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High-dosage tutoring-defined as consistently scheduled tutorials during the school day in which students work with a trained tutor in small groups (for example, four students to one tutor) at least three times per week--is among the most promising educational interventions for improving student learning. Despite their appeal, high-dosage tutoring programs can be challenging to deliver; in particular, research has shown that it is difficult to ensure that students participate in the expected number of tutoring sessions per week. Students can only benefit from tutoring interventions if they are able to attend. The Personalized Learning Initiative (PLI)--led by the University of Chicago Education Lab in collaboration with MDRC--seeks to understand how school districts can expand the benefits of high-dosage tutoring (and more affordable alternatives). This brief elevates the perspectives of school staff members and tutors at schools that are part of PLI. Hearing from people who are directly involved in the delivery of tutoring programs can bring to the surface new and innovative ideas and can help to ensure that those ideas are feasible for school staff members to implement. During the 2022-2023 school year, the PLI research team conducted an implementation study in two urban school districts--Chicago Public Schools and Fulton County Schools in Georgia--to explore how participating elementary, middle, and high schools implemented high-dosage tutoring programs during the school day. The research team fielded surveys and conducted semistructured interviews with school staff members and tutors who were involved with high-dosage tutoring programs. This brief is based on surveys from 30 schools and interviews from a subsample of 18 schools. Drawing on staff members' insights, the brief offers recommendations that schools and district offices can use to help students receive the intended amount of their school-day tutoring programs. [The Personalized Learning Initiative receives substantial support from Accelerate--The National Collaborative for Accelerated Learning, which is supported by Citadel founder and CEO Kenneth C. Griffin and Griffin Catalyst, Arnold Ventures, the Bill & Melinda Gates Foundation, Overdeck Family Foundation, and the Walton Family Foundation. Additional funding comes from the AbbVie Foundation, Crown Family Philanthropies, Citadel founder and CEO Kenneth C. Griffin and Griffin Catalyst, IMC Chicago Charitable Foundation, and Vivo Foundation.]

This paper presents a rationale for using professional and continuing education (PaCE) units at post-secondary institutions throughout California to design and implement talent-pipelines, research and development collaborations, and other knowledge ecosystems where emerging and returning professionals can acquire the knowledge, skills, and abilities (KSAs), as well as the experience, they need to address the challenges of the Fourth Industrial Revolution (4IR). The paper provides an analysis of the reasons why PaCE units are uniquely positioned to address the needs of industry and job seekers, and on a timetable that keeps pace with 4IR velocity.

Boosting literacy among school-age children remains a national priority. Nearly one third of students in the United States have not developed the foundational reading skills needed to succeed academically, with students living in poverty, students with disabilities, and English learners especially at risk. Starting in 2010, Congress invested more than $1 billion for state literacy improvement efforts through the Striving Readers Comprehensive Literacy (SRCL) program. SRCL was intended to focus funding on disadvantaged schools, encourage schools to use evidence-based practices, and support schools and teachers in providing comprehensive literacy instruction. These efforts were expected to lead to improved literacy outcomes for students. This study assesses how well SRCL implementation was aligned with these goals, using information collected from states, districts, and schools in all 11 states awarded grants in 2017.

The set of appendices in this volume is a companion to the report "Federal Efforts Towards Investing in Innovation in Education Through the i3 Fund: A Summary of Grantmaking and Evidence-Building." The goal of the U.S. Department of Education's Investing in Innovation Fund (i3) was to build high-quality evidence about effective educational strategies and to expand implementation of these strategies. Between 2010 and 2016, i3 awarded 172 grants totaling $1.4 billion to universities, school districts, and private non-profit organizations. The i3 Fund intentionally awarded different types of grants either to develop and test new, innovative but as-yet-unproven strategies or to learn more about the circumstances under which previously tested strategies are effective. Grantees were required to fund independent evaluations that would meet high standards for quality. Appendices included in this document are: (1) Additional Details about the Investing in Innovation (i3) Fund Program; (2) Study Design and Methods; and (3) Supplemental Tables and Information on Study Findings.

Finding innovative educational strategies that work is important to help improve student learning and close equity gaps nationwide. The goal of the U.S. Department of Education's Investing in Innovation Fund (i3) was to build high-quality evidence about effective educational strategies and to expand implementation of these strategies. Between 2010 and 2016, i3 awarded 172 grants totaling $1.4 billion to universities, school districts, and private non-profit organizations. The i3 Fund intentionally awarded different types of grants either to develop and test new, innovative but as-yet-unproven strategies or to learn more about the circumstances under which previously tested strategies are effective. Grantees were required to fund independent evaluations that would meet high standards for quality. This report examines the quality and findings of the 148 i3 grants with completed evaluations and provides information about the focus of the strategies those grantees tested.

There is increasing interest in looking at dialects in NLP. However, most work to date still treats dialects as discrete categories. For instance, evaluative work in variation-oriented NLP for English often works with Indian English or African-American Venacular English as homogeneous categories (Faisal et al., 2024; Ziems et al., 2023), yet even within one variety there is substantial variation. We examine within-dialect variation and show that performance critically varies within categories. We measure speech-to-text performance on Italian dialects, and empirically observe a geographical performance disparity. This disparity correlates substantially (-0.5) with linguistic similarity to the highest performing dialect variety. We cross-examine our results against dialectometry methods, and interpret the performance disparity to be due to a bias towards dialects that are more similar to the standard variety in the speech-to-text model examined. We additionally leverage geostatistical methods to predict zero-shot performance at unseen sites, and find the incorporation of geographical information to substantially improve prediction performance, indicating there to be geographical structure in the performance distribution.

Light-induced phenomena in materials can exhibit exotic behavior that extends beyond equilibrium properties, offering new avenues for understanding and controlling electronic phases. So far, non-equilibrium phenomena in solids have been predominantly explored using femtosecond laser pulses, which generate transient, ultra-fast dynamics. Here, we investigate the steady non-equilibrium regime in graphene induced by a continuous-wave (CW) mid-infrared laser. Our transport measurements reveal signatures of a long-lived Floquet phase, where a non-equilibrium electronic population is stabilized by the interplay between coherent photoexcitation and incoherent phonon cooling. The observation of non-equilibrium steady states using CW lasers opens a new regime for low-temperature Floquet phenomena, paving the way toward Floquet engineering of steady-state phases of matter., Comment: 21 pages + supplemental materials

We propose L3DG, the first approach for generative 3D modeling of 3D Gaussians through a latent 3D Gaussian diffusion formulation. This enables effective generative 3D modeling, scaling to generation of entire room-scale scenes which can be very efficiently rendered. To enable effective synthesis of 3D Gaussians, we propose a latent diffusion formulation, operating in a compressed latent space of 3D Gaussians. This compressed latent space is learned by a vector-quantized variational autoencoder (VQ-VAE), for which we employ a sparse convolutional architecture to efficiently operate on room-scale scenes. This way, the complexity of the costly generation process via diffusion is substantially reduced, allowing higher detail on object-level generation, as well as scalability to large scenes. By leveraging the 3D Gaussian representation, the generated scenes can be rendered from arbitrary viewpoints in real-time. We demonstrate that our approach significantly improves visual quality over prior work on unconditional object-level radiance field synthesis and showcase its applicability to room-scale scene generation., Comment: SIGGRAPH Asia 2024, project page: https://barbararoessle.github.io/l3dg , video: https://youtu.be/UHEEiXCYeLU

As relevant examples such as the future criminal detection software [1] show, fairness of AI-based and social domain affecting decision support tools constitutes an important area of research. In this contribution, we investigate the applications of AI to socioeconomically relevant infrastructures such as those of water distribution networks (WDNs), where fairness issues have yet to gain a foothold. To establish the notion of fairness in this domain, we propose an appropriate definition of protected groups and group fairness in WDNs as an extension of existing definitions. We demonstrate that typical methods for the detection of leakages in WDNs are unfair in this sense. Further, we thus propose a remedy to increase the fairness which can be applied even to non-differentiable ensemble classification methods as used in this context.

Research on methods for planning and controlling water distribution networks gains increasing relevance as the availability of drinking water will decrease as a consequence of climate change. So far, the majority of approaches is based on hydraulics and engineering expertise. However, with the increasing availability of sensors, machine learning techniques constitute a promising tool. This work presents the main tasks in water distribution networks, discusses how they relate to machine learning and analyses how the particularities of the domain pose challenges to and can be leveraged by machine learning approaches. Besides, it provides a technical toolkit by presenting evaluation benchmarks and a structured survey of the exemplary task of leakage detection and localization., Comment: This preprint has not undergone any post-submission improvements or corrections. The Version of Record of this contribution is published in Artificial Neural Networks and Machine Learning -- ICANN 2024

Fairness is an important objective throughout society. From the distribution of limited goods such as education, over hiring and payment, to taxes, legislation, and jurisprudence. Due to the increasing importance of machine learning approaches in all areas of daily life including those related to health, security, and equity, an increasing amount of research focuses on fair machine learning. In this work, we focus on the fairness of partition- and prototype-based models. The contribution of this work is twofold: 1) we develop a general framework for fair machine learning of partition-based models that does not depend on a specific fairness definition, and 2) we derive a fair version of learning vector quantization (LVQ) as a specific instantiation. We compare the resulting algorithm against other algorithms from the literature on theoretical and real-world data showing its practical relevance., Comment: This preprint has not undergone any post-submission improvements or corrections. The Version of Record of this contribution is published in Advances in Self-Organizing Maps, Learning Vector Quantization, Interpretable Machine Learning, and Beyond

Consensus-based optimization (CBO) is a versatile multi-particle optimization method for performing nonconvex and nonsmooth global optimizations in high dimensions. Proofs of global convergence in probability have been achieved for a broad class of objective functions in unconstrained optimizations. In this work we adapt the algorithm for solving constrained optimizations on compact and unbounded domains with boundary by leveraging emerging reflective boundary conditions. In particular, we close a relevant gap in the literature by providing a global convergence proof for the many-particle regime comprehensive of convergence rates. On the one hand, for the sake of minimizing running cost, it is desirable to keep the number of particles small. On the other hand, reducing the number of particles implies a diminished capability of exploration of the algorithm. Hence numerical heuristics are needed to ensure convergence of CBO in the few-particle regime. In this work, we also significantly improve the convergence and complexity of CBO by utilizing an adaptive region control mechanism and by choosing geometry-specific random noise. In particular, by combining a hierarchical noise structure with a multigrid finite element method, we are able to compute global minimizers for a constrained $p$-Allen-Cahn problem with obstacles, a very challenging variational problem.

As premodern texts are passed down over centuries, errors inevitably accrue. These errors can be challenging to identify, as some have survived undetected for so long precisely because they are so elusive. While prior work has evaluated error detection methods on artificially-generated errors, we introduce the first dataset of real errors in premodern Greek, enabling the evaluation of error detection methods on errors that genuinely accumulated at some stage in the centuries-long copying process. To create this dataset, we use metrics derived from BERT conditionals to sample 1,000 words more likely to contain errors, which are then annotated and labeled by a domain expert as errors or not. We then propose and evaluate new error detection methods and find that our discriminator-based detector outperforms all other methods, improving the true positive rate for classifying real errors by 5%. We additionally observe that scribal errors are more difficult to detect than print or digitization errors. Our dataset enables the evaluation of error detection methods on real errors in premodern texts for the first time, providing a benchmark for developing more effective error detection algorithms to assist scholars in restoring premodern works.

A high degree of structural complexity arises in dynamic neuronal dendrites due to extensive branching patterns and diverse spine morphologies, which enable the nervous system to adjust function, construct complex input pathways and thereby enhance the computational power of the system. Owing to the determinant role of dendrite morphology in the functionality of the nervous system, recognition of pathological changes due to neurodegenerative disorders is of crucial importance. We show that the statistical analysis of a temporary signal generated by cargos that have diffusively passed through the complex dendritic structure yields vital information about dendrite morphology. As a feasible scenario, we propose engineering mRNA-carrying multilamellar liposomes to diffusively reach the soma and release mRNAs, which encodes a specific protein upon encountering ribosomes. The concentration of this protein over a large population of neurons can be externally measured, as a detectable temporary signal. By developing a stochastic coarse-grained approach for first-passage through dendrites, we connect the model parameters to the characteristics of the evolving signal and, on the other hand, map them to the key morphological properties affected by neurodegenerative diseases -- including the density and size of spines, the extent of the tree, and the segmental increase of dendrite diameter towards soma. Thus, we establish a direct link between the dendrite morphology and the statistical characteristics of the detectable signal. Our approach provides a fast noninvasive measurement technique to indirectly extract vital information about the morphological evolution of dendrites in the course of neurodegenerative disease progression., Comment: 13 pages, 7 figures

Low-Latency and Low-Power Edge AI is essential for Virtual Reality and Augmented Reality applications. Recent advances show that hybrid models, combining convolution layers (CNN) and transformers (ViT), often achieve superior accuracy/performance tradeoff on various computer vision and machine learning (ML) tasks. However, hybrid ML models can pose system challenges for latency and energy-efficiency due to their diverse nature in dataflow and memory access patterns. In this work, we leverage the architecture heterogeneity from Neural Processing Units (NPU) and Compute-In-Memory (CIM) and perform diverse execution schemas to efficiently execute these hybrid models. We also introduce H4H-NAS, a Neural Architecture Search framework to design efficient hybrid CNN/ViT models for heterogeneous edge systems with both NPU and CIM. Our H4H-NAS approach is powered by a performance estimator built with NPU performance results measured on real silicon, and CIM performance based on industry IPs. H4H-NAS searches hybrid CNN/ViT models with fine granularity and achieves significant (up to 1.34%) top-1 accuracy improvement on ImageNet dataset. Moreover, results from our Algo/HW co-design reveal up to 56.08% overall latency and 41.72% energy improvements by introducing such heterogeneous computing over baseline solutions. The framework guides the design of hybrid network architectures and system architectures of NPU+CIM heterogeneous systems.

We investigate models that can induce significant modifications to the couplings of first- and second-generation quarks with Higgs bosons. Specifically, we identify all simplified models featuring two vector-like quark states which can lead to substantial enhancements in these couplings. In addition, these models generate operators in Standard Model Effective Field Theory, both at tree-level and one-loop, that are constrained by electroweak precision and Higgs data. We show how to evade constraints from flavour physics and consider direct searches for vector-like quarks. Ultimately, we demonstrate that viable ultraviolet models can be found with first-generation quark Yukawa couplings enhanced by several hundred times their Standard Model value, while the Higgs couplings to charm (strange) quarks can be increased by factors of a few (few tens). Given the importance of electroweak precision data in constraining these models, we also discuss projections for future measurements at the Tera-$Z$ FCC-ee machine., Comment: 35 pages + appendices

In this study, we propose a novel approach to uncover subgroup-specific and subgroup-common latent factors addressing the challenges posed by the heterogeneity of neurological and mental disorders, which hinder disease understanding, treatment development, and outcome prediction. The proposed approach, sparse Group Factor Analysis (GFA) with regularised horseshoe priors, was implemented with probabilistic programming and can uncover associations (or latent factors) among multiple data modalities differentially expressed in sample subgroups. Synthetic data experiments showed the robustness of our sparse GFA by correctly inferring latent factors and model parameters. When applied to the Genetic Frontotemporal Dementia Initiative (GENFI) dataset, which comprises patients with frontotemporal dementia (FTD) with genetically defined subgroups, the sparse GFA identified latent disease factors differentially expressed across the subgroups, distinguishing between "subgroup-specific" latent factors within homogeneous groups and "subgroup common" latent factors shared across subgroups. The latent disease factors captured associations between brain structure and non-imaging variables (i.e., questionnaires assessing behaviour and disease severity) across the different genetic subgroups, offering insights into disease profiles. Importantly, two latent factors were more pronounced in the two more homogeneous FTD patient subgroups (progranulin (GRN) and microtubule-associated protein tau (MAPT) mutation), showcasing the method's ability to reveal subgroup-specific characteristics. These findings underscore the potential of sparse GFA for integrating multiple data modalities and identifying interpretable latent disease factors that can improve the characterization and stratification of patients with neurological and mental health disorders., Comment: 38 pages, 14 figures

Soft robots have the potential to revolutionize the use of robotic systems with their capability of establishing safe, robust, and adaptable interactions with their environment, but their precise control remains challenging. In contrast, traditional rigid robots offer high accuracy and repeatability but lack the flexibility of soft robots. We argue that combining these characteristics in a hybrid robotic platform can significantly enhance overall capabilities. This work presents a novel hybrid robotic platform that integrates a rigid manipulator with a fully developed soft arm. This system is equipped with the intelligence necessary to perform flexible and generalizable tasks through imitation learning autonomously. The physical softness and machine learning enable our platform to achieve highly generalizable skills, while the rigid components ensure precision and repeatability., Comment: Corrected missing citation

Quantum adiabatic optimization has long been expected to outperform classical methods in solving NP-type problems. While this has been proven in certain experiments, its main applications still reside in academic problems where the size of the system to be solved would not represent an obstacle to any modern desktop computer. Here we develop a systematic procedure to find the global optima of the modularity function to discover community structure in complex networks solely relying on pure annealers rather than hybrid solutions. We bypass the one-hot encoding constraints by hierarchically and recursively encoding binary instances of the problem that can be solved without the need to guess the exact penalties for the Lagrange multipliers. We study the variability, and robustness of the annealer as a function of network size, directness of connections, topology, and the resolution of the communities. We show how our approach produces meaningful and at least equally optimal solutions to state-of-the-art community detection algorithms while maintaining tractable computing times. Lastly, due to its recursive nature, the annealing process returns intermediate subdivisions thus offering interpretable rather than black-box solutions. These \textit{dendrograms} can be used to unveil normal and pathological hidden hierarchies in brain networks hence opening the door to clinical workflows. Overall, this represents a first step towards an applicable practice-oriented usage of pure quantum annealing potentially bridging two segregated communities in modern science and engineering; that of network science and quantum computing.

We utilize a method using frequency combs to construct waves that feature superoscillations - local regions of the wave that exhibit a change in phase that the bandlimits of the wave should not otherwise allow. This method has been shown to create superoscillating regions that mimic any analytic function - even ones well outside the bandlimits - to an arbitrary degree of accuracy. We experimentally demonstrate that these waves are extremely robust against noise, allowing for accurate reconstruction of a superoscillating target function thoroughly buried in noise. We additionally show that such a construction can be easily used to range-resolve a signal well below the commonly accepted fundamental limit.

It has been a widely shared concern that social media reinforces echo chambers of like-minded users and exacerbate political polarization. While fostering interactions across party lines is recognized as an important strategy to break echo chambers, there is a lack of empirical evidence on whether users will actually become more integrated or instead more segregated following such interactions on real social media platforms. We fill this gap by inspecting how users change their community engagement after receiving a cross-party reply in the U.S. politics discussion on Reddit. More specifically, we investigate if they increase their activity in communities of the opposing party, or in communities of their own party. We find that receiving a cross-party reply to a comment in a non-partisan discussion space is not significantly associated with increased out-party subreddit activity, unless the comment itself is already a reply to another comment. Meanwhile, receiving a cross-party reply is significantly associated with increased in-party subreddit activity, but the effect is comparable to that of receiving a same-party reply. Our results reveal a highly conditional depolarization effect following cross-party interactions in spurring activity in out-party communities, which is likely part of a more general dynamic of feedback-boosted engagement., Comment: To appear in Proceedings of the International AAAI Conference on Web and Social Media (ICWSM 2025)

Previous work shows evidence that cerebrospinal fluid (CSF) plays a crucial role in brain waste clearance processes, and that altered flow patterns are associated with various diseases of the central nervous system. In this study, we investigate the potential of deep learning to predict the distribution in human brain of a gadolinium-based CSF contrast agent (tracer) administered intrathecal. For this, T1-weighted magnetic resonance imaging (MRI) scans taken at multiple time points before and after intrathecal injection were utilized. We propose a U-net-based supervised learning model to predict pixel-wise signal increases at their peak after 24 hours. Its performance is evaluated based on different tracer distribution stages provided during training, including predictions from baseline scans taken before injection. Our findings indicate that using imaging data from just the first two hours post-injection for training yields tracer flow predictions comparable to those trained with additional later-stage scans. The model was further validated by comparing ventricular reflux gradings provided by neuroradiologists, and inter-rater grading among medical experts and the model showed excellent agreement. Our results demonstrate the potential of deep learning-based methods for CSF flow prediction, suggesting that fewer MRI scans could be sufficient for clinical analysis, which might significantly improve clinical efficiency, patient well-being, and lower healthcare costs., Comment: 13 pages, 7 figures

We present a novel approach to calibrating linguistic expressions of certainty, e.g., "Maybe" and "Likely". Unlike prior work that assigns a single score to each certainty phrase, we model uncertainty as distributions over the simplex to capture their semantics more accurately. To accommodate this new representation of certainty, we generalize existing measures of miscalibration and introduce a novel post-hoc calibration method. Leveraging these tools, we analyze the calibration of both humans (e.g., radiologists) and computational models (e.g., language models) and provide interpretable suggestions to improve their calibration.

We study scattering equations of hyperplane arrangements from the perspective of combinatorial commutative algebra and numerical algebraic geometry. We formulate the problem as linear equations on a reciprocal linear space and develop a degeneration-based homotopy algorithm for solving them. We investigate the Hilbert regularity of the corresponding homogeneous ideal and apply our methods to CHY scattering equations.

Training a language model to be both helpful and harmless requires careful calibration of refusal behaviours: Models should refuse to follow malicious instructions or give harmful advice (e.g. "how do I kill someone?"), but they should not refuse safe requests, even if they superficially resemble unsafe ones (e.g. "how do I kill a Python process?"). Avoiding such false refusal, as prior work has shown, is challenging even for highly-capable language models. In this paper, we propose a simple and surgical method for mitigating false refusal in language models via single vector ablation. For a given model, we extract a false refusal vector and show that ablating this vector reduces false refusal rate without negatively impacting model safety and general model capabilities. We also show that our approach can be used for fine-grained calibration of model safety. Our approach is training-free and model-agnostic, making it useful for mitigating the problem of false refusal in current and future language models.

We investigate two one-dimensional tight-binding models with disorder that have extended states at zero energy. We use exact and partial diagonalisation of the Hamiltonian to obtain the eigenmodes and the associated participation ratios, and the transfer-matrix method to determine the localisation length. The first model has no on-site disorder, but random couplings. While the participation ratio remains finite at zero energy, the localisation length diverges logarithmically as the energy goes to zero. We provide an intuitive derivation of this logarithmic divergence based on the weak coupling of the two sublattices. The second model has a conserved quantity as the row sums of the Hamiltonian are zero. This model can be represented as a harmonic chain with random couplings, or as a diffusion model on a lattice with random links. We find, in agreement with existing analytical calculations, that the number of system-spanning eigenmodes increases proportionally to the square root of the system size, and we related this power law to other power laws that characterise the scaling behaviour of the eigenmodes, the participation ratio, the localisation length, and their dependence on energy and system size. When disorder is so strong that the smallest hopping terms can be arbitrarily close to zero, all these power laws change, and we show a crossover between the two scaling regimes. All these results are explained by intuitive arguments based on scaling.

The main goal of this paper is to introduce a new fractional anisotropic Sobolev space with variable exponent where the basic qualitative properties (completeness, separability, reflexivity, ...) are established, including the continuous and compact embedding results. Moreover, some functional proprieties of anisotropic fractional $\vec{p}(.,.)$-Laplacian operator are proved. As an application, we use the mountain pass theorem and Ekeland's variational principle to ensure the existence of a weak solution for a nonlocal anisotropic problem with variable exponent.

Originally rooted in game theory, the Shapley Value (SV) has recently become an important tool in machine learning research. Perhaps most notably, it is used for feature attribution and data valuation in explainable artificial intelligence. Shapley Interactions (SIs) naturally extend the SV and address its limitations by assigning joint contributions to groups of entities, which enhance understanding of black box machine learning models. Due to the exponential complexity of computing SVs and SIs, various methods have been proposed that exploit structural assumptions or yield probabilistic estimates given limited resources. In this work, we introduce shapiq, an open-source Python package that unifies state-of-the-art algorithms to efficiently compute SVs and any-order SIs in an application-agnostic framework. Moreover, it includes a benchmarking suite containing 11 machine learning applications of SIs with pre-computed games and ground-truth values to systematically assess computational performance across domains. For practitioners, shapiq is able to explain and visualize any-order feature interactions in predictions of models, including vision transformers, language models, as well as XGBoost and LightGBM with TreeSHAP-IQ. With shapiq, we extend shap beyond feature attributions and consolidate the application of SVs and SIs in machine learning that facilitates future research. The source code and documentation are available at https://github.com/mmschlk/shapiq., Comment: NeurIPS 2024

The high binding affinity of antibodies towards their cognate targets is key to eliciting effective immune responses, as well as to the use of antibodies as research and therapeutic tools. Here, we propose ANTIPASTI, a Convolutional Neural Network model that achieves state-of-the-art performance in the prediction of antibody binding affinity using as input a representation of antibody-antigen structures in terms of Normal Mode correlation maps derived from Elastic Network Models. This representation captures not only structural features but energetic patterns of local and global residue fluctuations. The learnt representations are interpretable: they reveal similarities of binding patterns among antibodies targeting the same antigen type, and can be used to quantify the importance of antibody regions contributing to binding affinity. Our results show the importance of the antigen imprint in the Normal Mode landscape, and the dominance of cooperative effects and long-range correlations between antibody regions to determine binding affinity.

A fundamental question in interpretability research is to what extent neural networks, particularly language models, implement reusable functions via subnetworks that can be composed to perform more complex tasks. Recent developments in mechanistic interpretability have made progress in identifying subnetworks, often referred to as circuits, which represent the minimal computational subgraph responsible for a model's behavior on specific tasks. However, most studies focus on identifying circuits for individual tasks without investigating how functionally similar circuits relate to each other. To address this gap, we examine the modularity of neural networks by analyzing circuits for highly compositional subtasks within a transformer-based language model. Specifically, given a probabilistic context-free grammar, we identify and compare circuits responsible for ten modular string-edit operations. Our results indicate that functionally similar circuits exhibit both notable node overlap and cross-task faithfulness. Moreover, we demonstrate that the circuits identified can be reused and combined through subnetwork set operations to represent more complex functional capabilities of the model., Comment: 24 pages, 17 figures

Understanding solar wind flows is crucial for unraveling the dynamics of the Sun's corona and improving space weather forecasting. However, the complex nature of the solar wind and the absence of reliable ground-truth data present significant challenges to current tracking methods. To address this, the Flow Tracking Challenge was established, providing a platform for testing and refining flow tracking algorithms using synthetic images generated by the Synthetic Coronal Outflow Model (SynCOM). The challenge is divided into two phases: a preliminary phase focusing on simpler flow scenarios and a main phase featuring more complex synthetic images that mimic solar outflows. These phases allow researchers to evaluate their methods under various simulated conditions. SynCOM's synthetic data benchmark improves accuracy in velocity estimations. This paper presents the preliminary results of the Flow Tracking Challenge, where the synthetic images from SynCOM are used to test solar wind velocity tracking methods. Initial findings demonstrate SynCOM's value as a benchmark, guiding improvements for upcoming missions like PUNCH., Comment: 10 pages, 4 Figures

This paper investigates the enhancement of spatial resolution in Sentinel-2 bands that contain spectral information using advanced super-resolution techniques by a factor of 2. State-of-the-art CNN models are compared with enhanced GAN approaches in terms of quality and feasibility. Therefore, a representative dataset comprising Sentinel-2 low-resolution images and corresponding high-resolution aerial orthophotos is required. Literature study reveals no feasible dataset for the land type of interest (forests), for which reason an adequate dataset had to be generated in addition, accounting for accurate alignment and image source optimization. The results reveal that while CNN-based approaches produce satisfactory outcomes, they tend to yield blurry images. In contrast, GAN-based models not only provide clear and detailed images, but also demonstrate superior performance in terms of quantitative assessment, underlying the potential of the framework beyond the specific land type investigated., Comment: This work has been submitted to the IEEE for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessible

SynCOM is a package of procedures written in IDL (Interactive Data Language) that simulates transient solar wind flows. Each function within SynCOM handles specific tasks, such as initializing parameters, generating synthetic profiles, creating Gaussian blobs to represent solar wind features, and producing high-resolution images of the solar corona. This modular design allows users to call or customize individual functions independently, providing flexibility to adjust simulations to different observational or solar wind conditions. The software architecture is designed to facilitate SynCOM, which effectively creates synthetic datasets for testing and verifying feature tracking algorithms. It also takes advantage of the robust capabilities of the IDL for high-performance scientific computing, Comment: 10 pages, 2 figures

This article discusses the magnetic connectivity between the Sun and the Earth, which is essential for understanding solar wind and space weather events. Due to limited direct observations, reliable simulations are necessary. The most commonly used method is the two-step ballistic method, but it has many free parameters that affect the results. The authors propose a method based on self-consistent magnetohydrodynamic (MHD) models. They combine the COCONUT coronal model with the EUHFORIA heliospheric model to compute magnetic field lines from the Earth to the Sun and quantify spatial and temporal uncertainties. To validate their method, they analyze four events associated with high-speed streams from well-identified coronal holes. The results show partial overlap with the assumed coronal holes of origin, ranging from 19% to 100% depending on the event. They also examine the magnetic polarity observed on Earth, finding that MHD simulations provide a good polarity estimation, with agreement ranging from 36% to 69% across the events. Spatial and temporal uncertainties explain the mixed results for some cases. MHD models appear more effective during periods of maximum solar activity due to the latitudinal extent of the heliospheric current sheet (HCS). The authors conclude that MHD models offer results as good as the two-step ballistic method, with potential for improvement as more critical physics are integrated into the models., Comment: 11 pages, 6 figures, accepted in A&A

Neurological disorders that affect speech production, such as Alzheimer's Disease (AD), significantly impact the lives of both patients and caregivers, whether through social, psycho-emotional effects or other aspects not yet fully understood. Recent advancements in Large Language Model (LLM) architectures have developed many tools to identify representative features of neurological disorders through spontaneous speech. However, LLMs typically lack interpretability, meaning they do not provide clear and specific reasons for their decisions. Therefore, there is a need for methods capable of identifying the representative features of neurological disorders in speech and explaining clearly why these features are relevant. This paper presents an explainable LLM method, named SLIME (Statistical and Linguistic Insights for Model Explanation), capable of identifying lexical components representative of AD and indicating which components are most important for the LLM's decision. In developing this method, we used an English-language dataset consisting of transcriptions from the Cookie Theft picture description task. The LLM Bidirectional Encoder Representations from Transformers (BERT) classified the textual descriptions as either AD or control groups. To identify representative lexical features and determine which are most relevant to the model's decision, we used a pipeline involving Integrated Gradients (IG), Linguistic Inquiry and Word Count (LIWC), and statistical analysis. Our method demonstrates that BERT leverages lexical components that reflect a reduction in social references in AD and identifies which further improve the LLM's accuracy. Thus, we provide an explainability tool that enhances confidence in applying LLMs to neurological clinical contexts, particularly in the study of neurodegeneration., Comment: 27 pages, 6 figures, authors Marina Ribeiro and B\'arbara Malcorra have equal contribution, C\'esar Renn\'o-Costa is the corresponding author

Context. Understanding the dwarf galaxy population in low density environments is crucial for testing the LCDM cosmological model. The increase in diversity towards low mass galaxies is seen as an increase in the scatter of scaling relations such as the stellar mass-size and the baryonic Tully-Fisher relation (BTFR), and is also demonstrated by recent in-depth studies of an extreme subclass of dwarf galaxies of low surface brightness, but large physical sizes, called ultra-diffuse galaxies (UDGs). Aims. We select galaxies from the Apertif HI survey, and apply a constraint on their i-band absolute magnitude to exclude high mass systems. The sample consists of 24 galaxies, and span HI mass ranges of 8.6 < log ($M_{HI}/M_{Sun}$) < 9.7 and stellar mass range of 8.0 < log ($M_*/M_{Sun}$) < 9.7 (with only three galaxies having log ($M_*/M_{Sun}$) > 9). Methods. We determine the geometrical parameters of the HI and stellar discs, build kinematic models from the HI data using 3DBarolo, and extract surface brightness profiles in g-, r- and i-band from the Pan-STARRS 1 photometric survey. Results. We find that, at fixed stellar mass, our HI selected dwarfs have larger optical effective radii than isolated, optically-selected dwarfs from the literature. We find misalignments between the optical and HI morphologies for some of our sample. For most of our galaxies, we use the HI morphology to determine their kinematics, and we stress that deep optical observations are needed to trace the underlying stellar discs. Standard dwarfs in our sample follow the same BTFR of high-mass galaxies, whereas UDGs are slightly offset towards lower rotational velocities, in qualitative agreement with results from previous studies. Finally, our sample features a fraction (25%) of dwarf galaxies in pairs that is significantly larger with respect to previous estimates based on optical spectroscopic data., Comment: 22 pages, 11 figures, 5 tables. Accepted for publication in A&A

Climate change (CC) has attracted increasing attention in NLP in recent years. However, detecting the stance on CC in multimodal data is understudied and remains challenging due to a lack of reliable datasets. To improve the understanding of public opinions and communication strategies, this paper presents MultiClimate, the first open-source manually-annotated stance detection dataset with $100$ CC-related YouTube videos and $4,209$ frame-transcript pairs. We deploy state-of-the-art vision and language models, as well as multimodal models for MultiClimate stance detection. Results show that text-only BERT significantly outperforms image-only ResNet50 and ViT. Combining both modalities achieves state-of-the-art, $0.747$/$0.749$ in accuracy/F1. Our 100M-sized fusion models also beat CLIP and BLIP, as well as the much larger 9B-sized multimodal IDEFICS and text-only Llama3 and Gemma2, indicating that multimodal stance detection remains challenging for large language models. Our code, dataset, as well as supplementary materials, are available at https://github.com/werywjw/MultiClimate., Comment: 5 pages, 1 figure

In this work, we study an inverse reinforcement learning (IRL) problem where the experts are planning under a shared reward function but with different, unknown planning horizons. Without the knowledge of discount factors, the reward function has a larger feasible solution set, which makes it harder for existing IRL approaches to identify a reward function. To overcome this challenge, we develop algorithms that can learn a global multi-agent reward function with agent-specific discount factors that reconstruct the expert policies. We characterize the feasible solution space of the reward function and discount factors for both algorithms and demonstrate the generalizability of the learned reward function across multiple domains., Comment: Accepted at RLC 2024

We consider a one-dimensional version of a variational model for pattern formation in biological membranes. The driving term in the energy is a coupling between the order parameter and the local curvature of the membrane. We derive scaling laws for the minimal energy. As a main tool we present new nonlinear interpolation inequalities that bound fractional Sobolev seminorms in terms of a Cahn-Hillard/Modica-Mortola energy., Comment: 22 pages, 5 figures

Sulfur dioxide (SO$_2$) and nitrogen monoxide (NO) are some of the gas impurities present in the carbon dioxide (CO$_2$) separated from fume using post combustion capture process. Even in a small amount, these impurities may have an impact on storage process development. The dissolution of such gases in aqueous phase is part of the studies carried out to develop processes and analyze the risks in case of geological storage. The enthalpies of solution of SO$_2$ and NO in water were here investigated by calorimetry, using a flow calorimetric technique. The enthalpies of solution were determined at 323.15 K and 373.15 K for sulfur dioxide, and at 323.15 K for nitrogen monoxide. The experimental enthalpy data were used together with available solubility data to test thermodynamic models representative of vapor-liquid equilibrium.

COVID-19 pandemic has brought to the fore epidemiological models which, though describing a rich variety of behaviors, have previously received little attention in the signal processing literature. During the pandemic, several works successfully leveraged state-of-the-art signal processing strategies to robustly infer epidemiological indicators despite the low quality of COVID-19 data. In the present work, a novel nonstationary autoregressive model is introduced, encompassing, but not reducing to, one of the most popular models for the propagation of viral epidemics. Using a variational framework, penalized likelihood estimators of the parameters of this new model are designed. In practice, the main bottleneck is that the estimation accuracy strongly depends on hyperparameters tuning. Without available ground truth, hyperparameters are selected by minimizing specifically designed data-driven oracles, used as proxy for the estimation error. Focusing on the nonstationary autoregressive Poisson model, the Stein's Unbiased Risk Estimate formalism is generalized to construct asymptotically unbiased risk estimators based on the derivation of an original autoregressive counterpart of Stein's lemma. The accuracy of these oracles and of the resulting estimates are assessed through intensive Monte Carlo simulations on synthetic data. Then, elaborating on recent epidemiological models, a novel weekly scaled Poisson model is proposed, enabling to better account for intrinsic variability of the contamination while being robust to reporting errors. Finally, the overall data-driven procedure is particularized to the estimation of COVID-19 reproduction number and exemplified on real COVID-19 infection counts in different countries and at different stages of the pandemic, demonstrating its ability to yield consistent estimates.

Human activities exhibit a strong correlation between actions and the places where these are performed, such as washing something at a sink. More specifically, in daily living environments we may identify particular locations, hereinafter named activity-centric zones, which may afford a set of homogeneous actions. Their knowledge can serve as a prior to favor vision models to recognize human activities. However, the appearance of these zones is scene-specific, limiting the transferability of this prior information to unfamiliar areas and domains. This problem is particularly relevant in egocentric vision, where the environment takes up most of the image, making it even more difficult to separate the action from the context. In this paper, we discuss the importance of decoupling the domain-specific appearance of activity-centric zones from their universal, domain-agnostic representations, and show how the latter can improve the cross-domain transferability of Egocentric Action Recognition (EAR) models. We validate our solution on the EPIC-Kitchens-100 and Argo1M datasets, Comment: Project webpage: https://gabrielegoletto.github.io/EgoZAR/

In tabular biomedical data analysis, tuning models to high accuracy is considered a prerequisite for discussing feature importance, as medical practitioners expect the validity of feature importance to correlate with performance. In this work, we challenge the prevailing belief, showing that low-performing models may also be used for feature importance. We propose experiments to observe changes in feature rank as performance degrades sequentially. Using three synthetic datasets and six real biomedical datasets, we compare the rank of features from full datasets to those with reduced sample sizes (data cutting) or fewer features (feature cutting). In synthetic datasets, feature cutting does not change feature rank, while data cutting shows higher discrepancies with lower performance. In real datasets, feature cutting shows similar or smaller changes than data cutting, though some datasets exhibit the opposite. When feature interactions are controlled by removing correlations, feature cutting consistently shows better stability. By analyzing the distribution of feature importance values and theoretically examining the probability that the model cannot distinguish feature importance between features, we reveal that models can still distinguish feature importance despite performance degradation through feature cutting, but not through data cutting. We conclude that the validity of feature importance can be maintained even at low performance levels if the data size is adequate, which is a significant factor contributing to suboptimal performance in tabular medical data analysis. This paper demonstrates the potential for utilizing feature importance analysis alongside statistical analysis to compare features relatively, even when classifier performance is not satisfactory.

In this paper, we present a novel visual servoing (VS) approach based on latent Denoising Diffusion Probabilistic Models (DDPMs). Opposite to classical VS methods, the proposed approach allows reaching the desired target view, even when the target is initially not visible. This is possible thanks to the learning of a latent representation that the DDPM uses for planning and a dataset of trajectories encompassing target-invisible initial views. The latent representation is learned using a Cross-Modal Variational Autoencoder, and used to estimate the return for conditioning the trajectory generation of the DDPM. Given the current image, the DDPM generates trajectories in the latent space driving the robotic platform to the desired visual target. The approach is applicable to any velocity-based controlled platform. We test our method with simulated and real-world experiments using generic multi-rotor Uncrewed Aerial Vehicles (UAVs). A video of our experiments can be found at https://youtu.be/yu-aTxqceOA.

Product attribute value identification (PAVI) involves automatically identifying attributes and their values from product information, enabling features like product search, recommendation, and comparison. Existing methods primarily rely on fine-tuning pre-trained language models, such as BART and T5, which require extensive task-specific training data and struggle to generalize to new attributes. This paper explores large language models (LLMs), such as LLaMA and Mistral, as data-efficient and robust alternatives for PAVI. We propose various strategies: comparing one-step and two-step prompt-based approaches in zero-shot settings and utilizing parametric and non-parametric knowledge through in-context learning examples. We also introduce a dense demonstration retriever based on a pre-trained T5 model and perform instruction fine-tuning to explicitly train LLMs on task-specific instructions. Extensive experiments on two product benchmarks show that our two-step approach significantly improves performance in zero-shot settings, and instruction fine-tuning further boosts performance when using training data, demonstrating the practical benefits of using LLMs for PAVI.

Large vision-language models frequently struggle to accurately predict responses provided by multiple human annotators, particularly when those responses exhibit human uncertainty. In this study, we focus on the Visual Question Answering (VQA) task, and we comprehensively evaluate how well the state-of-the-art vision-language models correlate with the distribution of human responses. To do so, we categorize our samples based on their levels (low, medium, high) of human uncertainty in disagreement (HUD) and employ not only accuracy but also three new human-correlated metrics in VQA, to investigate the impact of HUD. To better align models with humans, we also verify the effect of common calibration and human calibration. Our results show that even BEiT3, currently the best model for this task, struggles to capture the multi-label distribution inherent in diverse human responses. Additionally, we observe that the commonly used accuracy-oriented calibration technique adversely affects BEiT3's ability to capture HUD, further widening the gap between model predictions and human distributions. In contrast, we show the benefits of calibrating models towards human distributions for VQA, better aligning model confidence with human uncertainty. Our findings highlight that for VQA, the consistent alignment between human responses and model predictions is understudied and should become the next crucial target of future studies.

The data volumes generated by the WALLABY atomic Hydrogen (HI) survey using the Australiian Square Kilometre Array Pathfinder (ASKAP) necessitate greater automation and reliable automation in the task of source-finding and cataloguing. To this end, we introduce and explore a novel deep learning framework for detecting low Signal-to-Noise Ratio (SNR) HI sources in an automated fashion. Specfically, our proposed method provides an automated process for separating true HI detections from false positives when used in combination with the Source Finding Application (SoFiA) output candidate catalogues. Leveraging the spatial and depth capabilities of 3D Convolutional Neural Networks (CNNs), our method is specifically designed to recognise patterns and features in three-dimensional space, making it uniquely suited for rejecting false positive sources in low SNR scenarios generated by conventional linear methods. As a result, our approach is significantly more accurate in source detection and results in considerably fewer false detections compared to previous linear statistics-based source finding algorithms. Performance tests using mock galaxies injected into real ASKAP data cubes reveal our method's capability to achieve near-100% completeness and reliability at a relatively low integrated SNR~3-5. An at-scale version of this tool will greatly maximise the science output from the upcoming widefield HI surveys., Comment: 14 pages, 12 figures, accepted for publication in the Publications of the Astronomical Society of Australia

Biot's consolidation model is a classical model for the evolution of deformable porous media saturated by a fluid and has various interdisciplinary applications. While numerical solution methods to solve poroelasticity by typical schemes such as finite differences, finite volumes or finite elements have been intensely studied, lattice Boltzmann methods for poroelasticity have not been developed yet. In this work, we propose a novel semi-implicit coupling of lattice Boltzmann methods to solve Biot's consolidation model in two dimensions. To this end, we use a single-relaxation-time lattice Boltzmann method for reaction-diffusion equations to solve the Darcy flow and combine it with a recent pseudo-time multi-relaxation-time lattice Boltzmann scheme for quasi-static linear elasticity by Boolakee, Geier and De Lorenzis (2023, DOI: 10.1016/j.cma.2022.115756). The numerical results demonstrate that naive coupling schemes lead to instabilities when the poroelastic system is strongly coupled. However, the newly developed centered coupling scheme using fully explicit and semi-implicit contributions is stable and accurate in all considered cases, even for the Biot--Willis coefficient being one. Furthermore, the numerical results for Terzaghi's consolidation problem and a two-dimensional extension thereof highlight that the scheme is even able to capture discontinuous solutions arising from instantaneous loading., Comment: 21 pages, 11 figures