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Verified and Optimized Implementation of Orthologic Proof Search

We report on the development of an optimized and verified decision procedure for orthologic equalities and inequalities. We start by formalizing, in the Coq proof assistant, a proof system in sequent-...

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The article presents a novel and verified implementation of a decision procedure for orthologic proof search, which is significant for the field of formal methods and automated reasoning. The use of the Coq proof assistant enhances the rigor of the presented work. The findings address existing gaps in the literature, as indicated by the correction of a previously unaddressed case in a published proof. The optimization of runtime performance also adds practical value, making the research relevant for application in automated theorem proving and related areas.

Chiral Dissociation of Bound Photon Pairs for a Non-Hermitian Skin Effect

We theoretically study the bound states of interacting photons propagating in a waveguide chirally coupled to an array of atoms. We demonstrate that the bound photon pairs can concentrate at the edge ...

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The article presents a novel theoretical examination of the chiral dissociation of bound photon pairs, linking it to the non-Hermitian skin effect—a relatively unexplored area. This combination enhances its novelty and relevance to current research trends in quantum optics and non-Hermitian physics.

Physical Layer Design for Ambient IoT

There is a growing demand for ultra low power and ultra low complexity devices for applications which require maintenance-free and battery-less operation. One way to serve such applications is through...

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The article presents a timely and relevant exploration of physical layer design tailored for Ambient IoT, a critical area given the increasing need for low-power, batteryless communication devices. The novelty lies in tackling existing limitations of traditional backscatter devices, indicating potential for significant advancements in this field. The methodological approach includes link level simulations which add rigor to the findings, enhancing its impact for industry applications.

Slowly decaying strain solitons in nonlinear viscoelastic waveguides

This paper is devoted to the modeling of longitudinal strain waves in a rod composed of a nonlinear viscoelastic material characterized by frequency-dependent second- and third-order elastic constants...

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The article presents a novel modeling approach for strain waves in nonlinear viscoelastic materials and introduces a key insight regarding the role of frequency-dependent elastic constants. The methodological rigor is highlighted by the successful comparison between theoretical predictions and 3D simulations, indicating a strong validity of the findings. This contribution could advance understanding in material science and wave theory, particularly for applications in engineering and physics. However, while the results are promising, further experimental validation would enhance its impact.

Taming Penguins: Towards High Precision Measurements in $φ_d$ and $φ_s$

Experimentally, the phases $φ_d$ and $φ_s$ are determined from CP asymmetry measurements in the "golden modes" $B_d^0\to J/ψK_{\mathrm{S}}^0$ and $B_s^0\to J/ψφ&#...

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This article addresses the critical need for precision in measuring the phases $φ_d$ and $φ_s$, which are vital for testing the Standard Model and searching for new physics. The innovative methods involving penguin topology corrections and recent experimental data from prominent collaborations enhance its applicability and relevance. The methodological rigor in discussing corrections and using SU(3) flavour symmetry showcases a robust approach, making it a significant contribution to the field.

Quantum algorithm for the gradient of a logarithm-determinant

The logarithm-determinant is a common quantity in many areas of physics and computer science. Derivatives of the logarithm-determinant compute physically relevant quantities in statistical physics mod...

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The paper presents a novel quantum algorithm with significant implications for evaluating a common mathematical operation across several fields. It demonstrates methodological rigor by offering a clear complexity analysis and discusses potential real-world implementations in current quantum computing paradigms. The algorithm's applicability in quantum machine learning also contributes to its relevance, pointing to future research directions in this interdisciplinary domain.

FASP: Fast and Accurate Structured Pruning of Large Language Models

The rapid increase in the size of large language models (LLMs) has significantly escalated their computational and memory demands, posing challenges for efficient deployment, especially on resource-co...

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The paper presents a promising new framework for structured pruning of large language models, addressing a critical challenge in the field: resource efficiency. The methodological rigor in evaluating FASP against state-of-the-art methods strengthens the paper's impact. Its practical applications for deploying LLMs on resource-constrained devices enhance its relevance, impacting both industrial and academic research directions. The proposed restoration mechanism is particularly novel and could inspire further research in model optimization.

Towards Robust and Realistic Human Pose Estimation via WiFi Signals

Robust WiFi-based human pose estimation is a challenging task that bridges discrete and subtle WiFi signals to human skeletons. This paper revisits this problem and reveals two critical yet overlooked...

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This article presents a significant advancement in WiFi-based human pose estimation by addressing critical gaps in existing methods. The proposed framework, DT-Pose, is innovative and effectively incorporates domain-consistent learning and topology constraints, enhancing the robustness and accuracy of pose estimation. The methodology is rigorously tested across various datasets, underscoring its effectiveness and potential utility in both research and practical applications.

MoE $^2$ : Optimizing Collaborative Inference for Edge Large Language Models

Large language models (LLMs) have demonstrated remarkable capabilities across a wide range of natural language processing tasks. Exploiting the heterogeneous capabilities of edge LLMs is crucial for d...

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The article presents a novel framework for optimizing inference in edge large language models, addressing critical issues of energy efficiency and latency reduction in a relevant and emerging area. The methodological rigor is evident through robust experimental validation and advanced optimization techniques employed, which are crucial for practical applications in edge computing environments. The combination of novel algorithms with real-world implementations enhances its applicability and relevance.

mGeNTE: A Multilingual Resource for Gender-Neutral Language and Translation

Gender-neutral language reflects societal and linguistic shifts towards greater inclusivity by avoiding the implication that one gender is the norm over others. This is particularly relevant for gramm...

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The article introduces a novel dataset, mGeNTe, which addresses a significant gap in inclusive language resources. Its focus on gender-neutral language in translation is timely and critical, as societal values increasingly emphasize inclusivity. The comprehensive approach to extending a bilingual corpus to include multiple grammatical gender languages enhances its utility for researchers in this area. The methodological rigor in dataset generation and its applications in automatic translation further elevate its relevance. This work could inspire future research on inclusivity in AI and language technologies, making it highly impactful.

On the distribution of the statistical sum related to BSC

The distribution function of the sum of i.i.d. random variables of the special form is considered. Such sum describes messages posterior probabilities for random coding in binary symmetric channel. Cl...

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The article addresses a specific aspect of statistical distributions related to binary symmetric channels, which is significant in the fields of information theory and communications. The derivation of interesting lower and upper bounds for distribution functions adds methodological rigor and could lead to improved performance in coding strategies for communications. The relevance is enhanced by its potential application in practical scenarios involving error-correcting codes and their optimization. However, more application-focused validation would boost its relevance further.

NEBULA: A National Scale Dataset for Neighbourhood-Level Urban Building Energy Modelling for England and Wales

Buildings are significant contributors to global greenhouse gas emissions, accounting for 26% of global energy sector emissions in 2022. Meeting net zero goals requires a rapid reduction in building e...

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The NEBULA dataset presents a novel and comprehensive resource for urban building energy modelling, addressing a significant gap in high-resolution data availability in Europe. Its focus on neighbourhood-level data offers potential insights for energy consumption patterns that can inform regional and national energy policies, particularly regarding net zero targets. The methodological rigor in integrating diverse datasets enhances its applicability, making it a valuable asset for researchers and policymakers alike.

The $α$ condensate states of atomic nuclei ${}^{12}C$ , ${}^{16}O$ and ${}^{20}Ne$ in an analytical solvable model

The $α$ condensation in the ${}^{12}C$ , ${}^{16}O$ and ${}^{20}Ne$ nuclei is investigated within an analytical solvable model. It is found that the calculated ratio of ...

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The article presents novel insights into $\alpha$ condensation within atomic nuclei, which has significant implications for nuclear structure physics. The use of an analytical solvable model allows for a clearer understanding of complex phenomena, and the results align closely with experimental values, enhancing the reliability of the findings. However, the discrepancy in the ground state energy for ${}^{20}Ne}$ suggests there are limitations and areas for further exploration.

Artificial Intelligence, Ambient Backscatter Communication and Non-Terrestrial Networks: A 6G Commixture

The advent of Non-Terrestrial Networks (NTN) represents a compelling response to the International Mobile Telecommunications 2030 (IMT-2030) framework, enabling the delivery of advanced, seamless conn...

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The article presents a novel intersection of three cutting-edge technologies: Artificial Intelligence (AI), Ambient Backscatter Communication (AmBC), and Non-Terrestrial Networks (NTN). This integration addresses important challenges within the evolving landscape of 6G communication networks, indicating strong potential for significant advancements in both theoretical and practical applications. The focus on energy efficiency and the adaptability of network parameters through AI suggests methodological rigor and immediate applicability, which are crucial for future research development.

Agent-Based Simulation of a Perpetual Futures Market

I introduce an agent-based model of a Perpetual Futures market with heterogeneous agents trading via a central limit order book. Perpetual Futures (henceforth Perps) are financial derivatives introduc...

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The paper presents a novel agent-based model addressing a specific and increasingly relevant area in finance, namely Perpetual Futures markets. Its methodological rigor in adapting existing models to a new context, including the ability to successfully simulate key market features, shows potential for significant contributions to both theoretical understanding and practical applications in market dynamics. However, while robust, it does not fully explore potential real-world implications of its findings, which slightly limits its overall impact.

PISCO: Self-Supervised k-Space Regularization for Improved Neural Implicit k-Space Representations of Dynamic MRI

Neural implicit k-space representations (NIK) have shown promising results for dynamic magnetic resonance imaging (MRI) at high temporal resolutions. Yet, reducing acquisition time, and thereby availa...

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The article introduces a novel self-supervised loss function that addresses a critical issue in dynamic MRI concerning overfitting due to limited training data. Its high relevance is marked by the promising results it achieves in significantly improving reconstruction quality at high acceleration factors, showcasing methodological rigor and potential for widespread application. The integration of PISCO not only enhances existing architectures but also offers a versatile tool for future developments in the field.

A practical review on promoting connectivity in topology optimization

Topology optimization facilitates the automated design of high-performance structures across various engineering fields but, if unconstrained, often produces designs that are complex and difficult to ...

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This article provides a thorough review of connectivity constraints in topology optimization, highlighting their importance in generating manufacturable designs. It merges practical engineering challenges with theoretical advancements, making the findings relevant for real-world applications. The comparative analysis of different constraints offers valuable insights for future research, fostering further exploration in the field.

Towards a Framework for Enterprise Architecture in Mobile Government: A Case Study

Mobile government (m-government) represents a distinct paradigm shift from electronic government (e-government), offering a new avenue for governments worldwide to deliver services and applications to...

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The article addresses a significant gap in the literature regarding the application of Enterprise Architecture (EA) in the context of mobile government (m-government), particularly in developing countries. This is a novel area given the shift from e-government, and the proposed framework could help streamline the integration of services, thus enhancing the efficiency of public sector IT infrastructure. The methodology indicates rigor in developing the framework, and the practical implications could lead to better resource allocation and less redundancy in investments. However, the effectiveness of the proposed framework in practice remains to be seen, which slightly affects the score.

Joint Antenna Selection and Beamforming Design for Active RIS-aided ISAC Systems

Active reconfigurable intelligent surface (A-RIS) aided integrated sensing and communications (ISAC) system has been considered as a promising paradigm to improve spectrum efficiency. However, massive...

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This article addresses significant challenges in the integration of active reconfigurable intelligent surfaces (A-RIS) in wireless systems, particularly in the context of integrated sensing and communications (ISAC). The proposed solutions contribute both theoretically and practically to the efficiency of resource allocation in these systems. The novelty of combining antenna selection with advanced optimization techniques enhances its value for future research in ISAC and related fields.

Fast Searching of Extreme Operating Conditions for Relay Protection Setting Calculation Based on Graph Neural Network and Reinforcement Learning

Searching for the Extreme Operating Conditions (EOCs) is one of the core problems of power system relay protection setting calculation. The current methods based on brute-force search, heuristic algor...

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The article presents a novel approach to a significant problem in power system engineering, integrating advanced techniques such as graph neural networks and reinforcement learning. The proposed method shows substantial improvements in computation speed while maintaining accuracy, which is crucial given the increasing complexity of power systems with renewable energy sources. The methodological rigor is highlighted by extensive case studies. Its applicability in real-world scenarios and potential to influence future developments in related fields makes it highly relevant.