Miracle-dz/UniFAR-FTUs huggingface dataset:
Adaptive Algorithms for Relaxed Pareto Set Identification

In this paper we revisit the fixed-confidence identification of the Pareto optimal set in a multi-objective multi-armed bandit model. As the sample complexity to identify the exact Pareto set can be very large, a relaxation allowing to output some additional near-optimal arms has been studied. In this work we also tackle alternative relaxations that allow instead to identify a relevant subset of the Pareto set. Notably, we propose a single sampling strategy, called Adaptive Pareto Exploration, that can be used in conjunction with different stopping rules to take into account different relaxations of the Pareto Set Identification problem. We analyze the sample complexity of these different combinations, quantifying in particular the reduction in sample complexity that occurs when one seeks to identify at most $k$ Pareto optimal arms. We showcase the good practical performance of Adaptive Pareto Exploration on a real-world scenario, in which we adaptively explore several vaccination strategies against Covid-19 in order to find the optimal ones when multiple immunogenicity criteria are taken into account.
A Framework for Image Alignment of TerraSAR-X Images Using Fractional Derivatives and View Synthesis Approach

Abstract Conventional integer order differential operators suffer from poor feature detection accuracy and noise immunity, which leads to image misalignment. A new affine-based fractional order feature detection algorithm is proposed to detect syntactic and semantic structures from the backscattered signal of a TerraSAR-X band stripmap image. To further improve the alignment accuracy, we propose to adapt a view synthesis approach in the standard pipeline of feature-based image alignment. Experiments were performed to test the effectiveness and robustness of the view synthesis approach using a fractional order feature detector. The evaluation results showed that the proposed method achieves high precision and robust alignment of look-angle-varied TerraSAR-X images. The affine features detected using the fractional order operator are more stable and have strong capacity to reduce sturdy speckle noise.
The Forward-Forward Algorithm as a feature extractor for skin lesion classification: A preliminary study

Skin cancer, a deadly form of cancer, exhibits a 23% survival rate in the USA with late diagnosis. Early detection can significantly increase the survival rate, and facilitate timely treatment. Accurate biomedical image classification is vital in medical analysis, aiding clinicians in disease diagnosis and treatment. Deep learning (DL) techniques, such as convolutional neural networks and transformers, have revolutionized clinical decision-making automation. However, computational cost and hardware constraints limit the implementation of state-of-the-art DL architectures. In this work, we explore a new type of neural network that does not need backpropagation (BP), namely the Forward-Forward Algorithm (FFA), for skin lesion classification. While FFA is claimed to use very low-power analog hardware, BP still tends to be superior in terms of classification accuracy. In addition, our experimental results suggest that the combination of FFA and BP can be a better alternative to achieve a more accurate prediction.
Accretion–ablation mechanics

In this paper, we formulate a geometric nonlinear theory of the mechanics of accreting–ablating bodies. This is a generalization of the theory of accretion mechanics of Sozio & Yavari (Sozio & Yavari 2019 J. Nonlinear Sci. 29, 1813–1863 (doi:10.1007/s00332-019-09531-w)). More specifically, we are interested in large deformation analysis of bodies that undergo a continuous and simultaneous accretion and ablation on their boundaries while under external loads. In this formulation, the natural configuration of an accreting–ablating body is a time-dependent Riemannian 3-manifold with a metric that is an unknown a priori and is determined after solving the accretion–ablation initial-boundary-value problem. In addition to the time of attachment map, we introduce a time of detachment map that along with the time of attachment map, and the accretion and ablation velocities, describes the time-dependent reference configuration of the body. The kinematics, material manifold, material metric, constitutive equations and the balance laws are discussed in detail. As a concrete example and application of the geometric theory, we analyse a thick hollow circular cylinder made of an arbitrary incompressible isotropic material that is under a finite time-dependent extension while undergoing continuous ablation on its inner cylinder boundary and accretion on its outer cylinder boundary. The state of deformation and stress during the accretion–ablation process, and the residual stretch and stress after the completion of the accretion–ablation process, are computed. This article is part of the theme issue ‘Foundational issues, analysis and geometry in continuum mechanics’.
Stochastic Approach for Price Optimization Problems with Decision-dependent Uncertainty

Price determination is a central research topic of revenue management in marketing. The important aspect in pricing is controlling the stochastic behavior of demand, and the previous studies have tackled price optimization problems with uncertainties. However, many of those studies assumed that uncertainties are independent of decision variables (i.e., prices) and did not consider situations where demand uncertainty depends on price. Although some price optimization studies have dealt with decision-dependent uncertainty, they make application-specific assumptions in order to obtain an optimal solution or an approximation solution. To handle a wider range of applications with decision-dependent uncertainty, we propose a general non-convex stochastic optimization formulation. This approach aims to maximize the expectation of a revenue function with respect to a random variable representing demand under a decision-dependent distribution. We derived an unbiased stochastic gradient estimator by using a well-tuned variance reduction parameter and used it for a projected stochastic gradient descent method to find a stationary point of our problem. We conducted synthetic experiments and simulation experiments with real data on a retail service application. The results show that the proposed method outputs solutions with higher total revenues than baselines.
Contextual Prompt Learning for Vision-Language Understanding

Recent advances in multimodal learning has resulted in powerful vision-language models, whose representations are generalizable across a variety of downstream tasks. Recently, their generalization ability has been further extended by incorporating trainable prompts, borrowed from the natural language processing literature. While such prompt learning techniques have shown impressive results, we identify that these prompts are trained based on global image features which limits itself in two aspects: First, by using global features, these prompts could be focusing less on the discriminative foreground image, resulting in poor generalization to various out-of-distribution test cases. Second, existing work weights all prompts equally whereas intuitively, prompts should be reweighed according to the semantics of the image. We address these as part of our proposed Contextual Prompt Learning (CoPL) framework, capable of aligning the prompts to the localized features of the image. Our key innovations over earlier works include using local image features as part of the prompt learning process, and more crucially, learning to weight these prompts based on local features that are appropriate for the task at hand. This gives us dynamic prompts that are both aligned to local image features as well as aware of local contextual relationships. Our extensive set of experiments on a variety of standard and few-shot datasets show that our method produces substantially improved performance when compared to the current state of the art methods. We also demonstrate both few-shot and out-of-distribution performance to establish the utility of learning dynamic prompts that are aligned to local image features.
Understanding the impact of numerical solvers on inference for differential equation models

Most ordinary differential equation (ODE) models used to describe biological or physical systems must be solved approximately using numerical methods. Perniciously, even those solvers that seem sufficiently accurate for the forward problem, i.e. for obtaining an accurate simulation, might not be sufficiently accurate for the inverse problem, i.e. for inferring the model parameters from data. We show that for both fixed step and adaptive step ODE solvers, solving the forward problem with insufficient accuracy can distort likelihood surfaces, which might become jagged, causing inference algorithms to get stuck in local ‘phantom’ optima. We demonstrate that biases in inference arising from numerical approximation of ODEs are potentially most severe in systems involving low noise and rapid nonlinear dynamics. We reanalyse an ODE change point model previously fit to the COVID-19 outbreak in Germany and show the effect of the step size on simulation and inference results. We then fit a more complicated rainfall run-off model to hydrological data and illustrate the importance of tuning solver tolerances to avoid distorted likelihood surfaces. Our results indicate that, when performing inference for ODE model parameters, adaptive step size solver tolerances must be set cautiously and likelihood surfaces should be inspected for characteristic signs of numerical issues.
CasTGAN: Cascaded Generative Adversarial Network for Realistic Tabular Data Synthesis

Generative adversarial networks (GANs) have drawn considerable attention in recent years for their proven capability in generating synthetic data which can be utilised for multiple purposes. While GANs have demonstrated tremendous successes in producing synthetic data samples that replicate the dynamics of the original datasets, the validity of the synthetic data and the underlying privacy concerns represent major challenges which are not sufficiently addressed. In this work, we design a cascaded tabular GAN framework (CasTGAN) for generating realistic tabular data with a specific focus on the validity of the output. In this context, validity refers to the the dependency between features that can be found in the real data, but is typically misrepresented by traditional generative models. Our key idea entails that employing a cascaded architecture in which a dedicated generator samples each feature, the synthetic output becomes more representative of the real data. Our experimental results demonstrate that our model is capable of generating synthetic tabular data that can be used for fitting machine learning models, as CasTGAN’s classification performance only falls under the real training data’s PR-AUC score by 4.88% on average for classification datasets, and exhibits an average reduction of the real training data’s $R^{2}$ score by 0.139 for regression datasets. In addition, our model captures well the constraints and the correlations between the features of the real data, especially the high dimensional datasets. Assessing the generation of invalid records demonstrates that CasTGAN reduces the number of invalid data observations by up to 622% in comparison to the second best performing baseline tabular GAN model. Furthermore, we evaluate the risk of white-box privacy attacks on our model and subsequently show that applying some perturbations to the auxiliary learners in CasTGAN increases the overall robustness of our model against targeted attacks.
In ischemic stroke, adding EVT to usual care at 6 to 24 h improved functional status at 90 d

SOURCE CITATION Olthuis SGH, Pirson FAV, Pinckaers FME, et al; MR CLEAN-LATE investigators. Endovascular treatment versus no endovascular treatment after 6-24 h in patients with ischaemic stroke and collateral flow on CT angiography (MR CLEAN-LATE) in the Netherlands: a multicentre, open-label, blinded-endpoint, randomised, controlled, phase 3 trial. Lancet. 2023;401:1371-1380. 37003289.
Adonis: Practical and Efficient Control Flow Recovery through OS-Level Traces

Control flow recovery is critical to promise the software quality, especially for large-scale software in production environment. However, the efficiency of most current control flow recovery techniques is compromised due to their runtime overheads along with deployment and development costs. To tackle this problem, we propose a novel solution, Adonis, which harnesses OS-level traces, such as dynamic library calls and system call traces, to efficiently and safely recover control flows in practice. Adonis operates in two steps: it first identifies the call-sites of trace entries, then it executes a pair-wise symbolic execution to recover valid execution paths. This technique has several advantages. First, Adonis does not require the insertion of any probes into existing applications, thereby minimizing runtime cost. Second, given that OS-level traces are hardware-independent, Adonis can be implemented across various hardware configurations without the need for hardware-specific engineering efforts, thus reducing deployment cost. Third, as Adonis is fully automated and does not depend on manually created logs, it circumvents additional development cost. We conducted an evaluation of Adonis on representative desktop applications and real-world IoT applications. Adonis can faithfully recover the control flow with 86.8% recall and 81.7% precision. Compared to the state-of-the-art log-based approach, Adonis can not only cover all the execution paths recovered, but also recover 74.9% of statements that cannot be covered. In addition, the runtime cost of Adonis is 18.3 × lower than the instrument-based approach; the analysis time and storage cost (indicative of the deployment cost) of Adonis is 50 × smaller and 443 × smaller than the hardware-based approach, respectively. To facilitate future replication and extension of this work, we have made the code and data publicly available.
Fuzzy Logic Control of a Rotary Double Inverted Pendulum System

This work presents a fuzzy logic control of a rotary double inverted pendulum (RDIP) system. The RDIP system consists of two inverted pendulums mounted on a rotating disc which is driven by a DC motor. The longer pendulum is hinged on the right side of the disc whereas the shorter pendulum is hinged on the left side of the disc. The RDIP is an extremely nonlinear, unstable and under-actuated system of high order. A mathematical model is built for the RDIP using the Lagrange-Euler equation. A Fuzzy Logic Controller is then designed for swing-up and stabilization control of the RDIP system and its stability analysis is presented. The fuzzy controller takes the angles and angular velocities of the two pendulums and the angle and angular velocity of the rotary disc as its inputs, and the driving force as its output. A PID controller is also developed for this system for the purpose of comparison. The simulation results of these two control schemes with their comparative analysis show that, although both of the classical PID and the fuzzy controllers can control the system properly, the latter performs better especially on the steady state behavior. The simulation results also show the capability of the fuzzy logic control strategy to control a highly nonlinear and unstable system such as the RDIP.
Classifying data using near-term quantum devices

The goal of this work is to define a notion of a “quantum neural network” to classify data, which exploits the low-energy spectrum of a local Hamiltonian. As a concrete application, we build a binary classifier, train it on some actual data and then test its performance on a simple classification task. More specifically, we use Microsoft’s quantum simulator, LIQ[Formula: see text][Formula: see text], to construct local Hamiltonians that can encode trained classifier functions in their ground space, and which can be probed by measuring the overlap with test states corresponding to the data to be classified. To obtain such a classifier Hamiltonian, we further propose a training scheme based on quantum annealing which is completely closed-off to the environment and which does not depend on external measurements until the very end, avoiding unnecessary decoherence during the annealing procedure. For a network of size [Formula: see text], the trained network can be stored as a list of [Formula: see text] coupling strengths. We address the question of which interactions are most suitable for a given classification task, and develop a qubit-saving optimization for the training procedure on a simulated annealing device. Furthermore, a small neural network to classify colors into red versus blue is trained and tested, and benchmarked against the annealing parameters.
Application of Adaptive Observer to Sensorless Induction Motor via Parameter-Dependent Transformation

This brief addresses the adaptive observer design problem for sensorless induction motor (IM) drives with unknown resistances. In the literature, difficulty is found when applying several well-developed adaptive observer designs to IM dynamics. Hence, by introducing a parameter-dependent transformation, in the new coordinate, the IM dynamics manifest the attributes that facilitate the adaptive observer design. Although the classic designs that require linear-in-parameters result in overparameterization, the proposed nonlinearly parameterized adaptive observer demonstrates a good performance, as illustrated by the simulation and experimental results.
A feasibility trial of online Acceptance and Commitment Therapy for women with provoked vestibulodynia

Abstract Objectives Acceptance and Commitment Therapy (ACT) is an established treatment for chronic pain. However, it is a form of treatment that have not yet been applied much in the treatment of persistent vulvar pain disorders. This study examines the feasibility and preliminary effects of online ACT for patients with provoked vestibulodynia. Methods Women diagnosed with provoked vestibulodynia were assigned randomly either to online ACT or to a waitlist control group. Feasibility was assessed in terms of recruitment potential, treatment credibility, completions rates, retention in trial, and data quality. Participants completed measures of pain with sexual activity, sexual functioning, emotional and relational adjustment, and potential treatment processes before and after treatment. Results Of the 111 women who were invited to participate in the study, 44 were included (39.6 % recruitment rate). Thirty seven participants (84.1 %) completed the pre-treatment assessment. Participants who received online ACT rated treatment credibility positively, and completed on average 4.31 (SD=1.60) of the six treatment modules. Of participants, 34 provided post treatment data, giving a trial retention rate of 77 %. Effects of online ACT, as compared to waitlist, were large for pain acceptance and quality of life, medium for anxiety and pain catastrophizing, and small for sexual satisfaction, pain with sexual activity, and relationship adjustment. Conclusions With some adjustments to recruitment procedures, a full scale randomized controlled trial of online ACT for provoked vestibulodynia appears feasible.
Comparison of Sigma metrics computed by three bias estimation approaches for 33 chemistry and 26 immunoassay analytes

Abstract Objectives Sigma metric can be calculated using a simple equation. However, there are multiple sources for the elements in the equation that may produce different Sigma values. This study aimed to investigate the importance of different bias estimation approaches for Sigma metric calculation. Methods Sigma metrics were computed for 33 chemistry and 26 immunoassay analytes on the Roche Cobas 6000 analyzer. Bias was estimated by three approaches: (1) averaging the monthly bias values obtained from the external quality assurance (EQA) studies; (2) calculating the bias values from the regression equation derived from the EQA data; and (3) averaging the monthly bias values from the internal quality control (IQC) events. Sigma metrics were separately calculated for the two levels of the IQC samples using three bias estimation approaches. The resulting Sigma values were classified into five categories considering Westgard Sigma Rules as ≥6, <6 and ≥5, <5 and ≥4, <4 and ≥3, and <3. Results When classifying Sigma metrics estimated by three bias estimation approaches for each assay, 16 chemistry assays at the IQC level 1 and 2 were observed to fall into different Sigma categories under at least one bias estimation approach. Similarly, for 12 immunoassays at the IQC level 1 and 2, Sigma category was different depending on bias estimation approach. Conclusions Sigma metrics may differ depending on bias estimation approaches. This should be considered when using Six Sigma for assessing analytical performance or scheduling the IQC events.
Dihydroartemisinin ameliorates cerebral I/R injury in rats via regulating VWF and autophagy-mediated SIRT1/FOXO1 pathway

Abstract Dihydroartemisinin (DHA) has been found to inhibit the expression of von Willebrand factor (VWF), a marker of endothelial cell injury, but its mechanism in cerebral ischemia/reperfusion (I/R) injury remains obscure. In this study, I/R model was constructed through middle cerebral artery occlusion (MCAO) in rats, followed by DHA administration. The effect of DHA on rat cerebral I/R injury was investigated by 2,3,5-triphenyltetrazolium chloride staining, hematoxylin and eosin staining, TUNEL staining, and Western blot. Brain microvascular endothelial cells (BMVECs) isolated from newborn rats were exposed to oxygen–glucose deprivation/reoxygenation (OGD/R), and then treated with DHA. The results showed that MCAO treatment induced infarction, nerve cell apoptosis, and brain tissue impairment in rats, which was mitigated by DHA. OGD/R inhibited viability and accelerated apoptosis of BMVECs, which was alleviated by DHA. I/R procedures or OGD/R up-regulated expressions of VWF, ATG7, Beclin1, and LC3-II/LC3-I ratio, while down-regulating Occludin, Claudin-5, ZO-1, P62, SIRT1, and FOXO1 expressions in vivo and in vitro; however, these effects of I/R procedures or OGD/R were offset by DHA. VWF overexpression reversed the above effects of DHA on OGD/R-induced BMVECs. In summary, DHA ameliorates cerebral I/R injury in rats by reducing VWF level and activating autophagy-mediated SIRT1/FOXO1 signaling pathway.