Miracle-dz/UniFAR-FTUs huggingface dataset:
A General Framework for the Representation of Function and Affordance: A Cognitive, Causal, and Grounded Approach, and a Step Toward AGI

The function and affordance of natural or artificial objects and various physical constructs feature prominently in the functioning of an intelligent system or an intelligent autonomous system (IAS). In navigating an environment, an IAS needs to understand the function and affordance of roads, stairs, bridges, and various physical constructs that may assist with or obstruct its movement to achieve its goal(s). When encountering objects, understanding their function and affordance so that they may be manipulated or made use of to support the IAS in performing certain required tasks is a key aspect of the intelligent functioning of the IAS. In AI research, so far, the attention paid to the characterization and representation of function and affordance has been sporadic and sparse, and it has not received the same attention as, say, object categorization or natural language processing, even though this aspect features prominently in an intelligent system’s functioning, and is more fundamental and important in many ways. In the sporadic and sparse, though commendable efforts so far devoted to the characterization and understanding of function and affordance, there has also been no general framework that could unify all the different use domains and situations related to functionality, and that could provide an intelligent system or an IAS the necessary computational, representational, processing, and reasoning constructs to effect recognition, generation, and application of functional concepts. This paper develops just such a general framework, with an approach that emphasizes the fact that the representations involved must be explicitly cognitive and conceptual, and they must also contain the causal characterizations of the events and processes involved, as well as employ conceptual constructs that are grounded in the referents to which they refer, in order to achieve maximal generality for the framework to be applicable to a wide range of domains and situations. The basic general framework is described, along with a set of basic guiding principles with regards to representation of functionality. To properly and adequately characterize and represent functionality, a descriptive representation language is needed. This language is defined and developed, and many examples of its use are described. The general framework is developed based on an extension of the general language meaning representational framework called conceptual dependency. To support the general characterization and representation of functionality, the basic conceptual dependency framework is enhanced with representational devices called structure anchor and conceptual dependency elaboration, together with the definition of a set of ground level concepts. These novel representational constructs are defined, developed, and described. A general framework dealing with functionality would represent a major step toward achieving Artificial General Intelligence.
PRO-TIP: Phantom for RObust automatic ultrasound calibration by TIP detection

We propose a novel method to automatically calibrate tracked ultrasound probes. To this end we design a custom phantom consisting of nine cones with different heights. The tips are used as key points to be matched between multiple sweeps. We extract them using a convolutional neural network to segment the cones in every ultrasound frame and then track them across the sweep. The calibration is robustly estimated using RANSAC and later refined employing image based techniques. Our phantom can be 3D-printed and offers many advantages over state-of-the-art methods. The phantom design and algorithm code are freely available online. Since our phantom does not require a tracking target on itself, ease of use is improved over currently used techniques. The fully automatic method generalizes to new probes and different vendors, as shown in our experiments. Our approach produces results comparable to calibrations obtained by a domain expert.
Mitigating health disparities in hospital readmissions

Background : The management of hyperglycemia in hospitalized patients has a significant impact on both morbidity and mortality. This study used a large clinical database to predict the need for diabetic patients to be hospitalized, which could lead to improvements in patient safety. These predictions, however, may be vulnerable to health disparities caused by social determinants such as race, age, and gender. These biases must be removed early in the data collection process, before they enter the system and are reinforced by model predictions, resulting in biases in the model’s decisions. In this paper, we propose a machine learning pipeline capable of making predictions as well as detecting and mitigating biases. This pipeline analyses clinical data, determines whether biases exist, removes them, and then make predictions. We demonstrate the classification accuracy and fairness in model predictions using experiments. Results : The results show that when we mitigate biases early in a model, we get fairer predictions. we also find that as we get better fairness, we sacrifice a certain level of accuracy, which is also validated in the previous studies. Conclusion : We invite the research community to contribute to identifying additional factors that contribute to health disparities that can be addressed through this pipeline.
Applications of Deep Learning in Fish Habitat Monitoring: A Tutorial and Survey

Marine ecosystems and their fish habitats are becoming increasingly important due to their integral role in providing a valuable food source and conservation outcomes. Due to their remote and difficult to access nature, marine environments and fish habitats are often monitored using underwater cameras. These cameras generate a massive volume of digital data, which cannot be efficiently analysed by current manual processing methods, which involve a human observer. DL is a cutting-edge AI technology that has demonstrated unprecedented performance in analysing visual data. Despite its application to a myriad of domains, its use in underwater fish habitat monitoring remains under explored. In this paper, we provide a tutorial that covers the key concepts of DL, which help the reader grasp a high-level understanding of how DL works. The tutorial also explains a step-by-step procedure on how DL algorithms should be developed for challenging applications such as underwater fish monitoring. In addition, we provide a comprehensive survey of key deep learning techniques for fish habitat monitoring including classification, counting, localization, and segmentation. Furthermore, we survey publicly available underwater fish datasets, and compare various DL techniques in the underwater fish monitoring domains. We also discuss some challenges and opportunities in the emerging field of deep learning for fish habitat processing. This paper is written to serve as a tutorial for marine scientists who would like to grasp a high-level understanding of DL, develop it for their applications by following our step-by-step tutorial, and see how it is evolving to facilitate their research efforts. At the same time, it is suitable for computer scientists who would like to survey state-of-the-art DL-based methodologies for fish habitat monitoring.
Simple kinesthetic haptics for object recognition

Object recognition is an essential capability when performing various tasks. Humans naturally use either or both visual and tactile perception to extract object class and properties. Typical approaches for robots, however, require complex visual systems or multiple high-density tactile sensors which can be highly expensive. In addition, they usually require actual collection of a large dataset from real objects through direct interaction. In this paper, we propose a kinesthetic-based object recognition method that can be performed with any multi-fingered robotic hand in which the kinematics is known. The method does not require tactile sensors and is based on observing grasps of the objects. We utilize a unique and frame invariant parameterization of grasps to learn instances of object shapes. To train a classifier, training data is generated rapidly and solely in a computational process without interaction with real objects. We then propose and compare between two iterative algorithms that can integrate any trained classifier. The classifiers and algorithms are independent of any particular robot hand and, therefore, can be exerted on various ones. We show in experiments, that with few grasps, the algorithms acquire accurate classification. Furthermore, we show that the object recognition approach is scalable to objects of various sizes. Similarly, a global classifier is trained to identify general geometries (e.g., an ellipsoid or a box) rather than particular ones and demonstrated on a large set of objects. Full scale experiments and analysis are provided to show the performance of the method.
Variational Bayes Deep Operator Network: A data-driven Bayesian solver for parametric differential equations

Neural network based data-driven operator learning schemes have shown tremendous potential in computational mechanics. DeepONet is one such neural network architecture which has gained widespread appreciation owing to its excellent prediction capabilities. Having said that, being set in a deterministic framework exposes DeepONet architecture to the risk of overfitting, poor generalization and in its unaltered form, it is incapable of quantifying the uncertainties associated with its predictions. We propose in this paper, a Variational Bayes DeepONet (VB-DeepONet) for operator learning, which can alleviate these limitations of DeepONet architecture to a great extent and give user additional information regarding the associated uncertainty at the prediction stage. The key idea behind neural networks set in Bayesian framework is that, the weights and bias of the neural network are treated as probability distributions instead of point estimates and, Bayesian inference is used to update their prior distribution. Now, to manage the computational cost associated with approximating the posterior distribution, the proposed VB-DeepONet uses \textit{variational inference}. Unlike Markov Chain Monte Carlo schemes, variational inference has the capacity to take into account high dimensional posterior distributions while keeping the associated computational cost low. Different examples covering mechanics problems like diffusion reaction, gravity pendulum, advection diffusion have been shown to illustrate the performance of the proposed VB-DeepONet and comparisons have also been drawn against DeepONet set in deterministic framework.
A Survey on Uncertainty Reasoning and Quantification for Decision Making: Belief Theory Meets Deep Learning

An in-depth understanding of uncertainty is the first step to making effective decisions under uncertainty. Deep/machine learning (ML/DL) has been hugely leveraged to solve complex problems involved with processing high-dimensional data. However, reasoning and quantifying different types of uncertainties to achieve effective decision-making have been much less explored in ML/DL than in other Artificial Intelligence (AI) domains. In particular, belief/evidence theories have been studied in KRR since the 1960s to reason and measure uncertainties to enhance decision-making effectiveness. We found that only a few studies have leveraged the mature uncertainty research in belief/evidence theories in ML/DL to tackle complex problems under different types of uncertainty. In this survey paper, we discuss several popular belief theories and their core ideas dealing with uncertainty causes and types and quantifying them, along with the discussions of their applicability in ML/DL. In addition, we discuss three main approaches that leverage belief theories in Deep Neural Networks (DNNs), including Evidential DNNs, Fuzzy DNNs, and Rough DNNs, in terms of their uncertainty causes, types, and quantification methods along with their applicability in diverse problem domains. Based on our in-depth survey, we discuss insights, lessons learned, limitations of the current state-of-the-art bridging belief theories and ML/DL, and finally, future research directions.
Bayesian design with sampling windows for complex spatial processes

Optimal design facilitates intelligent data collection. In this paper, we introduce a fully Bayesian design approach for spatial processes with complex covariance structures, like those typically exhibited in natural ecosystems. Coordinate exchange algorithms are commonly used to find optimal design points. However, collecting data at specific points is often infeasible in practice. Currently, there is no provision to allow for flexibility in the choice of design. Accordingly, we also propose an approach to find Bayesian sampling windows, rather than points, via Gaussian process emulation to identify regions of high design efficiency across a multi-dimensional space. These developments are motivated by two ecological case studies: monitoring water temperature in a river network system in the northwestern United States and monitoring submerged coral reefs off the north-west coast of Australia.
EGRU: Event-based GRU for activity-sparse inference and learning

The scalability of recurrent neural networks (RNNs) is hindered by the sequential dependence of each time step’s computation on the previous time step’s output. Therefore, one way to speed up and scale RNNs is to reduce the computation required at each time step independent of model size and task. In this paper, we propose a model that reformulates Gated Recurrent Units (GRU) as an event-based activity-sparse model that we call the Event-based GRU (EGRU), where units compute updates only on receipt of input events (event-based) from other units. When combined with having only a small fraction of the units active at a time (activity-sparse), this model has the potential to be vastly more compute efﬁcient than current RNNs. Notably, activity-sparsity in our model also translates into sparse parameter updates during gradient descent, extending this compute efﬁciency to the training phase. We show that the EGRU demonstrates competitive performance compared to state-of-the-art recurrent network models in real-world tasks, including language modeling while maintaining high activity sparsity naturally during inference and training. This sets the stage for the next generation of recurrent networks that are scalable and more suitable for novel neuromorphic hardware.
Hellinger’s distance to normal distribution as market invariant

Main purpose of distance based portfolio constructions is for portfolio imitation. Here we used distance from normal distribution for other purpose. We attempted to find static market invariant within possible linear combinations of given random variables. We conjectured that “closeness” to normal distribution of possible portfolios in market may reliably represent market microstructure with possible correlations between assets. Taking the squared Hellinger’s distance, we sought for each level of desired mean return the portfolio whose return distribution is closest to Gaussian, with variance taken from efficient frontier found by initially solving mean-variance problem. We found that minimal distance differs significantly from market to market. The sensitivity check showed small average sensitivity for 5% change of a 5% portion of data, small sensitivity for adding new variables in simulated market, and extreme sensitivity to bin numbers. Though distance to normality differed among markets, its sensitivity being small enough in average sometimes showed extreme changes.
Gaia Data Release 3: The Gaia Andromeda Photometric Survey

Context. As part of Gaia Data Release 3 (Gaia DR3), epoch photometry has been released for 1.2 million sources centred on M31. This is a taster for Gaia Data Release 4 where all the epoch photometry will be released. Aims. In this paper the content of the Gaia Andromeda Photometric Survey is described, including statistics to assess the quality of the data. Known issues with the photometry are also outlined. Methods. Methods are given to improve interpretation of the photometry, in particular, a method for error renormalization. Also, use of correlations between the three photometric passbands allows clearer identification of variables that is not affected by false detections caused by systematic effects. Results. The Gaia Andromeda Photometric Survey presents a unique opportunity to look at Gaia epoch photometry that has not been preselected due to variability. This allows investigations to be carried out that can be applied to the rest of the sky using the mean source results. Additionally scientific studies of variability can be carried out on M31 and the Milky Way in general.
Trans-HHS Workshop: Diet, DNA Methylation Processes and Health Genistein Alters Methylation Patterns in Mice

In this study we examine the effect of the phytoestrogen genistein on DNA methylation. DNA methylation is thought to inhibit transcription of genes by regulating alterations in chromatin structure. Estrogenic compounds have been reported to regulate DNA methylation in a small number of studies. Additionally, phytoestrogens are believed to affect progression of some human diseases, such as estrogen-dependent cancers, osteoporosis and cardiovascular disease. Specifically, our working hypothesis is that certain soy phytoestrogens, such as genistein, may be involved in preventing the development of certain prostate and mammary cancers by maintaining a protective DNA methylation profile. The objective of the present study is to use mouse differential methylation hybridization (DMH) arrays to test for changes in the methylation status of the cytosine guanine dinucleotide (CpG) islands in the mouse genome by examining how these methylation patterns are affected by genistein. Male mice were fed a casein-based diet (control) or the same diet containing 300 mg genistein/kg according to one of four regimens: control diet for 4 wk, genistein diet for 4 wk, control diet for 2 wk followed by genistein diet for 2 wk and genistein diet for 2 wk followed by control diet for 2 wk. DNA from liver, brain and prostate were then screened with DMH arrays. Clones with methylation differences were sequenced and compared with known sequences. In conclusion, consumption of genistein diet was positively correlated with changes in prostate DNA methylation at CpG islands of specific mouse genes. J. Nutr. 132: 2419S–2423S, 2002.
Allen’s Interval Algebra and Smart-type Environments

—Allen’s interval algebra is a calculus for temporal reasoning that was introduced in 1983. Reasoning with qualitative time in Allen’s full interval algebra is nondeterministic polynomial time (NP) complete. Research since 1995 identiﬁed maximal tractable subclasses of this algebra via exhaustive computer search and also other ad-hoc methods. In 2003, the full classiﬁcation of complexity for satisﬁability problems over constraints in Allen’s interval algebra was established alge-braically. Recent research proposed scheduling based on the Fishburn-Shepp correlation inequality for posets. This article ﬁrst reviews Allen’s calculus and surrounding computational issues in temporal reasoning. We then go on to describe three potential temporal-related application areas as candidates for scheduling using the Fishburn-Shepp inequality. We also illustrate through concrete examples, and conclude the importance of Fishburn-Shepp inequality for the suggested application areas that are the development of smart homes, intelligent conversational agents and in physiology with emphasis during time-trial physical exercise. The Fishburn-Shepp inequality will enable the development of smart type devices, which will in turn help us to have a better standard of living.
Design , testing , and modelling of a novel robotic system for trans-esophageal ultrasound

Background: Trans-esophageal echocardiography (TEE) has been widely utilized for cardiac disease diagnosis and interventional procedure guidance. However, the TEE operator is required to manually manipulate the probe, often for long periods of time and sometimes in an X-ray environment where there is exposure to ionising radiation. Methods: A novel robotic manipulation system for remote control of commercial TEE probes has been developed and tested. The system has four degrees of freedom (DOF) and characterized by a kinematic model. The accuracy of the model and the error propagation were analysed. Results: The prototype system was shown to exhibit the required function in terms of the mechanical reliability and range of motion. The forward kinematic model can accurately predict the trajectory of the probe tip movement. The average point to point error was 2.60mm and 3.55°. Conclusions: Robotic assistance provided by the proposed system may improve the TEE operating environment. The proposed forward kinematic model can be further employed for automatic control.
A Multilevel Converter with a Floating Bridge for Open-End Winding Motor Drive

In this paper, a circuit topology is analyzed, which is used as a three-level open-end winding IM drive. This topology uses dual inverters with only one dc voltage source at the primary side of the converter. The second bridge converter is connected to a floating capacitor bank. The aim of this topology is to eliminate the requirement for a bulky isolation transformer while achieving multilevel output voltage waveforms. The voltage across the floating capacitor bank is controlled using the redundant switching vectors along with a modified space vector modulation (SVM) scheme, which avoids unwanted voltage levels.
Dividend Policies in Volatile Transitioning Markets

Abstract This paper examines the implicit impact of an individual company financial parameters on dividends payments. The empirical research is conducted within the environment that cross-examines fifteen European transition economies with shared traits of frontier to emerging capital markets development stage and exposure to exogenic global volatility from 2007/8 and Covid-19 economic crises spilling over at magnitude. The purpose of this paper is to test whether companies establish stable dividend policy. Dividends payments are sensitive to earnings and hence adjust imminently. The reason stems from uncertainty on future financial performance and on investor protection. Results yield negative link between solvency and dividends based on the fact that the weaker solvency position decreases the priority of dividends likelihood. Comparably dividends are less desirable if competing with company growth opportunities although investors are less willing to wait for future profits. Altogether transitioning markets are less responsive and structurally feature fewer corporate events.