Miracle-dz/UniFAR-FTUs huggingface dataset:
Curvature effects in the spectral dimension of spin foams

It has been shown in [1] that a class of restricted spin foam models can feature a reduced spectral dimension of space-time. However, it is still an open question how curvature affects the flow of the spectral dimension. To answer this question, we consider another class of restricted spin foam models, so called spin foam frusta, which naturally exhibit oscillating amplitudes induced by curvature, as well as an extension of the parameter space by a cosmological constant. Numerically computing the spectral dimension of $1$-periodic frusta geometries using extrapolated quantum amplitudes, we find that quantum effects lead to a small change of spectral dimension at small scales and an agreement to semi-classical results at larger scales. Adding a cosmological constant $\Lambda$, we find additive corrections to the non-oscillating result at the diffusion scale $\tau\sim 1/\sqrt{\Lambda}$. Extending to $2$-periodic configurations, we observe a reduced effective dimension, the form of which sensitively depends on the values of the gravitational constant $G$ and the cosmological constant $\Lambda$. We provide an intuition for our results based on an analytical estimate of the spectral dimension. Furthermore, we present a simplified integrable model with oscillating measure that qualitatively explains the features found numerically. We argue that there exists a phase transition in the thermodynamic limit which crucially depends on the parameters $G$ and $\Lambda$. The dependence on $G$ and $\Lambda$ presents an exciting opportunity to infer phenomenological insights about quantum geometry from measurement of the spectral dimension, in principle. [1]: S. Steinhaus and J. Th\"urigen, Emergence of Spacetime in a restricted Spin-foam model, Phys. Rev. D 98 (2018) 026013
Multi-study factor regression model: an application in nutritional epidemiology

Diet is a risk factor for many diseases. In nutritional epidemiology, studying reproducible dietary patterns is critical to reveal important associations with health. However, it is challenging: diverse cultural and ethnic backgrounds may critically impact eating patterns, showing heterogeneity, leading to incorrect dietary patterns and obscuring the components shared across different groups or populations. Moreover, covariate effects generated from observed variables, such as demographics and other confounders, can further bias these dietary patterns. Identifying the shared and group-specific dietary components and covariate effects is essential to drive accurate conclusions. To address these issues, we introduce a new modeling factor regression, the Multi-Study Factor Regression (MSFR) model. The MSFR model analyzes different populations simultaneously, achieving three goals: capturing shared component(s) across populations, identifying group-specific structures, and correcting for covariate effects. We use this novel method to derive common and ethnic-specific dietary patterns in a multi-center epidemiological study in Hispanic/Latinos community. Our model improves the accuracy of common and group dietary signals and yields better prediction than other techniques, revealing significant associations with health. In summary, we provide a tool to integrate different groups, giving accurate dietary signals crucial to inform public health policy.
Efficient ML Lifecycle Transferring for Large-Scale and High-Dimensional Data via Core Set-Based Dataset Similarity

Developing an end-to-end machine learning (ML) lifecycle for an ML task can be costly and time-consuming. It involves exploring multiple configurations of ML pipelines, encompassing data preparation, ML model design, training, and deployment. While automated ML (AutoML) can assist in automatically searching and training an optimized ML pipeline, it is computationally intensive and lacks reusability for high-dimensional datasets. Transfer learning has emerged as a popular technique for fine-tuning pre-trained models on related datasets, yet it still requires manual tuning to achieve optimal results. To overcome these challenges, we present a version management system for the end-to-end ML lifecycle, enabling the transfer of lifecycle versions from similar datasets to new ML tasks. Specifically, we introduce an algorithm that leverages core sets to compute similarities for large-scale and high-dimensional datasets efficiently. To the best of our knowledge, we are the first to investigate ML lifecycle transfer for similar high-dimensional datasets. We conducted experiments on real-world datasets comprising computer vision and spatiotemporal sensor data. The experimental results demonstrate the effectiveness of our dataset similarity algorithm and the ML lifecycle version transferring procedure, reducing dataset similarity computation time by up to 60x while improving model accuracy compared to transfer learning. Furthermore, in a practical case study, our solution exhibited up to 3.5x greater efficiency in training time and memory consumption and 9% better model accuracy than manual tuning approaches.
Thompson Sampling Regret Bounds for Contextual Bandits with sub-Gaussian rewards

In this work, we study the performance of the Thompson Sampling algorithm for Contextual Bandit problems based on the framework introduced by [1] and their concept of lifted information ratio. First, we prove a comprehensive bound on the Thompson Sampling expected cumulative regret that depends on the mutual information of the environment parameters and the history. Then, we introduce new bounds on the lifted information ratio that hold for sub-Gaussian rewards, thus generalizing the results from [1] which analysis requires binary rewards. Finally, we provide explicit regret bounds for the special cases of unstructured bounded contextual bandits, structured bounded contextual bandits with Laplace likelihood, structured Bernoulli bandits, and bounded linear contextual bandits.
Introducing MBIB - The First Media Bias Identification Benchmark Task and Dataset Collection

Although media bias detection is a complex multi-task problem, there is, to date, no unified benchmark grouping these evaluation tasks. We introduce the Media Bias Identification Benchmark (MBIB), a comprehensive benchmark that groups different types of media bias (e.g., linguistic, cognitive, political) under a common framework to test how prospective detection techniques generalize. After reviewing 115 datasets, we select nine tasks and carefully propose 22 associated datasets for evaluating media bias detection techniques. We evaluate MBIB using state-of-the-art Transformer techniques (e.g., T5, BART). Our results suggest that while hate speech, racial bias, and gender bias are easier to detect, models struggle to handle certain bias types, e.g., cognitive and political bias. However, our results show that no single technique can outperform all the others significantly.We also find an uneven distribution of research interest and resource allocation to the individual tasks in media bias. A unified benchmark encourages the development of more robust systems and shifts the current paradigm in media bias detection evaluation towards solutions that tackle not one but multiple media bias types simultaneously.
Exact recovery for the non-uniform Hypergraph Stochastic Block Model

Consider the community detection problem in random hypergraphs under the non-uniform hypergraph stochastic block model (HSBM), where each hyperedge appears independently with some given probability depending only on the labels of its vertices. We establish, for the first time in the literature, a sharp threshold for exact recovery under this non-uniform case, subject to minor constraints; in particular, we consider the model with multiple communities ($K \geq 2$). One crucial point here is that by aggregating information from all the uniform layers, we may obtain exact recovery even in cases when this may appear impossible if each layer were considered alone. Two efficient algorithms that successfully achieve exact recovery above the threshold are provided. The theoretical analysis of our algorithms relies on the concentration and regularization of the adjacency matrix for non-uniform random hypergraphs, which could be of independent interest. We also address some open problems regarding parameter knowledge and estimation.
Learning battery model parameter dynamics from data with recursive Gaussian process regression

Estimating state of health is a critical function of a battery management system but remains challenging due to the variability of operating conditions and usage requirements of real applications. As a result, techniques based on fitting equivalent circuit models may exhibit inaccuracy at extremes of performance and over long-term ageing, or instability of parameter estimates. Pure data-driven techniques, on the other hand, suffer from lack of generality beyond their training dataset. In this paper, we propose a hybrid approach combining data- and model-driven techniques for battery health estimation. Specifically, we demonstrate a Bayesian data-driven method, Gaussian process regression, to estimate model parameters as functions of states, operating conditions, and lifetime. Computational efficiency is ensured through a recursive approach yielding a unified joint state-parameter estimator that learns parameter dynamics from data and is robust to gaps and varying operating conditions. Results show the efficacy of the method, on both simulated and measured data, including accurate estimates and forecasts of battery capacity and internal resistance. This opens up new opportunities to understand battery ageing in real applications.
Connector 0.5: A unified framework for graph representation learning

Graph representation learning models aim to represent the graph structure and its features into low-dimensional vectors in a latent space, which can benefit various downstream tasks, such as node classification and link prediction. Due to its powerful graph data modelling capabilities, various graph embedding models and libraries have been proposed to learn embeddings and help researchers ease conducting experiments. In this paper, we introduce a novel graph representation framework covering various graph embedding models, ranging from shallow to state-of-the-art models, namely Connector. First, we consider graph generation by constructing various types of graphs with different structural relations, including homogeneous, signed, heterogeneous, and knowledge graphs. Second, we introduce various graph representation learning models, ranging from shallow to deep graph embedding models. Finally, we plan to build an efficient open-source framework that can provide deep graph embedding models to represent structural relations in graphs. The framework is available at https://github.com/NSLab-CUK/Connector.
Strong Dispersive Coupling Between a Mechanical Resonator and a Fluxonium Superconducting Qubit

We demonstrate strong dispersive coupling between a fluxonium superconducting qubit and a 690 megahertz mechanical oscillator, extending the reach of circuit quantum acousto-dynamics (cQAD) experiments into a new range of frequencies. We have engineered a qubit-phonon coupling rate of $g\approx2\pi\times14~\text{MHz}$, and achieved a dispersive interaction that exceeds the decoherence rates of both systems while the qubit and mechanics are highly nonresonant ($\Delta/g\gtrsim10$). Leveraging this strong coupling, we perform phonon number-resolved measurements of the mechanical resonator and investigate its dissipation and dephasing properties. Our results demonstrate the potential for fluxonium-based hybrid quantum systems, and a path for developing new quantum sensing and information processing schemes with phonons at frequencies below 700 MHz to significantly expand the toolbox of cQAD.
Four-channel System for Characterization of Josephson Parametric Amplifiers

The axion search experiments based on haloscopes at the Center for Axion and Precision Physics Research (CAPP) of the Institute for Basic Science (IBS) in South Korea are performed in the frequency range from 1 GHz to 6 GHz. In order to perform the experiments in a strong magnetic field of 12 T and a large-volume cavity of close to 40 liters, we use He wet dilution refrigerators with immersed superconducting magnets. The measurements require continuous operation for months without interruptions for microwave component replacements. This is achieved by using different cryogenic engineering approaches including microwave RF-switching. The critical components, defining the scanning rate and the sensitivity of the setup, are the Josephson parametric amplifiers (JPA) and cryogenic low noise amplifiers (cLNA) based on high-electron-mobility-transistor (HEMT) technology. It is desirable for both devices to have a wide frequency range and low noise close to the quantum limit for the JPA. In this paper, we show a recent design of a 4-channel measurement setup for JPA and HEMT measurements. The setup is based on a 4-channel wideband noise source (NS) and is used for both JPA and HEMT gain and noise measurements. The setup is placed at 20 mK inside the dry dilution refrigerator. The NS is thermally decoupled from the environment using plastic spacers, superconducting wires and superconducting coaxial cables. We show the gain and noise temperature curves measured for 4 HEMT amplifiers and 2 JPAs in one cool-down
Towards Explainable and Safe Conversational Agents for Mental Health: A Survey

Virtual Mental Health Assistants (VMHAs) are seeing continual advancements to support the overburdened global healthcare system that gets 60 million primary care visits, and 6 million Emergency Room (ER) visits annually. These systems are built by clinical psychologists, psychiatrists, and Artificial Intelligence (AI) researchers for Cognitive Behavioral Therapy (CBT). At present, the role of VMHAs is to provide emotional support through information, focusing less on developing a reflective conversation with the patient. A more comprehensive, safe and explainable approach is required to build responsible VMHAs to ask follow-up questions or provide a well-informed response. This survey offers a systematic critical review of the existing conversational agents in mental health, followed by new insights into the improvements of VMHAs with contextual knowledge, datasets, and their emerging role in clinical decision support. We also provide new directions toward enriching the user experience of VMHAs with explainability, safety, and wholesome trustworthiness. Finally, we provide evaluation metrics and practical considerations for VMHAs beyond the current literature to build trust between VMHAs and patients in active communications.
Self-Supervised Multi-Modal Sequential Recommendation

With the increasing development of e-commerce and online services, personalized recommendation systems have become crucial for enhancing user satisfaction and driving business revenue. Traditional sequential recommendation methods that rely on explicit item IDs encounter challenges in handling item cold start and domain transfer problems. Recent approaches have attempted to use modal features associated with items as a replacement for item IDs, enabling the transfer of learned knowledge across different datasets. However, these methods typically calculate the correlation between the model's output and item embeddings, which may suffer from inconsistencies between high-level feature vectors and low-level feature embeddings, thereby hindering further model learning. To address this issue, we propose a dual-tower retrieval architecture for sequence recommendation. In this architecture, the predicted embedding from the user encoder is used to retrieve the generated embedding from the item encoder, thereby alleviating the issue of inconsistent feature levels. Moreover, in order to further improve the retrieval performance of the model, we also propose a self-supervised multi-modal pretraining method inspired by the consistency property of contrastive learning. This pretraining method enables the model to align various feature combinations of items, thereby effectively generalizing to diverse datasets with different item features. We evaluate the proposed method on five publicly available datasets and conduct extensive experiments. The results demonstrate significant performance improvement of our method.
Measuring Bias in AI Models with Application to Face Biometrics: An Statistical Approach

—The new regulatory framework proposal on Ar-tiﬁcial Intelligence (AI) published by the European Commission establishes a new risk-based legal approach. The proposal highlights the need to develop adequate risk assessments for the different uses of AI. This risk assessment should address, among others, the detection and mitigation of bias in AI. In this work we analyze statistical approaches to measure biases in automatic decision-making systems. We focus our experiments in face recognition technologies. We propose a novel way to measure the biases in machine learning models using a statistical approach based on the N-Sigma method. N-Sigma is a popular statistical approach used to validate hypotheses in general science such as physics and social areas and its application to machine learning is yet unexplored. In this work we study how to apply this methodology to develop new risk assessment frameworks based on bias analysis and we discuss the main advantages and drawbacks with respect to other popular statistical tests.
SmartChoices: Augmenting Software with Learned Implementations

We are living in a golden age of machine learning. Powerful models perform many tasks far better than is possible using traditional software engineering approaches alone. However, developing and deploying these models in existing software systems remains challenging. In this paper, we present SmartChoices, a novel approach to incorporating machine learning into mature software stacks easily, safely, and effectively. We highlight key design decisions and present case studies applying SmartChoices within a range of large-scale industrial systems.
FLCC: Efficient Distributed Federated Learning on IoMT over CSMA/CA

Federated Learning (FL) has emerged as a promising approach for privacy preservation, allowing sharing of the model parameters between users and the cloud server rather than the raw local data. FL approaches have been adopted as a cornerstone of distributed machine learning (ML) to solve several complex use cases. FL presents an interesting interplay between communication and ML performance when implemented over distributed wireless nodes. Both the dynamics of networking and learning play an important role. In this article, we investigate the performance of FL on an application that might be used to improve a remote healthcare system over ad hoc networks which employ CSMA/CA to schedule its transmissions. Our FL over CSMA/CA (FLCC) model is designed to eliminate untrusted devices and harness frequency reuse and spatial clustering techniques to improve the throughput required for coordinating a distributed implementation of FL in the wireless network.In our proposed model, frequency allocation is performed on the basis of spatial clustering performed using virtual cells. Each cell assigns an FL server and dedicated carrier frequencies to exchange the updated model’s parameters within the cell. We present two metrics to evaluate the network performance: 1) probability of successful transmission while minimizing the interference, and 2) performance of distributed FL model in terms of accuracy and loss while considering the networking dynamics.We benchmark the proposed approach using a well-known MNIST dataset for performance evaluation. We demonstrate that the proposed approach outperforms the baseline FL algorithms in terms of explicitly defining the chosen users’ criteria and achieving high accuracy in a robust network.
Scalar and Spinor Excitations in a Ferromagnetic Bose-Einstein Condensate

Author(s): Marti, George Edward | Advisor(s): Stamper-Kurn, Dan M | Abstract: At low temperatures, the thermodynamic properties of a many-body system are determined by the low energy excursions from the ground state. In quantum degenerate rubidium, the low energy excitations are phonons—phase and density modes—and magnons—spin modes. This thesis reports on the construction of an ultracold rubidium apparatus and techniques to create and image phonons and magnons. We propose and study matter wave interferometry of phonons in a ring trap as a potential rotation sensor in a compact sensor. Precision measurements of coherent magnons allow us to determine the magnon dispersion relation, including a gap induced by magnetic dipole-dipole interactions.
Key-value information extraction from full handwritten pages

We propose a Transformer-based approach for information extraction from digitized handwritten documents. Our approach combines, in a single model, the different steps that were so far performed by separate models: feature extraction, handwriting recognition and named entity recognition. We compare this integrated approach with traditional two-stage methods that perform handwriting recognition before named entity recognition, and present results at different levels: line, paragraph, and page. Our experiments show that attention-based models are especially interesting when applied on full pages, as they do not require any prior segmentation step. Finally, we show that they are able to learn from key-value annotations: a list of important words with their corresponding named entities. We compare our models to state-of-the-art methods on three public databases (IAM, ESPOSALLES, and POPP) and outperform previous performances on all three datasets.
SDSS J094002.56+274942.0: an SU UMa star with an orbital period of 3.92 hours and an apparently unevolved secondary

We found that SDSS J094002.56+274942.0 underwent a superoutburst in 2019 February based on our observations and Zwicky Transient Facility (ZTF) data. This object showed shallow eclipses during this superoutburst and we established the orbital period to be 0.1635015(1) d in combination with the ZTF and Asteroid Terrestrial-impact Last Alert System (ATLAS) data in quiescence. Superhumps apparently started to develop soon after the object reached the plateau phase and fully grown superhumps were recorded within the initial 6 d of the plateau phase. Using the superhump and orbital periods, we obtained a mass ratio (q) of 0.39(3) and obtained an inclination of 70.5(5) deg by eclipse modeling. These values reproduced the quiescent ellipsoidal variations very well. Using the Gaia parallax and 2MASS observations, we confirmed that the secondary is indistinguishable from an unevolved main-sequence star. The resultant mass ratio and orbital period were the highest among SU UMa stars, and this provided a proof that the 3:1 resonance can develop in less than 6 d even in q=0.39(3). The superoutburst faded relatively rapidly and was followed by a rebrightening, suggesting that the tidal effect in a large-q system was insufficient to maintain a long superoutburst and the remnant matter caused a rebrightening. The presence of such a system among dwarf novae is against the conventional idea that outbursts in dwarf novae are not long enough to develop superhumps, in contrast to novalike variables, under a weak tidal effect. The present observation also supports that the 3:1 resonance is the cause of a long outburst, and not its consequence, even under extreme q. The rapid growth of the 3:1 resonance in a high-q system challenges the generally accepted results of hydrodynamic simulations.