Miracle-dz/UniFAR-FTUs huggingface dataset:
A Confidence-based Multipath Neural-symbolic Approach for Visual Question Answering

Neural-symbolic (NS) learning provides an efficient approach to visual question answering (VQA) by combining the advantages of neural network learning and symbolic reasoning. However, the uncertainty of the neural networks (NN) learning in the existing NS methods has not been considered, and one single answer is provided for a question without confidence evaluation. To tackle this problem, we propose a confidence-based NS (CBNS) framework to evaluate the confidence of the NN modules based on uncertainty quantification and make inferences based on the confidence evaluations. Specifically, CBNS includes a probabilistic question parser which generates multiple program candidates with confidence evaluations. CBNS also includes a probabilistic scene perception module which provides object-based scene representation and confidence evaluations for each attribute of objects in an image. The object-based scene representation and the programs with confidence evaluations are used for evaluating the confidence of answers through the inference process. The proposed framework is model-agnostic and compatible with mainstream NS VQA architectures. Experiments on CLEVR demonstrate that the proposed framework enables confidence-based reasoning for the complex VQA task and leads to a promising performance improvement with a significantly reduced computation cost.
Mitigating Prototype Shift: Few-Shot Nested Named Entity Recognition with Prototype-Attention

Nested entities are prone to obtain similar rep-001 resentations in pre-trained language models, 002 posing challenges for Named Entity Recog-003 nition (NER), especially in the few-shot set-004 ting where prototype shifts often occur due to 005 distribution differences between the support 006 and query sets. In this paper, we regard en-007 tity representation as the combination of proto-008 type and non-prototype representations. With 009 a hypothesis that using the prototype repre-010 sentation specifically can help mitigate poten-011 tial prototype shifts, we propose a Prototype-012 Attention mechanism in the Contrastive Learn-013 ing framework (PACL) for the few-shot nested 014 NER. PACL first generates prototype-enhanced 015 span representations to mitigate the prototype 016 shift by applying a prototype attention mech-017 anism. It then adopts a novel prototype-span 018 contrastive loss to reduce prototype differences 019 further and overcome the O-type’s non-unique 020 prototype limitation by comparing prototype-021 enhanced span representations with prototypes 022 and original semantic representations. Our ex-023 periments on three English, German, and Rus-024 sian nested NER datasets show that the PACL 025 outperformed seven baseline models on the 026 1-shot and 5-shot tasks in terms of F 1 score. 027 Further analyses indicate that our Prototype-028 Attention mechanism has high generality, en-029 hancing the performance of two baseline mod-030 els, and can serve as a valuable tool for NLP 031 practitioners facing few-shot nested NER tasks. 032
The challenges of first-and second-order belief reasoning in explainable human-robot interaction

—Current approaches to implement eXplainable Autonomous Robots (XAR) are dominantly based on Rein-forcement Learning (RL), which are suitable for modelling and correcting people’s first-order mental state attributions to robots. Our recent findings show that people also rely on attributing second-order beliefs (i.e., beliefs about beliefs) to robots to interpret their behavior. However, robots arguably form and act primarily on first-order beliefs and desires (about things in the environment) and do not have a functional “theory of mind”. Moreover, RL models may be incapable to appropriately address second-order belief attribution errors. This paper aims to open a discussion of what our recent findings on second-order mental state attribution to robots imply for current approaches to XAR.
Evaluating and Improving Context Attention Distribution on Multi-Turn Response Generation using Self-Contained Distractions

Despite the rapid progress of open-domain generation-based conversational agents, most deployed systems treat dialogue contexts as single-turns, while systems dealing with multi-turn contexts are less studied. There is a lack of a reliable metric for evaluating multi-turn modelling, as well as an effective solution for improving it. In this paper, we focus on an essential component of multi-turn generation-based conversational agents: context attention distribution, i.e. how systems distribute their attention on dialogue’s context. For evaluation of this component, We introduce a novel attention-mechanism-based metric: DAS ratio. To improve performance on this component, we propose an optimization strategy that employs selfcontained distractions. Our experiments on the Ubuntu chatlogs dataset show that models with comparable perplexity can be distinguished by their ability on context attention distribution. Our proposed optimization strategy improves both non-hierarchical and hierarchical models on the proposed metric by about 10% from baselines.
A Novel Approach for Assessment of Clonal Hematopoiesis of Indeterminate Potential Using Deep Neural Networks

We propose a novel diagnostic method for clonal hematopoiesis of indeterminate potential (CHIP), a condition characterized by the presence of somatic mutations in hematopoietic stem cells without detectable hematologic malignancy, using deep-learning techniques. We developed a convolutional neural network (CNN) to predict CHIP status using 4 different views from standard delayed gadolinium-enhanced cardiac MRI. We used 5-fold cross validation on 82 patients to assess the performance of our model. Different algorithms were compared to find the optimal patient-level prediction method using the image-level CNN predictions. We found that the best model had an AUC of 0.85 and an accuracy of 82%. We conclude that a deep learning-based diagnostic approach for CHIP is promising.
RH20T: A Comprehensive Robotic Dataset for Learning Diverse Skills in One-Shot

A key challenge in robotic manipulation in open domains is how to acquire diverse and generalizable skills for robots. Recent research in one-shot imitation learning has shown promise in transferring trained policies to new tasks based on demonstrations. This feature is attractive for enabling robots to acquire new skills and improving task and motion planning. However, due to limitations in the training dataset, the current focus of the community has mainly been on simple cases, such as push or pick-place tasks, relying solely on visual guidance. In reality, there are many complex skills, some of which may even require both visual and tactile perception to solve. This paper aims to unlock the potential for an agent to generalize to hundreds of real-world skills with multi-modal perception. To achieve this, we have collected a dataset comprising over 110,000 contact-rich robot manipulation sequences across diverse skills, contexts, robots, and camera viewpoints, all collected in the real world. Each sequence in the dataset includes visual, force, audio, and action information. Moreover, we also provide a corresponding human demonstration video and a language description for each robot sequence. We have invested significant efforts in calibrating all the sensors and ensuring a high-quality dataset. The dataset is made publicly available at rh20t.github. io
A Comprehensive Evaluation of Biomedical Entity Linking and Normalization Models

Biomedical entity linking (BioEL) is the pro-001 cess of connecting entities referenced in doc-002 uments to entries in biomedical databases 003 such as the Unified Medical Language System 004 (UMLS) or Medical Subject Headings (MeSH). 005 The study objective was to comprehensively 006 evaluate nine recent state-of-the-art biomedical 007 entity linking models under a unified frame-008 work. We compare these models along axes of 009 (1) accuracy, (2) speed, (3) ease of use, (4) gen-010 eralization, and (5) adaptability to new ontolo-011 gies and datasets. We additionally quantify the 012 impact of various preprocessing choices such as 013 abbreviation detection. Systematic evaluation 014 reveals several notable gaps in current meth-015 ods. In particular, current methods struggle 016 to correctly link genes and proteins and often 017 have difficulty effectively incorporating context 018 into linking decisions. To expedite future de-019 velopment and baseline testing, we release our 020 unified evaluation framework and all included 021 models in our linked github repo (added upon 022 publication). 023
Learning Neuro-Symbolic World Models with Conversational Proprioception

The recent emergence of Neuro-Symbolic Agent (NeSA) approaches to natural language-based interactions calls for the investigation of model-based approaches. In contrast to model-free approaches, which existing NeSAs take, learning an explicit world model has an interesting potential especially in the explainability, which is one of the key selling points of NeSA. To learn useful world models, we leverage one of the recent neuro-symbolic architectures, Logical Neural Networks (LNN). Here, we describe a method that can learn neuro-symbolic world models on the TextWorld-Commonsense set of games.We then show how this can be improved further by taking inspiration from the concept of proprioception, but for conversation. This is done by enhancing the internal logic state with a memory of previous actions while also guiding future actions by augmenting the learned model with constraints based on this memory.This greatly improves the game-solving agents performance in a TextWorld setting, where the advantage over the baseline is an 85% average steps reduction and x2.3 average score.
Application of computer vision systems for assessing bergamot fruit external features

Bergamot Citrus x bergamia Risso & Poiteau is an emblematic Citrus species of Reggio Calabria province (Southern Italy) where more than 90% of the global production thrives. The present work deals with the use of a non­ destructive technique based on a computer vision system to evaluate bergamot fruit peel colour, as well as dimensional features. To this purpose, experimental trials considered three bergamot cultivars, namely ‘Femminello’, ‘Castagnaro’ and ‘Fantastico’. Bergamot fruit RGB images were taken using a laboratory inspection chamber equipped with a lighting system and a digital camera Nikon D5200 directly connected to a personal computer, to enable remote image acquisition. First, images were pre­processed according to a previously created colour profile. After that, bergamot fruit colour was analysed and expressed in terms of Hunter L, a, and b coordinates, which were used to calculate Standard Citrus Colour Index (CCI). In addition, dimensional features and shape descrip­ tors were measured for each cultivar. Statistical data analysis, by applying the Kruskal­Wallis test at p<0.05 on CCI data highlighted significant differences between the assessed cultivars, and discriminant analysis (LDA) applied on CCI and dimensional features enabled a classification rate of 78.86% between culti­ vars, proving the reliability of computer vision techniques in assessing berg­ amot external features.
A logic for Petri nets

This paper presents a translation from Petri nets to linear logic with the objective of enhancing the analysis technics of Petri nets. The net markings are denoted by formulas generated by a connective, the transitions are denoted by implicatives formulas between marking formulas and the firing operation is expressed by a sequent (i.e. a reasoning scheme). Starting from this translation, a logical calculus on transition sequences is defined, which allows to compose transitions in a sequential way as well as in a concurrent one.
Thalamic connectivity topography in spina bifida newborns is linked to functional lesion level

Spina bifida affects spinal cord and cerebral development, leading to motor and cognitive delay. We investigated whether there are associations between thalamocortical connectivity topography, neurological function and developmental outcomes in open spina bifida. Diffusion tensor MRI was used to assess thalamocortical connectivity in 44 newborns with open spina bifida who underwent prenatal surgical repair. We quantified the volume of clusters formed based on the strongest probabilistic connectivity to the frontal, parietal, and temporal cortex. Developmental outcomes were assessed using the Bayley III Scales, while the functional level of the lesion was assessed by neurological examination at two years of age. Higher functional level was associated with smaller thalamo-parietal, while lower functional level was associated with smaller thalamo-temporal connectivity clusters (Bonferroni-corrected p<0.05). Lower functional levels were associated with weaker thalamic temporal connectivity, particularly in the ventrolateral and ventral anterior nuclei. No associations were found between thalamocortical connectivity and developmental outcomes. Our findings suggest that altered thalamocortical circuitry development in open spina bifida may contribute to impaired lower extremity function, impacting motor function and independent ambulation. We hypothesize that the neurologic function might not merely be caused by the spinal cord lesion, but further impacted by the disruption of cerebral neuronal circuitry.
Computational model of neuronal recruitment during ICMS for restoring somatosensation in the human somatosensory cortex

Objective. Intuitively providing touch feedback from artificial hands to users with sensory loss remains a challenge. Although localized fingertip sensations can be evoked via intracortical microstimulation (ICMS), feedback is generally optimized using psychometric tasks rather than mimicking the cortical response to touch. Approach. We created an anatomically-informed and participant-specific model of the human somatosensory cortex (S1) region with an implanted microelectrode array (MEA). We performed simultaneous stimulation-and-recording from the study participant S1 region to characterize cortical responses elicited by single ICMS pulses. Pulses were delivered to a set of pre-selected electrodes mapped to tactile receptive fields. We next performed a 2D (i.e., in the plane of the MEA probe tips) current source density (CSD) analysis of recorded cortical responses to inform cortical network model parameters on how ICMS activates neurons and lateral synaptic connections in the area of the S1 sampled by MEA electrodes. Using information from planar CSD profiles obtained from ground truth data, we reconstructed lateral connections in the S1 model needed to produce the desired responses to single ICMS pulses. The effect of multiple ICMS was then simulated in the biologically realistic cortical model and the results were validated against ground truth cortical responses from the study participant. Main results. A high-resolution cortical network model, calibrated to produce the known cortical responses to single ICMS pulses delivered to individual electrodes, predicted with a reasonable accuracy the cortical response to ICMS pulses delivered simultaneously to multiple electrodes. Significance. These preliminary results suggest that high-resolution biologically realistic cortical network models can potentially be reliable predictors of cortical response to a given pattern of ICMS presentations and therefore useful in designing biomimetic stimulation patterns.
High‐resolution vertical polarimetric imaging of pine forests

This paper describes a field campaign performed with a ground‐based polarimetric tomographic imager designed to measure the vertical response of several forest plots in order to better understand the mechanisms contributing to the total radar response. The campaign took place in the Mende forest, an artificial forest of European black pines, in the south of France. The objective is to perform a sensitivity study on the radar observables based on biophysical parameters. This paper presents a description of the experiment hardware and procedure, the results obtained for all sites, and a discussion of the results in the light of the ground truth. The results show the vertical distribution of the backscattered intensity for several parameters like tree density, forest growth, presence of undergrowth, and ground slope. The evolution of the energy backscattered by the ground, the volume, and the whole forest structure is also displayed. These results have shown the interest of such a campaign to improve the understanding of the distribution of radar mechanisms along the vertical axis.
Preliminary Validation Results of a Novel Concept of Operations for RPAS Integration in TMA and at Airports

The paper describes the results of recent research activities carried out to investigate the safe integration of Remotely Piloted Aircraft System (RPAS) into ATC controlled airspace with a focus on Terminal Manoeuvring Areas (TMAs) and airports. The research activities presented in this paper started with the definition of RPAS-specific use cases and the development of a preliminary novel Concept of Operations. This was followed by the definition and the execution of a simulation test plan for the validation of this new operational concept. The validation campaign has been carried out involving aerospace research centres in the Netherlands (Royal NLR), Germany (DLR), and Italy (CIRA), respectively. Different aspects of RPAS integration in the TMA were considered, such as latency of command & control (C2) link and voice communication link, Automatic Take-off and Landing (ATOL) operations, contingency procedures, and handover of control between two different remote pilot stations. The validation exercises were conducted using real-time human-in-the-loop simulation facilities to assess the human performance of both air traffic controllers and remote pilots. The results showed that the proposed novel concept of operations is suitable for the integration of RPAS at TMAs and airports, with 80% of the objectives fully and 20% partially achieved, and acceptable to excellent results regarding acceptance/situational awareness (8.2/9.0 out of 10 points; higher is better) and workload (2.6 out of 10 points; lower is better).
Path planning of autonomous UAVs using reinforcement learning

Autonomous BVLOS Unmanned Aerial Vehicles (UAVs) are gradually gaining their share in the drone market. Together with the demand for extended levels of autonomy comes the necessity for high-performance obstacle avoidance and navigation algorithms that will allow autonomous drones to operate with minimum or no human intervention. Traditional AI algorithms have been extensively used in the literature for finding the shortest path in 2-D or 3-D environments and navigating the drones successfully through a known and stable environment. However, the situation can become much more complicated when the environment is changing or not known in advance. In this work, we explore the use of advanced artificial intelligence techniques, such as reinforcement learning, to successfully navigate a drone within unspecified environments. We compare our approach against traditional AI algoriths in a set of validation experiments on a simulation environment, and the results show that using only a couple of low-cost distance sensors it is possible to successfully navigate the drone beyond the obstacles.
Investigation of the operational flexibility of a regional hybrid-electric aircraft

The complexity of hybrid-electric aircraft propulsion systems is also characterized by the greater number of degrees of freedom of the energy management system, whose objective is to split the required power to fly the aircraft to the different available powertrains (i.e., gas turbines, electric motors, fuel cells, etc.). Typically, a single design mission is considered for assessing the performance of a hybrid-electric propulsion system, often with a simple constant split power between the batteries and gas turbine. A probabilistic set-based design space exploration methodology is used and allows us to study the effects of lifecycle analysis of the battery pack of a hybrid-electric 50-seater turboprop, while different mission scenarios are considered. Using this approach, it is possible to flexibly find multiple families of energy management strategies that can satisfy battery capacity requirements and the reduction of emissions simultaneously. Furthermore, the generated data can help the designers to understand the hierarchy of the requirements that drive the design of the propulsion system for a range of operating scenarios, with emphasis on the energy storage system. Hence, the airliners are offered enhanced operational flexibility of the aircraft for different and desirable mission profiles.
Stochastic stability and stabilization of a class of piecewise‐homogeneous Markov jump linear systems with mixed uncertainties

This paper investigates the stochastic stability and stabilization problem for a general class of uncertain, continuous‐time Markov jump linear systems (MJLSs). The system under consideration is a piecewise‐homogenous Markovian structure subject to piecewise‐constant time‐varying transition rates (TRs). The time variation of the TRs is characterized by a high‐level Markovian signal, which is independent from the low‐level Markovian mechanism that governs the switching between the system dynamics. It is assumed that the structure is subject to mixed uncertainties in the form of additive norm‐bounded terms. The uncertainties help to consider the effect of imperfections induced by modeling errors for the system dynamics and the TRs of Markovian signals of both levels. This new uncertain, two‐level Markovian jump linear system is a more general model than the existing ones and is applicable to more practical situations. Besides, it is capable of being specialized to uncertain piecewise‐homogeneous MJLS with arbitrarily varying TRs, as well as the uncertain time‐homogeneous MJLS. The stability/stabilizability of this system is first examined by constructing a Lyapunov function which depends on both switching signals. Then, based on the analysis results, the corresponding robust controller gains are synthesized through solving a set of linear matrix inequalities (LMIs). Finally, simulation results for an industrial stirred tank reactor (CSTR) are used to demonstrate the applicability and potentials of the proposed theoretical method. Comparative simulations are also provided to show the superiority of the presented approach to the existing ones. Copyright © 2016 John Wiley & Sons, Ltd.