DZ39.88-2004%26url_ver%253DZ39.88-2004%26url_ctx_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Actx [53]: /lookup/external-ref?access_num=10.1348/000712604X15428&link_type=DOI [54]: /lookup/external-ref?access_num=15826327&link_type=MED&atom=%2Fsci%2F369%2F6510%2F1424.atom [55]: /lookup/external-ref?access_num=000227565900018&link_type=ISI [56]: #xref-ref-12-1 "View reference 12 in text" [57]: {openurl}?query=rft.jtitle%253DInterface%2BFocus%26rft_id%253Dinfo%253Adoi%252F10.1098%252Frsfs.2016.0112%26rft.genre%253Darticle%26rft_val_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Ajournal%26ctx_ver%253DZ39.88-2004%26url_ver%253DZ39.88-2004%26url_ctx_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Actx [58]: /lookup/external-ref?access_num=10.1098/rsfs.2016.0112&link_type=DOI [59]: #xref-ref-13-1 "View reference 13 in text" [60]: {openurl}?query=rft.jtitle%253DNature%26rft.stitle%253DNature%26rft.aulast%253DClayton%26rft.auinit1%253DN.%2BS.%26rft.volume%253D395%26rft.issue%253D6699%26rft.spage%253D272%26rft.epage%253D274%26rft.atitle%253DEpisodic-like%2Bmemory%2Bduring%2Bcache%2Brecovery%2Bby%2Bscrub%2Bjays.%26rft_id%253Dinfo%253Adoi%252F10.1038%252F26216%26rft_id%253Dinfo%253Apmid%252F9751053%26rft.genre%253Darticle%26rft_val_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Ajournal%26ctx_ver%253DZ39.88-2004%26url_ver%253DZ39.88-2004%26url_ctx_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Actx [61]: /lookup/external-ref?access_num=10.1038/26216&link_type=DOI [62]: /lookup/external-ref?access_num=9751053&link_type=MED&atom=%2Fsci%2F369%2F6510%2F1424.atom [63]: /lookup/external-ref?access_num=000075974600049&link_type=ISI [64]: #xref-ref-14-1 "View reference 14 in text" [65]: {openurl}?query=rft.jtitle%253DNature%26rft.stitle%253DNature%26rft.aulast%253DRaby%26rft.auinit1%253DC.%2BR.%26rft.volume%253D445%26rft.issue%253D7130%26rft.spage%253D919%26rft.epage%253D921%26rft.atitle%253DPlanning%2Bfor%2Bthe%2Bfuture%2Bby%2Bwestern%2Bscrub-jays.%26rft_id%253Dinfo%253Adoi%252F10.1038%252Fnature05575%26rft_id%253Dinfo%253Apmid%252F17314979%26rft.genre%253Darticle%26rft_val_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Ajournal%26ctx_ver%253DZ39.88-2004%26url_ver%253DZ39.88-2004%26url_ctx_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Actx [66]: /lookup/external-ref?access_num=10.1038/nature05575&link_type=DOI [67]: /lookup/external-ref?access_num=17314979&link_type=MED&atom=%2Fsci%2F369%2F6510%2F1424.atom [68]: /lookup/external-ref?access_num=000244341200050&link_type=ISI [69]: #xref-ref-15-1 "View reference 15 in text" [70]: 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