First, we thank all the reviewers for their invaluable assessment of our paper in this challenging time. To provide more reliable evidence that AdvFlow's distributional For the sake of completeness, we also add LID [31] The results are given in Table 1. This is indicating that the attacker's distributional properties are fooling the detectors. As seen, we get similar results to Table 2 of the paper, outperforming SimBA in defended baselines. Note that some of the current SOT A results in black-box adversarial attacks come from the attacker's knowledge about the However, once the target changes its training procedure (e.g., from vanilla See the official repo. of SimBA, where it clearly is indicated that the The results of Table 1 and 2 (as well as SVHN) will be added to the camera-ready version.

Latent Diffusion Models (LDM), a subclass of diffusion models, mitigate the computational complexity of pixel-space diffusion by operating within a compressed latent space constructed by Variational Autoencoders (VAEs), demonstrating significant advantages in Remote Sensing (RS) applications. Though numerous studies enhancing LDMs have been conducted, investigations explicitly targeting improvements within the intrinsic latent space remain scarce. This paper proposes an innovative perspective, utilizing the Discrete Wavelet Transform (DWT) to enhance the VAE's latent space representation, designed for satellite imagery. The proposed method, ExpDWT-VAE, introduces dual branches: one processes spatial domain input through convolutional operations, while the other extracts and processes frequency-domain features via 2D Haar wavelet decomposition, convolutional operation, and inverse DWT reconstruction. These branches merge to create an integrated spatial-frequency representation, further refined through convolutional and diagonal Gaussian mapping into a robust latent representation. We utilize a new satellite imagery dataset housed by the TerraFly mapping system to validate our method. Experimental results across several performance metrics highlight the efficacy of the proposed method at enhancing latent space representation.

Many real-world multivariate time series are collected from a network of physical objects embedded with software, electronics, and sensors. The quasi-periodic signals generated by these objects often follow a similar repetitive and periodic pattern, but have variations in the period, and come in different lengths caused by timing (synchronization) errors. Given a multitude of such quasi-periodic time series, can we build machine learning models to identify those time series that behave differently from the majority of the observations? In addition, can the models help human experts to understand how the decision was made? We propose a sequence to Gaussian Mixture Model (seq2GMM) framework. The overarching goal of this framework is to identify unusual and interesting time series within a network time series database. We further develop a surrogate-based optimization algorithm that can efficiently train the seq2GMM model. Seq2GMM exhibits strong empirical performance on a plurality of public benchmark datasets, outperforming state-of-the-art anomaly detection techniques by a significant margin. We also theoretically analyze the convergence property of the proposed training algorithm and provide numerical results to substantiate our theoretical claims.

Population pharmacokinetic (PopPK) modelling is a fundamental tool for understanding drug behaviour across diverse patient populations and enabling personalized dosing strategies to improve therapeutic outcomes. A key challenge in PopPK analysis lies in identifying and modelling covariates that influence drug absorption, as these relationships are often complex and nonlinear. Traditional methods may fail to capture hidden patterns within the data. In this study, we propose a data-driven, model-free framework that integrates Variational Autoencoders (VAEs) deep learning model and LASSO regression to uncover key covariates from simulated tacrolimus pharmacokinetic (PK) profiles. The VAE compresses high-dimensional PK signals into a structured latent space, achieving accurate reconstruction with a mean absolute percentage error (MAPE) of 2.26%. LASSO regression is then applied to map patient-specific covariates to the latent space, enabling sparse feature selection through L1 regularization. This approach consistently identifies clinically relevant covariates for tacrolimus including SNP, age, albumin, and hemoglobin which are retained across the tested regularization strength levels, while effectively discarding non-informative features. The proposed VAE-LASSO methodology offers a scalable, interpretable, and fully data-driven solution for covariate selection, with promising applications in drug development and precision pharmacotherapy.

--This paper introduces a deep learning framework for generating point clouds from WiFi Channel State Information data. We employ a two-stage autoencoder approach: a PointNet autoencoder with convolutional layers for point cloud generation, and a Convolutional Neural Network autoencoder to map CSI data to a matching latent space. By aligning these latent spaces, our method enables accurate environmental point cloud reconstruction from WiFi data. HE proliferation of wireless communication technologies has led to an increased interest in using WiFi signals for various sensing applications. Among these, Channel State Information (CSI) data from WiFi signals provides rich information about the environment, making it a valuable resource for tasks such as indoor localization [1], [2], activity recognition [3], [4], and environmental mapping [5], [6].

The Xeno -Canto bird audio repository is an invaluable resource for those interested in vocalizations and other sounds made by birds around the world. This is particularly the case for machine learning researchers attempting to improve on the bird species r ecognition accuracy of classification models. However, the task of extracting labeled datasets from th e recordings found in this crowd -sourced repository faces several challenges. One challenge of particular significance to machine learning practitioners i s that one bird species label is applied to each audio recording, but frequently other sounds are also captured including other bird species, other animal sounds, anthropogenic and other ambient sounds . These non -target bird species sounds can result in dataset labeling discrepanc ies referred to as label noise . In this work we present a cleaning process consisting of audio preprocessing followed by dimensionality reduction and unsupervised outlier detection (UOD) to reduce the label noise in a dataset derived from Xeno -Canto recordings . We investigate three neural network dimensionality reduction techniques: two flavors of convolutional autoencoder s and variational deep embedding (VaDE (Jiang, 2017)) . While both methods show some degree of effectiveness at detecting outliers for most bird species datasets, we f ound significant variation in the performance of the methods from one species to the next. We believe that the results of this investigation demonstrate that the application of our cleaning process can meaningfully reduce the label noise of bird species datasets derived from Xeno-Canto audio repository but results vary across species.

This paper proposes a combination of deep learning and stochastic optimal control for control of non-linear dynamical systems from image inputs. The system learns a latent space representation of the true system state based on images of the state using deep variational auto encoders. Additionally, a locally-linear dynamics model is learned on the latent space which is used to generate state space trajectories by applying stochastic optimal control techniques directly on the latent space. The system is tested on four control tasks and the performance is compared with multiple baselines showing good performance. Quality: --------- The paper is logical and sound.