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In this work, we study one-dimensional nonlocal elliptic transmission problems with piecewise constant coefficients that may change sign across an interface. In the local setting, we recall the T-coercive structure of the problem and characterize the critical contrast case. In the nonlocal setting, we focus on a simplified configuration in which the cross-interaction coefficient vanishes. Under this assumption, we prove a weak T-coercivity result for the global fractional problem and introduce a reconstructed formulation based on an explicit interface lifting. Then, we consider a simplified finite element discretization of the reconstructed model and prove its convergence toward the classical local transmission problem as the fractional parameter $s\to 1^-$ and the mesh size $h\to 0^+$. Numerical simulations in 1D illustrate the stability and consistency of the method, and a preliminary two-dimensional extension is presented as an exploratory perspective.

The sprint-based iterative approach in the Agile software development method allows continuous feedback and adaptation. One of the crucial Agile software development activities is the sprint planning session where developers estimate the effort required to complete tasks through a consensus-based estimation technique such as Planning Poker. In the Agile software development method, a common unit of measuring development effort is Story Point (SP) which is assigned to tasks to understand the complexity and development time needed to complete them. Despite the benefits of this process, it is an extremely time-consuming manual process. To mitigate this issue, in this study, we investigated if this manual process can be automated using Retrieval Augmented Generation (RAG) which comprises a "Retriever" and a "Generator". We applied two embedding models - bge-large-en-v1.5, and Sentence-Transformers' all-mpnet-base-v2 on 23 open-source software projects of varying sizes and examined four key aspects: 1) how retrieval hyper-parameters influence the performance, 2) whether estimation accuracy differs across different sizes of the projects, 3) whether embedding model choice affects accuracy

Over the last decade, the use of machine learning (ML) approaches in medicinal applications has increased manifold. Most of these approaches are based on deep learning, which aims to learn representations from grid data (like medical images). However, reinforcement learning (RL) applications in medicine are relatively less explored. Medical applications often involve a sequence of subtasks that form a diagnostic pipeline, and RL is uniquely suited to optimize over such sequential decision-making tasks. Ultrasound (US) image analysis is a quintessential example of such a sequential decision-making task, where the raw signal captured by the US transducer undergoes a series of signal processing and image post-processing steps, generally leading to a diagnostic suggestion. The application of RL in US remains limited. Deep Reinforcement Learning (DRL), that combines deep learning and RL, holds great promise in optimizing these pipelines by enabling intelligent and sequential decision-making. This review paper surveys the applications of RL in US over the last decade. We provide a succinct overview of the theoretic framework of RL and its application in US image processing and review exi

Software containers are widely adopted for developing and deploying software applications. Despite their popularity, major security concerns arise during container development and deployment. Software Engineering (SE) research literature reveals a lack of reviewed, aggregated, and organized knowledge of risks, vulnerabilities, security practices, and tools in container-based systems development and deployment. Therefore, we conducted a Systematic Mapping Study (SMS) based on 129 selected primary studies to explore and organize existing knowledge on security issues in software container systems. Data from the primary studies enabled us to identify critical risks and vulnerabilities across the container life-cycle and categorize them using a novel taxonomy. Additionally, the findings highlight the causes and implications and provide a list of mitigation techniques to overcome these risks and vulnerabilities. Furthermore, we provide an aggregation of security practices and tools that can help support and improve the overall security of container systems. This study offers critical insights into the current landscape of security issues within software container systems. Our analysis hi

Software development industries are increasingly adopting containers to enhance the scalability and flexibility of software applications. Security in containerized projects is a critical challenge that can lead to data breaches and performance degradation, thereby directly affecting the reliability and operations of the container services. Despite the ongoing effort to manage the security issues in containerized projects in software engineering (SE) research, more focused investigations are needed to explore the human perspective of security management and the technical approaches to security management in containerized projects. This research aims to explore security management in containerized projects by exploring how SE practitioners perceive the security issues in containerized software projects and their approach to managing such issues. A clear understanding of security management in containerized projects will enable industries to develop robust security strategies that enhance software reliability and trust. To achieve this, we conducted two separate semi-structured interview studies to examine how practitioners approach security management. The first study focused on prac

In this work, we study the existence and nonexistence of nonnegative solutions to a class of nonlocal elliptic systems set in a bounded open subset of $\mathbb{R}^N$. The diffusion operators are of type $u_i\mapsto d_i(-Δ)^{s_i}u_i$ where $0<s_1 eq s_2<1$, and the gradients of the unknowns act as source terms. Existence results are obtained by proving some fine estimates when data belong to weighted Lebesgue spaces. Those estimates are new and interesting in themselves.

We develop a system for solving logical deduction one-dimensional ordering problems by transforming natural language premises and candidate statements into first-order logic. Building on Heim and Kratzer's syntax-based compositional semantic rules which utilizes lambda calculus, we develop a semantic parsing algorithm with abstract types, templated rules, and a dynamic component for interpreting entities within a context constructed from the input. The resulting logical forms are executed via constraint logic programming to determine which candidate statements can be logically deduced from the premises. The symbolic system, the Formal Semantic Logic Inferer (FSLI), provides a formally grounded, linguistically driven system for natural language logical deduction. We evaluate it on both synthetic and derived logical deduction problems. FSLI achieves 100% accuracy on BIG-bench's logical deduction task and 88% on a syntactically simplified subset of AR-LSAT outperforming an LLM baseline, o1-preview. While current research in natural language reasoning emphasizes neural language models, FSLI highlights the potential of principled, interpretable systems for symbolic logical deduction in

Mixed Reality (MR) devices are being increasingly adopted across a wide range of real-world applications, ranging from education and healthcare to remote work and entertainment. However, the unique immersive features of MR devices, such as 3D spatial interactions and the encapsulation of virtual objects by invisible elements, introduce new vulnerabilities leading to interaction obstruction and misdirection. We implemented latency, click redirection, object occlusion, and spatial occlusion attacks within a remote collaborative MR platform using the Microsoft HoloLens 2 and evaluated user behavior and mitigations through a user study. We compared responses to MR-specific attacks, which exploit the unique characteristics of remote collaborative immersive environments, and traditional security attacks implemented in MR. Our findings indicate that users generally exhibit lower recognition rates for immersive attacks (e.g., spatial occlusion) compared to attacks inspired by traditional ones (e.g., click redirection). Our results demonstrate a clear gap in user awareness and responses when collaborating remotely in MR environments. Our findings emphasize the importance of training users t

In this paper, we prove the global-in-time existence of strong solutions to a class of fractional parabolic reaction-diffusion systems set in a bounded open subset of $\mathbb{R}^N$. The diffusion operators are of the form $u_i \mapsto d_i (-Δ)_{Sp}^{s_i} u_i$, where $0 < s_i < 1$. The operator $(-Δ)_{Sp}^{s}$ stands for the commonly called spectral fractional Laplacian. Moreover, the nonlinear reaction terms are assumed to fulfill natural structural conditions that ensure the nonnegativity of the solutions and provide uniform control of the total mass. We establish the global existence of strong solutions under the assumption that the nonlinearities exhibit at most polynomial growth. Our results extend previous results obtained when the diffusion operators are of the form $u_i \mapsto d_i (-Δ)^s u_i$, where $(-Δ)^s$ denotes the widely known regional fractional Laplacian. Furthermore, we present some numerical simulations to address a theoretical question that remains open to date.

Ecosystem respiration (Reco) represents a major component of the global carbon cycle, and accurate characterization of its dynamics is essential for a comprehensive understanding of ecosystem-climate interactions and the impacts of climate extremes on the ecosystem. This paper presents a novel data-driven and physics-aware method for estimating Reco dynamics using the dynamic mode decomposition with control input (DMDc) technique, an emerging tool for analyzing nonlinear dynamical systems. The proposed model represents Reco as a state space model with an autonomous component and an exogenous control input. The control input can be any ecosystem driver(s), such as air temperature, soil temperature, or soil water content. This unique modeling approach allows controlled intervention to study the effects of different inputs on the system. Experimental results using Fluxnet2015 data show that the prediction accuracy of Reco dynamics achieved with DMDc is comparable to state-of-the-art methods, making it a promising tool for analyzing the dynamic behavior of different vegetation ecosystems on multi-temporal scales in response to different climatic drivers.

Internet of Medical Things (IoMT) deals with a patient-data-rich segment, which makes security and privacy a severe concern for patients. Therefore, access control is a significant aspect of ensuring trust in the IoMT. However, deploying existing authentication and authorization solutions to the Internet of Medical Things (IoMT) is not straightforward because of highly dynamic and possibly unprotected environments and untrusted supply chain for the IoT devices. In this article, we propose Soter, a Zero-Trust based authentication system for the IoMT. Soter Incorporates trust negotiation mechanisms within the Zero Trust framework to enable dynamic trust establishment. When a user or device seeks access to a resource, initiate a trust negotiation process. During this process, credentials, attributes, and contextual information are exchanged between the requester and the resource owner. Soter defines access rules based on various factors, including user identity, device health, and location. Access is granted or denied based on these conditions.

Large language models (LLMs) have revolutionized natural language processing (NLP) with impressive performance across various text-based tasks. However, the extension of text-dominant LLMs to with speech generation tasks remains under-explored. In this work, we introduce a text-to-speech (TTS) system powered by a fine-tuned Llama model, named TTS-Llama, that achieves state-of-the-art speech synthesis performance. Building on TTS-Llama, we further propose MoLE-Llama, a text-and-speech multimodal LLM developed through purely late-fusion parameter-efficient fine-tuning (PEFT) and a mixture-of-expert architecture. Extensive empirical results demonstrate MoLE-Llama's competitive performance on both text-only question-answering (QA) and TTS tasks, mitigating catastrophic forgetting issue in either modality. Finally, we further explore MoLE-Llama in text-in-speech-out QA tasks, demonstrating its great potential as a multimodal dialog system capable of speech generation.

Deep learning-based disease diagnosis applications are essential for accurate diagnosis at various disease stages. However, using personal data exposes traditional centralized learning systems to privacy concerns. On the other hand, by positioning processing resources closer to the device and enabling more effective data analyses, a distributed computing paradigm has the potential to revolutionize disease diagnosis. Scalable architectures for data analytics are also crucial in healthcare, where data analytics results must have low latency and high dependability and reliability. This study proposes a microservices-based approach for IoT data analytics systems to satisfy privacy and performance requirements by arranging entities into fine-grained, loosely connected, and reusable collections. Our approach relies on federated learning, which can increase disease diagnosis accuracy while protecting data privacy. Additionally, we employ transfer learning to obtain more efficient models. Using more than 5800 chest X-ray images for pneumonia detection from a publicly available dataset, we ran experiments to assess the effectiveness of our approach. Our experiments reveal that our approach

Spin Hall nano oscillators (SHNOs) are promising candidates for neuromorphic computing due to their miniaturized dimensions, non-linearity, fast dynamics, and ability to synchronize in long chains and arrays. However, tuning the individual SHNOs in large chains/arrays, which is key to implementing synaptic control, has remained a challenge. Here, we demonstrate circular memristive nano-gates, both precisely aligned and shifted with respect to nano-constriction SHNOs of W/CoFeB/HfOx, with increased quality of the device tunability. Gating at the exact center of the nano-constriction region is found to cause irreversible degradation to the oxide layer, resulting in a permanent frequency shift of the auto-oscillating modes. As a remedy, gates shifted outside of the immediate nano-constriction region can tune the frequency dramatically (>200 MHz) without causing any permanent change to the constriction region. Circular memristive nano-gates can, therefore, be used in SHNO chains/arrays to manipulate the synchronization states precisely over large networks of oscillators.

Early diagnosis of Alzheimer's disease (AD) is essential in preventing the disease's progression. Therefore, detecting AD from neuroimaging data such as structural magnetic resonance imaging (sMRI) has been a topic of intense investigation in recent years. Deep learning has gained considerable attention in Alzheimer's detection. However, training a convolutional neural network from scratch is challenging since it demands more computational time and a significant amount of annotated data. By transferring knowledge learned from other image recognition tasks to medical image classification, transfer learning can provide a promising and effective solution. Irregularities in the dataset distribution present another difficulty. Class decomposition can tackle this issue by simplifying learning a dataset's class boundaries. Motivated by these approaches, this paper proposes a transfer learning method using class decomposition to detect Alzheimer's disease from sMRI images. We use two ImageNet-trained architectures: VGG19 and ResNet50, and an entropy-based technique to determine the most informative images. The proposed model achieved state-of-the-art performance in the Alzheimer's disease

Due to privacy or commercial constraints, large pre-trained language models (PLMs) are often offered as black-box APIs. Fine-tuning such models to downstream tasks is challenging because one can neither access the model's internal representations nor propagate gradients through it. This paper addresses these challenges by developing techniques for adapting PLMs with only API access. Building on recent work on soft prompt tuning, we develop methods to tune the soft prompts without requiring gradient computation. Further, we develop extensions that in addition to not requiring gradients also do not need to access any internal representation of the PLM beyond the input embeddings. Moreover, instead of learning a single prompt, our methods learn a distribution over prompts allowing us to quantify predictive uncertainty. Ours is the first work to consider uncertainty in prompts when only having API access to the PLM. Finally, through extensive experiments, we carefully vet the proposed methods and find them competitive with (and sometimes even improving on) gradient-based approaches with full access to the PLM.

Value-alignment in normative multi-agent systems is used to promote a certain value and to ensure the consistent behaviour of agents in autonomous intelligent systems with human values. However, the current literature is limited to the incorporation of effective norms for single-value alignment with no consideration of agents' heterogeneity and the requirement of simultaneous promotion and alignment of multiple values. This research proposes a multi-value promotion model that uses multi-objective evolutionary algorithms and decentralised reasoning to produce the optimum parametric set of norms that is aligned with multiple simultaneous values of heterogeneous agents and the system. To understand various aspects of this complex problem, several evolutionary algorithms were used to find a set of optimised norm parameters considering two toy tax scenarios with two and five values are considered. The results are analysed from different perspectives to show the impact of a selected evolutionary algorithm on the solution, and the importance of understanding the relation between values when prioritising them.