Miracle-dz/UniFAR-FTUs huggingface dataset:
Uncertainty-Aware Self-Supervised Learning for Cross-Domain Technical Skill Assessment in Robot-Assisted Surgery

Objective technical skill assessment is crucial for effective training of new surgeons in robot-assisted surgery. With advancements in surgical training programs in both physical and virtual environments, it is imperative to develop generalizable methods for automatically assessing skills. In this paper, we propose a novel approach for skill assessment by transferring domain knowledge from labeled kinematic data to unlabeled data. Our approach leverages labeled data from common surgical training tasks such as Suturing, Needle Passing, and Knot Tying to jointly train a model with both labeled and unlabeled data. Pseudo labels are generated for the unlabeled data through an iterative manner that incorporates uncertainty estimation to ensure accurate labeling. We evaluate our method on a virtual reality simulated training task (Ring Transfer) using data from the da Vinci Research Kit (dVRK). The results show that trainees with robotic assistance have significantly higher expert probability compared to these without any assistance, $p < 0.05$ , which aligns with previous studies showing the benefits of robotic assistance in improving training proficiency. Our method offers a significant advantage over other existing works as it does not require manual labeling or prior knowledge of the surgical training task for robot-assisted surgery.
Large Enhancements in Optical and Piezoelectric Properties in Ferroelectric Zn1‐xMgxO Thin Films through Engineering Electronic and Ionic Anharmonicities

Multifunctionality as a paradigm requires materials exhibiting multiple superior properties. Integrating second‐order optical nonlinearity and large bandgap with piezoelectricity can, for example, enable broadband, strain‐tunable photonics. Though very different phenomena at distinct frequencies, both second‐order optical nonlinearity and piezoelectricity are third‐rank polar tensors present only in acentric crystal structures. However, simultaneously enhancing both phenomena is highly challenging since it involves competing effects with tradeoffs. Recently, a large switchable ferroelectric polarization of ≈80 μC cm−2 was reported in Zn1‐xMgxO films. Here, ferroelectric Zn1‐xMgxO is demonstrated to be a platform that hosts simultaneously a 30% increase in the electronic bandgap, a 50% enhancement in the second harmonic generation (SHG) coefficients, and a near 200% improvement in the piezoelectric coefficients over pure ZnO. These enhancements are shown to be due to a 400% increase in the electronic anharmonicity and a ≈200% decrease in the ionic anharmonicity with Mg substitution. Precisely controllable periodic ferroelectric domain gratings are demonstrated down to 800 nm domain width, enabling ultraviolet quasi‐phase‐matched optical harmonic generation as well as domain‐engineered piezoelectric devices.
Using a Deep Learning Model to Simulate Human Stock Trader's Methods of Chart Analysis

Despite the efficient market hypothesis, many studies suggest the existence of inefficiencies in the stock market leading to the development of techniques to gain above-market returns. Systematic trading has undergone significant advances in recent decades with deep learning schemes emerging as a powerful tool for analyzing and predicting market behavior. In this paper, a method is proposed that is inspired by how professional technical analysts trade. This scheme looks at stock prices of the previous 600 days and predicts whether the stock price will rise or fall 10% or 20% within the next D days. Plus, the proposed method uses the Resnet's (a deep learning model) skip connections and logits to increase the probability of the prediction. The model was trained and tested using historical data from both the Korean and US stock markets. We show that using the period label of 5 gives the best result. On Korea market it achieved a profit more than 39% above the market return, and a profit more than 40% above the market return on the US market.
Counterfactual Explanation with Missing Values

Counterfactual Explanation (CE) is a post-hoc explanation method that provides a perturbation for altering the prediction result of a classifier. Users can interpret the perturbation as an"action"to obtain their desired decision results. Existing CE methods require complete information on the features of an input instance. However, we often encounter missing values in a given instance, and the previous methods do not work in such a practical situation. In this paper, we first empirically and theoretically show the risk that missing value imputation methods affect the validity of an action, as well as the features that the action suggests changing. Then, we propose a new framework of CE, named Counterfactual Explanation by Pairs of Imputation and Action (CEPIA), that enables users to obtain valid actions even with missing values and clarifies how actions are affected by imputation of the missing values. Specifically, our CEPIA provides a representative set of pairs of an imputation candidate for a given incomplete instance and its optimal action. We formulate the problem of finding such a set as a submodular maximization problem, which can be solved by a simple greedy algorithm with an approximation guarantee. Experimental results demonstrated the efficacy of our CEPIA in comparison with the baselines in the presence of missing values.
Model Predictive Control of Wind Turbines with Piecewise-Affine Power Coefficient Approximation

In this paper, an offset-free bilinear model predictive control approach for wind turbines is presented. State-of-the-art controllers employ different control loops for pitch angle and generator torque which switch depending on wind conditions. In contrast, the presented controller is based on one unified control law that works for all wind conditions. The inherent nonlinearity of wind turbines is addressed through a piecewise-affine approximation, which is modelled in a mixed-integer fashion. The presented controller is compared to a state-of-the-art baseline controller in a numerical case study using OpenFAST. Simulation results show that the presented controller ensures accurate reference power tracking. Additionally, damage equivalent loads are reduced for higher wind speeds.
From Explicit Communication to Tacit Cooperation: A Novel Paradigm for Cooperative MARL

Centralized training with decentralized execution (CTDE) is a widely-used learning paradigm that has achieved significant success in complex tasks. However, partial observability issues and the absence of effectively shared signals between agents often limit its effectiveness in fostering cooperation. While communication can address this challenge, it simultaneously reduces the algorithm's practicality. Drawing inspiration from human team cooperative learning, we propose a novel paradigm that facilitates a gradual shift from explicit communication to tacit cooperation. In the initial training stage, we promote cooperation by sharing relevant information among agents and concurrently reconstructing this information using each agent's local trajectory. We then combine the explicitly communicated information with the reconstructed information to obtain mixed information. Throughout the training process, we progressively reduce the proportion of explicitly communicated information, facilitating a seamless transition to fully decentralized execution without communication. Experimental results in various scenarios demonstrate that the performance of our method without communication can approaches or even surpasses that of QMIX and communication-based methods.
Calculation of Patient Doses Using a Human Voxel Phantom of Variable Diameter

The voxel phantom Golem was used for the dosimetric part of the EU research project 'Predictivity and Optimisation in Medical Radiation Protection'. A risk-related dose quantity, H Golems , was evaluated using calculated organ equivalent dose conversion coefficients and entrance air kerma values measured during the clinical examinations. Two types of examinations were considered: chest PA examinations, which were performed in 16 groups involving different speed classes, mean optical densities, tube voltages and methods of scatter reduction; and lumbar spine AP examinations, for which four different combinations of speed class and tube voltage were used. For the chest examinations, the expected effects of different examination techniques are clearly mirrored by the dosimetric results. For the lumbar spine examinations, part of the expected results was concealed by the unwanted influence of the patient diameters on the doses, which could be partially eliminated by adapting the voxel phantom to different sizes.
Ab initio
 calculations of superconducting transition temperatures: When going beyond RPA is essential

In ab initio calculations of superconducting properties, the Coulomb repulsion is accounted for at the GW level and is usually computed in RPA, which amounts to neglecting vertex corrections both at the polarizability level and in the self-energy. Although this approach is unjustified, the brute force inclusion of higher order corrections to the self-energy is computationally prohibitive. We propose to use a generalized GW self-energy, where vertex corrections are incorporated into W by employing the Kukkonen and Overhauser (KO) ansatz for the effective interaction between two electrons in the electron gas. By computing the KO interaction in the adiabatic local density approximation for a diverse set of conventional superconductors, and using it in the Eliashberg equations, we find that vertex corrections lead to a systematic decrease of the critical temperature (Tc), ranging from a few percent in bulk lead to more than 40% in compressed lithium. We propose a set of simple rules to identify those systems where large Tc corrections are to be expected and hence the use of the KO interaction is recommended. Our approach offers a rigorous extension of the RPA and GW methods for the prediction of superconducting properties at a negligible extra computational cost.
One-Step Distributional Reinforcement Learning

Reinforcement learning (RL) allows an agent interacting sequentially with an environment to maximize its long-term expected return. In the distributional RL (DistrRL) paradigm, the agent goes beyond the limit of the expected value, to capture the underlying probability distribution of the return across all time steps. The set of DistrRL algorithms has led to improved empirical performance. Nevertheless, the theory of DistrRL is still not fully understood, especially in the control case. In this paper, we present the simpler one-step distributional reinforcement learning (OS-DistrRL) framework encompassing only the randomness induced by the one-step dynamics of the environment. Contrary to DistrRL, we show that our approach comes with a unified theory for both policy evaluation and control. Indeed, we propose two OS-DistrRL algorithms for which we provide an almost sure convergence analysis. The proposed approach compares favorably with categorical DistrRL on various environments.
Using Perturbation to Improve Goodness-of-Fit Tests based on Kernelized Stein Discrepancy

Kernelized Stein discrepancy (KSD) is a score-based discrepancy widely used in goodness-of-fit tests. It can be applied even when the target distribution has an unknown normalising factor, such as in Bayesian analysis. We show theoretically and empirically that the KSD test can suffer from low power when the target and the alternative distributions have the same well-separated modes but differ in mixing proportions. We propose to perturb the observed sample via Markov transition kernels, with respect to which the target distribution is invariant. This allows us to then employ the KSD test on the perturbed sample. We provide numerical evidence that with suitably chosen transition kernels the proposed approach can lead to substantially higher power than the KSD test.
Human Activity Recognition Using Self-Supervised Representations of Wearable Data

Automated and accurate human activity recognition (HAR) using body-worn sensors enables practical and cost efficient remote monitoring of Activity of DailyLiving (ADL), which are shown to provide clinical insights across multiple therapeutic areas. Development of accurate algorithms for human activity recognition(HAR) is hindered by the lack of large real-world labeled datasets. Furthermore, algorithms seldom work beyond the specific sensor on which they are prototyped, prompting debate about whether accelerometer-based HAR is even possible [Tong et al., 2020]. Here we develop a 6-class HAR model with strong performance when evaluated on real-world datasets not seen during training. Our model is based on a frozen self-supervised representation learned on a large unlabeled dataset, combined with a shallow multi-layer perceptron with temporal smoothing. The model obtains in-dataset state-of-the art performance on the Capture24 dataset ($\kappa= 0.86$). Out-of-distribution (OOD) performance is $\kappa = 0.7$, with both the representation and the perceptron models being trained on data from a different sensor. This work represents a key step towards device-agnostic HAR models, which can help contribute to increased standardization of model evaluation in the HAR field.
Controllable One-Shot Face Video Synthesis With Semantic Aware Prior

The one-shot talking-head synthesis task aims to animate a source image to another pose and expression, which is dictated by a driving frame. Recent methods rely on warping the appearance feature extracted from the source, by using motion fields estimated from the sparse keypoints, that are learned in an unsupervised manner. Due to their lightweight formulation, they are suitable for video conferencing with reduced bandwidth. However, based on our study, current methods suffer from two major limitations: 1) unsatisfactory generation quality in the case of large head poses and the existence of observable pose misalignment between the source and the first frame in driving videos. 2) fail to capture fine yet critical face motion details due to the lack of semantic understanding and appropriate face geometry regularization. To address these shortcomings, we propose a novel method that leverages the rich face prior information, the proposed model can generate face videos with improved semantic consistency (improve baseline by $7\%$ in average keypoint distance) and expression-preserving (outperform baseline by $15 \%$ in average emotion embedding distance) under equivalent bandwidth. Additionally, incorporating such prior information provides us with a convenient interface to achieve highly controllable generation in terms of both pose and expression.
Data-Driven Volumetric Image Generation from Surface Structures using a Patient-Specific Deep Leaning Model

The advent of computed tomography significantly improves patients’ health regarding diagnosis, prognosis, and treatment planning and verification. However, tomographic imaging escalates concomitant radiation doses to patients, inducing potential secondary cancer by 4%. We demonstrate the feasibility of a data-driven approach to synthesize volumetric images using patients’ surface images, which can be obtained from a zero-dose surface imaging system. This study includes 500 computed tomography (CT) image sets from 50 patients. Compared to the ground truth CT, the synthetic images result in the evaluation metric values of 26.9 ± 4.1 Hounsfield units, 39.1 ± 1.0 dB, and 0.965 ± 0.011 regarding the mean absolute error, peak signal-to-noise ratio, and structural similarity index measure. This approach provides a data integration solution that can potentially enable real-time imaging, which is free of radiation-induced risk and could be applied to image-guided medical procedures.
Scalable Real-Time Recurrent Learning Using Columnar-Constructive Networks

Constructing states from sequences of observations is an important component of reinforcement learning agents. One solution for state construction is to use recurrent neural networks. Back-propagation through time (BPTT), and real-time recurrent learning (RTRL) are two popular gradient-based methods for recurrent learning. BPTT requires complete trajectories of observations before it can compute the gradients and is unsuitable for online updates. RTRL can do online updates but scales poorly to large networks. In this paper, we propose two constraints that make RTRL scalable. We show that by either decomposing the network into independent modules or learning the network in stages, we can make RTRL scale linearly with the number of parameters. Unlike prior scalable gradient estimation algorithms, such as UORO and Truncated-BPTT, our algorithms do not add noise or bias to the gradient estimate. Instead, they trade off the functional capacity of the network for computationally efficient learning. We demonstrate the effectiveness of our approach over Truncated-BPTT on a prediction benchmark inspired by animal learning and by doing policy evaluation of pre-trained policies for Atari 2600 games.
Low-Resource Music Genre Classification with Cross-Modal Neural Model Reprogramming

Transfer learning (TL) approaches have shown promising results when handling tasks with limited training data. However, considerable memory and computational resources are often required for fine-tuning pre-trained neural networks with target domain data. In this work, we introduce a novel method for leveraging pre-trained speech models for low-resource music classification based on the concept of Neural Model Reprogramming (NMR). NMR aims at re-purposing a pre-trained model from a source domain to a target domain by modifying the input of a frozen pre-trained models for cross-modal adaptation. In addition to the known, input-independent, re-programming method, we propose an new reprogramming paradigm: Input-dependent NMR, to increase adaptability to complex input data such as musical audio. Experimental results suggest that a neural model pre-trained on large-scale datasets can successfully perform music genre classification by using this reprogramming method. The two proposed Input-dependent NMR TL methods outperform fine-tuning-based TL methods on a small genre classification dataset.
The Entropy Law, Accounting Data, and Relevance to Decision-Making

This study uses the Entropy index of information developed by Claude Shannon and elaborated by Lev and others to measure the aggregation of accounting information. The question of interest is whether Entropy is a measure of communication to assess transmission channel in communication or the semantic measure of information. This question is discussed and evaluated using an experiment on making loan decisions by commercial lending officers. The results do not support the notion of using Entropy to convey meaning, consistent with Broadhurst and Darnell notion “eliciting meanings in others, requires a code-a set of symbols. ” The Entropy measure, however, offers a unique index of data disaggregation in the sense of combining or separating different units of communicated signals.
Association between sex and survival after out‐of‐hospital cardiac arrest: A systematic review and meta‐analysis

The current literature on sex differences in 30‐day survival following out‐of‐hospital cardiac arrest (OHCA) is conflicting, with 3 recent systematic reviews reporting opposing results. To address these contradictions, this systematic literature review and meta‐analysis aimed to synthesize the literature on sex differences in survival after OHCA by including only population‐based studies and through separate meta‐analyses of crude and adjusted effect estimates. MEDLINE and Embase databases were systematically searched from inception to March 23, 2022 to identify observational studies reporting sex‐specific 30‐day survival or survival until hospital discharge after OHCA. Two meta‐analyses were conducted. The first included unadjusted effect estimates of the association between sex and survival (comparing males vs females), whereas the second included effect estimates adjusted for possible mediating and/or confounding variables. The PROSPERO registration number was CRD42021237887, and the search identified 6712 articles. After the screening, 164 potentially relevant articles were identified, of which 26 were included. The pooled estimate for crude effect estimates (odds ratio [OR], 1.42; 95% confidence interval [CI], 1.22–1.66) indicated that males have a higher chance of survival after OHCA than females. However, the pooled estimate for adjusted effect estimates shows no difference in survival after OHCA between males and females (OR, 0.93; 95% CI, 0.84–1.03). Both meta‐analyses involved high statistical heterogeneity between studies: crude pooled estimate I2 = 95.7%, adjusted pooled estimate I2 = 91.3%. There does not appear to be a difference in survival between males and females when effect estimates are adjusted for possible confounding and/or mediating variables in non‐selected populations.
Effects of interwire gap and initial load diameter on long implosion time aluminum Z-pinches on Saturn

Summary form only given, as follows. Experiments to study aluminum K-shell emissions from long implosion time Z-pinches have been performed on the 8-MA Saturn accelerator. The experiments, designed to investigate the effects of initial load diameter and wire number on the Al K-shell radiation, were motivated in part by the need to understand the consequences of longer implosion time Z-pinch drivers. Typically, most pulsed power Z-pinch drivers have implosion times of 50-100 ns; Saturn in the long pulse mode can produce implosion times of 130-200 ns. In the wire number experiments, the initial load diameter was held constant at 30 mm, the mass was fixed near 620 /spl mu/g/cm (for an implosion time near 165 ns), and the number of wires in the array was varied from 32 to 282. Yields greater than 60 kJ were measured, and variations in the FWHM and risetime clearly show the interwire gap spacing where wire merger occurs. In a separate series of experiments, the initial load diameter was varied from 32 mm to 50 mm, with the implosion time also fixed near 165 ns. Al K-shell yields up to 60 kJ were measured, with the largest diameter loads producing the highest yields. These results will he compared to calculations, Haines' wire merger model, and with short implosion time Al results on Saturn.
A comprehensive view of Markov-Functional models and their application

Markov-FUnctional models are a very powerful class of market models which calibrate and compute prices and Greeks quickly. This dissertation explains, in detail, how Markov-FUnctional models work as well as discussing all of the specific models developed in the literature. It contains the key points that can be found in the present literature. We explain, in detail, all of the concepts, from the theoretical framework down to the numerical implementation of the specific models. This involves explaining the framework for Markov-FUnctional models , de cribing specific models, obtaining a deeper understanding of how the model parameters affect the results, discussing the issues involved in the implementation, implementing various models and investigating the effect of numerical and market parameters on the outcome. Various concepts, not discussed in the present literature, such as considerations for selecting a discretization grid for the numerical implementation, are developed. The practical application of MarkovFUnctional models is considered as well as alternative fields, such as Actuarial science, where the model can be applied. In summary, this dissertation embodies a complete discussion of the current class of Markov-FUnctional models.