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Large language models (LLMs) excel in tasks requiring processing and interpretation of input text. Abstract screening is a labour-intensive component of systematic review involving repetitive application of inclusion and exclusion criteria on a large volume of studies identified by a literature search. Here, LLMs (GPT-3.5 Turbo, GPT-4 Turbo, GPT-4o, Llama 3 70B, Gemini 1.5 Pro, and Claude Sonnet 3.5) were trialled on systematic reviews in a full issue of the Cochrane Library to evaluate their accuracy in zero-shot binary classification for abstract screening. Trials over a subset of 800 records identified optimal prompting strategies and demonstrated superior performance of LLMs to human researchers in terms of sensitivity (LLMmax = 1.000, humanmax = 0.775), precision (LLMmax = 0.927, humanmax = 0.911), and balanced accuracy (LLMmax = 0.904, humanmax = 0.865). The best performing LLM-prompt combinations were trialled across every replicated search result (n = 119,691), and exhibited consistent sensitivity (range 0.756-1.000) but diminished precision (range 0.004-0.096). 66 LLM-human and LLM-LLM ensembles exhibited perfect sensitivity with a maximal precision of 0.458, with less observed performance drop in larger trials. Significant variation in performance was observed between reviews, highlighting the importance of domain-specific validation before deployment. LLMs may reduce the human labour cost of systematic review with maintained or improved accuracy and sensitivity. Systematic review is the foundation of evidence-based medicine, and LLMs can contribute to increasing the efficiency and quality of this mode of research., Comment: RS and AJT are joint-first authors

Landmines remain a pervasive threat in conflict-affected regions worldwide, exacting a toll on innocent lives. Shockingly, every 95 minutes, another individual becomes a victim of these lethal explosive devices (Landmines Monitor 2022 2022), with a significant proportion being innocent civilians. Current methods for landmine detection suffer from inefficiency, high costs, and risks to the operator and system integrity. In this paper, we present a novel, efficient, safe, and cost-effective approach to unearth these hidden dangers. Our proposed method integrates an unmanned aerial vehicle (UAV) with a thermal camera to capture high-resolution images of minefields. These images are subsequently transmitted to a base computer, where a state-of-the-art image processing algorithm is applied to identify the presence of landmines. Notably, our solution performs exceptionally well, particularly during evening hours, achieving an impressive detection accuracy of nearly 88%. These results exhibit great promise when compared to existing methods constrained by their design limitations., Comment: Accepted for publication in "Int. J. Computational Vision and Robotics"

Evaluations are limited by benchmark availability. As models evolve, there is a need to create benchmarks that can measure progress on new generative capabilities. However, creating new benchmarks through human annotations is slow and expensive, restricting comprehensive evaluations for any capability. We introduce BENCHAGENTS, a framework that methodically leverages large language models (LLMs) to automate benchmark creation for complex capabilities while inherently ensuring data and metric quality. BENCHAGENTS decomposes the benchmark creation process into planning, generation, data verification, and evaluation, each of which is executed by an LLM agent. These agents interact with each other and utilize human-in-the-loop feedback from benchmark developers to explicitly improve and flexibly control data diversity and quality. We use BENCHAGENTS to create benchmarks to evaluate capabilities related to planning and constraint satisfaction during text generation. We then use these benchmarks to study seven state-of-the-art models and extract new insights on common failure modes and model differences.

Metallic materials such as brass, copper, and aluminum are used in numerous applications, including industrial manufacturing. The vibration characteristics of these objects are unique and can be used to identify these objects from a distance. This research presents a methodology for detecting and classifying these metallic objects using the vibration dynamics induced by their micro-Doppler signatures. The proposed approach utilizes image processing techniques to extract pivotal features from spectrograms. These spectrograms originate from micro-Doppler signatures of data collected during controlled laboratory experiments where signals were transmitted towards vibrating metal sheets, and the ensuing reflections were recorded using a software-defined radio (SDR). The spectrogram data was augmented using geometric transformation to train a convolutional neural network (CNN) based machine learning model for object classification. The results indicate that the proposed CNN model achieved an accuracy of more than 95% in classifying metals into brass, copper, and aluminum. This research could be used to understand the foundations of classifying spectrogram images using micro-Doppler signatures for its applications towards enhancing the sensing capabilities in industrial and defense applications., Comment: 11 pages, to be published in the May 2025 issue of the IEEE Instrumentation & Measurement Magazine

Reliable execution of large-scale quantum algorithms requires robust underlying operations and this challenge is addressed by quantum error correction (QEC). Most modern QEC protocols rely on measurements and feed-forward operations, which are experimentally demanding, and often slow and prone to high error rates. Additionally, no single error-correcting code intrinsically supports the full set of logical operations required for universal quantum computing, resulting in an increased operational overhead. In this work, we present a complete toolbox for fault-tolerant universal quantum computing without the need for measurements during algorithm execution by combining the strategies of code switching and concatenation. To this end, we develop new fault-tolerant, measurement-free protocols to transfer encoded information between 2D and 3D color codes, which offer complementary and in combination universal sets of robust logical gates. We identify experimentally realistic regimes where these protocols surpass state-of-the-art measurement-based approaches. Moreover, we extend the scheme to higher-distance codes by concatenating the 2D color code with itself and by integrating code switching for operations that lack a natively fault-tolerant implementation. Our measurement-free approach thereby provides a practical and scalable pathway for universal quantum computing on state-of-the-art quantum processors., Comment: 16 pages, 9 figures

One of the most intriguing 6G vertical markets is precision agriculture, where communications, sensing, control, and robotics technologies are used to improve agricultural outputs and decrease environmental impact. Ambient IoT (A-IoT), which uses a network of devices that harvest ambient energy to enable communications, is expected to play an important role in agricultural use cases due to its low costs, simplicity, and battery-free (or battery-assisted) operation. In this paper, we review the use cases of precision agriculture and discuss the challenges. We discuss how A-IoT can be used for precision agriculture and compare it with other ambient energy source technologies. We also discuss research directions related to both A-IoT and precision agriculture., Comment: 7 pages, 4 figures and 2 tables

Mid-infrared (MIR) spectroscopy is a powerful technique employed for a variety of applications, including gas sensing, industrial inspection, astronomy, surveillance, and imaging. Thin-film narrowband interference filters, targeted to specific absorption bands of target molecules, are commonly deployed for cost-effective MIR sensing systems. These devices require complex and time-consuming fabrication processes. Also, their customization on the micro-scale for emerging miniaturized applications is challenging. Plasmonic nanostructure arrays operating in reflection and transmission modes have been developed for MIR. However, they experience undesirable characteristics, such as broad spectra and low reflection/transmission efficiencies. All-dielectric metasurfaces have low intrinsic losses and have emerged as a substitute for plasmonic metasurfaces in MIR spectroscopy. Nevertheless, they typically operate only in reflection mode. In this work, we present a hybrid metal-dielectric metasurface for MIR spectroscopy operating in transmission mode. The metasurface is composed of germanium (Ge) atop aluminum (Al) cylinders, and we show that the transmission response arises because of the hybridization of modes arising from the Ge and the Al structures. The presented metasurface has a high transmission efficiency of 80 % at $\lambda = 2.6\ \mu\text{m}$, and a narrow full-width-at-half-maximum of $0.4\ \mu\text{m}$. We show numerical simulations, successful fabrication using a straightforward fabrication method, and deployment as the in-line optical filter in a CO$_2$ gas detection with a limit of detection of ~0.04% (a few hundred ppm). Our work demonstrates the potential for hybrid metasurfaces as in-line gas sensing optical filters in MIR spectroscopy.

We study a single exactly massless staggered fermion in the fundamental representation of an $SU(2)$ gauge group. We utilize an nHYP-smeared fermion action supplemented with additional heavy Pauli-Villars fields which serve to decrease lattice artifacts. The phase diagram exhibits a clear two-phase structure with a conformal phase at weak coupling and a novel new phase, the Symmetric Mass Generation (SMG) phase, appearing at strong coupling. The SMG phase is confining with all states gapped and chiral symmetry unbroken. Our finite size scaling analysis provides strong evidence that the phase transition between these two phases is continuous, which would allow for the existence of a continuum SMG phase. Furthermore, the RG flows are consistent with a $\beta$-function that vanishes quadratically at the new fixed point suggesting that the $N_f=4$ flavor SU(2) gauge theory lies at the opening of the conformal window., Comment: 7 pages, 6 figures; Spectroscopy data used in the analysis of this paper will be available at https://zenodo.org/uploads/13629600

This review paper investigates the diverse functions of ethical hacking within modern cybersecurity. By integrating current research, it analyzes the progression of ethical hacking techniques,their use in identifying vulnerabilities and conducting penetration tests, and their influence on strengthening organizational security. Additionally, the paper discusses the ethical considerations, legal contexts and challenges that arises with ethical hacking. This review ultimately enhances the understanding of how ethical hacking can bolster cybersecurity defenses., Comment: 5 pages

In recent years, the emergence of Transformers with self-attention mechanism has revolutionized the hyperspectral image (HSI) classification. However, these models face major challenges in computational efficiency, as their complexity increases quadratically with the sequence length. The Mamba architecture, leveraging a state space model (SSM), offers a more efficient alternative to Transformers. This paper introduces the Spatial-Spectral Morphological Mamba (MorpMamba) model in which, a token generation module first converts the HSI patch into spatial-spectral tokens. These tokens are then processed by morphological operations, which compute structural and shape information using depthwise separable convolutional operations. The extracted information is enhanced in a feature enhancement module that adjusts the spatial and spectral tokens based on the center region of the HSI sample, allowing for effective information fusion within each block. Subsequently, the tokens are refined through a multi-head self-attention which further improves the feature space. Finally, the combined information is fed into the state space block for classification and the creation of the ground truth map. Experiments on widely used HSI datasets demonstrate that the MorpMamba model outperforms (parametric efficiency) both CNN and Transformer models. The source code will be made publicly available at \url{https://github.com/MHassaanButt/MorpMamba}.

Spatial-Spectral Mamba (SSM) improves computational efficiency and captures long-range dependencies, addressing Transformer limitations. However, traditional Mamba models overlook rich spectral information in HSIs and struggle with high dimensionality and sequential data. To address these issues, we propose the SSM with multi-head self-attention and token enhancement (MHSSMamba). This model integrates spectral and spatial information by enhancing spectral tokens and using multi-head attention to capture complex relationships between spectral bands and spatial locations. It also manages long-range dependencies and the sequential nature of HSI data, preserving contextual information across spectral bands. MHSSMamba achieved remarkable classification accuracies of 97.62\% on Pavia University, 96.92\% on the University of Houston, 96.85\% on Salinas, and 99.49\% on Wuhan-longKou datasets. The source code is available at \href{https://github.com/MHassaanButt/MHA\_SS\_Mamba}{GitHub}.

The chameleon screening mechanism has been constrained many a time using dynamic and kinematic galaxy cluster observables. Current constraints are, however, insensitive to different mass components within galaxy clusters and have been mainly focused on a single mass density profile, the Navarro-Frenk-While mass density model. In this work, we extend the study of the Chameleon screening mechanism in galaxy clusters by considering a series of mass density models, namely: generalized-Navarro-Frenk-While, b-Navarro-Frenk-While, Burket, Isothermal and Einasto. The coupling strength ($\beta$) and asymptotic value of the chameleon field ($\phi_\infty$) are constrained by using kinematics analyses of simulated galaxy clusters, generated both assuming General Relativity and a strong chameleon scenario. By implementing a Bayesian analysis we comprehensively show that the biases introduced due to an incorrect assumption of the mass model are minimal. Similarly, we also demonstrate that a spurious detection of evidence for modifications to gravity is highly unlikely when utilizing the kinematics of galaxy clusters., Comment: 25 pages, 7 Figures, to be submitted to JCAP

Text-to-Image (T2I) generation has made significant advancements with the advent of diffusion models. These models exhibit remarkable abilities to produce images based on textual prompts. Current T2I models allow users to specify object colors using linguistic color names. However, these labels encompass broad color ranges, making it difficult to achieve precise color matching. To tackle this challenging task, named color prompt learning, we propose to learn specific color prompts tailored to user-selected colors. Existing T2I personalization methods tend to result in color-shape entanglement. To overcome this, we generate several basic geometric objects in the target color, allowing for color and shape disentanglement during the color prompt learning. Our method, denoted as ColorPeel, successfully assists the T2I models to peel off the novel color prompts from these colored shapes. In the experiments, we demonstrate the efficacy of ColorPeel in achieving precise color generation with T2I models. Furthermore, we generalize ColorPeel to effectively learn abstract attribute concepts, including textures, materials, etc. Our findings represent a significant step towards improving precision and versatility of T2I models, offering new opportunities for creative applications and design tasks. Our project is available at https://moatifbutt.github.io/colorpeel/., Comment: Accepted in ECCV 2024

A major hurdle for building a large-scale quantum computer is to scale up the number of qubits while maintaining connectivity between them. In trapped-ion devices, this connectivity can be provided by physically moving subregisters consisting of a few ions across the processor. The topology of the connectivity is given by the layout of the ion trap where one-dimensional and two-dimensional arrangements are possible. Here, we focus on an architecture based on a rectangular two-dimensional lattice, where each lattice site contains a subregister with a linear string of ions. We refer to this architecture as the Quantum Spring Array (QSA). Subregisters placed in neighboring lattice sites can be coupled by bringing the respective ion strings close to each other while avoiding merging them into a single trapping potential. Control of the separation of subregisters along one axis of the lattice, known as the axial direction, uses quasi-static voltages, while the second axis, the radial, requires control of radio frequency signals. In this work, we investigate key elements of the 2D lattice quantum computation architecture along both axes: We show that the coupling rate between neighboring lattice sites increases with the number of ions per site and the motion of the coupled system can be resilient to noise. The coherence of the coupling is assessed, and an entangled state of qubits in separate trapping regions along the radial axis is demonstrated. Moreover, we demonstrate control over radio frequency signals to adjust radial separation between strings, and thus tune their coupling rate. We further map the 2D lattice architecture to code primitives for fault-tolerant quantum error correction, providing a step towards a quantum processor architecture that is optimized for large-scale fault-tolerant operation., Comment: 23 pages, 19 figures (15 in main text, 4 in appendices)

With economic development, the complexity of infrastructure has increased drastically. Similarly, with the shift from fossil fuels to renewable sources of energy, there is a dire need for such systems that not only predict and forecast with accuracy but also help in understanding the process of predictions. Artificial intelligence and machine learning techniques have helped in finding out wellperforming solutions to different problems in the energy sector. However, the usage of state-of-the-art techniques like reinforcement learning is not surprisingly convincing. This paper discusses the application of reinforcement techniques in energy systems and how explanations of these models can be helpful

Brain tumor segmentation is a critical task in medical image analysis, aiding in the diagnosis and treatment planning of brain tumor patients. The importance of automated and accurate brain tumor segmentation cannot be overstated. It enables medical professionals to precisely delineate tumor regions, assess tumor growth or regression, and plan targeted treatments. Various deep learning-based techniques proposed in the literature have made significant progress in this field, however, they still face limitations in terms of accuracy due to the complex and variable nature of brain tumor morphology. In this research paper, we propose a novel Hybrid Multihead Attentive U-Net architecture, to address the challenges in accurate brain tumor segmentation, and to capture complex spatial relationships and subtle tumor boundaries. The U-Net architecture has proven effective in capturing contextual information and feature representations, while attention mechanisms enhance the model's ability to focus on informative regions and refine the segmentation boundaries. By integrating these two components, our proposed architecture improves accuracy in brain tumor segmentation. We test our proposed model on the BraTS 2020 benchmark dataset and compare its performance with the state-of-the-art well-known SegNet, FCN-8s, and Dense121 U-Net architectures. The results show that our proposed model outperforms the others in terms of the evaluated performance metrics.

With the impending arrival of the sixth generation (6G) of wireless communication technology, the telecommunications landscape is poised for another revolutionary transformation. At the forefront of this evolution are intelligent meta-surfaces (IS), emerging as a disruptive physical layer technology with the potential to redefine the capabilities and performance metrics of future wireless networks. As 6G evolves from concept to reality, industry stakeholders, standards organizations, and regulatory bodies are collaborating to define the specifications, protocols, and interoperability standards governing IS deployment. Against this background, this article delves into the ongoing standardization efforts, emerging trends, potential opportunities, and prevailing challenges surrounding the integration of IS into the framework of 6G and beyond networks. Specifically, it provides a tutorial-style overview of recent advancements in IS and explores their potential applications within future networks beyond 6G. Additionally, the article identifies key challenges in the design and implementation of various types of intelligent surfaces, along with considerations for their practical standardization. Finally, it highlights potential future prospects in this evolving field.

The traditional Transformer model encounters challenges with variable-length input sequences, particularly in Hyperspectral Image Classification (HSIC), leading to efficiency and scalability concerns. To overcome this, we propose a pyramid-based hierarchical transformer (PyFormer). This innovative approach organizes input data hierarchically into segments, each representing distinct abstraction levels, thereby enhancing processing efficiency for lengthy sequences. At each level, a dedicated transformer module is applied, effectively capturing both local and global context. Spatial and spectral information flow within the hierarchy facilitates communication and abstraction propagation. Integration of outputs from different levels culminates in the final input representation. Experimental results underscore the superiority of the proposed method over traditional approaches. Additionally, the incorporation of disjoint samples augments robustness and reliability, thereby highlighting the potential of our approach in advancing HSIC. The source code is available at https://github.com/mahmad00/PyFormer.

The paper focuses on the marginalization of indigenous language communities in the face of rapid technological advancements. We highlight the cultural richness of these languages and the risk they face of being overlooked in the realm of Natural Language Processing (NLP). We aim to bridge the gap between these communities and researchers, emphasizing the need for inclusive technological advancements that respect indigenous community perspectives. We show the NLP progress of indigenous Latin American languages and the survey that covers the status of indigenous languages in Latin America, their representation in NLP, and the challenges and innovations required for their preservation and development. The paper contributes to the current literature in understanding the need and progress of NLP for indigenous communities of Latin America, specifically low-resource and indigenous communities in general., Comment: Accepted at NAACL 2024

Safe overtakes in trucks are crucial to prevent accidents, reduce congestion, and ensure efficient traffic flow, making early prediction essential for timely and informed driving decisions. Accordingly, we investigate the detection of truck overtakes from CAN data. Three classifiers, Artificial Neural Networks (ANN), Random Forest, and Support Vector Machines (SVM), are employed for the task. Our analysis covers up to 10 seconds before the overtaking event, using an overlapping sliding window of 1 second to extract CAN features. We observe that the prediction scores of the overtake class tend to increase as we approach the overtake trigger, while the no-overtake class remain stable or oscillates depending on the classifier. Thus, the best accuracy is achieved when approaching the trigger, making early overtaking prediction challenging. The classifiers show good accuracy in classifying overtakes (Recall/TPR > 93%), but accuracy is suboptimal in classifying no-overtakes (TNR typically 80-90% and below 60% for one SVM variant). We further combine two classifiers (Random Forest and linear SVM) by averaging their output scores. The fusion is observed to improve no-overtake classification (TNR > 92%) at the expense of reducing overtake accuracy (TPR). However, the latter is kept above 91% near the overtake trigger. Therefore, the fusion balances TPR and TNR, providing more consistent performance than individual classifiers.

This paper introduces ReflectSumm, a novel summarization dataset specifically designed for summarizing students' reflective writing. The goal of ReflectSumm is to facilitate developing and evaluating novel summarization techniques tailored to real-world scenarios with little training data, %practical tasks with potential implications in the opinion summarization domain in general and the educational domain in particular. The dataset encompasses a diverse range of summarization tasks and includes comprehensive metadata, enabling the exploration of various research questions and supporting different applications. To showcase its utility, we conducted extensive evaluations using multiple state-of-the-art baselines. The results provide benchmarks for facilitating further research in this area., Comment: LREC-COLING 2024 camera ready; code and dataset are available at https://github.com/EngSalem/ReflectSUMM

Quantum error correction is a crucial tool for mitigating hardware errors in quantum computers by encoding logical information into multiple physical qubits. However, no single error-correcting code allows for an intrinsically fault-tolerant implementation of all the gates needed for universal quantum computing [1-3]. One way to tackle this problem is to switch between two suitable error-correcting codes, while preserving the encoded logical information, which in combination give access to a fault-tolerant universal gate set [4-6]. In this work, we present the first experimental implementation of fault-tolerant code switching between two codes. One is the seven-qubit color code [7], which features fault-tolerant CNOT and $H$ quantum gates, while the other one, the 10-qubit code [8], allows for a fault-tolerant $T$-gate implementation. Together they form a complementary universal gate set. Building on essential code switching building blocks, we construct logical circuits and prepare 12 different logical states which are not accessible natively in a fault-tolerant way within a single code. Finally, we use code switching to entangle two logical qubits employing the full universal gate set in a single logical quantum circuit. Our results experimentally open up a new route towards deterministic control over logical qubits with low auxiliary qubit overhead, not relying on the probabilistic preparation of resource states.