Miracle-dz/UniFAR-FTUs huggingface dataset:
Date of publication xxxx 00, 0000, date of current version xxxx 00, 0000

3D lung segmentation is essential since it processes the volumetric information of the lungs, removes the unnecessary areas of the scan, and segments the actual area of the lungs in a 3D volume. Recently, the deep learning model, such as U-Net outperforms other network architectures for biomedical image segmentation. In this paper, we propose a novel model, namely, Recurrent Residual 3D U-Net (RU3D), for the 3D lung segmentation task. In particular, the proposed model integrates 3D convolution into the Recurrent Residual Neural Network based on U-Net. It helps learn spatial dependencies in 3D and increases the propagation of 3D volumetric information. The proposed RU3D network is trained on the publicly available dataset LUNA16 and it achieves state-of-the-art performance on both LUNA16 (testing set) and VESSEL12 dataset. In addition, we show that training the RU3D model with a smaller number of CT scans, i.e., 100 scans, without applying data augmentation achieves an outstanding result in terms of Soft Dice Similarity Coefficient (Soft-DSC) of 0.9920. INDEX TERMS 3D Lung Segmentation, RU3D, Semantic Segmentation, Deep CNN, and Biomedical Image Analysis.
Effects of spin-phonon coupling in frustrated Heisenberg models

The existence and stability of spin-liquid phases represent a central topic in the field of frustrated magnetism. While a few examples of spin-liquid ground states are well established in specific models (e.g. the Kitaev model on the honeycomb lattice), recent investigations have suggested the possibility of their appearance in several Heisenberg-like models on frustrated lattices. An important related question concerns the stability of spin liquids in the presence of small perturbations in the Hamiltonian. In this respect, the magnetoelastic interaction between spins and phonons represents a relevant and physically motivated perturbation, which has been scarcely investigated so far. In this work, we study the effect of the spin-phonon coupling on prototypical models of frustrated magnetism. We adopt a variational framework based upon Gutzwiller-projected wave functions implemented with a spin-phonon Jastrow factor, providing a full quantum treatment of both spin and phonon degrees of freedom. The results on the frustrated J1 − J2 Heisenberg model on oneand two-dimensional (square) lattices show that, while a valence-bond crystal is prone to lattice distortions, a gapless spin liquid is stable for small spin-phonon couplings. In view of the ubiquitous presence of lattice vibrations, our results are particularly important to demonstrate the possibility that gapless spin liquids may be realized in real materials.
Estimating Presentation Competence using Multimodal Nonverbal Behavioral Cues

Public speaking and presentation competence plays an essential role in many areas of social interaction in our educational, professional, and everyday life. Since our intention during a speech can differ from what is actually understood by the audience, the ability to appropriately convey our message requires a complex set of skills. Presentation competence is cultivated in the early school years and continuously developed over time. One approach that can promote efficient development of presentation competence is the automated analysis of human behavior during a speech based on visual and audio features and machine learning. Furthermore, this analysis can be used to suggest improvements and the development of skills related to presentation competence. In this work, we investigate the contribution of different nonverbal behavioral cues, namely, facial, body pose-based, and audio-related features, to estimate presentation competence. The analyses were performed on videos of 251 students while the automated assessment is based on manual ratings according to the T\"ubingen Instrument for Presentation Competence (TIP). Our classification results reached the best performance with early fusion in the same dataset evaluation (accuracy of 71.25%) and late fusion of speech, face, and body pose features in the cross dataset evaluation (accuracy of 78.11%). Similarly, regression results performed the best with fusion strategies.
Evaluating Sensor Data Quality in Internet of Things Smart Agriculture Applications

The unprecedented growth of Internet of Things (IoT) underpinned by machine-to-machine communication, analytics, and actuation is spearheading the development of IoT-based smart agriculture applications (IoTSAs). Various factors (e.g., location and weather) impact data availability for such IoT applications, which impacts the decision making and actuation process of IoTSA. This article proposes a conceptual framework and a novel model to compute sensor data quality model that can be used by IoTSA in their decision/actuation process to adapt to uncertainty in IoT sensor data. To demonstrate the efficacy of the proposed data quality metrics and model, we apply them to an IoTSA for monitoring milk condition in dairy farms (an instance of IoTSA). Our real-world experimental evaluations conclude that the proposed model 1) can be employed by IoTSAs to adapt to different factors that may impact the quality of decision making (actuation) and 2) aids in developing data quality-aware IoTSA and beyond.
A Novel Multi-scale Dilated 3D CNN for Epileptic Seizure Prediction

Accurate prediction of epileptic seizures allows patients to take preventive measures in advance to avoid possible injuries. In this work, a novel convolutional neural network (CNN) is proposed to analyze time, frequency, and channel information of electroencephalography (EEG) signals. The model uses three-dimensional (3D) kernels to facilitate the feature extraction over the three dimensions. The application of multi-scale dilated convolution enables the 3D kernel to have more flexible receptive fields. The proposed CNN model is evaluated with the CHB-MIT EEG database, the experimental results indicate that our model outperforms the existing state-of-the-art, achieves 80.5% accuracy, 85.8% sensitivity and 75.1% specificity.
Time-Aware Q-Networks: Resolving Temporal Irregularity for Deep Reinforcement Learning

Deep Reinforcement Learning (DRL) has shown outstanding performance on inducing effective action policies that maximize expected long-term return on many complex tasks. Much of DRL work has been focused on sequences of events with discrete time steps and ignores the irregular time intervals between consecutive events. Given that in many real-world domains, data often consists of temporal sequences with irregular time intervals, and it is important to consider the time intervals between temporal events to capture latent progressive patterns of states. In this work, we present a general Time-Aware RL framework: Time-aware Q-Networks (TQN), which takes into account physical time intervals within a deep RL framework. TQN deals with time irregularity from two aspects: 1) elapsed time in the past and an expected next observation time for time-aware state approximation, and 2) action time window for the future for time-aware discounting of rewards. Experimental results show that by capturing the underlying structures in the sequences with time irregularities from both aspects, TQNs significantly outperform DQN in four types of contexts with irregular time intervals. More specifically, our results show that in classic RL tasks such as CartPole and MountainCar and Atari benchmark with randomly segmented time intervals, time-aware discounting alone is more important while in the real-world tasks such as nuclear reactor operation and septic patient treatment with intrinsic time intervals, both time-aware state and time-aware discounting are crucial. Moreover, to improve the agent's learning capacity, we explored three boosting methods: Double networks, Dueling networks, and Prioritized Experience Replay, and our results show that for the two real-world tasks, combining all three boosting methods with TQN is especially effective.
A Practical Ply-Based Appearance Modeling for Knitted Fabrics

Modeling the geometry and the appearance of knitted fabrics has been challenging due to their complex geometries and interactions with light. Previous surface-based models have difficulties capturing fine-grained knit geometries; Micro-appearance models, on the other hands, typically store individual cloth fibers explicitly and are expensive to be generated and rendered. Further, neither of the models have been matched the photographs to capture both the reflection and the transmission of light simultaneously. In this paper, we introduce an efficient technique to generate knit models with user-specified knitting patterns. Our model stores individual knit plies with fiber-level detailed depicted using normal and tangent mapping. We evaluate our generated models using a wide array of knitting patterns. Further, we compare qualitatively renderings to our models to photos of real samples.
Grid of pseudo-2D chemistry models for tidally locked exoplanets – I. The role of vertical and horizontal mixing

The atmospheres of synchronously rotating exoplanets are intrinsically 3D, and fast vertical and horizontal winds are expected to mix the atmosphere, driving the chemical composition out of equilibrium. Due to the longer computation times associated with multidimensional forward models, horizontal mixing has only been investigated for a few case studies. In this paper, we aim to generalize the impact of horizontal and vertical mixing on the chemistry of exoplanet atmospheres over a large parameter space. We do this by applying a sequence of post-processed forward models for a large grid of synchronously rotating gaseous exoplanets, where we vary the effective temperature (between 400 and 2600 K), surface gravity, and rotation rate. We find that there is a dichotomy in the horizontal homogeneity of the chemical abundances. Planets with effective temperatures below 1400 K tend to have horizontally homogeneous, vertically quenched chemical compositions, while planets hotter than 1400 K exhibit large compositional day-night differences for molecules such as CH4. Furthermore, we find that the planet’s rotation rate impacts the planetary climate, and thus also the molecular abundances and transmission spectrum. By employing a hierarchical modelling approach, we assess the relative importance of disequilibrium chemistry on the exoplanet transmission spectrum, and conclude that the temperature has the most profound impact. Temperature differences are also the main cause of limb asymmetries, which we estimate could be observable with the James Webb Space Telescope. This work highlights the value of applying a consistent modelling setup to a broad parameter space in exploratory theoretical research.
Pursuing the Origin of the Gamma Rays in RX J1713.7-3946 Quantifying the Hadronic and Leptonic Components

We analyzed the TeV gamma-ray image of a supernova remnant RX J1713.7−3946 (RX J1713) through a comparison with the interstellar medium (ISM) and nonthermal X-rays. The gamma-ray data sets at two energy bands of >2 TeV and >250–300 GeV were obtained with H.E. S.S. and utilized in the analysis. We employed a new methodology, which assumes that the gamma-ray counts can be expressed as a linear combination of two terms: one is proportional to the ISM column density and the other proportional to the X-ray count. We then assume that these represent the hadronic and leptonic components, respectively. By fitting the expression to the data pixels, we find that the gamma-ray counts are well represented by a flat plane in the 3D space formed by the gamma-ray counts, the ISM column density, and the X-ray counts. The results using the latest H.E. S.S. data at 4. ′8 resolution show that the hadronic and leptonic components constitute (67 ± 8)% and (33 ± 8)% of the total gamma rays, respectively, where the two components have been quantified for the first time. The hadronic component is greater than the leptonic component, which reflects the massive ISM of ∼104 M ⊙ associated with the remnant, lending support for the acceleration of cosmic-ray protons. There is a marginal hint that the gamma rays are suppressed at high gamma-ray counts, which may be ascribed to second-order effects including the shock–cloud interaction and the effect of penetration depth.
Analysis of induced current density in grounded and ungrounded prolate spheroid models in concurrent ELF electric and magnetic fields

Analyzed in this paper is the induced current density in the homogeneous prolate spheroid model of a biological object exposed to concurrent 60‐Hz vertical electrical field (1 kV/m), and horizontal and vertical magnetic fields (1 to 5 μT) with different phase angle. The analysis has been carried out separately considering the presence of electric and magnetic fields. The current density induced by the electric field is calculated using the finite element method, whereas the current density induced by the magnetic fields is calculated with exact solution for the prolate spheriod model. The total induced current density is the vector sum of the current density components induced by the electric and magnetic fields. It is found that the density of the total current is determined by the vertical electric field and horizontal magnetic field. The horizontal magnetic field has an important effect on the total induced current density distribution. The distribution of the density of the total current varies with the phase difference between the vertical electric and horizontal magnetic fields. As the model gets close to the ground, however, the contribution of the horizontal magnetic field to the total induced current density is found to decrease. © 2001 Scripta Technica, Electr Eng Jpn, 135(3): 8–15, 2001
Online Preconditioning of Experimental Inkjet Hardware by Bayesian Optimization in Loop

High-performance semiconductor optoelectronics such as perovskites have high-dimensional and vast composition spaces that govern the performance properties of the material. To cost-effectively search these composition spaces, we utilize a high-throughput experimentation method of rapidly printing discrete droplets via inkjet deposition, in which each droplet is comprised of a unique permutation of semiconductor materials. However, inkjet printer systems are not optimized to run high-throughput experimentation on semiconductor materials. Thus, in this work, we develop a computer vision-driven Bayesian optimization framework for optimizing the deposited droplet structures from an inkjet printer such that it is tuned to perform high-throughput experimentation on semiconductor materials. The goal of this framework is to tune to the hardware conditions of the inkjet printer in the shortest amount of time using the fewest number of droplet samples such that we minimize the time and resources spent on setting the system up for material discovery applications. We demonstrate convergence on optimum inkjet hardware conditions in 10 minutes using Bayesian optimization of computer vision-scored droplet structures. We compare our Bayesian optimization results with stochastic gradient descent.
SGG: Learning to Select, Guide, and Generate for Keyphrase Generation

Keyphrases, that concisely summarize the high-level topics discussed in a document, can be categorized into present keyphrase which explicitly appears in the source text and absent keyphrase which does not match any contiguous subsequence but is highly semantically related to the source. Most existing keyphrase generation approaches synchronously generate present and absent keyphrases without explicitly distinguishing these two categories. In this paper, a Select-Guide-Generate (SGG) approach is proposed to deal with present and absent keyphrases generation separately with different mechanisms. Specifically, SGG is a hierarchical neural network which consists of a pointing-based selector at low layer concentrated on present keyphrase generation, a selection-guided generator at high layer dedicated to absent keyphrase generation, and a guider in the middle to transfer information from selector to generator. Experimental results on four keyphrase generation benchmarks demonstrate the effectiveness of our model, which significantly outperforms the strong baselines for both present and absent keyphrases generation. Furthermore, we extend SGG to a title generation task which indicates its extensibility in natural language generation tasks.
A framework of explanation generation toward reliable autonomous robots

ABSTRACT To realize autonomous collaborative robots, it is important to increase the trust that users have in them. Toward this goal, this paper proposes an algorithm that endows an autonomous agent with the ability to explain the transition from the current state to the target state in a Markov decision process (MDP). According to cognitive science, to generate an explanation that is acceptable to humans, it is important to present the minimum information necessary to sufficiently understand an event. To meet this requirement, we propose a framework for identifying important elements in the decision-making process using a prediction model for the world and generating explanations based on these elements. To verify the ability of the proposed method, we conducted an experiment using a grid environment. It was inferred from the result of a simulation experiment that the explanation generated using the proposed method was composed of the minimum elements important for understanding the transition from the current state to the target state. Furthermore, subject experiments showed that the generated explanation was a good summary of the process of state transition, and that a high evaluation was obtained for the explanation of the reason for an action. GRAPHICAL ABSTRACT
Bayesian Active Learning by Disagreements: A Geometric Perspective

We present geometric Bayesian active learning by disagreements (GBALD), a framework that performs BALD on its core-set construction interacting with model uncertainty estimation. Technically, GBALD constructs core-set on ellipsoid, not typical sphere, preventing low-representative elements from spherical boundaries. The improvements are twofold: 1) relieve uninformative prior and 2) reduce redundant estimations. Theoretically, geodesic search with ellipsoid can derive tighter lower bound on error and easier to achieve zero error than with sphere. Experiments show that GBALD has slight perturbations to noisy and repeated samples, and outperforms BALD, BatchBALD and other existing deep active learning approaches.
GPF-Forecast: um Sistema Fuzzy-Genético para Previsão

Resumo— Este trabalho apresenta um Sistema FuzzyGenético para Previsão, denominado GPF-Forecast (Genetic Programming Fuzzy for Forecasting). Este modelo difere da abordagem tradicional de utilizar a meta-heurística genética como uma forma de aprender regras do tipo “se-então”. Além disso, não necessita da otimização dos coeficientes do consequente da regra, comum em modelos do tipo TakagiSugeno-Kang geralmente utilizados em problemas de previsão. O GPF-Forecast explora o potencial da Programação Genética Multigênica na busca das melhores variáveis de entrada e a forma funcional que mais bem se adeque ao problema. Ao mesmo tempo, proporciona ao usuário um entendimento linguístico da solução auferida. A base teórica do modelo é apresentada e são realizados quatro testes, visando a uma avaliação preliminar do modelo frente a métodos de previsão bem estabelecidos na literatura. Análises estatísticas das previsões realizadas e um exemplo da compreensão linguística fornecida pelo modelo acompanham os resultados.
The Role of Plan Robustness Evaluation in Comparing Protons and Photons Plans - An Application on IMPT and IMRT Plans in Skull Base Chordomas

Purpose: To analyze robustness of treatment plans optimized using different approaches in intensity modulated proton therapy (IMPT) and investigate the necessity of robust optimization and evaluation in intensity modulated radiotherapy (IMRT) plans for skull base chordomas. Materials and Methods: Two photon plans, standard IMRT and robustly optimized IMRT (RB-IMRT), and two IMPT plans, robustly optimized multi field optimization (MFO) and hybrid-MFO (HB-MFO), were created in RayStation TPS for five patients previously treated using single field uniform optimization (SFO). Both set-up and range uncertainties were incorporated during robust optimization of IMPT plans whereas only set-up uncertainty was used in RB-IMRT. The dosimetric outcomes from the five planning techniques were compared for every patient using standard dose volume indices and integral dose (ID) estimated for target and organs at risk (OARs). Robustness of each treatment plan was assessed by introducing set-up uncertainties of ±3 mm along the three translational axes and, only in protons, an additional range uncertainty of ±3.5%. Results: All the five nominal plans provided comparable and clinically acceptable target coverage. In comparison to nominal plans, worst case decrease in D95% of clinical target volume-high risk (CTV-HR) were 11.1%, 13.5%, and 13.6% for SFO, MFO, and HB-MFO plans respectively. The corresponding values were 13.7% for standard IMRT which improved to 11.5% for RB-IMRT. The worst case increased in high dose (D1%) to CTV-HR was highest in IMRT (2.1%) and lowest in SFO (0.7%) plans. Moreover, IMRT showed worst case increases in D1% for all neurological OARs and were lowest for SFO plans. The worst case D1% for brainstem, chiasm, spinal cord, optic nerves, and temporal lobes were increased by 29%, 41%, 30%, 41% and 14% for IMRT and 18%, 21%, 21%, 24%, and 7% for SFO plans, respectively. In comparison to IMRT, RB-IMRT improved D1% of all neurological OARs ranging from 5% to 14% in worst case scenarios. Conclusion: Based on the five cases presented in the current study, all proton planning techniques (SFO, MFO and HB-MFO) were robust both for target coverage and OARs sparing. Standard IMRT plans were less robust than proton plans in regards to high doses to neurological OARs. However, robust optimization applied to IMRT resulted in improved robustness in both target coverage and high doses to OARs. Robustness evaluation may be considered as a part of plan evaluation procedure even in IMRT.
A surprising journey into the conversion of urinary protein creatinine ratio to urinary albumin creatinine ratio as needed in the Kidney Failure Risk Equation

When trying to validate the Kidney Failure Risk Equation (KFRE) in our patients with chronic kidney disease Stage 4–5 [estimated glomerular filtration rate (eGFR) <30 mL/min/1.73 m], we came upon the need to convert urinary protein creatinine ratio (UPCR) to urinary albumin creatinine ratio (UACR) [1, 2]. Indeed, in our retrospective database, a large part of the extracted patients had only UPCR measured. Tangri et al. seem to have encountered the same problem. In their original study calculating the KFRE [1], they mention that 24-h protein excretion was converted to UACR. The authors report that in 1723 patients, UACR was measured on a spot urine sample, while in 1923 patients it was calculated from 24-h protein excretion. However, the formula for this conversion was not described. Instead we read: