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Objective: To propose and validate an unsupervised MRI reconstruction method that does not require fully sampled k-space data. Materials and Methods: The proposed method, deep image prior with structured sparsity (DISCUS), extends the deep image prior (DIP) by introducing group sparsity to frame-specific code vectors, enabling the discovery of a low-dimensional manifold for capturing temporal variations. \discus was validated using four studies: (I) simulation of a dynamic Shepp-Logan phantom to demonstrate its manifold discovery capabilities, (II) comparison with compressed sensing and DIP-based methods using simulated single-shot late gadolinium enhancement (LGE) image series from six distinct digital cardiac phantoms in terms of normalized mean square error (NMSE) and structural similarity index measure (SSIM), (III) evaluation on retrospectively undersampled single-shot LGE data from eight patients, and (IV) evaluation on prospectively undersampled single-shot LGE data from eight patients, assessed via blind scoring from two expert readers. Results: DISCUS outperformed competing methods, demonstrating superior reconstruction quality in terms of NMSE and SSIM (Studies I--III) and expert reader scoring (Study IV). Discussion: An unsupervised image reconstruction method is presented and validated on simulated and measured data. These developments can benefit applications where acquiring fully sampled data is challenging.

One of the most urgent problems is the overcrowding in emergency departments (EDs), caused by an aging population and rising healthcare costs. Patient dispositions have become more complex as a result of the strain on hospital infrastructure and the scarcity of medical resources. Individuals with more dangerous health issues should be prioritized in the emergency room. Thus, our research aims to develop a prediction model for patient disposition using EF-Net. This model will incorporate categorical features into the neural network layer and add numerical features with the embedded categorical features. We combine the EF-Net and XGBoost models to attain higher accuracy in our results. The result is generated using the soft voting technique. In EF-Net, we attained an accuracy of 95.33%, whereas in the Ensemble Model, we achieved an accuracy of 96%. The experiment's analysis shows that EF-Net surpasses existing works in accuracy, AUROC, and F1-Score on the MIMIC-IV-ED dataset, demonstrating its potential as a scalable solution for patient disposition assessment. Our code is available at https://github.com/nafisa67/thesis, Comment: Accepted to ICCIT2024

Conflict scholars have used rule-based approaches to extract information about political violence from news reports and texts. Recent Natural Language Processing developments move beyond rigid rule-based approaches. We review our recent ConfliBERT language model (Hu et al. 2022) to process political and violence related texts. The model can be used to extract actor and action classifications from texts about political conflict. When fine-tuned, results show that ConfliBERT has superior performance in accuracy, precision and recall over other large language models (LLM) like Google's Gemma 2 (9B), Meta's Llama 3.1 (7B), and Alibaba's Qwen 2.5 (14B) within its relevant domains. It is also hundreds of times faster than these more generalist LLMs. These results are illustrated using texts from the BBC, re3d, and the Global Terrorism Dataset (GTD)., Comment: 30 pages, 4 figures, 5 tables

We consider the Benjamin-Ono equation in the spatially quasiperiodic setting. We establish local well-posedness of the initial value problem with initial data in quasiperiodic Sobolev spaces. This requires developing some of the fundamental properties of Sobolev spaces and the energy method for quasiperiodic functions. We discuss prospects for global existence. We demonstrate that while conservation laws still hold, these quantities no longer control the associated Sobolev norms, thereby preventing the establishment of global results by usual arguments.

Motivated by two news states $h_1(1911)$ and $h_1(2316)$ observed by BESIII, we have investigated the mass spectrum and the strong decay properties of the strangeonium mesons within the modified Godfrey-Isgur model by considering the screening effects. We have determined the free parameters using the masses and widths of the well established $s\bar{s}$ states $\phi(1020)$, $\phi(1680)$, $h_1(1415)$, $f_2^\prime(1525)$, and $\phi_3(1850)$. According to our results, $h_1(1911)$ and $h_1(2316)$ could be well explained as states $h_1(2^1P_1)$ and $h_1(3^1P_1)$ $s\bar{s}$ states, respectively. Meanwhile, the possible assignments of $X(2000)$, $\eta_2(1870)$, and $\phi(2170)$ as $3^3S_1$, $1^1D_2$, and $2^3D_1$ are also discussed. Furthermore, the masses and widths of the $2S$, $3S$, $1P$, $2P$, $3P$, $1D$, and $2D$ $s\bar{s}$ states are also given and compared with various theoretical predictions, which is helpful for the observations and confirmations of these states in future.

Active imaging systems sample the Transient Light Transport Matrix (TLTM) for a scene by sequentially illuminating various positions in this scene using a controllable light source, and then measuring the resulting spatiotemporal light transport with time of flight (ToF) sensors. Time-resolved Non-line-of-sight (NLOS) imaging employs an active imaging system that measures part of the TLTM of an intermediary relay surface, and uses the indirect reflections of light encoded within this TLTM to "see around corners". Such imaging systems have applications in diverse areas such as disaster response, remote surveillance, and autonomous navigation. While existing NLOS imaging systems usually measure a subset of the full TLTM, development of customized gated Single Photon Avalanche Diode (SPAD) arrays \cite{riccardo_fast-gated_2022} has made it feasible to probe the full measurement space. In this work, we demonstrate that the full TLTM on the relay surface can be processed with efficient algorithms to computationally focus and detect our illumination in different parts of the hidden scene, turning the relay surface into a second-order active imaging system. These algorithms allow us to iterate on the measured, first-order TLTM, and extract a \textbf{second order TLTM for surfaces in the hidden scene}. We showcase three applications of TLTMs in NLOS imaging: (1) Scene Relighting with novel illumination, (2) Separation of direct and indirect components of light transport in the hidden scene, and (3) Dual Photography. Additionally, we empirically demonstrate that SPAD arrays enable parallel acquisition of photons, effectively mitigating long acquisition times.

We present SmolTulu-1.7b-Instruct, referenced in this report as SmolTulu-DPO-1130, an instruction-tuned language model that adapts AllenAI's Tulu 3 post-training pipeline to enhance Huggingface's SmolLM2-1.7B base model. Through comprehensive empirical analysis using a 135M parameter model, we demonstrate that the relationship between learning rate and batch size significantly impacts model performance in a task-dependent manner. Our findings reveal a clear split: reasoning tasks like ARC and GSM8K benefit from higher learning rate to batch size ratios, while pattern recognition tasks such as HellaSwag and IFEval show optimal performance with lower ratios. These insights informed the development of SmolTulu, which achieves state-of-the-art performance among sub-2B parameter models on instruction following, scoring 67.7% on IFEval ($\Delta$11%), and mathematical reasoning with 51.6% on GSM8K ($\Delta$3.4%), with an alternate version achieving scoring 57.1% on ARC ($\Delta5.4%$). We release our model, training recipes, and ablation studies to facilitate further research in efficient model alignment, demonstrating that careful adaptation of optimization dynamics can help bridge the capability gap between small and large language models., Comment: 10 pages, 4 figures, and 13 tables. For the SmolTulu-1.7b-instruct model, see: https://huggingface.co/SultanR/SmolTulu-1.7b-Instruct

Emotion artificial intelligence is a field of study that focuses on figuring out how to recognize emotions, especially in the area of text mining. Today is the age of social media which has opened a door for us to share our individual expressions, emotions, and perspectives on any event. We can analyze sentiment on social media posts to detect positive, negative, or emotional behavior toward society. One of the key challenges in sentiment analysis is to identify depressed text from social media text that is a root cause of mental ill-health. Furthermore, depression leads to severe impairment in day-to-day living and is a major source of suicide incidents. In this paper, we apply natural language processing techniques on Facebook texts for conducting emotion analysis focusing on depression using multiple machine learning algorithms. Preprocessing steps like stemming, stop word removal, etc. are used to clean the collected data, and feature extraction techniques like stylometric feature, TF-IDF, word embedding, etc. are applied to the collected dataset which consists of 983 texts collected from social media posts. In the process of class prediction, LSTM, GRU, support vector machine, and Naive-Bayes classifiers have been used. We have presented the results using the primary classification metrics including F1-score, and accuracy. This work focuses on depression detection from social media posts to help psychologists to analyze sentiment from shared posts which may reduce the undesirable behaviors of depressed individuals through diagnosis and treatment., Comment: Initial version with Bangla text. arXiv admin note: substantial text overlap with arXiv:2411.04542

Cardiovascular magnetic resonance imaging is a powerful diagnostic tool for assessing cardiac structure and function. Traditional breath-held imaging protocols, however, pose challenges for patients with arrhythmias or limited breath-holding capacity. We introduce Motion-Guided Deep Image prior (M-DIP), a novel unsupervised reconstruction framework for accelerated real-time cardiac MRI. M-DIP employs a spatial dictionary to synthesize a time-dependent template image, which is further refined using time-dependent deformation fields that model cardiac and respiratory motion. Unlike prior DIP-based methods, M-DIP simultaneously captures physiological motion and frame-to-frame content variations, making it applicable to a wide range of dynamic applications. We validate M-DIP using simulated MRXCAT cine phantom data as well as free-breathing real-time cine and single-shot late gadolinium enhancement data from clinical patients. Comparative analyses against state-of-the-art supervised and unsupervised approaches demonstrate M-DIP's performance and versatility. M-DIP achieved better image quality metrics on phantom data, as well as higher reader scores for in-vivo patient data.

The demand for computing education increases due to the rapid development of technology and its involvement in most daily activities. Academic institutes offer a variety of computing majors, such as Computer Engineering, Computer Science, Information Systems, Information Technology, Software Engineering, Cybersecurity, and Data Science. Since a major objective of earning a bachelor's degree is to improve career opportunities, it is crucial to understand how the job market perceives these computing majors. This study analyzed the relationships between various computing majors and the job market in Saudi Arabia, using LinkedIn public profile data, discovering insights into the strong relationship between the focus of certain computing majors and the employment of relevant job positions. Moreover, job category trends were analyzed over the past ten years, observing that demands for System Admin and Technical Support positions declined while demands for Business Analysis and Artificial Intelligence and Data Science inclined. This study also compared earned professional certifications between different computing major graduates that correspond to job position findings.

Software security is crucial in any field where breaches can exploit sensitive data, and lead to financial losses. As a result, vulnerability detection becomes an essential part of the software development process. One of the key steps in maintaining software integrity is identifying vulnerabilities in the source code before deployment. A security breach like CWE-476, which stands for NULL pointer dereferences (NPD), is crucial because it can cause software crashes, unpredictable behavior, and security vulnerabilities. In this scientific era, there are several vulnerability checkers, where, previous tools often fall short in analyzing specific feature connections of the source code, which weakens the tools in real-world scenarios. In this study, we propose another novel approach using a fine-tuned Large Language Model (LLM) termed "DeLLNeuN". This model leverages the advantage of various layers to reduce both overfitting and non-linearity, enhancing its performance and reliability. Additionally, this method provides dropout and dimensionality reduction to help streamline the model, making it faster and more efficient. Our model showed 87% accuracy with 88% precision using the Draper VDISC dataset. As software becomes more complex and cyber threats continuously evolve, the need for proactive security measures will keep growing. In this particular case, the proposed model looks promising to use as an early vulnerability checker in software development.

In software, a vulnerability is a defect in a program that attackers might utilize to acquire unauthorized access, alter system functions, and acquire information. These vulnerabilities arise from programming faults, design flaws, incorrect setups, and a lack of security protective measures. To mitigate these vulnerabilities, regular software upgrades, code reviews, safe development techniques, and the use of security tools to find and fix problems have been important. Several ways have been delivered in recent studies to address difficulties related to software vulnerabilities. However, previous approaches have significant limitations, notably in feature embedding and precisely recognizing specific vulnerabilities. To overcome these drawbacks, we present CodeGraphNet, an experimental method that combines GraphCodeBERT and Graph Convolutional Network (GCN) approaches, where, CodeGraphNet reveals data in a high-dimensional vector space, with comparable or related properties grouped closer together. This method captures intricate relationships between features, providing for more exact identification and separation of vulnerabilities. Using this feature embedding approach, we employed four machine learning models, applying both independent testing and 10-fold cross-validation. The DeepTree model, which is a hybrid of a Decision Tree and a Neural Network, outperforms state-of-the-art approaches. In additional validation, we evaluated our model using feature embeddings from LSA, GloVe, FastText, CodeBERT and GraphCodeBERT, and found that the CodeGraphNet method presented improved vulnerability identification with 98% of accuracy. Our model was tested on a real-time dataset to determine its capacity to handle real-world data and to focus on defect localization, which might influence future studies.

In the diverse field of medical imaging, automatic segmentation has numerous applications and must handle a wide variety of input domains, such as different types of Computed Tomography (CT) scans and Magnetic Resonance (MR) images. This heterogeneity challenges automatic segmentation algorithms to maintain consistent performance across different modalities due to the requirement for spatially aligned and paired images. Typically, segmentation models are trained using a single modality, which limits their ability to generalize to other types of input data without employing transfer learning techniques. Additionally, leveraging complementary information from different modalities to enhance segmentation precision often necessitates substantial modifications to popular encoder-decoder designs, such as introducing multiple branched encoding or decoding paths for each modality. In this work, we propose a simple Multi-Modal Segmentation (MulModSeg) strategy to enhance medical image segmentation across multiple modalities, specifically CT and MR. It incorporates two key designs: a modality-conditioned text embedding framework via a frozen text encoder that adds modality awareness to existing segmentation frameworks without significant structural modifications or computational overhead, and an alternating training procedure that facilitates the integration of essential features from unpaired CT and MR inputs. Through extensive experiments with both Fully Convolutional Network and Transformer-based backbones, MulModSeg consistently outperforms previous methods in segmenting abdominal multi-organ and cardiac substructures for both CT and MR modalities. The code is available in this {\href{https://github.com/ChengyinLee/MulModSeg_2024}{link}}., Comment: Accepted by WACV-2025

This study examines a home healthcare scheduling and routing problem (HHSRP) with a lunch break requirement. This problem especially consists of lunch break constraints for caregivers in addition to other typical features of the HHSRP in literature such as hard time window constraints for both patients and caregivers and patient preferences. The objective is to minimize both travel distance in a route and unvisited patient (penalty) cost. For this NP-Hard problem, we developed an effective Adaptive Large Neighborhood Search algorithm to provide high-quality solutions in a short amount of time. We tested the proposed four variants of the algorithm with the selected problem instances from the literature. The algorithms provided nearly all optimal solutions for 30-patient problem instances in 12 seconds on average. Additionally, they provided better solutions to 36 problem instances up to 36% improvement in some instance classes. Moreover, the improved solutions achieved to visit up to 10 more patients. The algorithms are also shown to be very robust due to their low coefficient variance of 0.3 on average. The algorithm also requires a very reasonable amount of time to generate solutions up to 54 seconds for solving 100-patient instances. A decision support system, namely Home Healthcare Decision Support System (HHCSS) was also designed to play a positive role in preventing the COVID-19 global pandemic. The system employs the proposed ALNS algorithm to solve various instances of approximately generated COVID-19 patient data from Turkey. The main aim of developing HHCSS is to support the administrative staff of home healthcare from the tedious task of scheduling and routing of caregivers and to increase service responsiveness.

This study investigates the automation of meta-analysis in scientific documents using large language models (LLMs). Meta-analysis is a robust statistical method that synthesizes the findings of multiple studies support articles to provide a comprehensive understanding. We know that a meta-article provides a structured analysis of several articles. However, conducting meta-analysis by hand is labor-intensive, time-consuming, and susceptible to human error, highlighting the need for automated pipelines to streamline the process. Our research introduces a novel approach that fine-tunes the LLM on extensive scientific datasets to address challenges in big data handling and structured data extraction. We automate and optimize the meta-analysis process by integrating Retrieval Augmented Generation (RAG). Tailored through prompt engineering and a new loss metric, Inverse Cosine Distance (ICD), designed for fine-tuning on large contextual datasets, LLMs efficiently generate structured meta-analysis content. Human evaluation then assesses relevance and provides information on model performance in key metrics. This research demonstrates that fine-tuned models outperform non-fine-tuned models, with fine-tuned LLMs generating 87.6% relevant meta-analysis abstracts. The relevance of the context, based on human evaluation, shows a reduction in irrelevancy from 4.56% to 1.9%. These experiments were conducted in a low-resource environment, highlighting the study's contribution to enhancing the efficiency and reliability of meta-analysis automation., Comment: Accepted in 2024 IEEE International Conference on Big Data (IEEE BigData)

We present an approach that can be utilized in order to account for the covariate shift between two datasets of the same observable with different distributions, so as to improve the generalizability of a neural network model trained on in-distribution samples (IDs) when inferring cosmology at the field level on out-of-distribution samples (OODs) of {\it unknown labels}. We make use of HI maps from the two simulation suites in CAMELS, IllustrisTNG and SIMBA. We consider two different techniques, namely adversarial approach and optimal transport, to adapt a target network whose initial weights are those of a source network pre-trained on a labeled dataset. Results show that after adaptation, salient features that are extracted by source and target encoders are well aligned in the embedding space, indicating that the target encoder has learned the representations of the target domain via the adversarial training and optimal transport. Furthermore, in all scenarios considered in our analyses, the target encoder, which does not have access to any labels ($\Omega_{\rm m}$) during adaptation phase, is able to retrieve the underlying $\Omega_{\rm m}$ from out-of-distribution maps to a great accuracy of $R^{2}$ score $\ge$ 0.9, comparable to the performance of the source encoder trained in a supervised learning setup. We further test the viability of the techniques when only a few out-of-distribution instances are available and find that the target encoder still reasonably recovers the matter density. Our approach is critical in extracting information from upcoming large scale surveys., Comment: 10 pages, 5 figures, 2 tables

We present the design, development, and experimental validation of BlueME, a compact magnetoelectric (ME) antenna array system for underwater robot-to-robot communication. BlueME employs ME antennas operating at their natural mechanical resonance frequency to efficiently transmit and receive very-low-frequency (VLF) electromagnetic signals underwater. We outline the design, simulation, fabrication, and integration of the proposed system on low-power embedded platforms focusing on portable and scalable applications. For performance evaluation, we deployed BlueME on an autonomous surface vehicle (ASV) and a remotely operated vehicle (ROV) in open-water field trials. Our tests demonstrate that BlueME maintains reliable signal transmission at distances beyond 200 meters while consuming only 1 watt of power. Field trials show that the system operates effectively in challenging underwater conditions such as turbidity, obstacles, and multipath interference -- that generally affect acoustics and optics. Our analysis also examines the impact of complete submersion on system performance and identifies key deployment considerations. This work represents the first practical underwater deployment of ME antennas outside the laboratory, and implements the largest VLF ME array system to date. BlueME demonstrates significant potential for marine robotics and automation in multi-robot cooperative systems and remote sensor networks.

As large language models (LLMs) grow and develop, so do their data demands. This is especially true for multilingual LLMs, where the scarcity of high-quality and readily available data online has led to a multitude of synthetic dataset generation approaches. A key technique in this space is machine translation (MT), where high-quality English text is adapted to a target, comparatively low-resource language. This report introduces FineWeb-Edu-Ar, a machine-translated version of the exceedingly popular (deduplicated) FineWeb-Edu dataset from HuggingFace. To the best of our knowledge, FineWeb-Edu-Ar is the largest publicly available machine-translated Arabic dataset out there, with its size of 202B tokens of an Arabic-trained tokenizer.

The increasing growth of social media provides us with an instant opportunity to be informed of the opinions of a large number of politically active individuals in real-time. We can get an overall idea of the ideologies of these individuals on governmental issues by analyzing the social media texts. Nowadays, different kinds of news websites and popular social media such as Facebook, YouTube, Instagram, etc. are the most popular means of communication for the mass population. So the political perception of the users toward different parties in the country is reflected in the data collected from these social sites. In this work, we have extracted three types of features, such as the stylometric feature, the word-embedding feature, and the TF-IDF feature. Traditional machine learning classifiers and deep learning models are employed to identify political ideology from the text. We have compared our methodology with the research work in different languages. Among them, the word embedding feature with LSTM outperforms all other models with 88.28% accuracy., Comment: 8 Pages !

The Gender Identification (GI) problem is concerned with determining the gender of the author from a given text. It has numerous applications in different fields like forensics, literature, security, marketing, trade, etc. Due to its importance, researchers have put extensive efforts into identifying gender from the text for different languages. Unfortunately, the same statement is not true for the Bangla language despite its being the 7th most spoken language in the world. In this work, we explore Gender Identification from Social media Bangla Text. Specially, we consider two approaches for feature extraction. The first one is Bag-Of-Words(BOW) approach and another one is based on computing features from sentiment and emotions. There is a common stereotype that female authors write in a more emotional way than male authors. One goal of this work is to validate this stereotype for the Bangla language., Comment: 7 pages

The mass spectrum of the charmed mesons is investigated by considering the coupled channel effects within the nonrelativistic potential model. The predicted masses of the charmed mesons are in agreement with experimental data. The strong decay properties are further analyzed within the $^3P_0$ model by using numerical wave functions obtained from nonrelativistic potential model. Based on the predicted masses and decay properties, we give a classification of the recently observed charmed states. Especially, we have effectively explained the masses and decay properties of the $D_1^*(2600)$ and $D_1^*(2760)$ by considering the $S$-$D$ mixing. Furthermore, the predicted masses and decay properties of the $2P$ wave states are helpful to search for them experimentally in future.

The circumgalactic medium (CGM) in $\gtrsim 10^{12}$ $\mathrm{M}_{\odot}$ halos is dominated by a hot phase ($T \gtrsim 10^{6}$ K). While many models exist for the hot gas structure, there is as yet no consensus. We compare cooling flow models, in which the hot CGM flows inward due to radiative cooling, to the CGM of $\sim 10^{12}-10^{13}$ $\mathrm{M}_{\odot}$ halos in galaxy formation simulations from the FIRE project at $z\sim0$. The simulations include realistic cosmological evolution and feedback from stars but neglect AGN feedback. At both mass scales, CGM inflows are typically dominated by the hot phase rather than by the `precipitation' of cold gas. Despite being highly idealized, we find that cooling flows describe $\sim 10^{13}$ $\mathrm{M}_{\odot}$ halos very well, with median agreement in the density and temperature profiles of $\sim 20\%$ and $\sim 10\%$, respectively. This indicates that stellar feedback has little impact on CGM scales in those halos. For $\sim 10^{12}$ $\mathrm{M}_{\odot}$ halos, the thermodynamic profiles are also accurately reproduced in the outer CGM. For some of these lower-mass halos, cooling flows significantly overpredict the hot gas density in the inner CGM. This could be due to multidimensional angular momentum effects not well captured by our 1D cooling flow models and/or to the larger cold gas fractions in these regions. Turbulence, which contributes $\sim 10-40\%$ of the total pressure, must be included to accurately reproduce the temperature profiles. Overall, cooling flows predict entropy profiles in better agreement with the FIRE simulations than other idealized models in the literature., Comment: 20 pages, 14 figures. Submitted to MNRAS

Effective analysis of tabular data still poses a significant problem in deep learning, mainly because features in tabular datasets are often heterogeneous and have different levels of relevance. This work introduces TabSeq, a novel framework for the sequential ordering of features, addressing the vital necessity to optimize the learning process. Features are not always equally informative, and for certain deep learning models, their random arrangement can hinder the model's learning capacity. Finding the optimum sequence order for such features could improve the deep learning models' learning process. The novel feature ordering technique we provide in this work is based on clustering and incorporates both local ordering and global ordering. It is designed to be used with a multi-head attention mechanism in a denoising autoencoder network. Our framework uses clustering to align comparable features and improve data organization. Multi-head attention focuses on essential characteristics, whereas the denoising autoencoder highlights important aspects by rebuilding from distorted inputs. This method improves the capability to learn from tabular data while lowering redundancy. Our research, demonstrating improved performance through appropriate feature sequence rearrangement using raw antibody microarray and two other real-world biomedical datasets, validates the impact of feature ordering. These results demonstrate that feature ordering can be a viable approach to improved deep learning of tabular data., Comment: This paper has been accepted for presentation at the 27th International Conference on Pattern Recognition (ICPR 2024) in Kolkata, India

DNA-Encoded Libraries (DEL) are combinatorial small molecule libraries that offer an efficient way to characterize diverse chemical spaces. Selection experiments using DELs are pivotal to drug discovery efforts, enabling high-throughput screens for hit finding. However, limited availability of public DEL datasets hinders the advancement of computational techniques designed to process such data. To bridge this gap, we present KinDEL, one of the first large, publicly available DEL datasets on two kinases: Mitogen-Activated Protein Kinase 14 (MAPK14) and Discoidin Domain Receptor Tyrosine Kinase 1 (DDR1). Interest in this data modality is growing due to its ability to generate extensive supervised chemical data that densely samples around select molecular structures. Demonstrating one such application of the data, we benchmark different machine learning techniques to develop predictive models for hit identification; in particular, we highlight recent structure-based probabilistic approaches. Finally, we provide biophysical assay data, both on- and off-DNA, to validate our models on a smaller subset of molecules. Data and code for our benchmarks can be found at: https://github.com/insitro/kindel.

The vision for 6G aims to enhance network capabilities with faster data rates, near-zero latency, and higher capacity, supporting more connected devices and seamless experiences within an intelligent digital ecosystem where artificial intelligence (AI) plays a crucial role in network management and data analysis. This advancement seeks to enable immersive mixed-reality experiences, holographic communications, and smart city infrastructures. However, the expansion of 6G raises critical security and privacy concerns, such as unauthorized access and data breaches. This is due to the increased integration of IoT devices, edge computing, and AI-driven analytics. This paper provides a comprehensive overview of 6G protocols, focusing on security and privacy, identifying risks, and presenting mitigation strategies. The survey examines current risk assessment frameworks and advocates for tailored 6G solutions. We further discuss industry visions, government projects, and standardization efforts to balance technological innovation with robust security and privacy measures.

We present a practical distillation approach to fine-tune LLMs for invoking tools in real-time applications. We focus on visual editing tasks; specifically, we modify images and videos by interpreting user stylistic requests, specified in natural language ("golden hour"), using an LLM to select the appropriate tools and their parameters to achieve the desired visual effect. We found that proprietary LLMs such as GPT-3.5-Turbo show potential in this task, but their high cost and latency make them unsuitable for real-time applications. In our approach, we fine-tune a (smaller) student LLM with guidance from a (larger) teacher LLM and behavioral signals. We introduce offline metrics to evaluate student LLMs. Both online and offline experiments show that our student models manage to match the performance of our teacher model (GPT-3.5-Turbo), significantly reducing costs and latency. Lastly, we show that fine-tuning was improved by 25% in low-data regimes using augmentation., Comment: EMNLP 2024

We investigate the potential of using gravitational wave (GW) signals from rotating core-collapse supernovae to probe the equation of state (EOS) of nuclear matter. By generating GW signals from simulations with various EOSs, we train machine learning models to classify them and evaluate their performance. Our study builds on previous work by examining how different machine learning models, parameters, and data preprocessing techniques impact classification accuracy. We test convolutional and recurrent neural networks, as well as six classical algorithms: random forest, support vector machines, naive Bayes, logistic regression, k-nearest neighbors, and eXtreme gradient boosting. All models, except naive Bayes, achieve over 90 per cent accuracy on our dataset. Additionally, we assess the impact of approximating the GW signal using the general relativistic effective potential (GREP) on EOS classification. We find that models trained on GREP data exhibit low classification accuracy. However, normalizing time by the peak signal frequency, which partially compensates for the absence of the time dilation effect in GREP, leads to a notable improvement in accuracy., Comment: Submitted to Machine Learning: Science and Technology

Based upon a combined formalism of Schwinger-Dyson and Bethe-Salpeter equations in quantum chromodynamics (QCD), we propose a QCD kindred algebraic model for the dressed quark propagator, for the Bethe-Salpeter amplitude of the pion and the electromagnetic quark-photon interaction vertex. We then compute the $\gamma^{*}\pi^0\gamma$ transition form factor $G^{\gamma^{*}\pi^0\gamma}(Q^2)$ for a wide range of photon momentum transfer squared $Q^2$. The quark propagator is expanded out in its perturbative functional form but with dynamically generated dressed quark mass. It has complex conjugate pole singularities in the complex-momentum plane which is motivated by the solution of the quark gap equation with rainbow-ladder truncation of the infinite set of Schwinger-Dyson equations. This complex pole singularity structure of the quark propagator can be associated with a signal of confinement which prevents quarks to become stable asymptotic states. The Bethe-Salpeter amplitude is expressed without a spectral density function, which encapsulate its low and large momentum behaviour. The QCD evolution of the distribution amplitude is also incorporated into our model through the direct implementation of Efremov-Radyushkin-Brodsky-Lepage evolution equations. We include the effects of the quark anomalous magnetic moment in the description of the quark-photon vertex whose infrared enhancement is known to dictate hadronic properties. Once the QCD kindred model is constructed, we calculate the form factor $G^{\gamma^{*}\pi^0\gamma}(Q^2)$ and find it consistent with direct QCD-based studies as well as most available experimental data. It slightly exceeds the conformal limit for large $Q^2$ which might be attributed to the scaling violations in QCD. The associated interaction radius and neutral pion decay width turn out to be compatible with experimental data., Comment: 11 pages, 4 figures

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

The $\mathcal{V}\to\mathcal{P}\gamma$ and $\eta(\eta^\prime) \to \gamma\gamma$ decays are evaluated within a Dyson-Schwinger and Bethe-Salpeter equations framework (here $\mathcal{V}=\{\rho^{\pm},K^{\star\pm},\phi\}$ and $\mathcal{P}=\{\pi^{\pm},K^{\pm},\eta,\eta^{\prime}\}$). The so-called impulse approximation (IA) is employed in the computation of the decay constants involved and decay widths, and so in the estimation of the associated charge and interaction radii. For their part, the required propagators and vertices stem from a contact interaction model, embedded within a beyond rainbow-ladder (RL) truncation that accounts for the typical ladder exchanges, quark anomalous magnetic moment, as well as the non-Abelian anomaly. While the examined transitions produce decay widths plainly compatible with the available experimental data, those processes involving the $\eta-\eta'$ mesons highlight the incompleteness of the IA when considering beyond RL effects in the interaction kernels., Comment: 9 pages

This study explores the use of historical data from Global Navigation Satellite System (GNSS) scintillation monitoring receivers to predict the severity of amplitude scintillation, a phenomenon where electron density irregularities in the ionosphere cause fluctuations in GNSS signal power. These fluctuations can be measured using the S4 index, but real-time data is not always available. The research focuses on developing a machine learning (ML) model that can forecast the intensity of amplitude scintillation, categorizing it into low, medium, or high severity levels based on various time and space-related factors. Among six different ML models tested, the XGBoost model emerged as the most effective, demonstrating a remarkable 77% prediction accuracy when trained with a balanced dataset. This work underscores the effectiveness of machine learning in enhancing the reliability and performance of GNSS signals and navigation systems by accurately predicting amplitude scintillation severity.

Aligning large language models (LLMs) with a human reasoning approach ensures that LLMs produce morally correct and human-like decisions. Ethical concerns are raised because current models are prone to generating false positives and providing malicious responses. To contribute to this issue, we have curated an ethics dataset named Dataset for Aligning Reasons (DFAR), designed to aid in aligning language models to generate human-like reasons. The dataset comprises statements with ethical-unethical labels and their corresponding reasons. In this study, we employed a unique and novel fine-tuning approach that utilizes ethics labels and their corresponding reasons (L+R), in contrast to the existing fine-tuning approach that only uses labels (L). The original pre-trained versions, the existing fine-tuned versions, and our proposed fine-tuned versions of LLMs were then evaluated on an ethical-unethical classification task and a reason-generation task. Our proposed fine-tuning strategy notably outperforms the others in both tasks, achieving significantly higher accuracy scores in the classification task and lower misalignment rates in the reason-generation task. The increase in classification accuracies and decrease in misalignment rates indicate that the L+R fine-tuned models align more with human ethics. Hence, this study illustrates that injecting reasons has substantially improved the alignment of LLMs, resulting in more human-like responses. We have made the DFAR dataset and corresponding codes publicly available at https://github.com/apurba-nsu-rnd-lab/DFAR., Comment: Accepted in ICPR 2024

Interpretable classification of time series presents significant challenges in high dimensions. Traditional feature selection methods in the frequency domain often assume sparsity in spectral density matrices (SDMs) or their inverses, which can be restrictive for real-world applications. In this article, we propose a model-based approach for classifying high-dimensional stationary time series by assuming sparsity in the difference between inverse SDMs. Our approach emphasizes the interpretability of model parameters, making it especially suitable for fields like neuroscience, where understanding differences in brain network connectivity across various states is crucial. The estimators for model parameters demonstrate consistency under appropriate conditions. We further propose using standard deep learning optimizers for parameter estimation, employing techniques such as mini-batching and learning rate scheduling. Additionally, we introduce a method to screen the most discriminatory frequencies for classification, which exhibits the sure screening property under general conditions. The flexibility of the proposed model allows the significance of covariates to vary across frequencies, enabling nuanced inferences and deeper insights into the underlying problem. The novelty of our method lies in the interpretability of the model parameters, addressing critical needs in neuroscience. The proposed approaches have been evaluated on simulated examples and the `Alert-vs-Drowsy' EEG dataset.

Iris recognition is widely used in several fields such as mobile phones, financial transactions, identification cards, airport security, international border control, voter registration for living persons. However, the possibility of identifying deceased individuals based on their iris patterns has emerged recently as a supplementary or alternative method valuable in forensic analysis. Simultaneously, it poses numerous new technological challenges and one of the most challenging among them is the image segmentation stage as conventional iris recognition approaches have struggled to reliably execute it. This paper presents and compares Deep Learning (DL) models designed for segmenting iris images collected from the deceased subjects, by training SegNet and DeepLabV3+ semantic segmentation methods where using VGG19, ResNet18, ResNet50, MobileNetv2, Xception, or InceptionResNetv2 as backbones. In this study, our experiments demonstrate that our proposed method effectively learns and identifies specific deformations inherent in post-mortem samples and providing a significant improvement in accuracy. By employing our novel method MobileNetv2 as the backbone of DeepLabV3+ and replacing the final layer with a hybrid loss function combining Boundary and Dice loss, we achieve Mean Intersection over Union of 95.54% on the Warsaw-BioBase-PostMortem-Iris-v1 dataset. To the best of our knowledge, this study provides the most extensive evaluation of DL models for post-mortem iris segmentation., Comment: submitted to ijcb 2024 special session

Ear recognition as a biometric modality is becoming increasingly popular, with promising broader application areas. While current applications involve adults, one of the challenges in ear recognition for children is the rapid structural changes in the ear as they age. This work introduces a foundational longitudinal dataset collected from children aged 4 to 14 years over a 2.5-year period and evaluates ear recognition performance in this demographic. We present a deep learning based approach for ear recognition, using an ensemble of VGG16 and MobileNet, focusing on both adult and child datasets, with an emphasis on longitudinal evaluation for children., Comment: Submitted to Biosig 2024