Hybrid variational quantum algorithms (VQAs) are promising for solving practical problems such as combinatorial optimization, quantum chemistry simulation, quantum machine learning, and quantum error correction on noisy quantum computers. However, with typical random ansatz or quantum alternating operator ansatz, derived variational quantum algorithms become a black box for model interpretation. In this paper we construct a quantum regression algorithm wherein the quantum state directly encodes the classical data table and the variational parameters correspond directly to the regression coefficients which are real numbers by construction, providing a high degree of model interpretability and minimal cost to optimize with the right expressiveness. Instead of assuming the state preparation is given by granted, we discuss the state preparation with different encoders and their time complexity and overall resource cost. We can take advantage of the encoded data structure to cut down the algorithm time complexity. To the best of our knowledge, we show for the first time explicitly how the linkage of the classical data structure can be taken advantage of directly through quantum subroutines by construction. For nonlinear regression, our algorithm can be extended by building nonlinear features into the training data as demonstrated by numerical results. In addition, we demonstrate that the model trainability is achievable only when the number of features $M$ is much less than the number of records $L$ for the encoded data structure to justify $L\gg M$ in our resource estimation.

The aim of this paper is to present a mathematical framework for tensor PCA. The proposed approach is able to overcome the limitations of previous methods that extract a low dimensional subspace by iteratively solving an optimization problem. The core of the proposed approach is the derivation of a basis in tensor space from a real self-adjoint tensor operator, thus reducing the problem of deriving a basis to an eigenvalue problem. Three different cases have been studied to derive: i) a basis from a self-adjoint tensor operator; ii) a rank-1 basis; iii) a basis in a subspace. In particular, the equivalence between eigenvalue equation for a real self-adjoint tensor operator and standard matrix eigenvalue equation has been proven. For all the three cases considered, a subspace approach has been adopted to derive a tensor PCA. Experiments on image datasets validate the proposed mathematical framework.

This post contains a list of the AI-related seminars that are scheduled to take place between 23 January and 29 February 2024. All events detailed here are free and open for anyone to attend virtually. What to expect of Europe's ubiquitous digital identification infrastructure Speaker: Thomas Lohninger Organised by: The Digital Humanism (DIGHUM) Initiative Zoom link is here. Planning and Acting to Learn Speaker: Paolo Traverso Organised by: Italian Association for Artificial Intelligence Watch live on YouTube here. Understanding Cellular Biology across multiple scales using machine learning Speaker: Mohammad Lotfollahi Organised by: Cambridge Centre for AI in Medicine Sign up to the mailing list to receive invite to attend.

We investigate the application of the factor graph framework for blind joint channel estimation and symbol detection on time-variant linear inter-symbol interference channels. In particular, we consider the expectation maximization (EM) algorithm for maximum likelihood estimation, which typically suffers from high complexity as it requires the computation of the symbol-wise posterior distributions in every iteration. We address this issue by efficiently approximating the posteriors using the belief propagation (BP) algorithm on a suitable factor graph. By interweaving the iterations of BP and EM, the detection complexity can be further reduced to a single BP iteration per EM step. In addition, we propose a data-driven version of our algorithm that introduces momentum in the BP updates and learns a suitable EM parameter update schedule, thereby significantly improving the performance-complexity tradeoff with a few offline training samples. Our numerical experiments demonstrate the excellent performance of the proposed blind detector and show that it even outperforms coherent BP detection in high signal-to-noise scenarios.

The aim of this work is to create and apply a methodological approach for predicting gas traps from 3D seismic data and gas well testing. The proposed approach is based on binary classification algorithms with training on well data. The study includes the following sequence of operations: interpretation of gas well test results to determine the radius of radial gas filtration (IARF); correlation of the top and bottom of productive horizons based on seismic data in the near-wellbore space; creating volumes of space for positive and negative class; dividing the sample into training and validation; creation of a separate test sample for the metamodel; creation of a feature space - extraction of seismic wavefield attributes; creation of data sampling - assignment of a vector of seismic attributes to each point of space within the volumes of classes; selection of features; basic model training; generalized assessment of trait contribution; creation of an ensemble of classification models using a metamodel - logistic regression; prediction of the probability of space belonging to gas reservoirs; evaluation of forecast quality on a test sample. The paper formalizes an approach to creating a training dataset by selecting volumes with established gas saturation and filtration properties within a seismic wavefield. The volumes divide the studied space into positive and negative classes. Positive class is a volume of gas-saturated sands, identified by the results of detailed correlation of the gas sandstone top and bottom, within which there is a region with a boundary along the radius of continuous radial gas filtration.

Recent works have demonstrated the effectiveness of machine learning (ML) techniques in detecting anxiety and stress using physiological signals, but it is unclear whether ML models are learning physiological features specific to stress. To address this ambiguity, we evaluated the generalizability of physiological features that have been shown to be correlated with anxiety and stress to high-arousal emotions. Specifically, we examine features extracted from electrocardiogram (ECG) and electrodermal (EDA) signals from the following three datasets: Anxiety Phases Dataset (APD), Wearable Stress and Affect Detection (WESAD), and the Continuously Annotated Signals of Emotion (CASE) dataset. We aim to understand whether these features are specific to anxiety or general to other high-arousal emotions through a statistical regression analysis, in addition to a within-corpus, cross-corpus, and leave-one-corpus-out cross-validation across instances of stress and arousal. We used the following classifiers: Support Vector Machines, LightGBM, Random Forest, XGBoost, and an ensemble of the aforementioned models. We found that models trained on an arousal dataset perform relatively well on a previously unseen stress dataset, and vice versa. Our experimental results suggest that the evaluated models may be identifying emotional arousal instead of stress. This work is the first cross-corpus evaluation across stress and arousal from ECG and EDA signals, contributing new findings about the generalizability of stress detection.

The integration of Machine Learning (ML) with domain-specific knowledge is a pivotal advancement in predictive modeling [1, 2]. This combination has brought a new level of precision and insight to fields within engineering and environmental sciences [3, 4]. While the synergy has notably improved accuracy and decision-making processes [5, 6], the challenge of seamlessly blending domain knowledge with ML algorithms continues to evolve. To bridge this gap, the Ladder of Knowledge-integrated Machine Learning has been introduced [7]. This framework aims to optimize the utilization of domain-specific insights, offering a comprehensive approach to integrating prior knowledge information into ML applications. Inspired by the long debate between holistic and reductionist approaches in ML [8], the framework aims firstly to synergize multidisciplinary domain knowledge with data-driven processes in two principal dimensions: firstly, by identifying and understanding the complementary nature of uncertainties in data, knowledge-based methodologies, and data-driven methods; secondly, by exploring knowledge decomposition from various perspectives and aligning these insights with our paradigm. Finally, building upon the previous two foundations in the specific domain context, the ladder unfolds across three progressive levels of integrating domain expertise into ML approaches [7]. In the pursuit of sustainable energy solutions, Proton Exchange Membrane Water Electrolyzers (PEMWEs) stand out for their high energy efficiency and minimal environmental impact [9] in hydrogen production.

Safeguarding intellectual property and preventing potential misuse of AI-generated images are of paramount importance. This paper introduces a robust and agile plug-and-play watermark detection framework, dubbed as RAW. As a departure from traditional encoder-decoder methods, which incorporate fixed binary codes as watermarks within latent representations, our approach introduces learnable watermarks directly into the original image data. Subsequently, we employ a classifier that is jointly trained with the watermark to detect the presence of the watermark. The proposed framework is compatible with various generative architectures and supports on-the-fly watermark injection after training. By incorporating state-of-the-art smoothing techniques, we show that the framework provides provable guarantees regarding the false positive rate for misclassifying a watermarked image, even in the presence of certain adversarial attacks targeting watermark removal. Experiments on a diverse range of images generated by state-of-the-art diffusion models reveal substantial performance enhancements compared to existing approaches. For instance, our method demonstrates a notable increase in AUROC, from 0.48 to 0.82, when compared to state-of-the-art approaches in detecting watermarked images under adversarial attacks, while maintaining image quality, as indicated by closely aligned FID and CLIP scores.

Non-significant randomized control trials can hide subgroups of good responders to experimental drugs, thus hindering subsequent development. Identifying such heterogeneous treatment effects is key for precision medicine and many post-hoc analysis methods have been developed for that purpose. While several benchmarks have been carried out to identify the strengths and weaknesses of these methods, notably for binary and continuous endpoints, similar systematic empirical evaluation of subgroup analysis for time-to-event endpoints are lacking. This work aims to fill this gap by evaluating several subgroup analysis algorithms in the context of time-to-event outcomes, by means of three different research questions: Is there heterogeneity? What are the biomarkers responsible for such heterogeneity? Who are the good responders to treatment? In this context, we propose a new synthetic and semi-synthetic data generation process that allows one to explore a wide range of heterogeneity scenarios with precise control on the level of heterogeneity. We provide an open source Python package, available on Github, containing our generation process and our comprehensive benchmark framework. We hope this package will be useful to the research community for future investigations of heterogeneity of treatment effects and subgroup analysis methods benchmarking.

The need to understand the structure of hierarchical or high-dimensional data is present in a variety of fields. Hyperbolic spaces have proven to be an important tool for embedding computations and analysis tasks as their non-linear nature lends itself well to tree or graph data. Subsequently, they have also been used in the visualization of high-dimensional data, where they exhibit increased embedding performance. However, none of the existing dimensionality reduction methods for embedding into hyperbolic spaces scale well with the size of the input data. That is because the embeddings are computed via iterative optimization schemes and the computation cost of every iteration is quadratic in the size of the input. Furthermore, due to the non-linear nature of hyperbolic spaces, Euclidean acceleration structures cannot directly be translated to the hyperbolic setting. This paper introduces the first acceleration structure for hyperbolic embeddings, building upon a polar quadtree. We compare our approach with existing methods and demonstrate that it computes embeddings of similar quality in significantly less time. Implementation and scripts for the experiments can be found at https://graphics.tudelft.nl/accelerating-hyperbolic-tsne.