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Objectives: Lung cancer poses a significant global health challenge, necessitating improved prognostic methods for personalized treatment. This study introduces a censor-aware semi-supervised learning (SSL) framework that integrates clinical and imaging data, addressing biases in traditional models handling censored data. Methods: We analyzed clinical, PET and CT data from 199 lung cancer patients from public and local data respositories, focusing on overall survival (OS) time as the primary outcome. Handcrafted (HRF) and Deep Radiomics features (DRF) were extracted after preprocessing using ViSERA software and were combined with clinical features (CF). Feature dimensions were optimized using Principal Component Analysis (PCA), followed by the application of supervised learning (SL) and SSL. SSL incorporated pseudo-labeling of censored data to improve performance. Seven regressors and three hazard ratio survival analysis (HRSA) algorithms were optimized using five-fold cross-validation, grid search and external test bootstrapping. Results: For PET HRFs, SSL reduced the mean absolute error (MAE) by 26.5%, achieving 1.55 years with PCA+decision tree regression, compared to SL's 2.11 years with PCA+KNNR (p<0.05). Combining HRFs (CT_HRF) and DRFs from CT images using SSL+PCA+KNNR achieved an MAE of 2.08 years, outperforming SL's 2.26 years by 7.96% (p<0.05). In HRSA, CT_HRF applied to PCA+Component Wise Gradient Boosting Survival Analysis achieved an external c-index of 0.65, effectively differentiating high- and low-risk groups. Conclusions: We demonstrated that the SSL strategy significantly outperforms SL across PET, CT, and CF. As such, censor-aware SSL applied to HRFs from PET images significantly improved survival prediction performance by 26.5% compared to the SL approach., Comment: 11 pages, 4 Figures and 4 Tables

Liquid metal (LM)-based composites hold promise for soft electronics due to their high conductivity and fluidic nature. However, the presence of {\alpha}_Ga2O3 and GaOOH layers around LM droplets impairs conductivity and performance. We tackle this issue by replacing the oxide layer with conductive silver (Ag) using an ultrasonic_assisted galvanic replacement reaction. The Ag_coated nanoparticles form aggregated, porous microparticles that are mixed with styrene_isoprene_styrene (SIS) polymers, resulting in a digitally printable composite with superior electrical conductivity and electromechanical properties compared to conventional fillers. Adding more LM enhances these properties further. The composite achieves EMI shielding effectiveness (SE) exceeding 75 dB in the X_band frequency range, even at 200 per cent strain, meeting stringent military and medical standards. It is applicable in wireless communications and Bluetooth signal blocking and as a thermal interface material (TIM). Additionally, we highlight its recyclability using a biodegradable solvent, underscoring its eco_friendly potential. This composite represents a significant advancement in stretchable electronics and EMI shielding, with implications for wearable and bioelectronic applications.

Despite advances in soft, sticker_like electronics, few efforts have dealt with the challenge of electronic waste. Here, this is addressed by introducing an eco friendly conductive ink for thin_film circuitry composed of silver flakes and a water_based polyurethane dispersion. This ink uniquely combines high electrical conductivity (1.6 x 105 S m_1), high resolution digital printability, robust adhesion for microchip integration, mechanical resilience, and recyclability. Recycling is achieved with an ecologically friendly processing method to decompose the circuits into constituent elements and recover the conductive ink with a decrease of only 2.4 per cent in conductivity. Moreover, adding liquid metal enables stretchability of up to 200 per cent strain, although this introduces the need for more complex recycling steps. Finally, on_skin electrophysiological monitoring biostickers along with a recyclable smart package with integrated sensors for monitoring safe storage of perishable foods are demonstrated.

Energy storage devices with liquid_metal electrodes have attracted interest in recent years due to their potential for mechanical resilience, self_healing, dendrite_free operation, and fast reaction kinetics. Gallium alloys like Eutectic Gallium Indium (EGaIn) are appealing due to their low melting point and high theoretical specific capacity. However, EGaIn electrodes are unstable in highly alkaline electrolytes due to Gallium oxide dissolution. In this letter, this bottleneck is addressed by introducing chemically stable films in which nanoscale droplets of EGaIn are coated with trace amounts of graphene oxide (GO). It is demonstrated that a GO to EGaIn weight ratio as low as 0.01 provides enough protection for a thin film formed by GO EGaIn nanocomposite against significantly acidic or alkaline environments (pH 1-14). It is shown that GO coating significantly enhances the surface stability in such environments, thus improving the energy storage capacity by over 10x. Microstructural analysis confirms GO EGaIn composite stability and enhanced electrochemical performance. Utilizing this, a thin film supercapacitor is fabricated. Results indicate that when coating the EGaIn with GO to EGaIn ratio of 0.001 the areal capacitance improves by 10 times, reaching 20.02 mF cm_2. This breakthrough paves the way for advanced liquid metal-based thin film electrodes, promising significant improvements in energy storage applications.

In this paper, we calculate the unit groups and the $2$-class numbers of the fields $ \mathbb{K}= \mathbb{Q}(\sqrt{2}, \sqrt{p_1}, \sqrt{p_2})$ and $ \mathbb{L}= \mathbb{Q}( \sqrt{-1},\sqrt{2}, \sqrt{p_1}, \sqrt{p_2})$, where $p_1$ and $p_2$ are two prime numbers satisfying $p_1\equiv p_2 \equiv 1 \pmod{4}$., Comment: 36 pages

Beamforming plays a crucial role in millimeter wave (mmWave) communication systems to mitigate the severe attenuation inherent to this spectrum. However, the use of large active antenna arrays in conventional architectures often results in high implementation costs and excessive power consumption, limiting their practicality. As an alternative, deploying large arrays at transceivers using passive devices, such as reconfigurable intelligent surfaces (RISs), offers a more cost-effective and energy-efficient solution. In this paper, we investigate a promising base station (BS) architecture that integrates a beyond diagonal RIS (BD-RIS) within the BS to enable passive beamforming. By utilizing Takagi's decomposition and leveraging the effective beamforming vector, the RIS profile can be designed to enable passive beamforming directed toward the target. Through the beamforming analysis, we reveal that BD-RIS provides robust beamforming performance across various system configurations, whereas the traditional diagonal RIS (D-RIS) exhibits instability with increasing RIS size and decreasing BS-RIS separation-two critical factors in optimizing RIS-assisted systems. Comprehensive computer simulation results across various aspects validate the superiority of the proposed BS-integrated BD-RIS over conventional D-RIS architectures, showcasing performance comparable to active analog beamforming antenna arrays., Comment: 13 pages, 9 figures, submitted to IEEE

Plug-and-play approaches to solving inverse problems such as restoration and super-resolution have recently benefited from Diffusion-based generative priors for natural as well as medical images. However, solutions often use the standard albeit computationally intensive route of training and inferring with the whole image on the diffusion prior. While patch-based approaches to evaluating diffusion priors in plug-and-play methods have received some interest, they remain an open area of study. In this work, we explore the feasibility of the usage of patches for training and inference of a diffusion prior on MRI images. We explore the minor adaptation necessary for artifact avoidance, the performance and the efficiency of memory usage of patch-based methods as well as the adaptability of whole image training to patch-based evaluation - evaluating across multiple plug-and-play methods, tasks and datasets., Comment: Accepted at IEEE International Symposium for Biomedical Imaging (ISBI) 2025

Studying the interstellar medium in nearby starbursts is essential for understanding the physical mechanisms driving these objects, thought to resemble young star-forming galaxies. This study aims to analyze the physical properties of the first spatially-resolved multi-wavelength SED of an extragalactic source, spanning six decades in frequency (from near-UV to cm wavelengths) at an angular resolution of 3$^{\prime\prime}$ (51 pc at the distance of NGC,253). We focus on the central molecular zone (CMZ) of NGC,253, which contains giant molecular clouds (GMCs) responsible for half of the galaxy's star formation. We use archival data, spanning optical to centimeter wavelengths, to compute SEDs with the GalaPy and CIGALE codes for validation, and analyze stellar optical spectra with the \textsc{starlight} code. Our results show significant differences between central and external GMCs in terms of stellar and dust masses, star formation rates (SFRs), and bolometric luminosities. We identify the best SFR tracers as radio continuum bands at 33 GHz, radio recombination lines, and the total infrared luminosity (L$_{\rm IR}$; 8-1000$\mu$m), as well as 60$\mu$m IR emission. BPT and WHAN diagrams indicate shock signatures in NGC~253's nuclear region, associating it with AGN/star-forming hybrids, though the AGN fraction is negligible ($\leq$7.5%). Our findings show significant heterogeneity in the CMZ, with central GMCs exhibiting higher densities, SFRs, and dust masses compared to external GMCs. We confirm that certain centimeter photometric bands can reliably estimate global SFR at GMC scales., Comment: Submitted to A&A. 33 pages (20 main text), 20 figures (13 main text)

A comprehensive study on thermomechanical processing of pure Mg was conducted through sequential hot extrusion, hot rolling, and cold drawing operations. Three different extrusion ratios (6:1, 25:1, and 39:1) were investigated at 350{\deg}C, revealing that 39:1 ratio produced an optimal bimodal grain structure with beneficial twin morphology. Subsequently, hot rolling experiments were performed at varying linear speeds (26- and 130-mm s-1) and interpass annealing times (2.5 and 10 minutes). Results demonstrated that higher rolling speeds led to finer microstructure, while longer interpass annealing times resulted in reduced twin fraction and more inhomogeneous microstructure. The processed material was then subjected to cold drawing with approximately 12% true strain per pass. Different annealing conditions (275{\deg}C and 375{\deg}C for 2.5-10 minutes) between drawing passes were evaluated. Analysis showed that annealing at 375{\deg}C for 2.5-5 minutes provided optimal softening for subsequent deformation. Fracture analysis revealed a mixed ductile-brittle behavior, with twin-matrix interfaces serving as preferred crack propagation paths This study establishes optimal processing parameters for pure Mg wire production, highlighting the critical role of twin characteristics and restoration processes in determining material formability during multi-step thermomechanical processing., Comment: 13 International Conference on Materials engineering and Metallurgy-Karaj, Iran

We study the instability properties of Nesterov's ODE in non-conservative settings, where the driving term is not necessarily the gradient of a potential function. While convergence properties under Nesterov's ODE are well-characterized for optimization settings with gradient-based driving terms, we show that the presence of arbitrarily small non-conservative terms can lead to instability, a phenomenon previously observed empirically via numerical studies in optimization and game-theoretic problems. Our instability analysis combines multi-time scale techniques, such as averaging via variations of constants formula, and Floquet Theory, focusing on systems where the vector field is linear and its Helmholtz decomposition reveals a non-vanishing non-conservative component. To stabilize the dynamics under non-vanishing non-conservative components, we study a regularization mechanism based on restarting. The resulting system is a hybrid dynamical system that mirrors Nesterov's ODE during intervals of flow, and implements resets of the momentum state through discrete periodic jumps. For this hybrid system, we establish novel explicit bounds on the resetting period that ensure the decrease of a suitable Lyapunov function, guaranteeing not only stability but also "accelerated" convergence rates under suitable smoothness and strong monotonicity properties on the driving term. Numerical simulations support our theoretical results.

We have performed a study of the orbital properties of seven eclipsing cataclysmic variable (CV) binary systems by analyzing photometric time series from the Transiting Exoplanet Survey Satellite (TESS). We employed Python code to determine the eclipse epochs and orbital periods for each system, and constructed O-C diagrams from observed and predicted eclipse epochs. By analyzing the O-C diagrams of our target CVs, we have constrained values for changes in orbital period with time. Our targets include a sample of sources from each class of non-magnetic, eclipsing CVs: dwarf novae variables, Z Cam type, and U Gem subclasses. We include in our study classical novae variables, nova-like variables (including the VY Scl and UX UMa subclasses), and recurrent novae variable stars. We approached this project with goals of developing time series analysis techniques for future undergraduate-level studies of eclipsing CVs, and how they may contribute to the understanding of their orbital evolution., Comment: 12 pages, 24 figures, submitted to JAAVSO

The present study focuses on the mesonic potential contributions to the Lagrangian of the extended linear-sigma model (eLSM) for scalar and pseudoscalar meson fields across various quark flavors. The present study focuses on the low-energy phenomenology associated with quantum chromodynamics (QCD), where mesons and their interactions serve as the pertinent degrees of freedom, rather than the fundamental constituents of quarks and gluons. Given that SU(4) configurations are completely based on SU(3) configurations, the possible relationships between meson states in SU(3) and those in SU(4) are explored at finite temperature. Meson states, which are defined by distinct chiral properties, are grouped according to their orbital angular momentum $J$, parity $P$, and charge conjugation $C$. Consequently, this organization yields scalar mesons with quantum numbers $J^{PC}=0^{++}$, pseudoscalar mesons with $J^{PC}=0^{-+}$, vector mesons with $J^{PC}=1^{--}$, and axialvector mesons with $J^{PC}=1^{++}$. We accomplished the derivation of analytical expressions for a total of seventeen noncharmed meson states and twenty-nine charmed meson states so that an analytical comparison of the noncharmed and charmed meson states at different temperatures becomes feasible and the contributions SU(3) and SU(4) configurations can be estimated, analytically., Comment: 18 pages, no figure, no table, accepted for publication in MDPI Particles

Large Language Models (LLMs) have made notable progress in mathematical reasoning, yet they often rely on single-paradigm reasoning that limits their effectiveness across diverse tasks. In this paper, we introduce Chain-of-Reasoning (CoR), a novel unified framework that integrates multiple reasoning paradigms--Natural Language Reasoning (NLR), Algorithmic Reasoning (AR), and Symbolic Reasoning (SR)--to enable synergistic collaboration. CoR generates multiple potential answers using different reasoning paradigms and synthesizes them into a coherent final solution. We propose a Progressive Paradigm Training (PPT) strategy that allows models to progressively master these paradigms, culminating in the development of CoR-Math-7B. Experimental results demonstrate that CoR-Math-7B significantly outperforms current SOTA models, achieving up to a 41.0% absolute improvement over GPT-4 in theorem proving tasks and a 7.9% improvement over RL-based methods in arithmetic tasks. These results showcase the enhanced mathematical comprehensive ability of our model, achieving significant performance gains on specific tasks and enabling zero-shot generalization across tasks.

Over the past years, rapid progress has been made on soft-matter electronics for wearable and implantable devices, for bioelectronics and optogenetics. Liquid Metal (LM) based electronics were especially popular, due to their long-term durability, when subject to repetitive strain cycles. However, one major limitation has been the need for tethering bioelectronics circuits to external power, or the use of rigid bulky batteries. This has motivated a growing interest in wireless energy transfer, which demands circuit miniaturization. However, miniaturization of LM circuits is challenging due to low LM-substrate adhesion, LM smearing, and challenges on microchip-interfacing. In this article, we address these challenges by high-resolution laser-assisted micropatterning of biphasic LM composites and vapor-assisted LM microchip soldering. Through development of a search algorithm for optimization of the biphasic ink coil performance, we designed and implemented micro coils with trace spacing of 50 {\mu}m that can harvest a significant amount of energy (178 mW/cm2) through near field inductive coupling. We show miniaturized soft-matter circuits with integrated SMD chips such as NFC chips, capacitors, and LEDs that are implemented in a few minutes through laser patterning, and vaporassisted soldering. In the context of optogenetics, where lightweight, miniaturized systems are needed to provide optical stimulation, soft coils stand out in terms of their improved conformability and flexibility. Thus, this article explores the applications of soft coils in wearable and implantable devices, with a specific focus on their use in optogenetics.

We present the GenAI Content Detection Task~1 -- a shared task on binary machine generated text detection, conducted as a part of the GenAI workshop at COLING 2025. The task consists of two subtasks: Monolingual (English) and Multilingual. The shared task attracted many participants: 36 teams made official submissions to the Monolingual subtask during the test phase and 26 teams -- to the Multilingual. We provide a comprehensive overview of the data, a summary of the results -- including system rankings and performance scores -- detailed descriptions of the participating systems, and an in-depth analysis of submissions. https://github.com/mbzuai-nlp/COLING-2025-Workshop-on-MGT-Detection-Task1, Comment: 18 pages

Navigating safely in dynamic and uncertain environments is challenging due to uncertainties in perception and motion. This letter presents C2U-MPPI, a robust sampling-based Model Predictive Control (MPC) framework that addresses these challenges by leveraging the Unscented Model Predictive Path Integral (U-MPPI) control strategy with integrated probabilistic chance constraints, ensuring more reliable and efficient navigation under uncertainty. Unlike gradient-based MPC methods, our approach (i) avoids linearization of system dynamics and directly applies non-convex and nonlinear chance constraints, enabling more accurate and flexible optimization, and (ii) enhances computational efficiency by reformulating probabilistic constraints into a deterministic form and employing a layered dynamic obstacle representation, enabling real-time handling of multiple obstacles. Extensive experiments in simulated and real-world human-shared environments validate the effectiveness of our algorithm against baseline methods, showcasing its capability to generate feasible trajectories and control inputs that adhere to system dynamics and constraints in dynamic settings, enabled by unscented-based sampling strategy and risk-sensitive trajectory evaluation. A supplementary video is available at: https://youtu.be/FptAhvJlQm8, Comment: This paper has 8 pages, 2 figures, 5 tables

For semiconductor device fabrication, Pulsed Laser Annealing (PLA) offers significant advantages over conventional thermal processes. Notably, it can provide ultrafast (~ns) and high temperature profiles ($>1000^\circ$C). When the maximum temperature exceeds the melting point, a solid-liquid phase transition is observed, immediately followed by rapid recrystallization. This unique annealing mechanism gives raises questions about dopant diffusion and residual defects, in not only in the recrystallized region, but also just below it. As power devices require micrometer-sized junctions, high laser energy densities are needed, which were proved to promote the incorporation of complex impurities from the surface and the creation of defects at the liquid/solid interface. This paper reports on the impact of laser annealing at high energy densities (up to 8.0 J/cm$^2$) on the leakage current, using Schottky and PN diodes, and DLTS measurements. Various laser annealing conditions were used: energy densities between 1.7 and 8.0 J/cm$^2$ with 1 to 10 pulses. Our results suggest that the liquid and solid solubility of vacancies in silicon are fixed by the maximum temperature reached, so to the energy density. Increasing the number of laser pulses allows, not only to reach this maximum vacancy concentration but also to promote their diffusion towards the surface. Concomitantly, the in-diffusion of complex impurities inside the melted region allows the coupling between both defect types to create trap centers, responsible for the degradation of the leakage current.

Edge computing has become critical for enabling latency-sensitive applications, especially when paired with cloud resources to form cloud-assisted edge clusters. However, efficient resource management remains challenging due to edge nodes' limited capacity and unreliable connectivity. This paper introduces KubeDSM, a Kubernetes-based dynamic scheduling and migration framework tailored for cloud-assisted edge environments. KubeDSM addresses the challenges of resource fragmentation, dynamic scheduling, and live migration while ensuring Quality of Service (QoS) for latency-sensitive applications. Unlike Kubernetes' default scheduler, KubeDSM adopts batch scheduling to minimize resource fragmentation and incorporates a live migration mechanism to optimize edge resource utilization. Specifically, KubeDSM facilitates three key operations: intra-edge migration to reduce fragmentation, edge-to-cloud migration during resource shortages, and cloud-to-edge migration when resources become available, thereby increasing the number of pods allocated to the edge. Our results demonstrate that KubeDSM consistently achieves a higher average edge ratio and a lower standard deviation in edge ratios, highlighting its ability to provide more effective and stable scheduling across different deployments. We also explore the impact of migration strategies and Quality of Service (QoS) configurations on the edge ratios achieved by KubeDSM. The findings reveal that enabling migrations significantly enhances the edge ratio by reducing fragmentation. Additionally, KubeDSM's adaptability in respecting QoS requirements while maximizing overall edge ratios is confirmed through different QoS scenarios.

Understanding objects in 3D at the part level is essential for humans and robots to navigate and interact with the environment. Current datasets for part-level 3D object understanding encompass a limited range of categories. For instance, the ShapeNet-Part and PartNet datasets only include 16, and 24 object categories respectively. The 3DCoMPaT dataset, specifically designed for compositional understanding of parts and materials, contains only 42 object categories. To foster richer and fine-grained part-level 3D understanding, we introduce 3DCoMPaT200, a large-scale dataset tailored for compositional understanding of object parts and materials, with 200 object categories with $\approx$5 times larger object vocabulary compared to 3DCoMPaT and $\approx$ 4 times larger part categories. Concretely, 3DCoMPaT200 significantly expands upon 3DCoMPaT, featuring 1,031 fine-grained part categories and 293 distinct material classes for compositional application to 3D object parts. Additionally, to address the complexities of compositional 3D modeling, we propose a novel task of Compositional Part Shape Retrieval using ULIP to provide a strong 3D foundational model for 3D Compositional Understanding. This method evaluates the model shape retrieval performance given one, three, or six parts described in text format. These results show that the model's performance improves with an increasing number of style compositions, highlighting the critical role of the compositional dataset. Such results underscore the dataset's effectiveness in enhancing models' capability to understand complex 3D shapes from a compositional perspective. Code and Data can be found at http://github.com/3DCoMPaT200/3DCoMPaT200

Continual learning has become essential in many practical applications such as online news summaries and product classification. The primary challenge is known as catastrophic forgetting, a phenomenon where a model inadvertently discards previously learned knowledge when it is trained on new tasks. Existing solutions involve storing exemplars from previous classes, regularizing parameters during the fine-tuning process, or assigning different model parameters to each task. The proposed solution LSEBMCL (Latent Space Energy-Based Model for Continual Learning) in this work is to use energy-based models (EBMs) to prevent catastrophic forgetting by sampling data points from previous tasks when training on new ones. The EBM is a machine learning model that associates an energy value with each input data point. The proposed method uses an EBM layer as an outer-generator in the continual learning framework for NLP tasks. The study demonstrates the efficacy of EBM in NLP tasks, achieving state-of-the-art results in all experiments., Comment: In the 7th International Conference on Artificial Intelligence in Information and Communication (ICAIIC 2025)

Tunneling spectroscopy between parallel two-dimensional (2D) electronic systems provides a powerful method to probe the underlying electronic properties by measuring tunneling conductance. In this work, we present a theoretical framework for spin transport in 2D-to-2D tunneling systems, driven by spin pumping. This theory applies to a vertical heterostructure where two layers of metallic 2D electron systems are separated by an insulating barrier, with one layer exchange-coupled to a magnetic layer driven at resonance. Utilizing a non-perturbative Floquet-Keldysh formalism, we derive general expressions for the tunneling spin and charge currents across a broad range of driving frequencies, extending beyond the traditional adiabatic pumping regime. At low frequencies, we obtain analytical results that recover the known behaviors in the adiabatic regime. However, at higher frequencies, our numerical findings reveal significant deviations in the dependence of spin and charge currents on both frequency and precession angle. This work offers fresh insights into the role of magnetization dynamics in tunneling transport, opening up new avenues for exploring non-adiabatic spin pumping phenomena.

We consider the problem of weight uncertainty proposed by [Blundell et al. (2015). Weight uncertainty in neural network. In International conference on machine learning, 1613-1622, PMLR.] in neural networks {(NNs)} specialized for regression tasks. {We further} investigate the effect of variance uncertainty in {their model}. We show that including the variance uncertainty can improve the prediction performance of the Bayesian {NN}. Variance uncertainty enhances the generalization of the model {by} considering the posterior distribution over the variance parameter. { We examine the generalization ability of the proposed model using a function approximation} example and {further illustrate it with} the riboflavin genetic data set. {We explore fully connected dense networks and dropout NNs with} Gaussian and spike-and-slab priors, respectively, for the network weights., Comment: Submitted to journal

Tomato anomalies/damages pose a significant challenge in greenhouse farming. While this method of cultivation benefits from efficient resource utilization, anomalies can significantly degrade the quality of farm produce. A common anomaly associated with tomatoes is splitting, characterized by the development of cracks on the tomato skin, which degrades its quality. Detecting this type of anomaly is challenging due to dynamic variations in appearance and sizes, compounded by dataset scarcity. We address this problem in an unsupervised manner by utilizing a tailored variational autoencoder (VAE) with hyperspectral input. Preliminary analysis of the dataset enabled us to select the optimal range of wavelengths for detecting this anomaly. Our findings indicate that the 530nm - 550nm range is suitable for identifying tomato dry splits. The analysis on reconstruction loss allow us to not only detect the anomalies but also to some degree estimate the anomalous regions., Comment: CVPPA Workshop

A critical part of creating code suggestion systems is the pre-training of Large Language Models on vast amounts of source code and natural language text, often of questionable origin or quality. This may contribute to the presence of bugs and vulnerabilities in code generated by LLMs. While efforts to identify bugs at or after code generation exist, it is preferable to pre-train or fine-tune LLMs on curated, high-quality, and compliant datasets. The need for vast amounts of training data necessitates that such curation be automated, minimizing human intervention. We propose an automated source code autocuration technique that leverages the complete version history of open-source software projects to improve the quality of training data. This approach leverages the version history of all OSS projects to identify training data samples that have been modified or have undergone changes in at least one OSS project, and pinpoint a subset of samples that include fixes for bugs or vulnerabilities. We evaluate this method using The Stack v2 dataset, and find that 17% of the code versions in the dataset have newer versions, with 17% of those representing bug fixes, including 2.36% addressing known CVEs. The deduplicated version of Stack v2 still includes blobs vulnerable to 6,947 known CVEs. Furthermore, 58% of the blobs in the dataset were never modified after creation, suggesting they likely represent software with minimal or no use. Misidentified blob origins present an additional challenge, as they lead to the inclusion of non-permissively licensed code, raising serious compliance concerns. By addressing these issues, the training of new models can avoid perpetuating buggy code patterns or license violations. We expect our results to inspire process improvements for automated data curation, with the potential to enhance the reliability of outputs generated by AI tools., Comment: Accepted in the Second International Workshop on Large Language Models for Code (LLM4Code 2025)

Any research endeavour needs to conform with some endorsed research ethics, such as the BERA ethical guidelines for educational research (BERA, 2018). These ethical guidelines can be applied to many research stages and procedures, including dealing with human participants (e.g. getting consent and approvals), data collection and analysis, and using pseudonyms instead of real names in the research report itself. Drawing on British Educational Research Association (BERA, 2018) as an authoritative source in this regard, I will explore how these ethical guidelines apply to educational research in the Arab world, and how they might inform and shape research practices in the light of a chosen experimental research study in language learning (Al-Jarf, 2006) by discussing the key ethical issues and concerns evident in the study, and suggesting alternative ways when needed. This would develop a methodological understanding of the proper conduct of the research process, and hence contribute to the advancement of knowledge in the educational arena. To investigate ethical issues in this paper, focus will be on the following points considering to what extent the researcher considered them: the researcher's qualities and virtues (honesty, objectivity, clarity, etc.), her interaction with participants, tackling previous literature, and research procedures.

Gliomas are the most common cause of mortality among primary brain tumors. Molecular markers, including Isocitrate Dehydrogenase (IDH) and O[6]-methylguanine-DNA methyltransferase (MGMT) influence treatment responses and prognosis. Deep learning (DL) models may provide a non-invasive method for predicting the status of these molecular markers. To achieve non-invasive determination of gene mutations in glioma patients, we compare 2D and 3D ResNet models to predict IDH and MGMT status, using T1, post-contrast T1, and FLAIR MRI sequences. USCF glioma dataset was used, which contains 495 patients with known IDH and 410 patients with known MGMT status. The dataset was divided into training (60%), tuning (20%), and test (20%) subsets at the patient level. The 2D models take axial, coronal, and sagittal tumor slices as three separate models. To ensemble the 2D predictions the three different views were combined using logistic regression. Various ResNet architectures (ResNet10, 18, 34, 50, 101, 152) were trained. For the 3D approach, we incorporated the entire brain tumor volume in the ResNet10, 18, and 34 models. After optimizing each model, the models with the lowest tuning loss were selected for further evaluation on the separate test sets. The best-performing models in IDH prediction were the 2D ResNet50, achieving a test area under the receiver operating characteristic curve (AUROC) of 0.9096, and the 3D ResNet34, which reached a test AUROC of 0.8999. For MGMT status prediction, the 2D ResNet152 achieved a test AUROC of 0.6168; however, all 3D models yielded AUROCs less than 0.5. Overall, the study indicated that both 2D and 3D models showed high predictive value for IDH prediction, with slightly better performance in 2D models., Comment: 11 PAGES, 2 Figures, 3 Tables

Hadith, the recorded words and actions of the prophet Muhammad, is a key source of the instructions and foundations of Islam, alongside the Quran. Interpreting individual hadiths and verifying their authenticity can be difficult, even controversial, and the subject has attracted the attention of many scholars who have established an entire science of Hadith criticism. Recent quantitative studies of hadiths focus on developing systems for automatic classification, authentication, and information retrieval that operate over existing hadith compilations. Qualitative studies on the other hand try to discuss different social and political issues from the perspective of hadiths, or they inspect how hadiths are used in specific contexts in official communications and press releases for argumentation and propaganda. However, there are no studies that attempt to understand the actual presence of hadiths among Muslims in their daily lives and interactions. In this study, we try to fill this gap by exploring the presence of hadiths on Twitter from January 2019 to January 2023. We highlight the challenges that quantitative methods should consider while processing texts that include hadiths and we provide a methodology for Islamic scholars to validate their hypotheses about hadiths on big data that better represent the position of the society and Hadith influence on it., Comment: accepted at CSCW 2025 (to appear in ACM Library)

Visual impairment affects the ability of people to live a life like normal people. Such people face challenges in performing activities of daily living, such as reading, writing, traveling and participating in social gatherings. Many traditional approaches are available to help visually impaired people; however, these are limited in obtaining contextually rich environmental information necessary for independent living. In order to overcome this limitation, this paper introduces a novel wearable vision assistance system that has a hat-mounted camera connected to a Raspberry Pi 4 Model B (8GB RAM) with artificial intelligence (AI) technology to deliver real-time feedback to a user through a sound beep mechanism. The key features of this system include a user-friendly procedure for the recognition of new people or objects through a one-click process that allows users to add data on new individuals and objects for later detection, enhancing the accuracy of the recognition over time. The system provides detailed descriptions of objects in the user's environment using a large vision language model (LVLM). In addition, it incorporates a distance sensor that activates a beeping sound using a buzzer as soon as the user is about to collide with an object, helping to ensure safety while navigating their environment. A comprehensive evaluation is carried out to evaluate the proposed AI-based solution against traditional support techniques. Comparative analysis shows that the proposed solution with its innovative combination of hardware and AI (including LVLMs with IoT), is a significant advancement in assistive technology that aims to solve the major issues faced by the community of visually impaired people, Comment: N-A

Cyberbullying significantly contributes to mental health issues in communities by negatively impacting the psychology of victims. It is a prevalent problem on social media platforms, necessitating effective, real-time detection and monitoring systems to identify harmful messages. However, current cyberbullying detection systems face challenges related to performance, dataset quality, time efficiency, and computational costs. This research aims to conduct a comparative study by adapting and evaluating existing text classification techniques within the cyberbullying detection domain. The study specifically evaluates the effectiveness and performance of these techniques in identifying cyberbullying instances on social media platforms. It focuses on leveraging and assessing large language models, including BERT, RoBERTa, XLNet, DistilBERT, and GPT-2.0, for their suitability in this domain. The results show that BERT strikes a balance between performance, time efficiency, and computational resources: Accuracy of 95%, Precision of 95%, Recall of 95%, F1 Score of 95%, Error Rate of 5%, Inference Time of 0.053 seconds, RAM Usage of 35.28 MB, CPU/GPU Usage of 0.4%, and Energy Consumption of 0.000263 kWh. The findings demonstrate that generative AI models, while powerful, do not consistently outperform fine-tuned models on the tested benchmarks. However, state-of-the-art performance can still be achieved through strategic adaptation and fine-tuning of existing models for specific datasets and tasks., Comment: 15 pages, 10 figures, 7 tables

Machine Unlearning has emerged as a critical area in artificial intelligence, addressing the need to selectively remove learned data from machine learning models in response to data privacy regulations. This paper provides a comprehensive comparative analysis of six state-of-theart unlearning techniques applied to image and text classification tasks. We evaluate their performance, efficiency, and compliance with regulatory requirements, highlighting their strengths and limitations in practical scenarios. By systematically analyzing these methods, we aim to provide insights into their applicability, challenges,and tradeoffs, fostering advancements in the field of ethical and adaptable machine learning.

Metal halide perovskite solar cells (MHPSCs) hold great promise related to their high efficiency and low fabrication costs, but their long-term stability under environmental conditions remains a major challenge. In this study, we demonstrate an effective protection strategy to enhance the stability of MHPSCs through the incorporation of a double passivation layer based on an adamantane-based plasma polymer (ADA) at both the electron transport layer (ETL)/perovskite and perovskite/hole transport layer (HTL) interfaces. Our results show that the implemented ADA deposition technique is compatible with delicate substrates such as perovskites thin films, as their optical, morphological and optoelectronic properties are unaltered upon ADA deposition. At the same time, it provides effective protection to the perovskite material in high humidity environments. The ADA-double passivation not only reduces the formation of mobile ionic defects that cause additional recombination, but also significantly reduces humidity-induced degradation and mitigates the photocatalytic degradation caused by TiO2 under UV exposure. Stability tests performed under 100% relative humidity and continuous AM 1.5G illumination (ISOS-L-1) show that ADA dual passivated devices retained 80% of their initial efficiency after 4000 minutes, while reference samples dropped to 30%. The enhanced performance of ADA-passivated cells is attributed to protective nature of the plasma polymer layer resulting in a preservation of photocurrent and the prevention of new recombination routes. This dual passivation strategy offers a promising route to improve the environmental stability of PSCs and extend their operational lifetime.

Street view imagery (SVI) has been instrumental in many studies in the past decade to understand and characterize street features and the built environment. Researchers across a variety of domains, such as transportation, health, architecture, human perception, and infrastructure have employed different methods to analyze SVI. However, these applications and image-processing procedures have not been standardized, and solutions have been implemented in isolation, often making it difficult for others to reproduce existing work and carry out new research. Using SVI for research requires multiple technical steps: accessing APIs for scalable data collection, preprocessing images to standardize formats, implementing computer vision models for feature extraction, and conducting spatial analysis. These technical requirements create barriers for researchers in urban studies, particularly those without extensive programming experience. We developed ZenSVI, a free and open-source Python package that integrates and implements the entire process of SVI analysis, supporting a wide range of use cases. Its end-to-end pipeline includes downloading SVI from multiple platforms (e.g., Mapillary and KartaView) efficiently, analyzing metadata of SVI, applying computer vision models to extract target features, transforming SVI into different projections (e.g., fish-eye and perspective) and different formats (e.g., depth map and point cloud), visualizing analyses with maps and plots, and exporting outputs to other software tools. We demonstrated its use in Singapore through a case study of data quality assessment and clustering analysis in a streamlined manner. Our software improves the transparency, reproducibility, and scalability of research relying on SVI and supports researchers in conducting urban analyses efficiently. Its modular design facilitates extensions of the package for new use cases.

Two-dimensional topological insulators with propagating topological edge states are promising for dissipationless transport, while their one-dimensional analogs are capable of hosting localized topological junction states that are mainly envisaged for quantum computing and spintronics. Here, in contrast, we propose to use localized nature of topological junction states for sensing applications. We report a systematic topological classification of a wide class of graphene nanoribbons represented by already synthesized extended chevron species. By using this classification, we theoretically model a double-junction transport device that shows enhanced interaction with NO$_2$ molecule. Our results show that topological junction states of nanoribbons can open an avenue for topological sensing and junction-assisted chemistry applications., Comment: 28 pages, 3 Figures, 2 Tables, 2 Schemes

Nuclear magnetic resonance (NMR) is a powerful spectroscopic technique that is sensitive to the local atomic structure of matter. Computational predictions of NMR parameters can help to interpret experimental data and validate structural models, and machine learning (ML) has emerged as an efficient route to making such predictions. Here, we systematically study graph-neural-network approaches to representing and learning tensor quantities for solid-state NMR -- specifically, the anisotropic magnetic shielding and the electric field gradient. We assess how the numerical accuracy of different ML models translates into prediction quality for experimentally relevant NMR properties: chemical shifts, quadrupolar coupling constants, tensor orientations, and even static 1D spectra. We apply these ML models to a structurally diverse dataset of amorphous SiO$_2$ configurations, spanning a wide range of density and local order, to larger configurations beyond the reach of traditional first-principles methods, and to the dynamics of the $\alpha\unicode{x2013}\beta$ inversion in cristobalite. Our work marks a step toward streamlining ML-driven NMR predictions for both static and dynamic behavior of complex materials, and toward bridging the gap between first-principles modeling and real-world experimental data., Comment: 13 pages, 7 figures

A long standing problem in abstract harmonic analysis concerns the strong Arens irregularity (sAir, for short) of the Fourier algebra $A(G)$ of a locally compact group $G.$ The groups for which $A(G)$ is known to be sAir are all amenable. So far this class includes the abelian groups, the discrete amenable groups, the second countable amenable groups $G$ such that $\overline{[G, G]}$ is not open in $G,$ the groups of the form $\prod_{i=0}^\infty G_i$ where each $G_i$, $i\ge1$, is a non-trivial metrizable compact group and $G_0$ is an amenable second countable locally compact group, the groups of the form $G_0\times G$, where $G$ is a compact group whose local weight $w(G)$ has uncountable cofinality and $G_0$ is any locally compact amenable group with $w(G_0)\le w(G)$, and the compact group $SU(2).$ We were primarily concerned with the groups for which $A(G)$ is sAir. We introduce a new class of $\ell^1$-bases in Banach algebras. These new $\ell^1$-bases enable us, among other results, to unify most of the results related to Arens products proved in the past seventy years since Arens defined his products. This includes the strong Arens irregularity of algebras in harmonic analysis, and in particular almost all the cases mentioned above for the Fourier algebras. In addition, we also show that $A(G)$ is sAir for compact connected groups with an infinite dual rank. The $\ell^1$-bases for the Fourier algebra are constructed with coefficients of certain irreducible representations of the group. With this new approach using $\ell^1$-bases, the rich algebraic structure of the algebras and semigroups under study such as the second dual of Banach algebras with an Arens product or certain semigroup compactifications (the Stone-\v Cech compactification of an infinite discrete group, for instance) is also unveiled

Neuromorphic computing aims to replicate the brain's capabilities for energy efficient and parallel information processing, promising a solution to the increasing demand for faster and more efficient computational systems. Efficient training of neural networks on neuromorphic hardware requires the development of training algorithms that retain the sparsity of spike-based communication during training. Here, we report on the first implementation of event-based backpropagation on the SpiNNaker2 neuromorphic hardware platform. We use EventProp, an algorithm for event-based backpropagation in spiking neural networks (SNNs), to compute exact gradients using sparse communication of error signals between neurons. Our implementation computes multi-layer networks of leaky integrate-and-fire neurons using discretized versions of the differential equations and their adjoints, and uses event packets to transmit spikes and error signals between network layers. We demonstrate a proof-of-concept of batch-parallelized, on-chip training of SNNs using the Yin Yang dataset, and provide an off-chip implementation for efficient prototyping, hyper-parameter search, and hybrid training methods., Comment: 38th Second Workshop on Machine Learning with New Compute Paradigms at NeurIPS 2024(MLNCP 2024) : Poster Presentation. NICE 2025 Neuromorphic Conference: Flash Talk Presentation

A remarkable characteristic of quantum computing is the potential for reliable computation despite faulty qubits. This can be achieved through quantum error correction, which is typically implemented by repeatedly applying static syndrome checks, permitting correction of logical information. Recently, the development of time-dynamic approaches to error correction has uncovered new codes and new code implementations. In this work, we experimentally demonstrate three time-dynamic implementations of the surface code, each offering a unique solution to hardware design challenges and introducing flexibility in surface code realization. First, we embed the surface code on a hexagonal lattice, reducing the necessary couplings per qubit from four to three. Second, we walk a surface code, swapping the role of data and measure qubits each round, achieving error correction with built-in removal of accumulated non-computational errors. Finally, we realize the surface code using iSWAP gates instead of the traditional CNOT, extending the set of viable gates for error correction without additional overhead. We measure the error suppression factor when scaling from distance-3 to distance-5 codes of $\Lambda_{35,\text{hex}} = 2.15(2)$, $\Lambda_{35,\text{walk}} = 1.69(6)$, and $\Lambda_{35,\text{iSWAP}} = 1.56(2)$, achieving state-of-the-art error suppression for each. With detailed error budgeting, we explore their performance trade-offs and implications for hardware design. This work demonstrates that dynamic circuit approaches satisfy the demands for fault-tolerance and opens new alternative avenues for scalable hardware design., Comment: 11 pages, 5 figures, Supplementary Information

Physical layer multicasting is an efficient transmission technique that exploits the beamforming potential at the transmitting nodes and the broadcast nature of the wireless channel, together with the demand for the same content from several UEs. This paper addresses the max-min fair multigroup multicast beamforming optimization, which is an NP-hard problem. We propose a novel iterative elimination procedure coupled with semidefinite relaxation (SDR) to find the near-global optimum rank-1 beamforming vectors in a cell-free massive MIMO (multiple-input multiple-output) network setup. The proposed optimization procedure shows significant improvements in computational complexity and spectral efficiency performance compared to the SDR followed by the commonly used randomization procedure and the state-of-the-art difference-of-convex approximation algorithm. The significance of the proposed procedure is that it can be utilized as a rank reduction method for any problem in conjunction with SDR., Comment: 6 pages, accepted at IEEE Global Communications Conference (Globecom), 2024