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Mobile health has the potential to revolutionize health care delivery and patient engagement. In this work, we discuss how integrating Artificial Intelligence into digital health applications-focused on supply chain, patient management, and capacity building, among other use cases-can improve the health system and public health performance. We present an Artificial Intelligence and Reinforcement Learning platform that allows the delivery of adaptive interventions whose impact can be optimized through experimentation and real-time monitoring. The system can integrate multiple data sources and digital health applications. The flexibility of this platform to connect to various mobile health applications and digital devices and send personalized recommendations based on past data and predictions can significantly improve the impact of digital tools on health system outcomes. The potential for resource-poor settings, where the impact of this approach on health outcomes could be more decisive, is discussed specifically. This framework is, however, similarly applicable to improving efficiency in health systems where scarcity is not an issue., Comment: This article has been accepted for publication in Health Systems & Reform, published by Taylor & Francis

The exponential growth of the mobile app market underscores the importance of constant innovation and rapid response to user demands. As user satisfaction is paramount to the success of a mobile application (app), developers typically rely on user reviews, which represent user feedback that includes ratings and comments to identify areas for improvement. However, the sheer volume of user reviews poses challenges in manual analysis, necessitating automated approaches. Existing automated approaches either analyze only the target apps reviews, neglecting the comparison of similar features to competitors or fail to provide suggestions for feature enhancement. To address these gaps, we propose a Large Language Model (LLM)-based Competitive User Review Analysis for Feature Enhancement) (LLM-Cure), an approach powered by LLMs to automatically generate suggestion s for mobile app feature improvements. More specifically, LLM-Cure identifies and categorizes features within reviews by applying LLMs. When provided with a complaint in a user review, LLM-Cure curates highly rated (4 and 5 stars) reviews in competing apps related to the complaint and proposes potential improvements tailored to the target application. We evaluate LLM-Cure on 1,056,739 reviews of 70 popular Android apps. Our evaluation demonstrates that LLM-Cure significantly outperforms the state-of-the-art approaches in assigning features to reviews by up to 13% in F1-score, up to 16% in recall and up to 11% in precision. Additionally, LLM-Cure demonstrates its capability to provide suggestions for resolving user complaints. We verify the suggestions using the release notes that reflect the changes of features in the target mobile app. LLM-Cure achieves a promising average of 73% of the implementation of the provided suggestions., Comment: 25 pages

Using properties of OSp(4|2) and PSL(2|2), we investigate the super geometry of the parametric D($2,1;\zeta $) labeled by variable $\zeta $ belonging to $\mathbb{C}\backslash \{-1,0\}$ and we give applications in the study of integrable superspin chains. This $9|8$ dimensional Lie supergroup has three orthogonal isospins in its even part SL($2,\mathbb{C}$)$^{\otimes 3}$ assembled by the tri-fundamental $2^{\otimes 3}$ with odd parity. It undergoes contractions at $\zeta =-1,0$ where an SL($2,\mathbb{C}$) gets decompactified into commutative $\mathbb{C}^{3}$ interpreted in terms of three central extensions. By help of the obtained characteristic features of D($2,1;\zeta $) and their local structures at the special points $\zeta =\pm 1$, we calculate the Lax operator $\mathcal{L}_{\mathfrak{d}(2,1;\zeta )}^{(\mathfrak{\eta})}$ solving the RLL equation describing the integrability of the superspin chain $\mathfrak{d}$($2,1;\zeta $). We also complete missing results regarding the calculation of $\mathcal{L}_{psl(2|2)}^{(\mathfrak{\mu })}$ and $\mathcal{L}_{osp(4|2)}^{(\mathfrak{\mu})}$. Other features of the four super Dynkin diagrams $S\mathfrak{DD}_{\mathfrak{d}(2,1;\zeta )}^{(\mathfrak{\eta})}$ and weight graphs of $\mathfrak{d}$($2,1;\zeta $) as well as discrete automorphisms are also given., Comment: LaTeX, 71 pages, 18 figures

The sixth-generation (6G) non-terrestrial networks (NTNs) are crucial for real-time monitoring in critical applications like disaster relief. However, limited bandwidth, latency, rain attenuation, long propagation delays, and co-channel interference pose challenges to efficient satellite communication. Therefore, semantic communication (SC) has emerged as a promising solution to improve transmission efficiency and address these issues. In this paper, we explore the potential of SC as a bandwidth-efficient, latency-minimizing strategy specifically suited to 6G satellite communications. While existing SC methods have demonstrated efficacy in direct satellite-terrestrial transmissions, they encounter limitations in satellite networks due to distortion accumulation across gateway hop-relays. Additionally, certain ground users (GUs) experience poor signal-to-noise ratios (SNR), making direct satellite communication challenging. To address these issues, we propose a novel framework that optimizes gateway hop-relay selection for GUs with low SNR and integrates gateway-based denoising mechanisms to ensure high-quality-of-service (QoS) in satellite-based SC networks. This approach directly mitigates distortion, leading to significant improvements in satellite service performance by delivering customized services tailored to the unique signal conditions of each GU. Our findings represent a critical advancement in reliable and efficient data transmission from the Earth observation satellites, thereby enabling fast and effective responses to urgent events. Simulation results demonstrate that our proposed strategy significantly enhances overall network performance, outperforming conventional methods by offering tailored communication services based on specific GU conditions., Comment: 13 pages, 8 figures, 2 tables

Version control systems (VCS) are essential for software development, yet centralized VCS present risks such as data loss, security breaches, and ownership disputes. While blockchain-based approaches to decentralized source code repository hosting have been explored, many existing solutions struggle with challenges related to security, scalability, efficiency, and real-time collaboration. This study seeks to enhance these efforts by proposing a novel decentralized solution that leverages the Ethereum blockchain and IPFS for secure, efficient, and resilient code repository hosting and governance. Our approach introduces a hybrid architecture that combines the immutable and decentralized nature of blockchain with the efficiency of IPFS for off-chain storage. To facilitate real-time collaboration, we integrate a temporary centralized Middleman IPFS that manages transaction processing and enhances operational efficiency without compromising long-term security. This Middleman IPFS acts as an intermediary, balancing the speed of centralized systems with the resilience of decentralized architectures. Our system uses smart contracts to maintain access control and key management by dynamically verifying access rights, ensuring that only authorized users can retrieve and decrypt data stored on IPFS. This integration allows for secure, real-time collaboration in environments where multiple collaborators need concurrent access to shared resources. Our system employs a hybrid encryption scheme that combines symmetric and asymmetric cryptography. The encrypted keys are stored on the blockchain, while IPFS handles the efficient storage of the codebase itself, with a Middleman IPFS maintaining concurrent collaboration, providing a robust and scalable solution for managing large-scale, collaborative coding projects., Comment: 24 pages, 3 figures, submitted manuscript to Plos one journal

The ER=EPR conjecture states that quantum entanglement between boundary degrees of freedom leads to the emergence of bulk spacetime itself. Although this has been tested extensively in String Theory for asymptotically anti-de Sitter spacetimes, its implications for an accelerating universe, such as our own, remain less explored. Assuming a cosmic version of ER=EPR for de Sitter space, we explore computational complexity corresponding to long-range entanglement responsible for bulk states on spacelike hypersurfaces. Rather remarkably, we find that the complexity (per unit volume) of the Euclidean vacuum, as an entangled state over two boundary CFT vacua, is finite both in the UV and the IR, which provides additional evidence for cosmic ER=EPR. Our result seems to be a universal feature of spacetimes with horizons and is explicitly independent of the details of the model under consideration., Comment: 5 pages, 1 figure, comments welcome

This study presents an uncertainty-aware stacked neural networks model for the reliable classification of COVID-19 from radiological images. The model addresses the critical gap in uncertainty-aware modeling by focusing on accurately identifying confidently correct predictions while alerting users to confidently incorrect and uncertain predictions, which can promote trust in automated systems. The architecture integrates uncertainty quantification methods, including Monte Carlo dropout and ensemble techniques, to enhance predictive reliability by assessing the certainty of diagnostic predictions. Within a two-tier model framework, the tier one model generates initial predictions and associated uncertainties, which the second tier model uses to produce a trust indicator alongside the diagnostic outcome. This dual-output model not only predicts COVID-19 cases but also provides a trust flag, indicating the reliability of each diagnosis and aiming to minimize the need for retesting and expert verification. The effectiveness of this approach is demonstrated through extensive experiments on the COVIDx CXR-4 dataset, showing a novel approach in identifying and handling confidently incorrect cases and uncertain cases, thus enhancing the trustworthiness of automated diagnostics in clinical settings., Comment: 15 pages, 7 figures, 6 tables

Accurate loss reserving is crucial in Property and Casualty (P&C) insurance for financial stability, regulatory compliance, and effective risk management. We propose a novel micro-level Cox model based on hidden Markov models (HMMs). Initially formulated as a continuous-time model, it addresses the complexity of incorporating temporal dependencies and policyholder risk attributes. However, the continuous-time model faces significant challenges in maximizing the likelihood and fitting right-truncated reporting delays. To overcome these issues, we introduce two discrete-time versions: one incorporating unsystematic randomness in reporting delays through a Dirichlet distribution and one without. We provide the EM algorithm for parameter estimation for all three models and apply them to an auto-insurance dataset to estimate IBNR claim counts. Our results show that while all models perform well, the discrete-time versions demonstrate superior performance by jointly modeling delay and frequency, with the Dirichlet-based model capturing additional variability in reporting delays. This approach enhances the accuracy and reliability of IBNR reserving, offering a flexible framework adaptable to different levels of granularity within an insurance portfolio.

While there has been progress towards aligning Large Language Models (LLMs) with human values and ensuring safe behaviour at inference time, safety-aligned LLMs are known to be vulnerable to training-time attacks such as supervised fine-tuning (SFT) on harmful datasets. In this paper, we ask if LLMs are vulnerable to adversarial reinforcement learning. Motivated by this goal, we propose Reverse Preference Attacks (RPA), a class of attacks to make LLMs learn harmful behavior using adversarial reward during reinforcement learning from human feedback (RLHF). RPAs expose a critical safety gap of safety-aligned LLMs in RL settings: they easily explore the harmful text generation policies to optimize adversarial reward. To protect against RPAs, we explore a host of mitigation strategies. Leveraging Constrained Markov-Decision Processes, we adapt a number of mechanisms to defend against harmful fine-tuning attacks into the RL setting. Our experiments show that ``online" defenses that are based on the idea of minimizing the negative log likelihood of refusals -- with the defender having control of the loss function -- can effectively protect LLMs against RPAs. However, trying to defend model weights using ``offline" defenses that operate under the assumption that the defender has no control over the loss function are less effective in the face of RPAs. These findings show that attacks done using RL can be used to successfully undo safety alignment in open-weight LLMs and use them for malicious purposes.

The study of interactions between biomimetic membranes and micron-sized particles serves as a fundamental cornerstone for understanding various biological processes. Here, we aim at controlling the engulfment of microparticles in absence of any externally applied force. We investigate systems involving particles and lipid vesicles, where interactions are primarily mediated by electrostatics. Our attention is directed towards dissipative phenomena at the micron and nano scales occurring between a vesicle membrane and a particle, highlighting the influence of the particle penetration depth and the impact of substructures on particle friction. Our work is complemented by hydrodynamic simulations considering the impacts of the shape of the membrane wrapping the particle and the water gap separating the lipid bilayer membrane and the particle on the translational particle drag. Lastly, we explore a system of aqueous microdroplets in oil, stabilized by a single lipid layer and compare the friction experienced by a particle in contact with this monolayer to the friction observed for particles wrapped by lipid bilayers. Surprisingly, the particle drag is higher on the lipid monolayer than the bilayer, which can be explained by introducing a drag contribution due to the partial wetting dynamics of the particle at the monolayer interface.

We present AutoMode-ASR, a novel framework that effectively integrates multiple ASR systems to enhance the overall transcription quality while optimizing cost. The idea is to train a decision model to select the optimal ASR system for each segment based solely on the audio input before running the systems. We achieve this by ensembling binary classifiers determining the preference between two systems. These classifiers are equipped with various features, such as audio embeddings, quality estimation, and signal properties. Additionally, we demonstrate how using a quality estimator can further improve performance with minimal cost increase. Experimental results show a relative reduction in WER of 16.2%, a cost saving of 65%, and a speed improvement of 75%, compared to using a single-best model for all segments. Our framework is compatible with commercial and open-source black-box ASR systems as it does not require changes in model codes., Comment: SPECOM 2024 Conference

Bushfire is one of the major natural disasters that cause huge losses to livelihoods and the environment. Understanding and analyzing the severity of bushfires is crucial for effective management and mitigation strategies, helping to prevent the extensive damage and loss caused by these natural disasters. This study presents an in-depth analysis of bushfire severity in Australia over the last twelve years, combining remote sensing data and machine learning techniques to predict future fire trends. By utilizing Landsat imagery and integrating spectral indices like NDVI, NBR, and Burn Index, along with topographical and climatic factors, we developed a robust predictive model using XGBoost. The model achieved high accuracy, 86.13%, demonstrating its effectiveness in predicting fire severity across diverse Australian ecosystems. By analyzing historical trends and integrating factors such as population density and vegetation cover, we identify areas at high risk of future severe bushfires. Additionally, this research identifies key regions at risk, providing data-driven recommendations for targeted firefighting efforts. The findings contribute valuable insights into fire management strategies, enhancing resilience to future fire events in Australia. Also, we propose future work on developing a UAV-based swarm coordination model to enhance fire prediction in real-time and firefighting capabilities in the most vulnerable regions., Comment: 15 pages

We present in this paper a novel approach for 3D/2D intraoperative registration during neurosurgery via cross-modal inverse neural rendering. Our approach separates implicit neural representation into two components, handling anatomical structure preoperatively and appearance intraoperatively. This disentanglement is achieved by controlling a Neural Radiance Field's appearance with a multi-style hypernetwork. Once trained, the implicit neural representation serves as a differentiable rendering engine, which can be used to estimate the surgical camera pose by minimizing the dissimilarity between its rendered images and the target intraoperative image. We tested our method on retrospective patients' data from clinical cases, showing that our method outperforms state-of-the-art while meeting current clinical standards for registration. Code and additional resources can be found at https://maxfehrentz.github.io/style-ngp/., Comment: Accepted at MICCAI 2024

This study presents BanStereoSet, a dataset designed to evaluate stereotypical social biases in multilingual LLMs for the Bangla language. In an effort to extend the focus of bias research beyond English-centric datasets, we have localized the content from the StereoSet, IndiBias, and Kamruzzaman et. al.'s datasets, producing a resource tailored to capture biases prevalent within the Bangla-speaking community. Our BanStereoSet dataset consists of 1,194 sentences spanning 9 categories of bias: race, profession, gender, ageism, beauty, beauty in profession, region, caste, and religion. This dataset not only serves as a crucial tool for measuring bias in multilingual LLMs but also facilitates the exploration of stereotypical bias across different social categories, potentially guiding the development of more equitable language technologies in Bangladeshi contexts. Our analysis of several language models using this dataset indicates significant biases, reinforcing the necessity for culturally and linguistically adapted datasets to develop more equitable language technologies.

As the deployment of large language models (LLMs) expands, there is an increasing demand for personalized LLMs. One method to personalize and guide the outputs of these models is by assigning a persona -- a role that describes the expected behavior of the LLM (e.g., a man, a woman, an engineer). This study investigates whether an LLM's understanding of social norms varies across assigned personas. Ideally, the perception of a social norm should remain consistent regardless of the persona, since acceptability of a social norm should be determined by the region the norm originates from, rather than by individual characteristics such as gender, body size, or race. A norm is universal within its cultural context. In our research, we tested 36 distinct personas from 12 sociodemographic categories (e.g., age, gender, beauty) across four different LLMs. We find that LLMs' cultural norm interpretation varies based on the persona used and the norm interpretation also varies within a sociodemographic category (e.g., a fat person and a thin person as in physical appearance group) where an LLM with the more socially desirable persona (e.g., a thin person) interprets social norms more accurately than with the less socially desirable persona (e.g., a fat person). We also discuss how different types of social biases may contribute to the results that we observe., Comment: Preprint, Under Review

Homodyned K-distribution (HK-distribution) parameter estimation in quantitative ultrasound (QUS) has been recently addressed using Bayesian Neural Networks (BNNs). BNNs have been shown to significantly reduce computational time in speckle statistics-based QUS without compromising accuracy and precision. Additionally, they provide estimates of feature uncertainty, which can guide the clinician's trust in the reported feature value. The total predictive uncertainty in Bayesian modeling can be decomposed into epistemic (uncertainty over the model parameters) and aleatoric (uncertainty inherent in the data) components. By decomposing the predictive uncertainty, we can gain insights into the factors contributing to the total uncertainty. In this study, we propose a method to compute epistemic and aleatoric uncertainties for HK-distribution parameters ($\alpha$ and $k$) estimated by a BNN, in both simulation and experimental data. In addition, we investigate the relationship between the prediction error and both uncertainties, shedding light on the interplay between these uncertainties and HK parameters errors., Comment: 4 pages, 2 figures

Weakly supervised video anomaly detection (WS-VAD) is a crucial area in computer vision for developing intelligent surveillance systems. This system uses three feature streams: RGB video, optical flow, and audio signals, where each stream extracts complementary spatial and temporal features using an enhanced attention module to improve detection accuracy and robustness. In the first stream, we employed an attention-based, multi-stage feature enhancement approach to improve spatial and temporal features from the RGB video where the first stage consists of a ViT-based CLIP module, with top-k features concatenated in parallel with I3D and Temporal Contextual Aggregation (TCA) based rich spatiotemporal features. The second stage effectively captures temporal dependencies using the Uncertainty-Regulated Dual Memory Units (UR-DMU) model, which learns representations of normal and abnormal data simultaneously, and the third stage is employed to select the most relevant spatiotemporal features. The second stream extracted enhanced attention-based spatiotemporal features from the flow data modality-based feature by taking advantage of the integration of the deep learning and attention module. The audio stream captures auditory cues using an attention module integrated with the VGGish model, aiming to detect anomalies based on sound patterns. These streams enrich the model by incorporating motion and audio signals often indicative of abnormal events undetectable through visual analysis alone. The concatenation of the multimodal fusion leverages the strengths of each modality, resulting in a comprehensive feature set that significantly improves anomaly detection accuracy and robustness across three datasets. The extensive experiment and high performance with the three benchmark datasets proved the effectiveness of the proposed system over the existing state-of-the-art system.

Reliable models are dependable and provide predictions acceptable given basic domain knowledge. Therefore, it is critical to develop and deploy reliable models, especially for healthcare applications. However, Multiple Instance Learning (MIL) models designed for Whole Slide Images (WSIs) classification in computational pathology are not evaluated in terms of reliability. Hence, in this paper we compare the reliability of MIL models with three suggested metrics and use three region-wise annotated datasets. We find the mean pooling instance (MEAN-POOL-INS) model more reliable than other networks despite its naive architecture design and computation efficiency. The code to reproduce the results is accessible at https://github.com/tueimage/MILs'R .

In state-of-the-art optical lattice clocks, beyond-electric-dipole polarizability terms lead to a break-down of magic wavelength trapping. In this Letter, we report a novel approach to evaluate lattice light shifts, specifically addressing recent discrepancies in the atomic multipolarizability term between experimental techniques and theoretical calculations. We combine imaging and multi-ensemble techniques to evaluate lattice light shift atomic coefficients, leveraging comparisons in a dual-ensemble lattice clock to rapidly evaluate differential frequency shifts. Further, we demonstrate application of a running wave field to probe both the multipolarizability and hyperpolarizability coefficients, establishing a new technique for future lattice light shift evaluations., Comment: 17 pages, 6 figures

The SoccerNet 2024 challenges represent the fourth annual video understanding challenges organized by the SoccerNet team. These challenges aim to advance research across multiple themes in football, including broadcast video understanding, field understanding, and player understanding. This year, the challenges encompass four vision-based tasks. (1) Ball Action Spotting, focusing on precisely localizing when and which soccer actions related to the ball occur, (2) Dense Video Captioning, focusing on describing the broadcast with natural language and anchored timestamps, (3) Multi-View Foul Recognition, a novel task focusing on analyzing multiple viewpoints of a potential foul incident to classify whether a foul occurred and assess its severity, (4) Game State Reconstruction, another novel task focusing on reconstructing the game state from broadcast videos onto a 2D top-view map of the field. Detailed information about the tasks, challenges, and leaderboards can be found at https://www.soccer-net.org, with baselines and development kits available at https://github.com/SoccerNet., Comment: 7 pages, 1 figure

The proliferation of open Pre-trained Language Models (PTLMs) on model registry platforms like Hugging Face (HF) presents both opportunities and challenges for companies building products around them. Similar to traditional software dependencies, PTLMs continue to evolve after a release. However, the current state of release practices of PTLMs on model registry platforms are plagued by a variety of inconsistencies, such as ambiguous naming conventions and inaccessible model training documentation. Given the knowledge gap on current PTLM release practices, our empirical study uses a mixed-methods approach to analyze the releases of 52,227 PTLMs on the most well-known model registry, HF. Our results reveal 148 different naming practices for PTLM releases, with 40.87% of changes to model weight files not represented in the adopted name-based versioning practice or their documentation. In addition, we identified that the 52,227 PTLMs are derived from only 299 different base models (the modified original models used to create 52,227 PTLMs), with Fine-tuning and Quantization being the most prevalent modification methods applied to these base models. Significant gaps in release transparency, in terms of training dataset specifications and model card availability, still exist, highlighting the need for standardized documentation. While we identified a model naming practice explicitly differentiating between major and minor PTLM releases, we did not find any significant difference in the types of changes that went into either type of releases, suggesting that major/minor version numbers for PTLMs often are chosen arbitrarily. Our findings provide valuable insights to improve PTLM release practices, nudging the field towards more formal semantic versioning practices.

Human Activity Recognition (HAR) systems aim to understand human behaviour and assign a label to each action, attracting significant attention in computer vision due to their wide range of applications. HAR can leverage various data modalities, such as RGB images and video, skeleton, depth, infrared, point cloud, event stream, audio, acceleration, and radar signals. Each modality provides unique and complementary information suited to different application scenarios. Consequently, numerous studies have investigated diverse approaches for HAR using these modalities. This paper presents a comprehensive survey of the latest advancements in HAR from 2014 to 2024, focusing on machine learning (ML) and deep learning (DL) approaches categorized by input data modalities. We review both single-modality and multi-modality techniques, highlighting fusion-based and co-learning frameworks. Additionally, we cover advancements in hand-crafted action features, methods for recognizing human-object interactions, and activity detection. Our survey includes a detailed dataset description for each modality and a summary of the latest HAR systems, offering comparative results on benchmark datasets. Finally, we provide insightful observations and propose effective future research directions in HAR.

Traditional star formation subgrid models implemented in cosmological galaxy formation simulations, such as that of Springel & Hernquist (2003, hereafter SH03), employ adjustable parameters to satisfy constraints measured in the local Universe. In recent years, however, theory and spatially-resolved simulations of the turbulent, multiphase, star-forming ISM have begun to produce new first-principles models, which when fully developed can replace traditional subgrid prescriptions. This approach has advantages of being physically motivated and predictive rather than empirically tuned, and allowing for varying environmental conditions rather than being tied to local Universe conditions. As a prototype of this new approach, by combining calibrations from the TIGRESS numerical framework with the Pressure-Regulated Feedback-Modulated (PRFM) theory, simple formulae can be obtained for both the gas depletion time and an effective equation of state. Considering galaxies in TNG50, we compare the "native" simulation outputs with post-processed predictions from PRFM. At TNG50 resolution, the total midplane pressure is nearly equal to the total ISM weight, indicating that galaxies in TNG50 are close to satisfying vertical equilibrium. The measured gas scale height is also close to theoretical equilibrium predictions. The slopes of the effective equations of states are similar, but with effective velocity dispersion normalization from SH03 slightly larger than that from current TIGRESS simulations. Because of this and the decrease in PRFM feedback yield at high pressure, the PRFM model predicts shorter gas depletion times than the SH03 model at high densities and redshift. Our results represent a first step towards implementing new, numerically calibrated subgrid algorithms in cosmological galaxy formation simulations., Comment: 33 pages, 12 figures, Accepted for publication in ApJ. This is a Learning the Universe Publication. All codes and data used to produce this work can be found at the following $\href{https://github.com/sultan-hassan/tng50-post-processing-prfm}{GitHub \,Link.}$

We present a suite of six high-resolution chemo-dynamical simulations of isolated galaxies, spanning observed disk-dominated environments on the star-forming main sequence, as well as quenched, bulge-dominated environments. We compare and contrast the physics driving star formation and stellar feedback amongst the galaxies, with a view to modeling these processes in cosmological simulations. We find that the mass-loading of galactic outflows is coupled to the clustering of supernova explosions, which varies strongly with the rate of galactic rotation $\Omega = v_c/R$ via the Toomre length, leading to smoother gas disks in the bulge-dominated galaxies. This sets an equation of state in the star-forming gas that also varies strongly with $\Omega$, so that the bulge-dominated galaxies have higher mid-plane densities, lower velocity dispersions, and higher molecular gas fractions than their main sequence counterparts. The star formation rate in five out of six galaxies is independent of $\Omega$, and is consistent with regulation by the mid-plane gas pressure alone. In the sixth galaxy, which has the most centrally-concentrated bulge and thus the highest $\Omega$, we reproduce dynamical suppression of the star formation efficiency (SFE) in agreement with observations. This produces a transition away from pressure-regulated star formation., Comment: This is a Learning the Universe Publication, accepted for publication in ApJ. 29 pages, 14 figures, and analysis code at: https://github.com/sjeffreson/pressure_regulated_SF_analysis

Daily activity monitoring systems used in households provide vital information for health status, particularly with aging residents. Multiple approaches have been introduced to achieve such goals, typically obtrusive and non-obtrusive. Amongst the obtrusive approaches are the wearable devices, and among the non-obtrusive approaches are the movement detection systems, including motion sensors and thermal sensor arrays (TSAs). TSA systems are advantageous when preserving a person's privacy and picking his precise spatial location. In this study, human daily living activities were monitored day and night, constructing the corresponding activity time series and spatial probability distribution and employing a TSA system. The monitored activities are classified into two categories: sleeping and daily activity. Results showed the possibility of distinguishing between classes regardless of day and night. The obtained sleep activity duration was compared with previous research using the same raw data. Results showed that the duration of sleep activity, on average, was 9 hours/day, and daily life activity was 7 hours/day. The person's spatial probability distribution was determined using the bivariate distribution for the monitored location. In conclusion, the results showed that sleeping activity was dominant. Our study showed that TSAs were the optimum choice when monitoring human activity. Our proposed approach tackled limitations encountered by previous human activity monitoring systems, such as preserving human privacy while knowing his precise spatial location.

We propose in this paper a texture-invariant 2D keypoints descriptor specifically designed for matching preoperative Magnetic Resonance (MR) images with intraoperative Ultrasound (US) images. We introduce a matching-by-synthesis strategy, where intraoperative US images are synthesized from MR images accounting for multiple MR modalities and intraoperative US variability. We build our training set by enforcing keypoints localization over all images then train a patient-specific descriptor network that learns texture-invariant discriminant features in a supervised contrastive manner, leading to robust keypoints descriptors. Our experiments on real cases with ground truth show the effectiveness of the proposed approach, outperforming the state-of-the-art methods and achieving 80.35% matching precision on average., Comment: Accepted for publication at the International Workshop of Advances in Simplifying Medical UltraSound (ASMUS) at MICCAI 2024

Selecting the appropriate communication protocol is crucial for optimizing the performance, scalability, and user experience of web applications. In the diverse ecosystem of web technologies, various protocols like RESTful APIs, gRPC, WebSockets, and others serve distinct purposes. RESTful APIs are widely favored for their simplicity and stateless nature, making them ideal for standard CRUD operations. They offer a straightforward approach to interacting with resources over HTTP/1.1, providing broad compatibility and ease of integration across different platforms. However, in scenarios where applications require high efficiency and real-time communication, gRPC and WebSockets emerge as powerful alternatives. Each protocol comes with its strengths and limitations, influencing factors such as ease of implementation, performance under load, and support for complex data structures. RESTful APIs, while easy to use and widely supported, may introduce overhead due to their stateless nature and reliance on multiple HTTP/1.1 requests. In contrast, gRPC advanced features, while powerful, require a steeper learning curve and more sophisticated infrastructure. Similarly, WebSockets, while excellent for real-time applications, require careful management of persistent connections and security considerations. This paper explores the key considerations in choosing the right communication protocol, emphasizing the need to align technical choices with application requirements and user expectations. By understanding the unique attributes of each protocol, developers can make informed decisions that enhance the responsiveness and reliability of their web applications. The choice of protocol can significantly impact the user experience, scalability, and maintainability of the application, making it a critical decision in the web development process.

Transition metal dichalcogenides have shown great promise in the field of gas sensing due to their high catalytic activity and unique electronic properties. They can effectively interact with various gas molecules, making them suitable materials for high performance gas sensors. In this work, we have studied the sensing properties of nitrogen containing gases on different heterostructures using density functional studies. The result shows that NH3 and NOx exhibit weak electronic interactions with MoS/WTe and MoTe/WS heterostructures and strong electronic interactions are observed between NH3 and NOx molecules with MoS/IrO and MoS/TiO heterostructures., Comment: 31 pages, 10 figures

In non-Hermitian open quantum systems, such as polariton condensates, local tailoring of gains and losses opens up an interesting possibility to realize functional optical elements. Here, we demonstrate that deliberately introducing losses via a photonic defect, realized by reducing the quality factor of a DBR mirror locally within an ultrahigh-quality microcavity, may be utilized to create directed polariton currents towards the defect. We discuss the role of polariton-polariton interactions in the process and how to tailor the effective decay time of a polariton condensate via coupling to the defect. Our results highlight the far-reaching potential of non-Hermitian physics in polaritonics., Comment: 9 pages, 4 figures

Methane (CH4) is a potent greenhouse gas (GHG) with a short atmospheric half-life (~8.4 years) and a high short-term impact on global warming, significantly higher than CO2 (Kleinberg, 2020; Balcombe et al., 2018). Traditional metrics such as the 100-year Global Warming Potential (GWP100) obscure methane's short-term, negative climatic effects, potentially leading to inadequate policy responses (Kleinberg, 2020). This letter examines the limitations of GWP100 in capturing methane's true climate impact, explores alternative metrics, and discusses the implications of underreporting methane emissions. We highlight the necessity of adopting a more immediate perspective on methane to accelerate climate emergency action, while noting the adverse effects of the rapid growth rate of methane emissions on reduction efforts. Additionally, we hope that in the immediate future, during COP29, policymakers will adopt actions that give appropriate attention to methane's short-term warming potential to dramatically reduce emissions and address the immediate climate crisis., Comment: 14 pages, 3 figures, to be submitted to AGU Letters

We introduce the notion of a conditional encryption scheme as an extension of public key encryption. In addition to the standard public key algorithms ($\mathsf{KG}$, $\mathsf{Enc}$, $\mathsf{Dec}$) for key generation, encryption and decryption, a conditional encryption scheme for a binary predicate $P$ adds a new conditional encryption algorithm $\mathsf{CEnc}$. The conditional encryption algorithm $c=\mathsf{CEnc}_{pk}(c_1,m_2,m_3)$ takes as input the public encryption key $pk$, a ciphertext $c_1 = \mathsf{Enc}_{pk}(m_1)$ for an unknown message $m_1$, a control message $m_2$ and a payload message $m_3$ and outputs a conditional ciphertext $c$. Intuitively, if $P(m_1,m_2)=1$ then the conditional ciphertext $c$ should decrypt to the payload message $m_3$. On the other hand if $P(m_1,m_2) = 0$ then the ciphertext should not leak any information about the control message $m_2$ or the payload message $m_3$ even if the attacker already has the secret decryption key $sk$. We formalize the notion of conditional encryption secrecy and provide concretely efficient constructions for a set of predicates relevant to password typo correction. Our practical constructions utilize the Paillier partially homomorphic encryption scheme as well as Shamir Secret Sharing. We prove that our constructions are secure and demonstrate how to use conditional encryption to improve the security of personalized password typo correction systems such as TypTop. We implement a C++ library for our practically efficient conditional encryption schemes and evaluate the performance empirically. We also update the implementation of TypTop to utilize conditional encryption for enhanced security guarantees and evaluate the performance of the updated implementation., Comment: Full version of CCS 2024 paper with the same title

Cyber-physical control systems are critical infrastructures designed around highly responsive feedback loops that are measured and manipulated by hundreds of sensors and controllers. Anomalous data, such as from cyber-attacks, greatly risk the safety of the infrastructure and human operators. With recent advances in the quantum computing paradigm, the application of quantum in anomaly detection can greatly improve identification of cyber-attacks in physical sensor data. In this paper, we explore the use of strong pre-processing methods and a quantum-hybrid Support Vector Machine (SVM) that takes advantage of fidelity in parameterized quantum circuits to efficiently and effectively flatten extremely high dimensional data. Our results show an F-1 Score of 0.86 and accuracy of 87% on the HAI CPS dataset using an 8-qubit, 16-feature quantum kernel, performing equally to existing work and 14% better than its classical counterpart., Comment: 6 pages, 5 figures, 2 tables, under ISVLSI 2024 proceedings

In this article, we investigate symmetric designs admitting a flag-transitive and point-primitive affine automorphism group. We prove that if an automorphism group $G$ of a symmetric $(v,k,\lambda)$ design with $\lambda$ prime is point-primitive of affine type, then $G=2^{6}{:}\mathrm{S}_{6}$ and $(v,k,\lambda)=(16,6,2)$, or $G$ is a subgroup of $\mathrm{A\Gamma L}_{1}(q)$ for some odd prime power $q$. In conclusion, we present a classification of flag-transitive and point-primitive symmetric designs with $\lambda$ prime, which says that such an incidence structure is a projective space $\mathrm{PG}(n,q)$, it has parameter set $(15,7,3)$, $(7, 4, 2)$, $(11, 5, 2)$, $(11, 6, 2)$, $(16,6,2)$ or $(45, 12, 3)$, or $v=p^d$ where $p$ is an odd prime and the automorphism group is a subgroup of $\mathrm{A\Gamma L}_{1}(q)$.

Scale-free networks are ubiquitous in social, biological and technological networked systems. Dynamic Scale-free networks and their synchronizations are important to understand and predict the behavior of social, biological and technological networked systems. In this research, computational experiments have been conducted to understand the sustainability of scale-free properties during the time of synchronizations in dynamic scale-free networks. Two synchronization phenomena which are synchronization based on states of nodes with coupling configuration matrix and synchronization based on states of nodes with network centralities have been implemented for the synchronization in dynamic scale-free networks. In experiments, dynamic scale-free networks have been generated with a network generation algorithm and analyzed to understand the fluctuation from the scale-free properties in their phases during the time of synchronizations.

We present a quantum protocol which securely and implicitly implements a random shuffle to realize differential privacy in the shuffle model. The shuffle model of differential privacy amplifies privacy achievable via local differential privacy by randomly permuting the tuple of outcomes from data contributors. In practice, one needs to address how this shuffle is implemented. Examples include implementing the shuffle via mix-networks, or shuffling via a trusted third-party. These implementation specific issues raise non-trivial computational and trust requirements in a classical system. We propose a quantum version of the protocol using entanglement of quantum states and show that the shuffle can be implemented without these extra requirements. Our protocol implements k-ary randomized response, for any value of k > 2, and furthermore, can be efficiently implemented using fault-tolerant computation.

In a platoon, multiple autonomous vehicles engage in data exchange to navigate toward their intended destination. Within this network, a designated leader shares its status information with followers based on a predefined communication graph. However, these vehicles are susceptible to disturbances, leading to deviations from their intended routes. Denial-of-service (DoS) attacks, a significant type of cyber threat, can impact the motion of the leader. This paper addresses the destabilizing effects of DoS attacks on platoons and introduces a novel vehicular resilient control strategy to restore stability. Upon detecting and measuring a DoS attack, modeled with a time-varying delay, the proposed method initiates a process to retrieve the attacked leader. Through a newly designed switching system, the attacked leader transitions to a follower role, and a new leader is identified within a restructured platoon configuration, enabling the platoon to maintain consensus. Specifically, in the event of losing the original leader due to a DoS attack, the remaining vehicles do experience destabilization. They adapt their motions as a cohesive network through a distributed resilient controller. The effectiveness of the proposed approach is validated through an illustrative case study, showing its applicability in real-world scenarios., Comment: 9 pages

Specifically focusing on the landscape of abstractive text summarization, as opposed to extractive techniques, this survey presents a comprehensive overview, delving into state-of-the-art techniques, prevailing challenges, and prospective research directions. We categorize the techniques into traditional sequence-to-sequence models, pre-trained large language models, reinforcement learning, hierarchical methods, and multi-modal summarization. Unlike prior works that did not examine complexities, scalability and comparisons of techniques in detail, this review takes a comprehensive approach encompassing state-of-the-art methods, challenges, solutions, comparisons, limitations and charts out future improvements - providing researchers an extensive overview to advance abstractive summarization research. We provide vital comparison tables across techniques categorized - offering insights into model complexity, scalability and appropriate applications. The paper highlights challenges such as inadequate meaning representation, factual consistency, controllable text summarization, cross-lingual summarization, and evaluation metrics, among others. Solutions leveraging knowledge incorporation and other innovative strategies are proposed to address these challenges. The paper concludes by highlighting emerging research areas like factual inconsistency, domain-specific, cross-lingual, multilingual, and long-document summarization, as well as handling noisy data. Our objective is to provide researchers and practitioners with a structured overview of the domain, enabling them to better understand the current landscape and identify potential areas for further research and improvement., Comment: 9 Tables, 7 Figures

The development of satellite constellation applications is rapidly advancing due to increasing user demands, reduced operational costs, and technological advancements. However, a significant gap in the existing literature concerns reconfiguration and retasking issues within satellite constellations, which is the primary focus of our research. In this work, we critically assess the impact of satellite failures on constellation performance and the associated task requirements. To facilitate this analysis, we introduce a system modeling approach for GPS satellite constellations, enabling an investigation into performance dynamics and task distribution strategies, particularly in scenarios where satellite failures occur during mission-critical operations. Additionally, we introduce reinforcement learning (RL) techniques, specifically Q-learning, Policy Gradient, Deep Q-Network (DQN), and Proximal Policy Optimization (PPO), for managing satellite constellations, addressing the challenges posed by reconfiguration and retasking following satellite failures. Our results demonstrate that DQN and PPO achieve effective outcomes in terms of average rewards, task completion rates, and response times., Comment: Accepted for publication in the IEEE Military Communications Conference (IEEE MILCOM 2024)

Background: This study aimed to evaluate and compare the performance of classical machine learning models (CMLs) and large language models (LLMs) in predicting mortality associated with COVID-19 by utilizing a high-dimensional tabular dataset. Materials and Methods: We analyzed data from 9,134 COVID-19 patients collected across four hospitals. Seven CML models, including XGBoost and random forest (RF), were trained and evaluated. The structured data was converted into text for zero-shot classification by eight LLMs, including GPT-4 and Mistral-7b. Additionally, Mistral-7b was fine-tuned using the QLoRA approach to enhance its predictive capabilities. Results: Among the CML models, XGBoost and RF achieved the highest accuracy, with F1 scores of 0.87 for internal validation and 0.83 for external validation. In the LLM category, GPT-4 was the top performer with an F1 score of 0.43. Fine-tuning Mistral-7b significantly improved its recall from 1% to 79%, resulting in an F1 score of 0.74, which was stable during external validation. Conclusion: While LLMs show moderate performance in zero-shot classification, fine-tuning can significantly enhance their effectiveness, potentially aligning them closer to CML models. However, CMLs still outperform LLMs in high-dimensional tabular data tasks., Comment: Code is available at: https://github.com/mohammad-gh009/Large-Language-Models-vs-Classical-Machine-learning and https://github.com/Sdamirsa/Tehran_COVID_Cohort. The datasets are available from the corresponding author on reasonable request (sdamirsa@ymail.com)

This paper describes the corrections made to the FLORES evaluation (dev and devtest) dataset for four African languages, namely Hausa, Northern Sotho (Sepedi), Xitsonga and isiZulu. The original dataset, though groundbreaking in its coverage of low-resource languages, exhibited various inconsistencies and inaccuracies in the reviewed languages that could potentially hinder the integrity of the evaluation of downstream tasks in natural language processing (NLP), especially machine translation. Through a meticulous review process by native speakers, several corrections were identified and implemented, improving the dataset's overall quality and reliability. For each language, we provide a concise summary of the errors encountered and corrected, and also present some statistical analysis that measure the difference between the existing and corrected datasets. We believe that our corrections enhance the linguistic accuracy and reliability of the data and, thereby, contributing to more effective evaluation of NLP tasks involving the four African languages.

Public transportation systems are experiencing an increase in commuter traffic. This increase underscores the need for resilience strategies to manage unexpected service disruptions, ensuring rapid and effective responses that minimize adverse effects on stakeholders and enhance the system's ability to maintain essential functions and recover quickly. This study aims to explore the management of public transport disruptions through resilience as a service (RaaS) strategies, developing an optimization model to effectively allocate resources and minimize the cost for operators and passengers. The proposed model includes multiple transportation options, such as buses, taxis, and automated vans, and evaluates them as bridging alternatives to rail-disrupted services based on factors such as their availability, capacity, speed, and proximity to the disrupted station. This ensures that the most suitable vehicles are deployed to maintain service continuity. Applied to a case study in the Ile de France region, Paris and suburbs, complemented by a microscopic simulation, the model is compared to existing solutions such as bus bridging and reserve fleets. The results highlight the model's performance in minimizing costs and enhancing stakeholder satisfaction, optimizing transport management during disruptions.

Deep supervised learning models require high volume of labeled data to attain sufficiently good results. Although, the practice of gathering and annotating such big data is costly and laborious. Recently, the application of self supervised learning (SSL) in vision tasks has gained significant attention. The intuition behind SSL is to exploit the synchronous relationships within the data as a form of self-supervision, which can be versatile. In the current big data era, most of the data is unlabeled, and the success of SSL thus relies in finding ways to utilize this vast amount of unlabeled data available. Thus it is better for deep learning algorithms to reduce reliance on human supervision and instead focus on self-supervision based on the inherent relationships within the data. With the advent of ViTs, which have achieved remarkable results in computer vision, it is crucial to explore and understand the various SSL mechanisms employed for training these models specifically in scenarios where there is limited labelled data available. In this survey, we develop a comprehensive taxonomy of systematically classifying the SSL techniques based upon their representations and pre-training tasks being applied. Additionally, we discuss the motivations behind SSL, review popular pre-training tasks, and highlight the challenges and advancements in this field. Furthermore, we present a comparative analysis of different SSL methods, evaluate their strengths and limitations, and identify potential avenues for future research., Comment: 34 Pages, 5 Figures, 7 Tables

We consider the Chern-Simons term coupled to the inflaton in the Palatini formulation of general relativity. In contrast to the metric formulation, here the Chern-Simons term affects also the background evolution. We approximately solve for the connection, insert it back into the action, and reduce the order of the equations to obtain an effective theory in the gradient approximation. We consider three cases: when the connection is unconstrained, and when non-metricity or torsion is put to zero. In the first two cases, the inflaton kinetic term is modified with a term proportional to the square of the potential. For polynomial potentials dominated by the highest power of the field, the Chern-Simons term solves the problem that higher order corrections spoil the flatness of the potential. For Higgs inflation, the tensor-to-scalar ratio can as large as the current observational bound, and the non-minimal coupling to the Ricci scalar can be as small as in the metric case. The Palatini contribution cures the known instability of the tensor modes due the Chern-Simons term in the metric formulation., Comment: 15+4 pages, no figures

Knowledge graphs (KGs) have become ubiquitous publicly available knowledge sources, and are nowadays covering an ever increasing array of domains. However, not all knowledge represented is useful or pertaining when considering a new application or specific task. Also, due to their increasing size, handling large KGs in their entirety entails scalability issues. These two aspects asks for efficient methods to extract subgraphs of interest from existing KGs. To this aim, we introduce KGPrune, a Web Application that, given seed entities of interest and properties to traverse, extracts their neighboring subgraphs from Wikidata. To avoid topical drift, KGPrune relies on a frugal pruning algorithm based on analogical reasoning to only keep relevant neighbors while pruning irrelevant ones. The interest of KGPrune is illustrated by two concrete applications, namely, bootstrapping an enterprise KG and extracting knowledge related to looted artworks., Comment: Accepted as a demo paper at ECAI 2024

Surface electromyography (EMG) serves as a pivotal tool in hand gesture recognition and human-computer interaction, offering a non-invasive means of signal acquisition. This study presents a novel methodology for classifying hand gestures using EMG signals. To address the challenges associated with feature extraction where, we explored 23 distinct morphological, time domain and frequency domain feature extraction techniques. However, the substantial size of the features may increase the computational complexity issues that can hinder machine learning algorithm performance. We employ an efficient feature selection approach, specifically an extra tree classifier, to mitigate this. The selected potential feature fed into the various machine learning-based classification algorithms where our model achieved 97.43\% accuracy with the KNN algorithm and selected feature. By leveraging a comprehensive feature extraction and selection strategy, our methodology enhances the accuracy and usability of EMG-based hand gesture recognition systems. The higher performance accuracy proves the effectiveness of the proposed model over the existing system. \keywords{EMG signal, machine learning approach, hand gesture recognition.

Background: Data quality is vital in software analytics, particularly for machine learning (ML) applications like software defect prediction (SDP). Despite the widespread use of ML in software engineering, the effect of data quality antipatterns on these models remains underexplored. Objective: This study develops a taxonomy of ML-specific data quality antipatterns and assesses their impact on software analytics models' performance and interpretation. Methods: We identified eight types and 14 sub-types of ML-specific data quality antipatterns through a literature review. We conducted experiments to determine the prevalence of these antipatterns in SDP data (RQ1), assess how cleaning order affects model performance (RQ2), evaluate the impact of antipattern removal on performance (RQ3), and examine the consistency of interpretation from models built with different antipatterns (RQ4). Results: In our SDP case study, we identified nine antipatterns. Over 90% of these overlapped at both row and column levels, complicating cleaning prioritization and risking excessive data removal. The order of cleaning significantly impacts ML model performance, with neural networks being more resilient to cleaning order changes than simpler models like logistic regression. Antipatterns such as Tailed Distributions and Class Overlap show a statistically significant correlation with performance metrics when other antipatterns are cleaned. Models built with different antipatterns showed moderate consistency in interpretation results. Conclusion: The cleaning order of different antipatterns impacts ML model performance. Five antipatterns have a statistically significant correlation with model performance when others are cleaned. Additionally, model interpretation is moderately affected by different data quality antipatterns.

The ultrathin structure and efficient spin dynamics of two-dimensional (2D) antiferromagnetic (AFM) materials hold unprecedented opportunities for ultrafast memory devices, artificial intelligence circuits, and novel computing technology. For example, chromium thiophosphate (CrPS4) is one of the most promising 2D A-type AFM materials due to its robust stability in diverse environmental conditions and net out-of-plane magnetic moment in each layer, attributed to anisotropy in crystal axes (a and b). However, their net zero magnetic moment poses a challenge for detecting the Neel state that is used to encode information. In this study, we demonstrate the detection of the Neel vector by detecting the magnetic order of the surface layer by employing defects in tungsten diselenide (WSe2). These defects are ideal candidates for optically active transducers to probe the magnetic order due to their narrow linewidth and high susceptibility to magnetic fields. We observed spin-polarized charge transfer in the heterostructure of bulk CrPS4 and single-layer WSe2 indicating type-II band alignment as supported by density functional theory (DFT) calculations. In the A-type AFM regime, the intensity of both right-handed and left-handed circularly polarized light emanating from the sample remains constant as a function of the applied magnetic field, indicating a constant polarized transition behavior. Our results showcase a new approach to optically characterizing the magnetic states of 2D bulk AFM material, highlighting avenues for future research and technological applications.

The rise of computer vision applications in the real world puts the security of the deep neural networks at risk. Recent works demonstrate that convolutional neural networks are susceptible to adversarial examples - where the input images look similar to the natural images but are classified incorrectly by the model. To provide a rebuttal to this problem, we propose a new method to build robust deep neural networks against adversarial attacks by reformulating the saddle point optimization problem in \cite{madry2017towards}. Our proposed method offers significant resistance and a concrete security guarantee against multiple adversaries. The goal of this paper is to act as a stepping stone for a new variation of deep learning models which would lead towards fully robust deep learning models.

Ultrasound plane wave imaging is a cutting-edge technique that enables high frame-rate imaging. However, one challenge associated with high frame-rate ultrasound imaging is the high noise associated with them, hindering their wider adoption. Therefore, the development of a denoising method becomes imperative to augment the quality of plane wave images. Drawing inspiration from Denoising Diffusion Probabilistic Models (DDPMs), our proposed solution aims to enhance plane wave image quality. Specifically, the method considers the distinction between low-angle and high-angle compounding plane waves as noise and effectively eliminates it by adapting a DDPM to beamformed radiofrequency (RF) data. The method underwent training using only 400 simulated images. In addition, our approach employs natural image segmentation masks as intensity maps for the generated images, resulting in accurate denoising for various anatomy shapes. The proposed method was assessed across simulation, phantom, and in vivo images. The results of the evaluations indicate that our approach not only enhances image quality on simulated data but also demonstrates effectiveness on phantom and in vivo data in terms of image quality. Comparative analysis with other methods underscores the superiority of our proposed method across various evaluation metrics. The source code and trained model will be released along with the dataset at: http://code.sonography.ai

Weight-preserving model editing techniques heavily rely on the scoping mechanism that decides when to apply an edit to the base model. These scoping mechanisms utilize distance functions in the representation space to ascertain the scope of the edit. In this work, we show that distance-based scoping functions grapple with lexical biases leading to issues such as misfires with irrelevant prompts that share similar lexical characteristics. To address this problem, we introduce, Projector Editor Networks for Model Editing (PENME),is a model editing approach that employs a compact adapter with a projection network trained via a contrastive learning objective. We demonstrate the efficacy of PENME in achieving superior results while being compute efficient and flexible to adapt across model architectures.