Miracle-dz/UniFAR-FTUs huggingface dataset:
Should artificial agents ask for help in human-robot collaborative problem-solving?

Transferring as fast as possible the functioning of our brain to artificial intelligence is an ambitious goal that would help advance the state of the art in AI and robotics. It is in this perspective that we propose to start from hypotheses derived from an empirical study in a human-robot interaction and to verify if they are validated in the same way for children as for a basic reinforcement learning algorithm. Thus, we check whether receiving help from an expert when solving a simple close-ended task (the Towers of Hanoi) allows to accelerate or not the learning of this task, depending on whether the intervention is canonical or requested by the player. Our experiences have allowed us to conclude that, whether requested or not, a Q-learning algorithm benefits in the same way from expert help as children do.
Analysis of spatial variability within mot+ and mot− Pseudomonas fluorescens biofilms using representative elements

The spatial variability for most measurable parameters contained within biofilms is very large. Therefore a procedure for determining statistically representative regions of analysis is desirable. Scanning confocal laser microscopy, a computer‐controlled xy stage, and fluorescence exclusion staining were used to obtain a series of optical thin sections of biofilms formed by motile (mot+) and nonmotile (mot−) Pseudomonas fluorescens on the surfaces of glass flow cells. Based on a representative elementary area (REA) analysis procedure, the images were used to construct montages large enough to encompass the range of variation in biofilm cell area. The minimum area of analysis required to be representative varied with depth in the biofilm and between the strains used. Biofilms consisting of mot− P. fluorescens were variable. Thus, large area (REA ≥ × 105‐μm−) were required for statistically valid comparisons of cell distribution. REAs for the mot+ biofilm reflected a more uniform distribution of cells at al.
Explainable Artificial Intelligence: a Systematic Review

Explainable Artificial Intelligence (XAI) has experienced a significant growth over the last few years. This is due to the widespread application of machine learning, particularly deep learning, that has led to the development of highly accurate models but lack explainability and interpretability. A plethora of methods to tackle this problem have been proposed, developed and tested. This systematic review contributes to the body of knowledge by clustering these methods with a hierarchical classification system with four main clusters: review articles, theories and notions, methods and their evaluation. It also summarises the state-of-the-art in XAI and recommends future research directions.
Learning to Recognize Code-switched Speech Without Forgetting Monolingual Speech Recognition

Recently, there has been significant progress made in Automatic Speech Recognition (ASR) of code-switched speech, leading to gains in accuracy on code-switched datasets in many language pairs. Code-switched speech co-occurs with monolingual speech in one or both languages being mixed. In this work, we show that fine-tuning ASR models on code-switched speech harms performance on monolingual speech. We point out the need to optimize models for code-switching while also ensuring that monolingual performance is not sacrificed. Monolingual models may be trained on thousands of hours of speech which may not be available for re-training a new model. We propose using the Learning Without Forgetting (LWF) framework for code-switched ASR when we only have access to a monolingual model and do not have the data it was trained on. We show that it is possible to train models using this framework that perform well on both code-switched and monolingual test sets. In cases where we have access to monolingual training data as well, we propose regularization strategies for fine-tuning models for code-switching without sacrificing monolingual accuracy. We report improvements in Word Error Rate (WER) in monolingual and code-switched test sets compared to baselines that use pooled data and simple fine-tuning.
Estimação de parâmetros genéticos para produção de leite de vacas da raça Holandesa via regressão aleatória

A total of 21,702 records of milk production from 2,429 first-lactation Holstein cows, sired by 233 bulls, collected in 33 herds in the State of Rio Grande do Sul from 1991 to 2003, were used to estimate genetic parameters for that characteristic. The random regression model adjusted to test day from the 6th and the 305th lactation day included the effect of herd-year-month of the test day, the age of the cow at parturition, and the order fourth Legendre polynomial parameters, in order to obtain the average curve for the milk production of the population and parameters from the same polynomial to estimate the additive genetic and permanent environmental random effects. The genetic and permanent environmental variances for test day milk yield ranged from 2.38 to 3.14 and from 7.55 to 10.35, respectively. The estimated heritabilities gradually increased from the beginning (0.14) to the end (0.20) of the lactation period, indicating that test day milk yield is a characteristic with moderate heritability. The genetic correlation between milk yield in different phases of lactation ranged from 0.33 to 0.99 and was higher between the adjacent test days. The permanent environmental correlations followed the same tendency of the genetic ones. The random regression animal model using Legendre polynomials of order four can be considered as a good tool to estimate genetic parameters for milk production throughout the lactation.
Field Simulation of Transgenic Seed Admixture Dispersion in Maize with a Blue Kernel Color Marker

Seed purity in maize (Zea mays L.) has been almost neglected in coexistence studies. In this study, the impact of initial seed admixture was assessed in 20 fields (17 integrated pest management [IPM] and three organics) in the Swiss Midlands in 2007 and 2008 and a seed admixture threshold was proposed. The bluekernelled French hybrid Adonis was used as a color-marker admixture. In 2009, its pollen production was assessed relative to those of two commercial hybrids (LG 32.12 and DKC 2960) that released 41 and 62%, respectively, less pollen than Adonis. Adonis was added to commercial yellow hybrid seeds at a proportion of 1:99. On average, the seed admixture at harvest was 2.8 times greater than the seed admixture at sowing. The greater pollen production of Adonis relative to yellow hybrids explained the increase in the harvest seed admixture via pollen flow. The lesser average kernel number per plant of Adonis (316 kernels plant−1; 451 kernels plant−1 for commercial hybrids) buffered the effect of pollen flow. Correcting for these differences provided simulation results compatible with the Hardy–Weinberg equilibrium for a trait with 1% frequency. When compared among the 20 field situations, (i) flower synchrony between admixture and commercial subpopulation and (ii) vertical distance between tassels of Adonis and ears of the commercial hybrid contributed most to field differences. To guarantee the 0.9% threshold imposed by the European Union for non-genetically-modified product, the seed admixture threshold should range between 0.2 and 0.5%.
Random matrix model for the QCD Kondo effect

We propose a novel random matrix model that describes the QCD Kondo effect. The model correctly implements both the chiral symmetry of light quarks and the $\mathrm{SU}(2)$ spin symmetry of heavy quarks. We take the large-$N$ limit with $N$ the matrix size and show that the model has three phases: the pure Kondo phase with no chiral condensate, the pure chirally broken phase with no Kondo condensate, and the coexistence phase. The model predicts that the pairing form of the Kondo condensate in the coexistence phase is significantly altered compared to the pure Kondo phase. For each of these phases we rigorously derive the low-energy effective theory of Nambu-Goldstone modes and obtain compact closed expressions for the partition function with external sources. We also include a chiral chemical potential into the model and examine the vacuum structure.
Heart Rate Estimation from Face Videos for Student Assessment: Experiments on edBB

In this study we estimate the heart rate from face videos for student assessment. This information could be very valuable to track their status along time and also to estimate other data such as their attention level or the presence of stress that may be caused by cheating attempts. The recent edBBplat, a platform for student behavior modelling in remote education, is considered in this study1. This platform permits to capture several signals from a set of sensors that capture biometric and behavioral data: RGB and near infrared cameras, microphone, EEG band, mouse, smartwatch, and keyboard, among others. In the experimental framework of this study, we focus on the RGB and near-infrared video sequences for performing heart rate estimation applying remote photoplethysmography techniques. The experiments include behavioral and physiological data from 25 different students completing a collection of tasks related to e-learning. Our proposed face heart rate estimation approach is compared with the heart rate provided by the smartwatch, achieving very promising results for its future deployment in e-learning applications.
When to sell an asset amid anxiety about drawdowns

We consider risk‐averse investors with different levels of anxiety about asset price drawdowns. The latter is defined as the distance of the current price away from its best performance since inception. These drawdowns can increase either continuously or by jumps, and will contribute toward the investor's overall impatience when breaching the investor's private tolerance level. We investigate the unusual reactions of investors when aiming to sell an asset under such adverse market conditions. Mathematically, we study the optimal stopping of the utility of an asset sale with a random discounting that captures the investor's overall impatience. The random discounting is given by the cumulative amount of time spent by the drawdowns in an undesirable high region, fine‐tuned by the investor's personal tolerance and anxiety about drawdowns. We prove that in addition to the traditional take‐profit sales, the real‐life employed stop‐loss orders and trailing stops may become part of the optimal selling strategy, depending on different personal characteristics. This paper thus provides insights on the effect of anxiety and its distinction with traditional risk aversion on decision making.
Reducing the X-ray radiation exposure frequency in cardio-angiography via deep-learning based video interpolation

Cardiac coronary angiography is a major technology to assist doctors during cardiac interventional surgeries. Under the exposure of X-ray radiation, doctors inject contrast agents through catheters to determine the position and status of coronary vessels in real time. To get a coronary angiography video with a high frame rate, the doctor needs to increase the exposure frequency and intensity of the X-ray. This will inevitably increase the X-ray harm to both patients and surgeons. In this work, we innovatively utilize a deep-learning based video interpolation algorithm to interpolate coronary angiography videos. Moreover, we establish a new coronary angiography image dataset ,which contains 95,039 triplets images to retrain the video interpolation network model. Using the retrained network we synthesize high frame rate coronary angiography video from the low frame rate coronary angiography video. The average peak signal to noise ratio(PSNR) of those synthesized video frames reaches 34dB. Extensive experiment results demonstrate the feasibility of using the video frame interpolation algorithm to synthesize continuous and clear high frame rate coronary angiography video. With the help of this technology, doctors can significantly reduce exposure frequency and intensity of the X-ray during coronary angiography.
Experimental Evaluation of Empirical NB-IoT Propagation Modelling in a Deep-Indoor Scenario

Path-loss modelling in deep-indoor scenarios is a difficult task. On one hand, the theoretical formulae solely dependent on transmitter-receiver distance are too simple; on the other hand, discovering all significant factors affecting the loss of signal power in a given situation may often be infeasible. In this paper, we experimentally investigate the influence of deep-indoor features such as indoor depth, indoor distance and distance to the closest tunnel corridor and the effect on received power using NB-IoT. We describe a measurement campaign performed in a system of long underground tunnels, and we analyse linear regression models involving the engineered features. We show that the current empirical models for NB-IoT signal attenuation are inaccurate in a deep-indoor scenario. We observe that 1) indoor distance and penetration depth do not explain the signal attenuation well and increase the error of the prediction by 2-12 dB using existing models, and 2) a promising feature of average distance to the nearest corridor is identified.
Challenge report: Recognizing Families In the Wild Data Challenge

This paper is a brief report to our submission to the Recognizing Families In the Wild Data Challenge (4th Edition), in conjunction with FG 2020 Forum. Automatic kinship recognition has attracted many researchers' attention for its full application, but it is still a very challenging task because of the limited information that can be used to determine whether a pair of faces are blood relatives or not. In this paper, we studied previous methods and proposed our method. We try many methods, like deep metric learning-based, to extract deep embedding feature for every image, then determine if they are blood relatives by Euclidean distance or method based on classes. Finally, we find some tricks like sampling more negative samples and high resolution that can help get better performance. Moreover, we proposed a symmetric network with a binary classification based method to get our best score in all tasks.
LIPIDS AND FATTY ACIDS OF WILD EDIBLE MUSHROOMS OF THE GENUS BOLETUS

Fifteen species of wild edible mushrooms belonging to the genus Boletus (phylum Basidiomycota) were examined by high-performance thin-layer chromatographic (HP-TLC) analysis and gas chromatography–mass spectrometry (GC-MS) analysis for the presence of neutral, glyco-, phospholipids, betaine lipids and fatty acids. The most abundant fatty acids were oleic acid (9-18:1, 15–42%), linoleic acid (9,12-18:2, 38–58%) and palmitic acid (16:0, 7–17%), but a great variation of the ester composition from one to another one was found. A comparison of neutral lipids, glycolipids, polar lipids and fatty acid composition of four species was done. Diacylglyceryltrimethylhomoserine and phosphatidylcholine were found as major components among polar lipids. Chemical constituents were characterized by GC-MS, HP-TLC and other chemical methods. Biologic activity of some isolated compounds from the genus Boletus is also discussed.
Retrieval of Family Members Using Siamese Neural Network

Retrieval of family members in the wild aims at finding family members of the given subject in the dataset, which is useful in finding the lost children and analyzing the kinship. However, due to the diversity in age, gender, pose and illumination of the collected data, this task is always challenging. To solve this problem, we propose our solution with deep Siamese neural network. Our solution can be divided into two parts: similarity computation and ranking. In training procedure, the Siamese network firstly takes two candidate images as input and produces two feature vectors. And then, the similarity between the two vectors is computed with several fully connected layers. While in inference procedure, we try another similarity computing method by dropping the followed several fully connected layers and directly computing the cosine similarity of the two feature vectors. After similarity computation, we use the ranking algorithm to merge the similarity scores with the same identity and output the ordered list according to their similarities. To gain further improvement, we try different combinations of backbones, training methods and similarity computing methods. Finally, we submit the best combination as our solution and our team(ustc-nelslip) obtains favorable result in the track3 of the RFIW2020 challenge with the first runner-up, which verifies the effectiveness of our method. Our code is available at: https://github.com/gniknoil/FG2020-kinship
Center-of-Mass-based Robust Grasp Planning for Unknown Objects Using Tactile-Visual Sensors

An unstable grasp pose can lead to slip, thus an unstable grasp pose can be predicted by slip detection. A regrasp is required afterwards to correct the grasp pose in order to finish the task. In this work, we propose a novel regrasp planner with multi-sensor modules to plan grasp adjustments with the feedback from a slip detector. Then a regrasp planner is trained to estimate the location of center of mass, which helps robots find an optimal grasp pose. The dataset in this work consists of 1 025 slip experiments and 1 347 regrasps collected by one pair of tactile sensors, an RGB-D camera and one Franka Emika robot arm equipped with joint force/torque sensors. We show that our algorithm can successfully detect and classify the slip for 5 unknown test objects with an accuracy of 76.88% and a regrasp planner increases the grasp success rate by 31.0% compared to the state-of-the-art vision-based grasping algorithm.
On Tilings of Asymmetric Limited-Magnitude Balls

We study whether an asymmetric limited-magnitude ball may tile ${\mathbb{Z}^n}$. This ball generalizes previously studied shapes: crosses, semi-crosses, and quasi-crosses. Such tilings act as perfect error-correcting codes in a channel which changes a transmitted integer vector in a bounded number of entries by limited-magnitude errors.A construction of lattice tilings based on perfect codes in the Hamming metric is given. Several non-existence results are proved, both for general tilings, and lattice tilings. A complete classification of lattice tilings for two certain cases is proved.
Neuro-Adaptive Formation Control and Target Tracking for Nonlinear Multi-Agent Systems With Time-Delay

This letter proposes an adaptive neural network-based backstepping controller that uses rigid graph theory to address the distance-based formation control problem and target tracking for nonlinear multi-agent systems with bounded time-delay and disturbance. The radial basis function neural network (RBFNN) is used to overcome and compensate for the unknown nonlinearity and disturbance in the system dynamics. The effect of the state time-delay of the agents is alleviated by using an appropriate control signal that is designed based on specific Lyapunov function and Young’s inequality. The adaptive neural network (NN) weights tuning law is derived using this Lyapunov function. An upper bound for the singular value of the normalized rigidity matrix is introduced, and uniform ultimate boundedness (UUB) of the formation distance error is rigorously proven based on the Lyapunov stability theory. Finally, the performance and effectiveness of the proposed method are validated through the simulation results on nonlinear multi-agent systems. Comparisons between the proposed distance-based method and an existing, displacement-based method are provided to evaluate the performance of the suggested method.
Hessian-based optimization of quantum dynamics under constrained control

Efficient optimization of quantum systems with large state or control spaces is a necessity for reaching fault tolerant thresholds. A standard tool for optimizing simulated quantum dynamics is the gradient-based \textsc{grape} algorithm, which has been successfully applied in a wide range of different branches of quantum physics. In this work, we derive and implement exact $2^{\mathrm{nd}}$ order analytical Hessian of the coherent dynamics and find improvements compared to the approximate $2^{\mathrm{nd}}$ order \textsc{bfgs} standard optimization. We demonstrate performance improvements for both the best and average errors of constrained unitary gate synthesis on a circuit-\textsc{qed} system over a broad range of different gate durations.
Linguistic Features for Readability Assessment

Readability assessment aims to automatically classify text by the level appropriate for learning readers. Traditional approaches to this task utilize a variety of linguistically motivated features paired with simple machine learning models. More recent methods have improved performance by discarding these features and utilizing deep learning models. However, it is unknown whether augmenting deep learning models with linguistically motivated features would improve performance further. This paper combines these two approaches with the goal of improving overall model performance and addressing this question. Evaluating on two large readability corpora, we find that, given sufficient training data, augmenting deep learning models with linguistically motivated features does not improve state-of-the-art performance. Our results provide preliminary evidence for the hypothesis that the state-of-the-art deep learning models represent linguistic features of the text related to readability. Future research on the nature of representations formed in these models can shed light on the learned features and their relations to linguistically motivated ones hypothesized in traditional approaches.
Enabling 5G on the Ocean: A Hybrid Satellite-UAV-Terrestrial Network Solution

Current fifth generation (5G) cellular networks mainly focus on the terrestrial scenario. Due to the difficulty of deploying communications infrastructure on the ocean, the performance of existing maritime communication networks (MCNs) is far behind 5G. This problem can be solved by using unmanned aerial vehicles (UAVs) as agile aerial platforms to enable on-demand maritime coverage, as a supplement to marine satellites and shore-based terrestrial based stations (TBSs). In this article, we study the integration of UAVs with existing MCNs, and investigate the potential gains of hybrid satellite-UAV-terrestrial networks for maritime coverage. Unlike the terrestrial scenario, vessels on the ocean keep to sea lanes and are sparsely distributed. This provides new opportunities to ease the scheduling of UAVs. Also, new challenges arise due to the more complicated maritime prorogation environment, as well as the mutual interference between UAVs and existing satellites/TBSs. We discuss these issues and show possible solutions considering practical constraints.
Robust Reinforcement Learning with Wasserstein Constraint

Robust Reinforcement Learning aims to find the optimal policy with some extent of robustness to environmental dynamics. Existing learning algorithms usually enable the robustness through disturbing the current state or simulating environmental parameters in a heuristic way, which lack quantified robustness to the system dynamics (i.e. transition probability). To overcome this issue, we leverage Wasserstein distance to measure the disturbance to the reference transition kernel. With Wasserstein distance, we are able to connect transition kernel disturbance to the state disturbance, i.e. reduce an infinite-dimensional optimization problem to a finite-dimensional risk-aware problem. Through the derived risk-aware optimal Bellman equation, we show the existence of optimal robust policies, provide a sensitivity analysis for the perturbations, and then design a novel robust learning algorithm--Wasserstein Robust Advantage Actor-Critic algorithm (WRAAC). The effectiveness of the proposed algorithm is verified in the Cart-Pole environment.