Search

The application of large language models (LLMs) in healthcare has gained significant attention due to their ability to process complex medical data and provide insights for clinical decision-making. These models have demonstrated substantial capabilities in understanding and generating natural language, which is crucial for medical documentation, diagnostics, and patient interaction. This review examines the trajectory of language models from their early stages to the current state-of-the-art LLMs, highlighting their strengths in healthcare applications and discussing challenges such as data privacy, bias, and ethical considerations. The potential of LLMs to enhance healthcare delivery is explored, alongside the necessary steps to ensure their ethical and effective integration into medical practice.

In the span of four decades, quantum computation has evolved from an intellectual curiosity to a potentially realizable technology. Today, small-scale demonstrations have become possible for quantum algorithmic primitives on hundreds of physical qubits and proof-of-principle error-correction on a single logical qubit. Nevertheless, despite significant progress and excitement, the path toward a full-stack scalable technology is largely unknown. There are significant outstanding quantum hardware, fabrication, software architecture, and algorithmic challenges that are either unresolved or overlooked. These issues could seriously undermine the arrival of utility-scale quantum computers for the foreseeable future. Here, we provide a comprehensive review of these scaling challenges. We show how the road to scaling could be paved by adopting existing semiconductor technology to build much higher-quality qubits, employing system engineering approaches, and performing distributed quantum computation within heterogeneous high-performance computing infrastructures. These opportunities for research and development could unlock certain promising applications, in particular, efficient quantum simulation/learning of quantum data generated by natural or engineered quantum systems. To estimate the true cost of such promises, we provide a detailed resource and sensitivity analysis for classically hard quantum chemistry calculations on surface-code error-corrected quantum computers given current, target, and desired hardware specifications based on superconducting qubits, accounting for a realistic distribution of errors. Furthermore, we argue that, to tackle industry-scale classical optimization and machine learning problems in a cost-effective manner, distributed quantum-assisted probabilistic computing with custom-designed accelerators should be considered as a complementary path toward scalability., Comment: 50 pages, 42 figures

For $\beta\in{\mathbb Z}$, let $G(\beta)=\langle A,B\,|\, A^{[A,B]}=A,\, B^{[B,A]}=B^\beta\rangle$ be the infinite Macdonald group, and set $C=[A,B]$. Then $G(\beta)$ is a nilpotent polycyclic group of the form $\langle A\rangle\ltimes\langle B,C\rangle$, where $A$ has infinite order. If $\beta\neq 1$, then $G(\beta)$ is of class 3 and $\langle B,C\rangle$ is a finite metacyclic group of order $|\beta-1|^3$, which is an extension of $C_{(\beta-1)^2}$ by $C_{|\beta-1|}$, split except when $v_2(\beta-1)=1$, while $G(1)$ is the integral Heisenberg group, of class 2 and $\langle B,C\rangle\cong{\mathbb Z}^2$. We give a full description of the automorphism group of $G(\beta)$. If $\beta\neq 1$, then $|\mathrm{Aut}(G(\beta))|=2(\beta-1)^4$ and we exhibit an imbedding $\mathrm{Aut}(G(\beta))\hookrightarrow {\mathrm GL}_4({\mathbb Z}/(\beta-1){\mathbb Z})$, but for the case $\beta\in\{-1,3\}$ when 5 is required instead of 4. When $\beta$ is even the automorphism group of $\langle B,C\rangle$ can be obtained from the work of Bidwell and Curran \cite{BC}, and we indicate which of their automorphisms extend to an automorphism of $G(\beta)$. In general, we give necessary and sufficient conditions for $G(\beta)$ to be isomorphic to $G(\gamma)$. When $\gcd(\beta-1,6)=1$, we determine the automorphism group of $L(\beta)=G(\beta)/\langle A^{\beta-1}\rangle$, which is a relative holomorph of $\langle B,C\rangle$, and $\langle A^{\beta-1}\rangle$ is a characteristic subgroup of $G(\beta)$. The map $\mathrm{Aut}(G(\beta))\to \mathrm{Aut}(L(\beta))$ is injective and $\mathrm{Aut}(L(\beta))$ is an extension of the Heisenberg group over ${\mathbb Z}/(\beta-1){\mathbb Z}$ direct product $C_{\beta-1}$, by the holomorph of $C_{\beta-1}$.

The mid-infrared region is crucial for elucidating the unique biochemical signatures of microorganisms. The MIR resonant structures turned out to facilitate exceptional performance owing to the enhance electric field confinement in the nano-sized aperture. However, the extension of such technique in bacteria-sensing remains limited, primarily due to its micrometre size. This work is the first demonstration of a MIR resonant structure, the gold-coated micro-structured inverted pyramid array of silicon exhibiting light-trapping capabilities, for the bacteria detection in entire MIR range. The electric-field localization within the micro-sized cavity of inverted pyramid amplifies the light-matter interaction by harnessing surface plasmon polaritons, leading to improved detection sensitivity. The confinement of electric field is further corroborated by electric-field simulations based on finite element method. In particular, we observed notable enhancement in both the quantitative and qualitative detection of Escherichia coli and Staphylococcus aureus for the bacteria cell with very low concentration, reflecting the efficacy of our detection method. Furthermore, the cost-effective micro structured silicon is fabricated using metal-assisted chemical etching method with the lithography-free method, along with the capabilities of wafer-scale fabrication. Moreover, our device configuration even demonstrates the characteristics of reusability and reproducibility offers substantial benefits over conventional detection schemes. Consequently, this CMOS technology-compatible biosensor signifies promising ways for the integration of this technology with forthcoming bio-applications.

The development of high-performance materials for microelectronics, energy storage, and extreme environments depends on our ability to describe and direct property-defining microstructural order. Our present understanding is typically derived from laborious manual analysis of imaging and spectroscopy data, which is difficult to scale, challenging to reproduce, and lacks the ability to reveal latent associations needed for mechanistic models. Here, we demonstrate a multi-modal machine learning (ML) approach to describe order from electron microscopy analysis of the complex oxide La$_{1-x}$Sr$_x$FeO$_3$. We construct a hybrid pipeline based on fully and semi-supervised classification, allowing us to evaluate both the characteristics of each data modality and the value each modality adds to the ensemble. We observe distinct differences in the performance of uni- and multi-modal models, from which we draw general lessons in describing crystal order using computer vision., Comment: 30 pages, 5 figures, 2 tables

The kicked top is one of the most extensively studied paradigms of quantum chaos. In this model, an intricate connection has been observed between entanglement entropy and classical dynamics. This connection appears surprising since both chaos and entanglement are understood to be exclusive to classical and quantum mechanics respectively. In this paper, we have argued that from an alternative standpoint on classical physics, this connection becomes completely natural. According to this view, classical states are more accurately represented by distributions instead of infinitely precise points in phase space. Many properties that have traditionally been held to be exclusively quantum, such as non-separability of states, appear in classical physics too in this picture. Looking at the kicked top from this paradigm of classical physics provides a completely fresh outlook to the chaos-entanglement discussion. This finding opens new avenues of understanding in quantum chaos and the more general problem of classical-quantum correspondence.

The PAC-Bayesian framework has significantly advanced our understanding of statistical learning, particularly in majority voting methods. However, its application to multi-view learning remains underexplored. In this paper, we extend PAC-Bayesian theory to the multi-view setting, introducing novel PAC-Bayesian bounds based on R\'enyi divergence. These bounds improve upon traditional Kullback-Leibler divergence and offer more refined complexity measures. We further propose first and second-order oracle PAC-Bayesian bounds, along with an extension of the C-bound for multi-view learning. To ensure practical applicability, we develop efficient optimization algorithms with self-bounding properties.

We propose a novel technique for optimizing a modular fault-tolerant quantum computing architecture, taking into account any desired space-time trade--offs between the number of physical qubits and the fault-tolerant execution time of a quantum algorithm. We consider a concept architecture comprising a dedicated zone as a multi-level magic state factory and a core processor for efficient logical operations, forming a supply chain network for production and consumption of magic states. Using a heuristic algorithm, we solve the multi-objective optimization problem of minimizing space and time subject to a user-defined error budget for the success of the computation, taking the performance of various fault-tolerant protocols such as quantum memory, state preparation, magic state distillation, code growth, and logical operations into account. As an application, we show that physical quantum resource estimation reduces to a simple model involving a small number of key parameters, namely, the circuit volume, the error prefactors ($\mu$) and error suppression rates ($\Lambda$) of the fault-tolerant protocols, and an allowed slowdown factor ($\beta$). We show that, in the proposed architecture, $10^5$--$10^8$ physical qubits are required for quantum algorithms with $T$-counts in the range $10^6$--$10^{15}$ and logical qubit counts in the range $10^2$--$10^4$, when run on quantum computers with quantum memory $\Lambda$ in the range 3--10, for all slowdown factors $\beta \geq 0.2$., Comment: 21 pages, 6 figures

This paper presents a methodology for training a transformer-based model to classify lexical and morphosyntactic features of Skolt Sami, an endangered Uralic language characterized by complex morphology. The goal of our approach is to create an effective system for understanding and analyzing Skolt Sami, given the limited data availability and linguistic intricacies inherent to the language. Our end-to-end pipeline includes data extraction, augmentation, and training a transformer-based model capable of predicting inflection classes. The motivation behind this work is to support language preservation and revitalization efforts for minority languages like Skolt Sami. Accurate classification not only helps improve the state of Finite-State Transducers (FSTs) by providing greater lexical coverage but also contributes to systematic linguistic documentation for researchers working with newly discovered words from literature and native speakers. Our model achieves an average weighted F1 score of 1.00 for POS classification and 0.81 for inflection class classification. The trained model and code will be released publicly to facilitate future research in endangered NLP., Comment: IWCLUL 2024

Inspired by the intriguing discrepancies observed in the $b\to s\ell^+\ell^-$ neutral current decays, we study the decay channel $B_c\to D_s^{(\ast)} \,\ell^+\ell^-$ ($\ell=\mu,\tau$), which is also the same flavor changing neutral current (FCNC) transition at the quark level. In this context, looking for the potential of physics beyond the standard model (SM) can offer compelling results to the decay $B_c\to D_s^{(\ast)} \,\ell^+\ell^-$. For this purpose, we use the helicity formalism for this decay by employing the effective theory approach where the vector and axial vector new physics (NP) operators are present. In this study, we have calculated several observables, such as the branching ratio $B_r$, the $D^\ast$ helicity fraction $f_L$, the lepton forward-backward asymmetry $A_{FB}$, and the lepton flavor universality ratio (LFU) $R^{\tau\mu}_{D_s^*}$. In addition, to a complimentary check on the LFU, we also calculate the ratio of different observables $R_{i}^{\tau\mu}$ where $i=A_{FB}$, $f_L$. We assume that the NP universal coupling is present for both muons and tauons, while the non-universal coupling is only present for muons. Regarding these couplings, we imply the latest global fit to the $b\to s\ell^+\ell^-$ data, which is recently computed in \cite{Alguero:2023jeh}. We give predictions of some of the mentioned observables within the SM and the various NP scenarios. We found that the considered observables are not only sensitive to the NP but also helpful in distinguishing among the different NP scenarios. These results can be tested at LHCb, HL-LHC, and FCC-ee, and therefore, the precise measurements of these observables not only deepen our understanding of the $b\to s\ell^+\ell^-$ process but also provide a complementary check of the status of different NP scenarios., Comment: 27 pages, 11 figures

Poisoning attacks are a primary threat to machine learning models, aiming to compromise their performance and reliability by manipulating training datasets. This paper introduces a novel attack - Outlier-Oriented Poisoning (OOP) attack, which manipulates labels of most distanced samples from the decision boundaries. The paper also investigates the adverse impact of such attacks on different machine learning algorithms within a multiclass classification scenario, analyzing their variance and correlation between different poisoning levels and performance degradation. To ascertain the severity of the OOP attack for different degrees (5% - 25%) of poisoning, we analyzed variance, accuracy, precision, recall, f1-score, and false positive rate for chosen ML models.Benchmarking our OOP attack, we have analyzed key characteristics of multiclass machine learning algorithms and their sensitivity to poisoning attacks. Our experimentation used three publicly available datasets: IRIS, MNIST, and ISIC. Our analysis shows that KNN and GNB are the most affected algorithms with a decrease in accuracy of 22.81% and 56.07% while increasing false positive rate to 17.14% and 40.45% for IRIS dataset with 15% poisoning. Further, Decision Trees and Random Forest are the most resilient algorithms with the least accuracy disruption of 12.28% and 17.52% with 15% poisoning of the IRIS dataset. We have also analyzed the correlation between number of dataset classes and the performance degradation of models. Our analysis highlighted that number of classes are inversely proportional to the performance degradation, specifically the decrease in accuracy of the models, which is normalized with increasing number of classes. Further, our analysis identified that imbalanced dataset distribution can aggravate the impact of poisoning for machine learning models

Inspired by the natural motion of insects, fish, and other animals, flapping airfoils have gained significant importance due to their applications in fields such as ship propulsion, micro aerial vehicles, and autonomous underwater vehicles. Over the past two decades, extensive research has focused on understanding the dynamics of these airfoils, their thrust production capabilities, and methods to enhance this thrust in unsteady flows. This study investigates how the presence of a cylinder oscillating due to incoming flow affects the thrust performance of the flapping airfoil. The results indicate that the flapping airfoil generates increased thrust when placed in the wake of the oscillating cylinder compared to a scenario without the cylinder's wake. A direct relationship has been found between the strouhal number ($St$) of the flapping airfoil and its pitching amplitude, which significantly influences the airfoil's performance. This analysis highlights the potential for optimizing flapping airfoil efficiency through strategic selection of flapping parameters and the placement of an oscillating cylinder.

This paper introduces a comprehensive framework for Post-Disaster Search and Rescue (PDSR), aiming to optimize search and rescue operations leveraging Unmanned Aerial Vehicles (UAVs). The primary goal is to improve the precision and availability of sensing capabilities, particularly in various catastrophic scenarios. Central to this concept is the rapid deployment of UAV swarms equipped with diverse sensing, communication, and intelligence capabilities, functioning as an integrated system that incorporates multiple technologies and approaches for efficient detection of individuals buried beneath rubble or debris following a disaster. Within this framework, we propose architectural solution and address associated challenges to ensure optimal performance in real-world disaster scenarios. The proposed framework aims to achieve complete coverage of damaged areas significantly faster than traditional methods using a multi-tier swarm architecture. Furthermore, integrating multi-modal sensing data with machine learning for data fusion could enhance detection accuracy, ensuring precise identification of survivors., Comment: This paper is currently under review at IEEE IoT Magazine

Electrocardiogram (ECG) is a reliable tool for medical professionals to detect and diagnose abnormal heart waves that may cause cardiovascular diseases. This paper proposes a methodology to create a new high-quality heartbeat dataset from all 48 of the MIT-BIH recordings. The proposed approach computes an optimal heartbeat size, by eliminating outliers and calculating the mean value over 10-second windows. This results in independent QRS-centered heartbeats avoiding the mixing of successive heartbeats problem. The quality of the newly constructed dataset has been evaluated and compared with existing datasets. To this end, we built and trained a PyTorch 1-D Resnet architecture model that achieved 99.24\% accuracy with a 5.7\% improvement compared to other methods. Additionally, downsampling the dataset has improved the model's execution time by 33\% and reduced 3x memory usage., Comment: 4 pages, 5 figures, 5 tables, presented during IEEE COINS 2023 Berlin. link to ieeexploere: https://ieeexplore.ieee.org/document/10189299

Dementia is a neurodegenerative condition that combines several diseases and impacts millions around the world and those around them. Although cognitive impairment is profoundly disabling, it is the noncognitive features of dementia, referred to as Neuropsychiatric Symptoms (NPS), that are most closely associated with a diminished quality of life. Agitation and aggression (AA) in people living with dementia (PwD) contribute to distress and increased healthcare demands. Current assessment methods rely on caregiver intervention and reporting of incidents, introducing subjectivity and bias. Artificial Intelligence (AI) and predictive algorithms offer a potential solution for detecting AA episodes in PwD when utilized in real-time. We present a 5-year study system that integrates a multimodal approach, utilizing the EmbracePlus wristband and a video detection system to predict AA in severe dementia patients. We conducted a pilot study with three participants at the Ontario Shores Mental Health Institute to validate the functionality of the system. The system collects and processes raw and digital biomarkers from the EmbracePlus wristband to accurately predict AA. The system also detected pre-agitation patterns at least six minutes before the AA event, which was not previously discovered from the EmbracePlus wristband. Furthermore, the privacy-preserving video system uses a masking tool to hide the features of the people in frames and employs a deep learning model for AA detection. The video system also helps identify the actual start and end time of the agitation events for labeling. The promising results of the preliminary data analysis underscore the ability of the system to predict AA events. The ability of the proposed system to run autonomously in real-time and identify AA and pre-agitation symptoms without external assistance represents a significant milestone in this research field.

The Workflows Community Summit gathered 111 participants from 18 countries to discuss emerging trends and challenges in scientific workflows, focusing on six key areas: time-sensitive workflows, AI-HPC convergence, multi-facility workflows, heterogeneous HPC environments, user experience, and FAIR computational workflows. The integration of AI and exascale computing has revolutionized scientific workflows, enabling higher-fidelity models and complex, time-sensitive processes, while introducing challenges in managing heterogeneous environments and multi-facility data dependencies. The rise of large language models is driving computational demands to zettaflop scales, necessitating modular, adaptable systems and cloud-service models to optimize resource utilization and ensure reproducibility. Multi-facility workflows present challenges in data movement, curation, and overcoming institutional silos, while diverse hardware architectures require integrating workflow considerations into early system design and developing standardized resource management tools. The summit emphasized improving user experience in workflow systems and ensuring FAIR workflows to enhance collaboration and accelerate scientific discovery. Key recommendations include developing standardized metrics for time-sensitive workflows, creating frameworks for cloud-HPC integration, implementing distributed-by-design workflow modeling, establishing multi-facility authentication protocols, and accelerating AI integration in HPC workflow management. The summit also called for comprehensive workflow benchmarks, workflow-specific UX principles, and a FAIR workflow maturity model, highlighting the need for continued collaboration in addressing the complex challenges posed by the convergence of AI, HPC, and multi-facility research environments.

This paper explores a GIS-based application of the conditional p-median problem (where p = 1) in last-mile delivery logistics. The rapid growth of e-commerce in Pakistan has primarily benefited logistics companies, which face the challenge of resolving inefficiencies in the existing infrastructure and scaling effectively to meet increasing demand. Addressing these challenges would not only reduce operational costs but also lower carbon footprints. We present an algorithm that utilizes road-network-based distances to determine the optimal location for a new hub facility, a problem known in operations research as the conditional p-median problem. The algorithm optimizes the placement of a new facility, given q existing facilities. The past delivery data for this research was provided by Muller and Phipps Logistics Pakistan. Our method involves constructing a distance matrix between candidate hub locations and past delivery points, followed by a grid search to identify the optimal hub location. To simulate the absence of past delivery data, we repeated the process using the population distribution of Lahore. Our results demonstrate a 16% reduction in average delivery distance with the addition of a new hub., Comment: 6 pages

This letter proposes using intermittent chaotic clocks, generated from chaotic maps, to drive cryptographic chips running the Advanced Encryption Standard as a countermeasure against Correlation Power Analysis attacks. Five different chaotic maps -- namely: the Logistic map, the Bernoulli shift map, the Henon map, the Tent map, and the Ikeda map -- are used in this work to generate chaotic clocks. The performance of these chaotic clocks is evaluated in terms of timing overhead and the resilience of the driven chip against Correlation Power Analysis attacks. All proposed chaotic clocking schemes successfully protect the driven chip against attacks, with the clocks produced by the optimized Ikeda, Henon, and Logistic maps achieving the lowest timing overhead. These optimized maps, due to their intermittent chaotic behavior, exhibit lower timing overhead compared to previous work. Notably, the chaotic clock generated by the optimized Ikeda map approaches the theoretical limit of timing overhead, i.e., half the execution time of a reference periodic clock.

We present the discovery and characterization of TOI-4364\,b, a young mini-Neptune in the tidal tails of the Hyades cluster, identified through TESS transit observations and ground-based follow-up photometry. The planet orbits a bright M dwarf ($K=9.1$\,mag) at a distance of 44\,pc, with an orbital period of 5.42\,days and an equilibrium temperature of $488^{+4}_{-4}$\,K. The host star's well-constrained age of 710\,Myr makes TOI-4364\,b an exceptional target for studying early planetary evolution around low-mass stars. We determined a planetary radius of $2.01^{+0.1}_{-0.08}$\,Earth radii, indicating that this planet is situated near the upper edge of the radius valley. This suggests that the planet retains a modest H/He envelope. As a result, TOI-4364\,b provides a unique opportunity to explore the transition between rocky super-Earths and gas-rich mini-Neptunes at the early stages of evolution. Its radius, which may still evolve as a result of ongoing atmospheric cooling, contraction, and photoevaporation, further enhances its significance for understanding planetary development. Furthermore, TOI-4364\,b possesses a moderately high Transmission Spectroscopy Metric of 44.2, positioning it as a viable candidate for atmospheric characterization with instruments such as JWST. This target has the potential to offer crucial insights into atmospheric retention and loss in young planetary systems., Comment: 18 pages, 9 figures

In the search for a light Higgs boson, the ATLAS and CMS experiments have observed excesses in both the diphoton ($\gamma\gamma$) and di-tau-pair ($\tau^+\tau^-$) decay channels at about $95$ GeV. The LEP collaboration has also previously reported an excess in the $b\bar{b}$ channel at a comparable Higgs mass. In this paper, we explore whether these excesses can be accommodated within the framework of the Two Higgs Doublet type II Seesaw Model (2HDMcT). By implementing various theoretical constraints and experimental limits on the parameter space, we first demonstrate that a light CP-even Higgs boson, $h_1$, with a mass around 95 GeV can simultaneously account for the excesses observed in the $\gamma\gamma$ and $b\bar{b}$ channels, provided a Type I Yukawa texture is employed. More interestingly, our analysis shows that the three excesses in $\gamma\gamma$, $b\bar{b}$ and $\tau^+\tau^-$ channels can well be accommodated simultaneously, reaching a $0.64 \sigma$ C.L. if the CP-odd Higgs boson $A_1$ is nearly mass degenerate and superposed to the light CP-even Higgs $h_1$., Comment: 20 pages, 8 figures, 4 Tables, Latex

Reanalysis data, such as ERA5, provide a comprehensive and detailed representation of the Earth's system by assimilating observations into climate models. While crucial for climate research, they pose significant challenges in terms of generation, storage, and management. For 3-hourly bivariate wind speed ensembles from ERA5, which face these challenges, this paper proposes an online stochastic generator (OSG) applicable to any global region, offering fast stochastic approximations while storing only model parameters. A key innovation is the incorporation of the online updating, which allows data to sequentially enter the model in blocks of time and contribute to parameter updates. This approach reduces storage demands during modeling by eliminating the need to store and analyze the entire dataset, and enables near real-time emulations that complement the generation of reanalysis data. The Slepian concentration technique supports the efficiency of the proposed OSG by representing the data in a lower-dimensional space spanned by data-independent Slepian bases optimally concentrated within the specified region. We demonstrate the flexibility and efficiency of the OSG through two case studies requiring long and short blocks, specified for the Arabian-Peninsula region (ARP). For both cases, the OSG performs well across several statistical metrics and is comparable to the SG trained on the full dataset.

This paper presents the foundational framework and initial findings of the Balanced Arabic Readability Evaluation Corpus (BAREC) project, designed to address the need for comprehensive Arabic language resources aligned with diverse readability levels. Inspired by the Taha/Arabi21 readability reference, BAREC aims to provide a standardized reference for assessing sentence-level Arabic text readability across 19 distinct levels, ranging in targets from kindergarten to postgraduate comprehension. Our ultimate goal with BAREC is to create a comprehensive and balanced corpus that represents a wide range of genres, topics, and regional variations through a multifaceted approach combining manual annotation with AI-driven tools. This paper focuses on our meticulous annotation guidelines, demonstrated through the analysis of 10,631 sentences/phrases (113,651 words). The average pairwise inter-annotator agreement, measured by Quadratic Weighted Kappa, is 79.9%, reflecting a high level of substantial agreement. We also report competitive results for benchmarking automatic readability assessment. We will make the BAREC corpus and guidelines openly accessible to support Arabic language research and education., Comment: 16 pages, 3 figures

The nuclear ground state properties of 67 80As nuclei have been investigated within the framework of relativistic mean field (RMF) approach. The RMF model with density dependent (DDME2) interaction is utilized for the calculation of potential energy curves and the nuclear ground state deformation parameters $\beta_2$ of selected As isotopes. Later, the $\beta$ decay properties of As isotopes were studied using the proton neutron quasi particle random phase approximation pnQRPA model. These include Gamow Tellar (GT) strength distributions, log ft values, $\beta$ decay half lives, stellar $\beta$ plus minus decays and stellar electron positron capture rates. The $\beta_2$ values computed from RMF model were employed in the on QRPA model as an input parameter for the calculations of $\beta$-decay properties for 67 80As. The calculated log ft values were in decent agreement with the measured data. The predicted $\beta$-decay half lives matched the experimental values within a factor of 10. The stellar rates were compared with the shell model results. Only at high temperature and density values, the sum of $\beta$ plus and electron capture rates had a finite contribution. On the other hand, the sum of $\beta$ negative and positron capture rates were sizeable only at low density and high temperature values. For all such cases, the pn QRPA rates were found to be bigger than the shell model rates up to a factor of 33 or more. The findings reported in the current investigation could prove valuable for simulating the late stage stellar evolution of massive stars., Comment: 16 pages 6 figures 5 tables, Chinese Journal of Physics (2024)

Morocco's Vision 2030, known as Maroc Digital 2030, aims to position the country as a regional leader in digital technology by boosting digital infrastructure, fostering innovation, and advancing digital skills. Complementing this initiative, the Pacte ESRI 2030 strategy, launched in 2023, seeks to transform the higher education, research, and innovation sectors by integrating state-of-the-art digital technologies. In alignment with these national strategies, this paper introduces BlockMEDC, a blockchain-based system for securing and managing Moroccan educational digital certificates. Leveraging Ethereum smart contracts and the InterPlanetary File System, BlockMEDC automates the issuance, management, and verification of academic credentials across Moroccan universities. The proposed system addresses key issues such as document authenticity, manual verification, and lack of interoperability, delivering a secure, transparent, and cost-effective solution that aligns with Morocco's digital transformation goals for the education sector.

Recent trends within computational and data sciences show an increasing recognition and adoption of computational workflows as tools for productivity, reproducibility, and democratized access to platforms and processing know-how. As digital objects to be shared, discovered, and reused, computational workflows benefit from the FAIR principles, which stand for Findable, Accessible, Interoperable, and Reusable. The Workflows Community Initiative's FAIR Workflows Working Group (WCI-FW), a global and open community of researchers and developers working with computational workflows across disciplines and domains, has systematically addressed the application of both FAIR data and software principles to computational workflows. We present our recommendations with commentary that reflects our discussions and justifies our choices and adaptations. Like the software and data principles on which they are based, these are offered to workflow users and authors, workflow management system developers, and providers of workflow services as guide rails for adoption and fodder for discussion. Workflows are becoming more prevalent as documented, automated instruments for data analysis, data collection, AI-based predictions, and simulations. The FAIR recommendations for workflows that we propose in this paper will maximize their value as research assets and facilitate their adoption by the wider community., Comment: 17 pages, 1 figure, 1 table

Kepler-51 is a $\lesssim 1\,\mathrm{Gyr}$-old Sun-like star hosting three transiting planets with radii $\approx 6$-$9\,R_\oplus$ and orbital periods $\approx 45$-$130\,\mathrm{days}$. Transit timing variations (TTVs) measured with past Kepler and Hubble Space Telescope (HST) observations have been successfully modeled by considering gravitational interactions between the three transiting planets, yielding low masses and low mean densities ($\lesssim 0.1\,\mathrm{g/cm^3}$) for all three planets. However, the transit time of the outermost transiting planet Kepler-51d recently measured by the James Webb Space Telescope (JWST) 10 years after the Kepler observations is significantly discrepant from the prediction made by the three-planet TTV model, which we confirmed with ground-based and follow-up HST observations. We show that the departure from the three-planet model is explained by including a fourth outer planet, Kepler-51e, in the TTV model. A wide range of masses ($\lesssim M_\mathrm{Jup}$) and orbital periods ($\lesssim 10\,\mathrm{yr}$) are possible for Kepler-51e. Nevertheless, all the coplanar solutions found from our brute-force search imply masses $\lesssim 10\,M_\oplus$ for the inner transiting planets. Thus their densities remain low, though with larger uncertainties than previously estimated. Unlike other possible solutions, the one in which Kepler-51e is around the $2:1$ mean motion resonance with Kepler-51d implies low orbital eccentricities ($\lesssim 0.05$) and comparable masses ($\sim 5\,M_\oplus$) for all four planets, as is seen in other compact multi-planet systems. This work demonstrates the importance of long-term follow-up of TTV systems for probing longer period planets in a system., Comment: 48 pages, 26 figures, accepted for publication in AJ

Secure navigation is pivotal for several applications including autonomous vehicles, robotics, and aviation. The inertial navigation system estimates position, velocity, and attitude through dead reckoning especially when external references like GPS are unavailable. However, the three accelerometers and three gyroscopes that compose the system are exposed to various types of errors including bias errors, scale factor errors, and noise, which can significantly degrade the accuracy of navigation constituting also a key vulnerability of this system. This work aims to adopt a supervised convolutional neural network (ConvNet) to address this vulnerability inherent in inertial navigation systems. In addition to this, this paper evaluates the impact of the ConvNet layer's depth on the accuracy of these corrections. This evaluation aims to determine the optimal layer configuration maximizing the effectiveness of error correction in INS (Inertial Navigation System) leading to precise navigation solutions.

We present a novel autonomous robot navigation algorithm for outdoor environments that is capable of handling diverse terrain traversability conditions. Our approach, VLM-GroNav, uses vision-language models (VLMs) and integrates them with physical grounding that is used to assess intrinsic terrain properties such as deformability and slipperiness. We use proprioceptive-based sensing, which provides direct measurements of these physical properties, and enhances the overall semantic understanding of the terrains. Our formulation uses in-context learning to ground the VLM's semantic understanding with proprioceptive data to allow dynamic updates of traversability estimates based on the robot's real-time physical interactions with the environment. We use the updated traversability estimations to inform both the local and global planners for real-time trajectory replanning. We validate our method on a legged robot (Ghost Vision 60) and a wheeled robot (Clearpath Husky), in diverse real-world outdoor environments with different deformable and slippery terrains. In practice, we observe significant improvements over state-of-the-art methods by up to 50% increase in navigation success rate.

We present CROSS-GAiT, a novel algorithm for quadruped robots that uses Cross Attention to fuse terrain representations derived from visual and time-series inputs, including linear accelerations, angular velocities, and joint efforts. These fused representations are used to adjust the robot's step height and hip splay, enabling adaptive gaits that respond dynamically to varying terrain conditions. We generate these terrain representations by processing visual inputs through a masked Vision Transformer (ViT) encoder and time-series data through a dilated causal convolutional encoder. The cross-attention mechanism then selects and integrates the most relevant features from each modality, combining terrain characteristics with robot dynamics for better-informed gait adjustments. CROSS-GAiT uses the combined representation to dynamically adjust gait parameters in response to varying and unpredictable terrains. We train CROSS-GAiT on data from diverse terrains, including asphalt, concrete, brick pavements, grass, dense vegetation, pebbles, gravel, and sand. Our algorithm generalizes well and adapts to unseen environmental conditions, enhancing real-time navigation performance. CROSS-GAiT was implemented on a Ghost Robotics Vision 60 robot and extensively tested in complex terrains with high vegetation density, uneven/unstable surfaces, sand banks, deformable substrates, etc. We observe at least a 7.04% reduction in IMU energy density and a 27.3% reduction in total joint effort, which directly correlates with increased stability and reduced energy usage when compared to state-of-the-art methods. Furthermore, CROSS-GAiT demonstrates at least a 64.5% increase in success rate and a 4.91% reduction in time to reach the goal in four complex scenarios. Additionally, the learned representations perform 4.48% better than the state-of-the-art on a terrain classification task.