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We investigate the impact of peculiar motion of Type Ia supernova host galaxies on cosmological parameter estimation. This motion causes their redshift to deviate from that of the comoving observer at their position and is a source of noise. To this end, we develop an estimator for parameter estimation in models with errors in independent variables. Using the Bayesian framework, errors in independent variables are treated as nuisance parameters by making the independent variables parameters of the model. Our method applied to the Pantheon sample of Type Ia supernova indicates a few percent shift in the central values of inferred cosmological parameters. For the $w$CDM model, we find that accounting for peculiar velocities makes the data marginally more consistent with the cosmological constant model. By using simulated data, we show that not accounting for peculiar velocities will significantly impact parameter estimation from higher precision future data sets., Comment: 18 pages, 6 figures, 4 tables

Shortcut learning, where machine learning models exploit spurious correlations in data instead of capturing meaningful features, poses a significant challenge to building robust and generalizable models. This phenomenon is prevalent across various machine learning applications, including vision, natural language processing, and speech recognition, where models may find unintended cues that minimize training loss but fail to capture the underlying structure of the data. Vision classifiers such as Convolutional Neural Networks (CNNs), Multi-Layer Perceptrons (MLPs), and Vision Transformers (ViTs) leverage distinct architectural principles to process spatial and structural information, making them differently susceptible to shortcut learning. In this study, we systematically evaluate these architectures by introducing deliberate shortcuts into the dataset that are positionally correlated with class labels, creating a controlled setup to assess whether models rely on these artificial cues or learn actual distinguishing features. We perform both quantitative evaluation by training on the shortcut-modified dataset and testing them on two different test sets -- one containing the same shortcuts and another without them -- to determine the extent of reliance on shortcuts. Additionally, qualitative evaluation is performed by using network inversion-based reconstruction techniques to analyze what the models internalize in their weights, aiming to reconstruct the training data as perceived by the classifiers. We evaluate shortcut learning behavior across multiple benchmark datasets, including MNIST, Fashion-MNIST, SVHN, and CIFAR-10, to compare the susceptibility of different vision classifier architectures to shortcut reliance and assess their varying degrees of sensitivity to spurious correlations.

We present the results of spectroscopic observations of host galaxies of eleven candidate giant radio galaxies (GRGs), powered by active galactic nuclei (AGNs), conducted with the 2-m Himalayan Chandra Telescope (HCT). The primary aim of these observations, performed with the Hanle Faint Object Spectrograph Camera (HFOSC), was to secure accurate spectroscopic redshifts, enabling precise calculations of their projected linear sizes. Based on these measurements, we confirm all eleven sources as giants, with linear sizes ranging from 0.7 to 2.9 Mpc, including ten GRGs and one giant radio quasar (GRQ). One of the GRGs shows evidence of a potential AGN jet-driven ionized outflow, extending up to $\sim$12 kpc, which, if confirmed, would represent a rarely observed feature. Two of the confirmed GRGs exceed 2 Mpc in size, which are relatively rare examples of GRG. The redshifts of the host galaxies span 0.09323 $\leq$ z $\leq$ 0.41134. Using the obtained spectroscopic data, we characterised their AGN states based on the optical emission line properties. To complement these observations, archival radio and optical survey data were utilised to characterise their large-scale radio morphology and estimate projected linear sizes, arm-length ratios, flux densities, luminosities, and core dominance factors. These results provide new insights into the properties of GRSs and form a critical foundation for further detailed studies of their environments, AGN activity, and evolution using future high-sensitivity optical and radio datasets., Comment: Accepted for publication in the A&A journal. Comments are welcome

Transformer-based video super-resolution (VSR) models have set new benchmarks in recent years, but their substantial computational demands make most of them unsuitable for deployment on resource-constrained devices. Achieving a balance between model complexity and output quality remains a formidable challenge in VSR. Although lightweight models have been introduced to address this issue, they often struggle to deliver state-of-the-art performance. We propose a novel lightweight, parameter-efficient deep residual deformable convolution network for VSR. Unlike prior methods, our model enhances feature utilization through residual connections and employs deformable convolution for precise frame alignment, addressing motion dynamics effectively. Furthermore, we introduce a single memory tensor to capture information accrued from the past frames and improve motion estimation across frames. This design enables an efficient balance between computational cost and reconstruction quality. With just 2.3 million parameters, our model achieves state-of-the-art SSIM of 0.9175 on the REDS4 dataset, surpassing existing lightweight and many heavy models in both accuracy and resource efficiency. Architectural insights from our model pave the way for real-time VSR on streaming data.

Vision classifiers are often trained on proprietary datasets containing sensitive information, yet the models themselves are frequently shared openly under the privacy-preserving assumption. Although these models are assumed to protect sensitive information in their training data, the extent to which this assumption holds for different architectures remains unexplored. This assumption is challenged by inversion attacks which attempt to reconstruct training data from model weights, exposing significant privacy vulnerabilities. In this study, we systematically evaluate the privacy-preserving properties of vision classifiers across diverse architectures, including Multi-Layer Perceptrons (MLPs), Convolutional Neural Networks (CNNs), and Vision Transformers (ViTs). Using network inversion-based reconstruction techniques, we assess the extent to which these architectures memorize and reveal training data, quantifying the relative ease of reconstruction across models. Our analysis highlights how architectural differences, such as input representation, feature extraction mechanisms, and weight structures, influence privacy risks. By comparing these architectures, we identify which are more resilient to inversion attacks and examine the trade-offs between model performance and privacy preservation, contributing to the development of secure and privacy-respecting machine learning models for sensitive applications. Our findings provide actionable insights into the design of secure and privacy-aware machine learning systems, emphasizing the importance of evaluating architectural decisions in sensitive applications involving proprietary or personal data.

The classification of radio-frequency (RF) signals is crucial for applications in robotics, traffic control, and medical devices. Spintronic devices, which respond to RF signals via ferromagnetic resonance, offer a promising solution. Recent studies have shown that a neural network of nanoscale magnetic tunnel junctions can classify RF signals without digitization. However, the complexity of these junctions poses challenges for rapid scaling. In this work, we demonstrate that simple spintronic devices, known as metallic spin-diodes, can effectively perform RF classification. These devices consist of NiFe/Pt bilayers and can implement weighted sums of RF inputs. We experimentally show that chains of four spin-diodes can execute 2x2 pixel filters, achieving high-quality convolutions on the Fashion-MNIST dataset. Integrating the hardware spin-diodes in a software network, we achieve a top-1 accuracy of 88 \% on the first 100 images, compared to 88.4 \% for full software with noise, and 90 \% without noise.

Federated Learning (FL) offers a decentralized paradigm for collaborative model training without direct data sharing, yet it poses unique challenges for Domain Generalization (DG), including strict privacy constraints, non-i.i.d. local data, and limited domain diversity. We introduce FedAlign, a lightweight, privacy-preserving framework designed to enhance DG in federated settings by simultaneously increasing feature diversity and promoting domain invariance. First, a cross-client feature extension module broadens local domain representations through domain-invariant feature perturbation and selective cross-client feature transfer, allowing each client to safely access a richer domain space. Second, a dual-stage alignment module refines global feature learning by aligning both feature embeddings and predictions across clients, thereby distilling robust, domain-invariant features. By integrating these modules, our method achieves superior generalization to unseen domains while maintaining data privacy and operating with minimal computational and communication overhead., Comment: 9 pages, 4 figures

Whole slide images (WSIs) are high-resolution, gigapixel sized images that pose significant computational challenges for traditional machine learning models due to their size and heterogeneity.In this paper, we present a scalable and efficient methodology for WSI classification by leveraging patch-based feature extraction, clustering, and Fisher vector encoding. Initially, WSIs are divided into fixed size patches, and deep feature embeddings are extracted from each patch using a pre-trained convolutional neural network (CNN). These patch-level embeddings are subsequently clustered using K-means clustering, where each cluster aggregates semantically similar regions of the WSI. To effectively summarize each cluster, Fisher vector representations are computed by modeling the distribution of patch embeddings in each cluster as a parametric Gaussian mixture model (GMM). The Fisher vectors from each cluster are concatenated into a high-dimensional feature vector, creating a compact and informative representation of the entire WSI. This feature vector is then used by a classifier to predict the WSI's diagnostic label. Our method captures local and global tissue structures and yields robust performance for large-scale WSI classification, demonstrating superior accuracy and scalability compared to other approaches.

Sound decision-making relies on accurate prediction for tangible outcomes ranging from military conflict to disease outbreaks. To improve crowdsourced forecasting accuracy, we developed SAGE, a hybrid forecasting system that combines human and machine generated forecasts. The system provides a platform where users can interact with machine models and thus anchor their judgments on an objective benchmark. The system also aggregates human and machine forecasts weighting both for propinquity and based on assessed skill while adjusting for overconfidence. We present results from the Hybrid Forecasting Competition (HFC) - larger than comparable forecasting tournaments - including 1085 users forecasting 398 real-world forecasting problems over eight months. Our main result is that the hybrid system generated more accurate forecasts compared to a human-only baseline which had no machine generated predictions. We found that skilled forecasters who had access to machine-generated forecasts outperformed those who only viewed historical data. We also demonstrated the inclusion of machine-generated forecasts in our aggregation algorithms improved performance, both in terms of accuracy and scalability. This suggests that hybrid forecasting systems, which potentially require fewer human resources, can be a viable approach for maintaining a competitive level of accuracy over a larger number of forecasting questions., Comment: 20 pages, 6 figures, 4 tables

Bankart lesions, or anterior-inferior glenoid labral tears, are diagnostically challenging on standard MRIs due to their subtle imaging features-often necessitating invasive MRI arthrograms (MRAs). This study develops deep learning (DL) models to detect Bankart lesions on both standard MRIs and MRAs, aiming to improve diagnostic accuracy and reduce reliance on MRAs. We curated a dataset of 586 shoulder MRIs (335 standard, 251 MRAs) from 558 patients who underwent arthroscopy. Ground truth labels were derived from intraoperative findings, the gold standard for Bankart lesion diagnosis. Separate DL models for MRAs and standard MRIs were trained using the Swin Transformer architecture, pre-trained on a public knee MRI dataset. Predictions from sagittal, axial, and coronal views were ensembled to optimize performance. The models were evaluated on a 20% hold-out test set (117 MRIs: 46 MRAs, 71 standard MRIs). Bankart lesions were identified in 31.9% of MRAs and 8.6% of standard MRIs. The models achieved AUCs of 0.87 (86% accuracy, 83% sensitivity, 86% specificity) and 0.90 (85% accuracy, 82% sensitivity, 86% specificity) on standard MRIs and MRAs, respectively. These results match or surpass radiologist performance on our dataset and reported literature metrics. Notably, our model's performance on non-invasive standard MRIs matched or surpassed the radiologists interpreting MRAs. This study demonstrates the feasibility of using DL to address the diagnostic challenges posed by subtle pathologies like Bankart lesions. Our models demonstrate potential to improve diagnostic confidence, reduce reliance on invasive imaging, and enhance accessibility to care., Comment: Accepted for presentation at SPIE Medical Imaging 2025: Computer-Aided Diagnosis. The manuscript is expected to appear in the conference proceedings

Federated Learning (FL) has gained popularity for fine-tuning large language models (LLMs) across multiple nodes, each with its own private data. While LoRA has been widely adopted for parameter efficient federated fine-tuning, recent theoretical and empirical studies highlight its suboptimal performance in the federated learning context. In response, we propose a novel, simple, and more effective parameter-efficient fine-tuning method that does not rely on LoRA. Our approach introduces a small multi-layer perceptron (MLP) layer between two existing MLP layers the up proj (the FFN projection layer following the self-attention module) and down proj within the feed forward network of the transformer block. This solution addresses the bottlenecks associated with LoRA in federated fine tuning and outperforms recent LoRA-based approaches, demonstrating superior performance for both language models and vision encoders.

Deep learning models rely heavily on large volumes of labeled data to achieve high performance. However, real-world datasets often contain noisy labels due to human error, ambiguity, or resource constraints during the annotation process. Instance-dependent label noise (IDN), where the probability of a label being corrupted depends on the input features, poses a significant challenge because it is more prevalent and harder to address than instance-independent noise. In this paper, we propose a novel hybrid framework that combines self-supervised learning using SimCLR with iterative pseudo-label refinement to mitigate the effects of IDN. The self-supervised pre-training phase enables the model to learn robust feature representations without relying on potentially noisy labels, establishing a noise-agnostic foundation. Subsequently, we employ an iterative training process with pseudo-label refinement, where confidently predicted samples are identified through a multistage approach and their labels are updated to improve label quality progressively. We evaluate our method on the CIFAR-10 and CIFAR-100 datasets augmented with synthetic instance-dependent noise at varying noise levels. Experimental results demonstrate that our approach significantly outperforms several state-of-the-art methods, particularly under high noise conditions, achieving notable improvements in classification accuracy and robustness. Our findings suggest that integrating self-supervised learning with iterative pseudo-label refinement offers an effective strategy for training deep neural networks on noisy datasets afflicted by instance-dependent label noise.

Neural networks have emerged as powerful tools across various applications, yet their decision-making process often remains opaque, leading to them being perceived as "black boxes." This opacity raises concerns about their interpretability and reliability, especially in safety-critical scenarios. Network inversion techniques offer a solution by allowing us to peek inside these black boxes, revealing the features and patterns learned by the networks behind their decision-making processes and thereby provide valuable insights into how neural networks arrive at their conclusions, making them more interpretable and trustworthy. This paper presents a simple yet effective approach to network inversion using a meticulously conditioned generator that learns the data distribution in the input space of the trained neural network, enabling the reconstruction of inputs that would most likely lead to the desired outputs. To capture the diversity in the input space for a given output, instead of simply revealing the conditioning labels to the generator, we encode the conditioning label information into vectors and intermediate matrices and further minimize the cosine similarity between features of the generated images. Additionally, we incorporate feature orthogonality as a regularization term to boost image diversity which penalises the deviations of the Gram matrix of the features from the identity matrix, ensuring orthogonality and promoting distinct, non-redundant representations for each label. The paper concludes by exploring immediate applications of the proposed network inversion approach in interpretability, out-of-distribution detection, and training data reconstruction., Comment: arXiv admin note: substantial text overlap with arXiv:2410.16884, arXiv:2407.18002

We introduce a new metric to assess the quality of generated images that is more reliable, data-efficient, compute-efficient, and adaptable to new domains than the previous metrics, such as Fr\'echet Inception Distance (FID). The proposed metric is based on normalizing flows, which allows for the computation of density (exact log-likelihood) of images from any domain. Thus, unlike FID, the proposed Flow-based Likelihood Distance Plus (FLD+) metric exhibits strongly monotonic behavior with respect to different types of image degradations, including noise, occlusion, diffusion steps, and generative model size. Additionally, because normalizing flow can be trained stably and efficiently, FLD+ achieves stable results with two orders of magnitude fewer images than FID (which requires more images to reliably compute Fr\'echet distance between features of large samples of real and generated images). We made FLD+ computationally even more efficient by applying normalizing flows to features extracted in a lower-dimensional latent space instead of using a pre-trained network. We also show that FLD+ can easily be retrained on new domains, such as medical images, unlike the networks behind previous metrics -- such as InceptionNetV3 pre-trained on ImageNet., Comment: 13 pages, 10 figures

The adoption of security protocols such as Transport Layer Security (TLS) has significantly improved the state of traffic encryption and integrity protection on the Internet. Despite rigorous analysis, vulnerabilities continue to emerge, sometimes due to fundamental flaws in the protocol specification. This paper examines the security of TLS when using Raw Public Key (RPK) authentication. This mode has not been as extensively studied as X.509 certificates and Pre-Shared Keys (PSK). We develop a formal model of TLS RPK using applied pi calculus and the ProVerif verification tool, revealing that the RPK mode is susceptible to identity misbinding attacks. Our contributions include formal models of TLS RPK with several mechanisms for binding the endpoint identity to its public key, verification results, practical scenarios demonstrating the misbinding attack, and recommendations for mitigating such vulnerabilities. These findings highlight the need for improved security measures in TLS RPK.

We address the challenge of automated classification of diffuse large B-cell lymphoma (DLBCL) into its two primary subtypes: activated B-cell-like (ABC) and germinal center B-cell-like (GCB). Accurate classification between these subtypes is essential for determining the appropriate therapeutic strategy, given their distinct molecular profiles and treatment responses. Our proposed deep learning model demonstrates robust performance, achieving an average area under the curve (AUC) of (87.4 pm 5.7)\% during cross-validation. It shows a high positive predictive value (PPV), highlighting its potential for clinical application, such as triaging for molecular testing. To gain biological insights, we performed an analysis of morphological features of ABC and GCB subtypes. We segmented cell nuclei using a pre-trained deep neural network and compared the statistics of geometric and color features for ABC and GCB. We found that the distributions of these features were not very different for the two subtypes, which suggests that the visual differences between them are more subtle. These results underscore the potential of our method to assist in more precise subtype classification and can contribute to improved treatment management and outcomes for patients of DLBCL.

The advancement of digital pathology, particularly through computational analysis of whole slide images (WSI), is poised to significantly enhance diagnostic precision and efficiency. However, the large size and complexity of WSIs make it difficult to analyze and classify them using computers. This study introduces a novel method for WSI classification by automating the identification and examination of the most informative patches, thus eliminating the need to process the entire slide. Our method involves two-stages: firstly, it extracts only a few patches from the WSIs based on their pathological significance; and secondly, it employs Fisher vectors (FVs) for representing features extracted from these patches, which is known for its robustness in capturing fine-grained details. This approach not only accentuates key pathological features within the WSI representation but also significantly reduces computational overhead, thus making the process more efficient and scalable. We have rigorously evaluated the proposed method across multiple datasets to benchmark its performance against comprehensive WSI analysis and contemporary weakly-supervised learning methodologies. The empirical results indicate that our focused analysis of select patches, combined with Fisher vector representation, not only aligns with, but at times surpasses, the classification accuracy of standard practices. Moreover, this strategy notably diminishes computational load and resource expenditure, thereby establishing an efficient and precise framework for WSI analysis in the realm of digital pathology.

Whole Slide Imaging (WSI) is a cornerstone of digital pathology, offering detailed insights critical for diagnosis and research. Yet, the gigapixel size of WSIs imposes significant computational challenges, limiting their practical utility. Our novel approach addresses these challenges by leveraging various encoders for intelligent data reduction and employing a different classification model to ensure robust, permutation-invariant representations of WSIs. A key innovation of our method is the ability to distill the complex information of an entire WSI into a single vector, effectively capturing the essential features needed for accurate analysis. This approach significantly enhances the computational efficiency of WSI analysis, enabling more accurate pathological assessments without the need for extensive computational resources. This breakthrough equips us with the capability to effectively address the challenges posed by large image resolutions in whole-slide imaging, paving the way for more scalable and effective utilization of WSIs in medical diagnostics and research, marking a significant advancement in the field., Comment: arXiv admin note: text overlap with arXiv:2411.08530

A space-dependent mean for cosmological perturbations negates the ansatz of statistical homogeneity and isotropy, and hence ergodicity. In this work, we construct such a primordial mean of scalar perturbations from an alternative quantum initial state (coherent state) and examine the associated power and bi-spectra. A multitude of cosmological tests based on these spectra are discussed. We find that current cosmological data doesn't favor a primordial mean over large scales and strong constraints arise from the limit on bispectrum from Planck data. At small scales, this hypothesis can be tested by future observables such as $\mu$-distortion of CMB., Comment: v1: 14 pages (including supplemental material), 2 figures; v2: made minor updates in discussions and added references, version to appear in Phys. Rev. D

Our study introduces ResNet-L2 (RL2), a novel metric for evaluating generative models and image quality in histopathology, addressing limitations of traditional metrics, such as Frechet inception distance (FID), when the data is scarce. RL2 leverages ResNet features with a normalizing flow to calculate RMSE distance in the latent space, providing reliable assessments across diverse histopathology datasets. We evaluated the performance of RL2 on degradation types, such as blur, Gaussian noise, salt-and-pepper noise, and rectangular patches, as well as diffusion processes. RL2's monotonic response to increasing degradation makes it well-suited for models that assess image quality, proving a valuable advancement for evaluating image generation techniques in histopathology. It can also be used to discard low-quality patches while sampling from a whole slide image. It is also significantly lighter and faster compared to traditional metrics and requires fewer images to give stable metric value., Comment: 7 pages, 5 figures. Accepted in ISBI 2025

Accurate survival prediction is essential for personalized cancer treatment. However, genomic data - often a more powerful predictor than pathology data - is costly and inaccessible. We present the cross-modal genomic feature translation and alignment network for enhanced survival prediction from histopathology images (PathoGen-X). It is a deep learning framework that leverages both genomic and imaging data during training, relying solely on imaging data at testing. PathoGen-X employs transformer-based networks to align and translate image features into the genomic feature space, enhancing weaker imaging signals with stronger genomic signals. Unlike other methods, PathoGen-X translates and aligns features without projecting them to a shared latent space and requires fewer paired samples. Evaluated on TCGA-BRCA, TCGA-LUAD, and TCGA-GBM datasets, PathoGen-X demonstrates strong survival prediction performance, emphasizing the potential of enriched imaging models for accessible cancer prognosis.

A wide range of non-equilibrium phenomena in nature involve non-reciprocal interactions. To understand the novel behaviors that can emerge in such systems, finding tractable models is essential. With this goal, we introduce a non-reciprocal generalization of the kinetic Ising model in one dimension and solve it exactly. Our solution uncovers novel properties driven by non-reciprocity, such as underdamped phases, critically damped phases where a system of size $N$ is described by an $N^{th}$-order exceptional point, and wave phenomena influenced by the parity of $N$. Additionally, we examine the low-energy behavior of these systems in various limits, demonstrating that non-reciprocity leads to unique scaling behavior at zero temperature., Comment: 8 pages, LaTeX, 3 figues. arXiv admin note: text overlap with arXiv:2410.23615

Non-reciprocal interactions are a generic feature of non-equilibrium systems. We define a non-reciprocal generalization of the kinetic Ising model in one spatial dimension. We solve the model exactly using two different approaches for infinite, semi-infinite and finite systems with either periodic or open boundary conditions. The exact solution allows us to explore a range of novel phenomena tied to non-reciprocity like non-reciprocity induced frustration and wave phenomena with interesting parity-dependence for finite systems of size $N$. We study dynamical questions like the approach to equilibrium with various boundary conditions. We find new regimes, separated by $N^{th}$-order exceptional points, which can be classified as overdamped, underdamped and critically damped phases. Despite these new regimes, long-time order is only present at zero temperature. Additionally, we explore the low-energy behavior of the system in various limits, including the ageing and spatio-temporal Porod regimes, demonstrating that non-reciprocity induces unique scaling behavior at zero temperature. Lastly, we present general results for systems where spins interact with no more than two spins, outlining the conditions under which long-time order may exist., Comment: 72 pages, LaTeX, 13 figures

Foundation models applied to bio-molecular space hold promise to accelerate drug discovery. Molecular representation is key to building such models. Previous works have typically focused on a single representation or view of the molecules. Here, we develop a multi-view foundation model approach, that integrates molecular views of graph, image and text. Single-view foundation models are each pre-trained on a dataset of up to 200M molecules and then aggregated into combined representations. Our multi-view model is validated on a diverse set of 18 tasks, encompassing ligand-protein binding, molecular solubility, metabolism and toxicity. We show that the multi-view models perform robustly and are able to balance the strengths and weaknesses of specific views. We then apply this model to screen compounds against a large (>100 targets) set of G Protein-Coupled receptors (GPCRs). From this library of targets, we identify 33 that are related to Alzheimer's disease. On this subset, we employ our model to identify strong binders, which are validated through structure-based modeling and identification of key binding motifs., Comment: 37 pages including supplement. 10 figures, 8 tables

As large language models (LLMs) become integral to recruitment processes, concerns about AI-induced bias have intensified. This study examines biases in candidate interview reports generated by Claude 3.5 Sonnet, GPT-4o, Gemini 1.5, and Llama 3.1 405B, focusing on characteristics such as gender, race, and age. We evaluate the effectiveness of LLM-based anonymization in reducing these biases. Findings indicate that while anonymization reduces certain biases, particularly gender bias, the degree of effectiveness varies across models and bias types. Notably, Llama 3.1 405B exhibited the lowest overall bias. Moreover, our methodology of comparing anonymized and non-anonymized data reveals a novel approach to assessing inherent biases in LLMs beyond recruitment applications. This study underscores the importance of careful LLM selection and suggests best practices for minimizing bias in AI applications, promoting fairness and inclusivity., Comment: 13 pages, 18 figures. This paper presents a technical analysis of bias in large language models, focusing on bias detection and mitigation

Machine Learning models are often trained on proprietary and private data that cannot be shared, though the trained models themselves are distributed openly assuming that sharing model weights is privacy preserving, as training data is not expected to be inferred from the model weights. In this paper, we present Training-Like Data Reconstruction (TLDR), a network inversion-based approach to reconstruct training-like data from trained models. To begin with, we introduce a comprehensive network inversion technique that learns the input space corresponding to different classes in the classifier using a single conditioned generator. While inversion may typically return random and arbitrary input images for a given output label, we modify the inversion process to incentivize the generator to reconstruct training-like data by exploiting key properties of the classifier with respect to the training data along with some prior knowledge about the images. To validate our approach, we conduct empirical evaluations on multiple standard vision classification datasets, thereby highlighting the potential privacy risks involved in sharing machine learning models.

We present Movie Gen, a cast of foundation models that generates high-quality, 1080p HD videos with different aspect ratios and synchronized audio. We also show additional capabilities such as precise instruction-based video editing and generation of personalized videos based on a user's image. Our models set a new state-of-the-art on multiple tasks: text-to-video synthesis, video personalization, video editing, video-to-audio generation, and text-to-audio generation. Our largest video generation model is a 30B parameter transformer trained with a maximum context length of 73K video tokens, corresponding to a generated video of 16 seconds at 16 frames-per-second. We show multiple technical innovations and simplifications on the architecture, latent spaces, training objectives and recipes, data curation, evaluation protocols, parallelization techniques, and inference optimizations that allow us to reap the benefits of scaling pre-training data, model size, and training compute for training large scale media generation models. We hope this paper helps the research community to accelerate progress and innovation in media generation models. All videos from this paper are available at https://go.fb.me/MovieGenResearchVideos.

Missing frequency channels pose a problem for estimating $P(k_\perp,k_\parallel)$ the redshifted 21-cm power spectrum (PS) from radio-interferometric visibility data. This is particularly severe for the Murchison Widefield Array (MWA), which has a periodic pattern of missing channels that introduce spikes along $k_\parallel$. The Tracking Tapered Gridded Estimator (TTGE) overcomes this by first correlating the visibilities in the frequency domain to estimate the multi-frequency angular power spectrum (MAPS) $C_\ell(\Delta\nu)$ that has no missing frequency separation $\Delta\nu$. We perform a Fourier transform along $\Delta\nu$ to estimate $P(k_\perp,k_\parallel)$. Considering our earlier work, simulations demonstrate that the TTGE can estimate $P(k_\perp,k_\parallel)$ without any artifacts due to the missing channels. However, the spikes were still found to persist for the actual data, which is foreground-dominated. The current work presents a detailed investigation considering both simulations and actual data. We find that the spikes arise due to a combination of the missing channels and the strong spectral dependence of the foregrounds. Based on this, we propose and demonstrate a technique to mitigate the spikes. Applying this, we find the values of $P(k_\perp,k_\parallel)$ in the region $0.004 \leq k_\perp \leq 0.048\,{\rm Mpc^{-1}}$ and $k_\parallel > 0.35 \,{\rm Mpc^{-1}}$ to be consistent with zero within the expected statistical fluctuations. We obtain the $2\sigma$ upper limit of $\Delta_{\rm UL}^2(k)=(918.17)^2\,{\rm mK^2}$ at $k=0.404\,{\rm Mpc^{-1}}$ for the mean squared brightness temperature fluctuations of the $z=8.2$ epoch of reionization (EoR) 21-cm signal. This upper limit is from just $\sim 17$ minutes of observation for a single pointing direction. We expect tighter constraints when we combine all $162$ different pointing directions of the drift scan observation., Comment: 11 pages, 16 figures, comments are welcome

Understanding the evolution of galaxies cannot exclude the important role played by the central supermassive black hole and the circumgalactic medium (CGM). Simulations have strongly suggested the negative feedback of AGN Jet/wind/outflows on the ISM/CGM of a galaxy leading to the eventual decline of star formation. However, no "smoking gun" evidence exists so far where relics of feedback, observed in any band, are consistent with the time scale of a major decline in star formation, in any sample of galaxies. Relics of any AGN-driven outflows will be observed as a faint and fuzzy structure which may be difficult to characterise by automated algorithms but trained citizen scientists can possibly perform better through their intuitive vision with additional heterogeneous data available anywhere on the Internet. RAD@home, launched on 15th April 2013, is not only the first Indian Citizen Science Research (CSR) platform in astronomy but also the only CSR publishing discoveries using any Indian telescope. We briefly report 11 CSR discoveries collected over the last eleven years. While searching for such relics we have spotted cases of offset relic lobes from elliptical and spiral, episodic radio galaxies with overlapping lobes as the host galaxy is in motion, large diffuse spiral-shaped emission, cases of jet-galaxy interaction, kinks and burls on the jets, a collimated synchrotron thread etc. Such exotic sources push the boundaries of our understanding of classical Seyferts and radio galaxies with jets and the process of discovery prepares the next generation for science with the upgraded GMRT and Square Kilometre Array Observatory (SKAO)., Comment: 14 pages, 8 figures. Accepted for publication in the Springer-Nature conference proceedings for "ISRA 2023: The Relativistic Universe: From Classical to Quantum Proceedings of the International Symposium on Recent Developments in Relativistic Astrophysics". Comments and collaborations, most welcome! Please visit #RADatHomeIndia website at radathomeindia.org

In this study, we investigate the performance of few-shot classification models across different domains, specifically natural images and histopathological images. We first train several few-shot classification models on natural images and evaluate their performance on histopathological images. Subsequently, we train the same models on histopathological images and compare their performance. We incorporated four histopathology datasets and one natural images dataset and assessed performance across 5-way 1-shot, 5-way 5-shot, and 5-way 10-shot scenarios using a selection of state-of-the-art classification techniques. Our experimental results reveal insights into the transferability and generalization capabilities of few-shot classification models between diverse image domains. We analyze the strengths and limitations of these models in adapting to new domains and provide recommendations for optimizing their performance in cross-domain scenarios. This research contributes to advancing our understanding of few-shot learning in the context of image classification across diverse domains.

Federated Learning (FL) facilitates data privacy by enabling collaborative in-situ training across decentralized clients. Despite its inherent advantages, FL faces significant challenges of performance and convergence when dealing with data that is not independently and identically distributed (non-i.i.d.). While previous research has primarily addressed the issue of skewed label distribution across clients, this study focuses on the less explored challenge of multi-domain FL, where client data originates from distinct domains with varying feature distributions. We introduce a novel method designed to address these challenges FedStein: Enhancing Multi-Domain Federated Learning Through the James-Stein Estimator. FedStein uniquely shares only the James-Stein (JS) estimates of batch normalization (BN) statistics across clients, while maintaining local BN parameters. The non-BN layer parameters are exchanged via standard FL techniques. Extensive experiments conducted across three datasets and multiple models demonstrate that FedStein surpasses existing methods such as FedAvg and FedBN, with accuracy improvements exceeding 14% in certain domains leading to enhanced domain generalization. The code is available at https://github.com/sunnyinAI/FedStein, Comment: 12 pages, 2 figures. Accepted at International Workshop on Federated Foundation Models In Conjunction with NeurIPS 2024 (FL@FM-NeurIPS'24)

Neurosymbolic learning enables the integration of symbolic reasoning with deep learning but faces significant challenges in scaling to complex symbolic programs, large datasets, or both. We introduce Dolphin, a framework that tackles these challenges by supporting neurosymbolic programs in Python, executing complex symbolic reasoning on the CPU while vectorizing probabilistic computations and gradient propagation on the GPU. Across 13 benchmarks spanning tasks over text, image, and video data, with symbolic reasoning features like recursion and black-box functions, Dolphin converges to state-of-the-art accuracies on the more complex benchmarks while existing frameworks such as Scallop, ISED, and IndeCateR+ fail to converge within the time limit. On simpler benchmarks, Dolphin matches their performance, while achieving these results 1.71x to 62x faster than the baselines. Overall, Dolphin advances the scalability of neurosymbolic frameworks, achieving state-of-the-art efficiency and convergence on difficult benchmarks where existing frameworks struggle.

We developed a software pipeline for quality control (QC) of histopathology whole slide images (WSIs) that segments various regions, such as blurs of different levels, tissue regions, tissue folds, and pen marks. Given the necessity and increasing availability of GPUs for processing WSIs, the proposed pipeline comprises multiple lightweight deep learning models to strike a balance between accuracy and speed. The pipeline was evaluated in all TCGAs, which is the largest publicly available WSI dataset containing more than 11,000 histopathological images from 28 organs. It was compared to a previous work, which was not based on deep learning, and it showed consistent improvement in segmentation results across organs. To minimize annotation effort for tissue and blur segmentation, annotated images were automatically prepared by mosaicking patches (sub-images) from various WSIs whose labels were identified using a patch classification tool HistoROI. Due to the generality of our trained QC pipeline and its extensive testing the potential impact of this work is broad. It can be used for automated pre-processing any WSI cohort to enhance the accuracy and reliability of large-scale histopathology image analysis for both research and clinical use. We have made the trained models, training scripts, training data, and inference results publicly available at https://github.com/abhijeetptl5/wsisegqc, which should enable the research community to use the pipeline right out of the box or further customize it to new datasets and applications in the future., Comment: 14 pages, 8 figures

We propose two new evaluation metrics to assess realness of generated images based on normalizing flows: a simpler and efficient flow-based likelihood distance (FLD) and a more exact dual-flow based likelihood distance (D-FLD). Because normalizing flows can be used to compute the exact likelihood, the proposed metrics assess how closely generated images align with the distribution of real images from a given domain. This property gives the proposed metrics a few advantages over the widely used Fr\'echet inception distance (FID) and other recent metrics. Firstly, the proposed metrics need only a few hundred images to stabilize (converge in mean), as opposed to tens of thousands needed for FID, and at least a few thousand for the other metrics. This allows confident evaluation of even small sets of generated images, such as validation batches inside training loops. Secondly, the network used to compute the proposed metric has over an order of magnitude fewer parameters compared to Inception-V3 used to compute FID, making it computationally more efficient. For assessing the realness of generated images in new domains (e.g., x-ray images), ideally these networks should be retrained on real images to model their distinct distributions. Thus, our smaller network will be even more advantageous for new domains. Extensive experiments show that the proposed metrics have the desired monotonic relationships with the extent of image degradation of various kinds., Comment: 15 pages, 16 figures

Histopathology whole slide images (WSIs) are being widely used to develop deep learning-based diagnostic solutions, especially for precision oncology. Most of these diagnostic softwares are vulnerable to biases and impurities in the training and test data which can lead to inaccurate diagnoses. For instance, WSIs contain multiple types of tissue regions, at least some of which might not be relevant to the diagnosis. We introduce HistoROI, a robust yet lightweight deep learning-based classifier to segregate WSI into six broad tissue regions -- epithelium, stroma, lymphocytes, adipose, artifacts, and miscellaneous. HistoROI is trained using a novel human-in-the-loop and active learning paradigm that ensures variations in training data for labeling-efficient generalization. HistoROI consistently performs well across multiple organs, despite being trained on only a single dataset, demonstrating strong generalization. Further, we have examined the utility of HistoROI in improving the performance of downstream deep learning-based tasks using the CAMELYON breast cancer lymph node and TCGA lung cancer datasets. For the former dataset, the area under the receiver operating characteristic curve (AUC) for metastasis versus normal tissue of a neural network trained using weakly supervised learning increased from 0.88 to 0.92 by filtering the data using HistoROI. Similarly, the AUC increased from 0.88 to 0.93 for the classification between adenocarcinoma and squamous cell carcinoma on the lung cancer dataset. We also found that the performance of the HistoROI improves upon HistoQC for artifact detection on a test dataset of 93 annotated WSIs. The limitations of the proposed model are analyzed, and potential extensions are also discussed., Comment: 18 pages

Federated learning faces a critical challenge in balancing communication efficiency with rapid convergence, especially for second-order methods. While Newton-type algorithms achieve linear convergence in communication rounds, transmitting full Hessian matrices is often impractical due to quadratic complexity. We introduce Federated Learning with Enhanced Nesterov-Newton Sketch (FLeNS), a novel method that harnesses both the acceleration capabilities of Nesterov's method and the dimensionality reduction benefits of Hessian sketching. FLeNS approximates the centralized Newton's method without relying on the exact Hessian, significantly reducing communication overhead. By combining Nesterov's acceleration with adaptive Hessian sketching, FLeNS preserves crucial second-order information while preserving the rapid convergence characteristics. Our theoretical analysis, grounded in statistical learning, demonstrates that FLeNS achieves super-linear convergence rates in communication rounds - a notable advancement in federated optimization. We provide rigorous convergence guarantees and characterize tradeoffs between acceleration, sketch size, and convergence speed. Extensive empirical evaluation validates our theoretical findings, showcasing FLeNS's state-of-the-art performance with reduced communication requirements, particularly in privacy-sensitive and edge-computing scenarios. The code is available at https://github.com/sunnyinAI/FLeNS, Comment: 10 pages, 3 figures, 2 Tables

Current video summarization methods largely rely on transformer-based architectures, which, due to their quadratic complexity, require substantial computational resources. In this work, we address these inefficiencies by enhancing the Direct-to-Summarize Network (DSNet) with more resource-efficient token mixing mechanisms. We show that replacing traditional attention with alternatives like Fourier, Wavelet transforms, and Nystr\"omformer improves efficiency and performance. Furthermore, we explore various pooling strategies within the Regional Proposal Network, including ROI pooling, Fast Fourier Transform pooling, and flat pooling. Our experimental results on TVSum and SumMe datasets demonstrate that these modifications significantly reduce computational costs while maintaining competitive summarization performance. Thus, our work offers a more scalable solution for video summarization tasks., Comment: 10 pages, 5 figures

Recent advancements in single image super-resolution have been predominantly driven by token mixers and transformer architectures. WaveMixSR utilized the WaveMix architecture, employing a two-dimensional discrete wavelet transform for spatial token mixing, achieving superior performance in super-resolution tasks with remarkable resource efficiency. In this work, we present an enhanced version of the WaveMixSR architecture by (1) replacing the traditional transpose convolution layer with a pixel shuffle operation and (2) implementing a multistage design for higher resolution tasks ($4\times$). Our experiments demonstrate that our enhanced model -- WaveMixSR-V2 -- outperforms other architectures in multiple super-resolution tasks, achieving state-of-the-art for the BSD100 dataset, while also consuming fewer resources, exhibits higher parameter efficiency, lower latency and higher throughput. Our code is available at https://github.com/pranavphoenix/WaveMixSR., Comment: 10 pages. Accepted in AAAI 2025. arXiv admin note: text overlap with arXiv:2307.00430

Quantum error correction (QEC) is necessary to run large scale quantum programs. Regardless of error correcting code, hardware platform, or systems architecture, QEC systems are limited by the types of gates which they can perform efficiently. In order to make the base code's gate set universal, they typically rely on the production of a single type of resource state, commonly T, in a different code which is then distilled and injected into the base code. This process is neither space nor time efficient and can account for a large portion of the total execution time and physical qubit cost of any program. In order to circumvent this problem, alternatives have been proposed, such as the production of continuous angle rotation states \cite{akahoshi2023partially, choi2023fault}. These proposals are powerful because they not only enable localized resource generation but also can potentially reduce total space requirements. However, the production of these states is non-deterministic and can require many repetitions in order to obtain the desired resource. The original proposals suggest architectures which do not actively account for realtime management of its resources to minimize total execution time. Without this, static compilation of programs to these systems will be unnecessarily expensive. In this work, we propose a realtime compilation of programs to these continuous angle systems and a generalized resource sharing architecture which actively minimizes total execution time based on expected production rates. To do so, we repeatedly redistribute resources on-demand which depending on the underlying hardware can cause excessive classical control overhead. We further address this by dynamically selecting the frequency of recompilation. Our compiler and architecture improves over the baseline proposals by an average of $2\times$., Comment: 14 pages, 14 figures

The current standard for detecting human epidermal growth factor receptor 2 (HER2) status in breast cancer patients relies on HER2 amplification, identified through fluorescence in situ hybridization (FISH) or immunohistochemistry (IHC). However, hematoxylin and eosin (H\&E) tumor stains are more widely available, and accurately predicting HER2 status using H\&E could reduce costs and expedite treatment selection. Deep Learning algorithms for H&E have shown effectiveness in predicting various cancer features and clinical outcomes, including moderate success in HER2 status prediction. In this work, we employed a customized weak supervision classification technique combined with MoCo-v2 contrastive learning to predict HER2 status. We trained our pipeline on 182 publicly available H&E Whole Slide Images (WSIs) from The Cancer Genome Atlas (TCGA), for which annotations by the pathology team at Yale School of Medicine are publicly available. Our pipeline achieved an Area Under the Curve (AUC) of 0.85 across four different test folds. Additionally, we tested our model on 44 H&E slides from the TCGA-BRCA dataset, which had an HER2 score of 2+ and included corresponding HER2 status and FISH test results. These cases are considered equivocal for IHC, requiring an expensive FISH test on their IHC slides for disambiguation. Our pipeline demonstrated an AUC of 0.81 on these challenging H&E slides. Reducing the need for FISH test can have significant implications in cancer treatment equity for underserved populations.

This paper presents a study on few-shot classification in the context of histopathology images. While few-shot learning has been studied for natural image classification, its application to histopathology is relatively unexplored. Given the scarcity of labeled data in medical imaging and the inherent challenges posed by diverse tissue types and data preparation techniques, this research evaluates the performance of state-of-the-art few-shot learning methods for various scenarios on histology data. We have considered four histopathology datasets for few-shot histopathology image classification and have evaluated 5-way 1-shot, 5-way 5-shot and 5-way 10-shot scenarios with a set of state-of-the-art classification techniques. The best methods have surpassed an accuracy of 70%, 80% and 85% in the cases of 5-way 1-shot, 5-way 5-shot and 5-way 10-shot cases, respectively. We found that for histology images popular meta-learning approaches is at par with standard fine-tuning and regularization methods. Our experiments underscore the challenges of working with images from different domains and underscore the significance of unbiased and focused evaluations in advancing computer vision techniques for specialized domains, such as histology images.

Volunteer crowdsourcing (VCS) leverages citizen interaction to address challenges by utilizing individuals' knowledge and skills. Complex social tasks often require collaboration among volunteers with diverse skill sets, and their willingness to engage is crucial. Matching tasks with the most suitable volunteers remains a significant challenge. VCS platforms face unpredictable demands in terms of tasks and volunteer requests, complicating the prediction of resource requirements for the volunteer-to-task assignment process. To address these challenges, we introduce the Skill and Willingness-Aware Volunteer Matching (SWAM) algorithm, which allocates volunteers to tasks based on skills, willingness, and task requirements. We also developed a serverless framework to deploy SWAM. Our method outperforms conventional solutions, achieving a 71% improvement in end-to-end latency efficiency. We achieved a 92% task completion ratio and reduced task waiting time by 56%, with an overall utility gain 30% higher than state-of-the-art baseline methods. This framework contributes to generating effective volunteer and task matches, supporting grassroots community coordination and fostering citizen involvement, ultimately contributing to social good.

Previous methods for audio-image matching generally fall into one of two categories: pipeline models or End-to-End models. Pipeline models first transcribe speech and then encode the resulting text; End-to-End models encode speech directly. Generally, pipeline models outperform end-to-end models, but the intermediate transcription necessarily discards some potentially useful non-textual information. In addition to textual information, speech can convey details such as accent, mood, and and emphasis, which should be effectively captured in the encoded representation. In this paper, we investigate whether non-textual information, which is overlooked by pipeline-based models, can be leveraged to improve speech-image matching performance. We thoroughly analyze and compare End-to-End models, pipeline models, and our proposed dual-channel model for robust audio-image retrieval on a variety of datasets. Our approach achieves a substantial performance gain over the previous state-of-the-art by leveraging strong pretrained models, a prompting mechanism and a bifurcated design.

Classical optimization and learning-based methods are the two reigning paradigms in deformable image registration. While optimization-based methods boast generalizability across modalities and robust performance, learning-based methods promise peak performance, incorporating weak supervision and amortized optimization. However, the exact conditions for either paradigm to perform well over the other are shrouded and not explicitly outlined in the existing literature. In this paper, we make an explicit correspondence between the mutual information of the distribution of per-pixel intensity and labels, and the performance of classical registration methods. This strong correlation hints to the fact that architectural designs in learning-based methods is unlikely to affect this correlation, and therefore, the performance of learning-based methods. This hypothesis is thoroughly validated with state-of-the-art classical and learning-based methods. However, learning-based methods with weak supervision can perform high-fidelity intensity and label registration, which is not possible with classical methods. Next, we show that this high-fidelity feature learning does not translate to invariance to domain shift, and learning-based methods are sensitive to such changes in the data distribution. Finally, we propose a general recipe to choose the best paradigm for a given registration problem, based on these observations., Comment: 16 pages; Accepted to NeurIPS 2024

In-situ sensing devices need to be deployed in remote environments for long periods of time; minimizing their power consumption is vital for maximising both their operational lifetime and coverage. We introduce Terracorder -- a versatile multi-sensor device -- and showcase its exceptionally low power consumption using an on-device reinforcement learning scheduler. We prototype a unique device setup for biodiversity monitoring and compare its battery life using our scheduler against a number of fixed schedules; the scheduler captures more than 80% of events at less than 50% of the number of activations of the best-performing fixed schedule. We then explore how a collaborative scheduler can maximise the useful operation of a network of devices, improving overall network power consumption and robustness., Comment: Preprint

One of the few cases of AdS/CFT where both sides of the duality are under good control relates tensionless $k=1$ strings on AdS$_3$ to a two-dimensional symmetric product CFT. Building on prior observations, we propose an exact duality between string theory on a spacetime which is not asymptotically AdS and a non-conformal field theory. The bulk theory is constructed as a marginal deformation of the $k=1$ AdS$_3$ string while the spacetime dual is a single trace $T\overline{T}$-deformed symmetric orbifold theory. As evidence for the duality, we match the one-loop bulk and boundary torus partition functions. This correspondence provides a framework to both learn about quantum gravity beyond AdS and understand how to define physical observables in $T\overline{T}$-deformed field theories., Comment: 32 pages; v2: references added, typos corrected