Miracle-dz/UniFAR-FTUs huggingface dataset:
Representing and Reasoning With Complex Aﬀordances

The concept of aﬀordances has been used in robotics research to endow artiﬁcial agents with the ability to reason about available actions and uses of objects. Previous research has demonstrated that a representation of aﬀordances improves performance when planning and reasoning about available actions, and has introduced a distinction between primitive and complex aﬀordances. Complex aﬀordances refer to actions the execution of which depends on several diﬀerent abilities, or aspects of object usage. Problem solving research in behavioural biology and psychology provides some evidence that ﬂexible tool use and creative problem solving require a type of abductive reasoning that can be supported by a representation of complex aﬀordances, following the deﬁnition described in previous works. The present study aimed to build on ﬁndings demonstrating the utility of primitive aﬀordances, by developing and implementing a representation of complex aﬀordances that capture the prerequisite conditions for complex actions which involve multiple intermediate steps. Complex aﬀordances are encoded and tested in Answer Set Prolog (ASP), a declarative programming paradigm that supports non-monotonic logical reasoning for planning and diagnostics. The utility of complex aﬀordances was evaluated by running paired trials, where planning was carried out with complete domain knowledge and missing domain axioms relating to complex aﬀordances. Use of complex aﬀordances led to the production of minimal plans, and resulted in a larger number of plans found for a given goal. Domain axioms involving complex aﬀordances led to a higher overall success rate, while exclusion of these axioms led to the production of invalid plans. The present study therefore demonstrated a successful implementation of complex aﬀordances, and their utility. Finally, areas of future work were explored, including the applicability of complex aﬀordances in the creation of complex plans, problem solving, and plan recognition.
PUBLIC SECTOR POLICY OF ESTIMATING MODEL FOR RENEWABLE ENERGY

Renewable energies are crucially needed right now. One of the them is ethanol as a non-fossil energy source. Data time-series of world demand for ethanol are very interesting to find its forecasting models, so that the production targets can be more accurate. Generalised auto-regressive conditional heteroscedasticity is one of the best models we use. Our findings AR(1) Generalised Auto-Regressive Conditional Heteroskedasticity (GARCH) (1,1) modelsare considered as a good-fit measurement in predicting ethanol demand. Increasing the number of demand should be considered with the number of its processes. In this paper, we combine an analysis of economic considerations (predicting demand levels) with a political analysis of policies (describing renewable energy policy options).
Deep Action-conditioned Tactile Predictive Model

: Enhanced robotic grasping and manipulation involves an intelligent 1 controller changing the manipulators trajectory to avoid failure due to slippage. 2 Tactile sensing is considered an essential feature for such dexterous robot ma-3 nipulations. Predicting the tactile readings given planned actions of the robot is 4 challenging and still remains an open problem. In this paper, we present a tac-5 tile predictive model by proposing a series of robot-action-conditioned recurrent 6 neural network models. Moreover, we created a data-set of simple pick-and-move 7 tasks as well as complex kinesthetic movements with a 7DOF robot manipulator 8 while grasping a bottle-shaped object with low resolution magnetic-based tactile 9 sensors on the ﬁngers of the robot. The tactile predictive models are trained us-10 ing the data-set and their performances on a test set are compared. The proposed 11 architecture is inspired by state of the art video prediction models and resolves 12 their shortcomings in a real-time manipulation tactile prediction task. The model 13 shows good test results, suitable for use in a future slip avoidance controller during 14 robotic manipulative movements. 15
Betting on mean reversion in the VIX? Evidence from ETP flows

We evaluate the ability of different asset pricing models to explain the flows into VIX ETPs with long volatility exposure. We find no evidence supporting that investors consider systematic risk when they evaluate VIX ETP performance. Instead, investors appear to follow a simple mean reversion strategy, buying (selling) when returns are negative (positive), coinciding with low (high) levels of the VIX. We provide evidence that this mean reversion strategy is a very likely explanation for the "low premium response puzzle" in the VIX premium.
APPLICATION OF PRINCIPAL COMPONENT ANALYSIS TO DROUGHT INDICATORS OF THREE REPRESENTATIVE CROATIAN REGIONS

Drought has become a very frequent hydrological event globally, including in Croatia. It can generally be explained by air temperature and precipitation changes on an annual and seasonal basis, owing to climate change. To contribute to the knowledge on drought phenomena in Croatia, the changes in air temperature and precipitation over a relatively long period between 1951 and 2018 were analyzed. The meteorological stations included in the research were Osijek, Zagreb, and Split, which represented the climate of the entire country. Drought was estimated using the standardized precipitation evapotranspiration index, which is one of the most comprehensive drought indices. Furthermore, the drought severity and duration were calculated using run theory. These parameters were tested for homogeneity using the standard normal homogeneity test. Only the air temperature exhibited inhomogeneity, with a break year in 1991 (Zagreb and Split) and 1998 (Osijek). The existence of significant temporal trends was tested using the non-parametric Mann–Kendall trend test. The probability of drought occurrence with a certain duration and severity was calculated using the copula function. Finally, principal component analysis was applied to the computed standardized Mann–Kendall test statistic (ZMK ) to define the relevance of each parameter change and their combination in drought occurrence on a seasonal basis. Drought occurrence was less recognizable from 1951 to 1991 (1998). In the second sub-period, the impact of an increasing air temperature was the most significant variable, particularly in Zagreb.
Damage Evolution of Sandstone under Constant-Amplitude Cyclic Loading Based on Acoustic Emission Parameters and Resistivity

In the real environment, besides static load, rock is more affected by cyclic load. There is a large difference in the mechanical properties exhibited by rocks under cyclic and static loading. Therefore, it is particularly necessary to investigate the mechanical characteristics of rock subjected to cyclic loading. These parameters of rock, acoustic emission (AE) and resistivity, are both sensitive to the failure process of rock, and they are complementary to the different stages of rock damage. Therefore, in this paper, the AE characteristics and resistivity properties of sandstone subjected to constant-amplitude cyclic loading and unloading were experimentally investigated using a typical sandstone in Chongqing. The same three-stage pattern was found for the AE evolution of sandstones during constant-amplitude cyclic loading. Initial evolution stage: the rock deformation is fast with strong changes in the AE signal. The rock deformation developed slowly while the changes of the AE signal were stable in the constant velocity evolution stage. The rock deformation developed dramatically while the AE signal became more intense in accelerated evolution stage. The change in resistivity is characterized by a rapid decrease during the loading stage and a rapid rebound during the unloading stage. Overall, from the beginning of the cycle to the end of the cycle, the resistivity of the sandstone showed a general trend of gradual decrease, until the sudden increase in resistivity at the time of damage. Finally, a damage model based on AE parameters and resistivity was constructed by combining damage mechanics.
Auditing Robot Learning for Safety and Compliance during Deployment

Robots of the future are going to exhibit increasingly human-like and super-human intelligence in a myriad of different tasks. They are also likely going to fail and be incompliant with human preferences in increasingly subtle ways. Towards the goal of achieving autonomous robots, the robot learning community has made rapid strides in applying machine learning techniques to train robots through data and interaction. This makes the study of how best to audit these algorithms for checking their compatibility with humans, pertinent and urgent. In this paper, we draw inspiration from the AI Safety and Alignment communities and make the case that we need to urgently consider ways in which we can best audit our robot learning algorithms to check for failure modes, and ensure that when operating autonomously, they are indeed behaving in ways that the human algorithm designers intend them to. We believe that this is a challenging problem that will require efforts from the entire robot learning community, and do not attempt to provide a concrete framework for auditing. Instead, we outline high-level guidance and a possible approach towards formulating this framework which we hope will serve as a useful starting point for thinking about auditing in the context of robot learning.
Architecture Aware Latency Constrained Sparse Neural Networks

Acceleration of deep neural networks to meet a specific latency constraint is essential for their deployment on mobile devices. In this paper, we design an architecture aware latency constrained sparse (ALCS) framework to prune and accelerate CNN models. Taking modern mobile computation architectures into consideration, we propose Single Instruction Multiple Data (SIMD)-structured pruning, along with a novel sparse convolution algorithm for efficient computation. Besides, we propose to estimate the run time of sparse models with piece-wise linear interpolation. The whole latency constrained pruning task is formulated as a constrained optimization problem that can be efficiently solved with Alternating Direction Method of Multipliers (ADMM). Extensive experiments show that our system-algorithm co-design framework can achieve much better Pareto frontier among network accuracy and latency on resource-constrained mobile devices.
Growth and maturation of white-fleshed dragon fruit

Pitaya has great potential for marketing and processing. However, the lack of knowledge about its cultivation is still a barrier that prevents its diffusion in the country. The objective of this study was to analyze the changes that occur during the growth and ripening of white-fleshed dragon fruit. Physical and physicochemical and chemical fruit characterization analyzes were conducted at several development stages (7, 14, 21, 28, 35 and 42 days after anthesis). Increases in length, fruit mass and pulp, yield and soluble solids were observed, as well as reductions in skin thickness, strength and pulp pH. Significant and important levels in mineral for the human diet were found, especially nitrogen, potassium, calcium, manganese, iron and zinc. Intense changes in seed maturation and biomass accumulation occoured during the fruit growth phase, while in the maturation stage the main changes are related to the improvement of the organoleptic characteristics such as acidity reduction and content of soluble solids, besides the reduction of the mass and thickness of the skin. The ideal harvest point, whereas organoleptic characteristics and visual aspects, is around 35 days, when fruit reached physiological maturity; however, at 42 days, the fruit pulp still had sufficient quality for consumption.
Association between alcohol and crack: Prevalence, effects, associated factors and experiences of combined use

Objective To estimate the prevalence and factors associated with the effect of alcohol on crack cocaine use and to analyze experiences related to combined use. Materials and methods: sequential mixed methods (qualitative and quantitative) research, carried out between August 2014 and August 2015 with people who use crack. In the quantitative approach, a cross-sectional study was conducted with 1,062 participants. Factors associated with “alcohol use with the effect of increasing the effect of crack/crack craving” were estimated by multiple regression. In the qualitative approach, 39 interviews were conducted using Bardin’s content analysis technique. Results 871 (82.0%) participants reported consuming alcohol, among them, 668 (76.7%) used alcohol combined with crack: 219 (32.8%) reported feeling an effect of reduction in paranoia and/or crack craving and 384 (57.5%) reported feeling an increase in the effect of crack and in the craving to consume the drug. This relationship was also observed in the narratives of the people who use crack, with the possibility of a cyclic effect of consumption of the two substances. Those who related alcohol use to the effect of increasing crack craving (384) were more likely to use alcohol before crack (OR: 1.81; 95%CI: 1.13–2.89); to consume more than 20 stones daily (OR: 1.48; 95%CI: 1.01–2.16); to remain in abstinence from crack for less than one month (OR: 3.20; 95%CI: 1.91–5.35); to use dependence treatment services (OR: 1.85; 95%CI: 1.26–2.71); and to commit physical violence (OR:1.67; 95%CI:1.08–2.56). Conclusion The findings of this study indicate that the modulation of the effect of alcohol use on crack cocaine depends on the moment when the drugs are consumed, and the use of alcohol before crack consumption is associated with characteristics that suggest a greater vulnerability to patterns of harmful crack use. Even though combined use is referred to as a way of reducing the negative effects of crack, the damage of this association may be greater than its possible benefits.
Multiset Canonical Correlations Analysis of Bidimensional Intrinsic Mode Functions for Automatic Target Recognition of SAR Images

A novel feature generation algorithm for the synthetic aperture radar image is designed in this study for automatic target recognition. As an adaptive 2D signal processing technique, bidimensional empirical mode decomposition is employed to generate multiscale bidimensional intrinsic mode functions from the original synthetic aperture radar images, which could better capture the broad spectral information and details of the target. And, the combination of the original image and decomposed bidimensional intrinsic mode functions could promisingly provide more discriminative information for correct target recognition. To reduce the high dimension of the original image as well as bidimensional intrinsic mode functions, multiset canonical correlations analysis is adopted to fuse them as a unified feature vector. The resultant feature vector highly reduces the high dimension while containing the inner correlations between the original image and decomposed bidimensional intrinsic mode functions, which could help improve the classification accuracy and efficiency. In the classification stage, the support vector machine is taken as the basic classifier to determine the target label of the test sample. In the experiments, the 10-class targets in the moving and stationary target acquisition and recognition dataset are classified to investigate the performance of the proposed method. Several operating conditions and reference methods are setup for comprehensive evaluation.
Machine Learning-Based Facial Beauty Prediction and Analysis of Frontal Facial Images Using Facial Landmarks and Traditional Image Descriptors

The beauty industry has seen rapid growth in multiple countries and due to its applications in entertainment, the analysis and assessment of facial attractiveness have received attention from scientists, physicians, and artists because of digital media, plastic surgery, and cosmetics. An analysis of techniques is used in the assessment of facial beauty that considers facial ratios and facial qualities as elements to predict facial beauty. Here, the facial landmarks are extracted to calculate facial ratios according to Golden Ratios and Symmetry Ratios, and an ablation study is performed to find the best performing feature set from extracted ratios. Subsequently, Gray Level Covariance Matrix (GLCM), Hu's Moments, and Color Histograms in the HSV space are extracted as texture, shape, and color features, respectively. Another ablation study is performed to find out which feature performs the best when concatenated with the facial landmarks. Experimental results show that the concatenation of primary facial characteristics with facial landmarks improved the prediction score of facial beauty. Four models are trained, K-Nearest Neighbors (KNN), Linear Regression (LR), Random Forest (RF), and Artificial Neural Network (ANN) on a dataset of 5500 frontal facial images, and amongst them, KNN performs the best for the concatenated features achieving a Pearson's Correlation Coefficient of 0.7836 and a Mean Squared Error of 0.0963. Our analysis also provides us with insights into how different machine learning models can understand the concept of facial beauty.
Measurement error using a SeeMaLab structured light 3D scanner against a Microscribe 3D digitizer

Background Geometric morphometrics is a powerful approach to capture and quantify morphological shape variation. Both 3D digitizer arms and structured light surface scanners are portable, easy to use, and relatively cheap, which makes these two capturing devices obvious choices for geometric morphometrics. While digitizer arms have been the “gold standard”, benefits of having full 3D models are manifold. We assessed the measurement error and investigate bias associated with the use of an open-source, high-resolution structured light scanner called SeeMaLab against the popular Microscribe 3D digitizer arm. Methodology The analyses were based on 22 grey seal (Halichoerus grypus) skulls. 31 fixed anatomical landmarks were annotated both directly using a Microscribe 3D digitizer and on reconstructed 3D digital models created from structured light surface scans. Each skull was scanned twice. Two operators annotated the landmarks, each twice on all the skulls and 3D models, allowing for the investigation of multiple sources of measurement error. We performed multiple Procrustes ANOVAs to compare the two devices in terms of within- and between-operator error, to quantify the measurement error induced by device, to compare between-device error with other sources of variation, and to assess the level of scanning-related error. We investigated the presence of general shape bias due to device and operator. Results Similar precision was obtained with both devices. If landmarks that were identified as less clearly defined and thus harder to place were omitted, the scanner pipeline would achieve higher precision than the digitizer. Between-operator error was biased and seemed to be smaller when using the scanner pipeline. There were systematic differences between devices, which was mainly driven by landmarks less clearly defined. The factors device, operator and landmark replica were all statistically significant and of similar size, but were minor sources of total shape variation, compared to the biological variation among grey seal skulls. The scanning-related error was small compared to all other error sources. Conclusions As the scanner showed precision similar to the digitizer, a scanner should be used if the advantages of obtaining detailed 3D models of a specimen are desired. To obtain high precision, a pre-study should be conducted to identify difficult landmarks. Due to the observed bias, data from different devices and/or operators should not be combined when the expected biological variation is small, without testing the landmarks for repeatability across platforms and operators. For any study necessitating the combination of landmark measurements from different operators, the scanner pipeline will be better suited. The small scanning-related error indicates that by following the same scanning protocol, different operators can be involved in the scanning process without introducing significant error.
Novel Cellulose Fibre-Based Flexible Plasmonic Membrane for Point-of-Care SERS Biomarker Detection in Chronic Wound Healing

Background Wound management is stretching the limits of health systems globally, challenging clinicians to evaluate the effectiveness of their treatments and deliver appropriate care to their patients. Visual inspection and manual measurement of wound size are subjective, often inaccurate and inconsistent. Growth factors, such as pro-inflammatory cytokines and proteases, play important roles in cutaneous wound healing. However, little is known about the point-of-care monitoring of the changes in such markers during the healing process. Here, we explore the capability of surface-enhanced Raman spectroscopy (SERS) as a viable point-of-care platform to monitor the changes of these surrogate indicators of healing status in chronic wounds. Methods We developed a biofunctionalized flexible, cost-effective, scalable and easy-to-fabricate plasmonic SERS substrate using cellulose fibre (CF), which is used for sensing of wound markers based on a modified immunoassay method. Results We evaluated and selected the reliable silver nano-island thickness that will be sputtered onto the CF-based substrate for the highest SERS enhancement. Using this biofunctionalized SERS substrate, we detected varying concentrations of MMP-9 (10–5000 ng/mL) and TNF-α (5–100 ng/mL) proteins to model the wound exudates. This SERS detection method demonstrates a linear response within biologically relevant concentrations, ranging from 10 to 500 ng/mL for MMP-9 and 5 to 25 ng/mL for TNF-α for these surrogate indicators. Conclusion Our SERS sensing platform achieved detection limits in the µM to sub-nM range and displayed high sensitivity and selectivity. This could result in a cheap, point-of-care device that provides a non-invasive measure of cutaneous wound healing in real time. We envision that these flexible substrates after activation may be incorporated into wound dressings in future for routine monitoring of wound healing status.