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Distributed quantum networks are not merely information conduits but intricate systems that embody the principles of quantum mechanics. In our study, we examine the underlying mechanisms of quantum connectivity within a distributed framework by exploring phenomena such as superposition and entanglement and their influence on information propagation. We investigate how these fundamental quantum effects interact with routing strategies that, while inspired by classical methods, must contend with quantum decoherence and measurement uncertainties. By simulating distributed networks of 10, 20, 50 and 100 nodes, we assess the performance of routing mechanisms through metrics that reflect both quantum fidelity and operational efficiency. Our findings reveal that the quantum coherence inherent in entangled states can enhance routing fidelity under specific conditions, yet also introduce challenges such as increased computational overhead and sensitivity to network scale. This work bridges the gap between the underlying principles of quantum systems and practical routing implementations, offering new insights into the design of robust distributed quantum networks.

Efficient exploration is essential for intelligent systems interacting with their environment, but existing language models often fall short in scenarios that require strategic information gathering. In this paper, we present PAPRIKA, a fine-tuning approach that enables language models to develop general decision-making capabilities that are not confined to particular environments. By training on synthetic interaction data from different tasks that require diverse strategies, PAPRIKA teaches models to explore and adapt their behavior on a new task based on environment feedback in-context without more gradient updates. Experimental results show that models fine-tuned with PAPRIKA can effectively transfer their learned decision-making capabilities to entirely unseen tasks without additional training. Unlike traditional training, our approach's primary bottleneck lies in sampling useful interaction data instead of model updates. To improve sample efficiency, we propose a curriculum learning strategy that prioritizes sampling trajectories from tasks with high learning potential. These results suggest a promising path towards AI systems that can autonomously solve novel sequential decision-making problems that require interactions with the external world., Comment: Project Website: https://paprika-llm.github.io

Sensor attacks compromise the reliability of cyber-physical systems (CPSs) by altering sensor outputs with the objective of leading the system to unsafe system states. This paper studies a probabilistic intrusion detection framework based on $\lambda$-sensor-attack detectability ($\lambda$-sa), a formal measure that evaluates the likelihood of a system being under attack based on observed behaviors. Our framework enhances detection by extending its capabilities to identify multiple sensor attack strategies using probabilistic information, which enables the detection of sensor attacks that were undetected by current detection methodologies. We develop a polynomial-time algorithm that verifies $\lambda$-sa detectability by constructing a weighted verifier automaton and solving the shortest path problem. Additionally, we propose a method to determine the maximum detection confidence level ($\lambda$*) achievable by the system, ensuring the highest probability of identifying attack-induced behaviors.

Centralized electronic health record repositories are critical for advancing disease surveillance, public health research, and evidence-based policymaking. However, developing countries face persistent challenges in achieving this due to fragmented healthcare data sources, inconsistent record-keeping practices, and the absence of standardized patient identifiers, limiting reliable record linkage, compromise data interoperability, and limit scalability-obstacles exacerbated by infrastructural constraints and privacy concerns. To address these barriers, this study proposes a scalable, privacy-preserving clinical data warehouse, NCDW, designed for heterogeneous EHR integration in resource-limited settings and tested with 1.16 million clinical records. The framework incorporates a wrapper-based data acquisition layer for secure, automated ingestion of multisource health data and introduces a soundex algorithm to resolve patient identity mismatches in the absence of unique IDs. A modular data mart is designed for disease-specific analytics, demonstrated through a dengue fever case study in Bangladesh, integrating clinical, demographic, and environmental data for outbreak prediction and resource planning. Quantitative assessment of the data mart underscores its utility in strengthening national decision-support systems, highlighting the model's adaptability for infectious disease management. Comparative evaluation of database technologies reveals NoSQL outperforms relational SQL by 40-69% in complex query processing, while system load estimates validate the architecture's capacity to manage 19 million daily records (34TB over 5 years). The framework can be adapted to various healthcare settings across developing nations by modifying the ingestion layer to accommodate standards like ICD-11 and HL7 FHIR, facilitating interoperability for managing infectious diseases (i.e., COVID, tuberculosis).

In this paper, we propose an efficient implementation of deep policy gradient method (PGM) for optimal control problems in continuous time. The proposed method has the ability to manage the allocation of computational resources, number of trajectories, and complexity of architecture of the neural network. This is, in particular, important for continuous-time problems that require a fine time discretization. Each step of this method focuses on a different time scale and learns a policy, modeled by a neural network, for a discretized optimal control problem. The first step has the coarsest time discretization. As we proceed to other steps, the time discretization becomes finer. The optimal trained policy in each step is also used to provide data for the next step. We accompany the multi-scale deep PGM with a theoretical result on allocation of computational resources to obtain a targeted efficiency and test our methods on the linear-quadratic stochastic optimal control problem., Comment: 20 pages, 4 figures

Human intelligence involves metacognitive abilities like self-regulation, recognizing limitations, and seeking assistance only when needed. While LLM Agents excel in many domains, they often lack this awareness. Overconfident agents risk catastrophic failures, while those that seek help excessively hinder efficiency. A key challenge is enabling agents with a limited intervention budget $C$ is to decide when to request assistance. In this paper, we propose an offline framework that trains a "helper" policy to request interventions, such as more powerful models or test-time compute, by combining LLM-based process reward models (PRMs) with tabular reinforcement learning. Using state transitions collected offline, we score optimal intervention timing with PRMs and train the helper model on these labeled trajectories. This offline approach significantly reduces costly intervention calls during training. Furthermore, the integration of PRMs with tabular RL enhances robustness to off-policy data while avoiding the inefficiencies of deep RL. We empirically find that our method delivers optimal helper behavior.

The rapid advancement of large language models has opened new avenues for automating complex problem-solving tasks such as algorithmic coding and competitive programming. This paper introduces a novel evaluation technique, LLM-ProS, to assess the performance of state-of-the-art LLMs on International Collegiate Programming Contest (ICPC) problems. Using a curated dataset of 166 World Finals problems from 2011 to 2024, we benchmark the models' reasoning, accuracy, and efficiency. We evaluate the five models-GPT-4o, Mistral Large, Llama-3.1-405B, and the o1 family, consisting of o1-mini and o1-preview, across critical metrics like correctness, resource utilization, and response calibration. Our results reveal significant differences in the models' abilities to generalize, adapt, and solve novel problems. We also investigated the impact of training methodologies, dataset contamination, and chain-of-thought reasoning on model performance. The findings provide new insights into optimizing LLMs for algorithmic tasks, highlighting both strengths and limitations of current models., Comment: To be published in LLM4Code 2025 workshop proceedings

Metasurface-based optical components are becoming increasingly important due to their unparalleled ability to shape and manipulate electromagnetic waves across a wide range of frequencies, from microwave and terahertz to visible light. In particular, planar meta-mirrors with high efficiency and broad operational bandwidth are expected to play a significant role in shaping channel characteristics for 6G communication links due to their reduced SWaP (size, weight, and power) and integrability. However, achieving large fractional bandwidth and high efficiency simultaneously using metasurface-based mirrors or reflectors have been challenging. In this work, highly efficient broadband focusing meta-mirror has been designed, fabricated and experimentally demonstrated. The device operates in the D-Band (110-170 GHz) and beyond (up to 200 GHz), and exhibits unmatched performance in terms of combined fractional bandwidth (58\%) and energy efficiency, exceeding 53\% over the entire bandwidth with a peak of 77.4\% at frequency 135 GHz. The high performance and advantageous design tradeoffs of this meta-mirror are discussed in terms of the principles of low Fresnel number optics.

We present an end-to-end workflow for superconducting qubit readout that embeds co-designed Neural Networks (NNs) into the Quantum Instrumentation Control Kit (QICK). Capitalizing on the custom firmware and software of the QICK platform, which is built on Xilinx RFSoC FPGAs, we aim to leverage machine learning (ML) to address critical challenges in qubit readout accuracy and scalability. The workflow utilizes the hls4ml package and employs quantization-aware training to translate ML models into hardware-efficient FPGA implementations via user-friendly Python APIs. We experimentally demonstrate the design, optimization, and integration of an ML algorithm for single transmon qubit readout, achieving 96% single-shot fidelity with a latency of 32ns and less than 16% FPGA look-up table resource utilization. Our results offer the community an accessible workflow to advance ML-driven readout and adaptive control in quantum information processing applications.

Many real-world decision-making problems are combinatorial in nature, where states (e.g., surrounding traffic of a self-driving car) can be seen as a combination of basic elements (e.g., pedestrians, trees, and other cars). Due to combinatorial complexity, observing all combinations of basic elements in the training set is infeasible, which leads to an essential yet understudied problem of zero-shot generalization to states that are unseen combinations of previously seen elements. In this work, we first formalize this problem and then demonstrate how existing value-based reinforcement learning (RL) algorithms struggle due to unreliable value predictions in unseen states. We argue that this problem cannot be addressed with exploration alone, but requires more expressive and generalizable models. We demonstrate that behavior cloning with a conditioned diffusion model trained on expert trajectory generalizes better to states formed by new combinations of seen elements than traditional RL methods. Through experiments in maze, driving, and multiagent environments, we show that conditioned diffusion models outperform traditional RL techniques and highlight the broad applicability of our problem formulation.

Materials exhibiting a martensitic phase transition are essential for applications in shape memory alloys, actuators and sensors. Hexamethylbenzene (HMB) has long been considered as a classical example of ferroelastic organic crystals since Mnyukh's pioneering work in 1970s. However, the atomistic mechanism underlying this phase transition has never been clarified. In this work, we present a direct molecular dynamics simulation to investigate the phase transition mechanism in HMB. For the first time, we report a simulation results that can accurately reproduce both the transition temperature and hysteresis loop observed in previous experimental studies. By analyzing the MD trajectories, the potential energy surface, we identified that a low-barrier atomic sliding mode along the close-packed (11$\overline{1}$) plane of the low-temperature phase is the key to trigger the phase transition at the critical temperature window. This is further confirmed by the observed continuous softening of shear modulus around the transition window. Our results demonstrate that the integration of various atomistic modeling techniques can provide invaluable insight into the martensitic phase transition mechanisms in organic crystals and guide the development of new organic martensites., Comment: 9 pages, 9 figures

We present Fanar, a platform for Arabic-centric multimodal generative AI systems, that supports language, speech and image generation tasks. At the heart of Fanar are Fanar Star and Fanar Prime, two highly capable Arabic Large Language Models (LLMs) that are best in the class on well established benchmarks for similar sized models. Fanar Star is a 7B (billion) parameter model that was trained from scratch on nearly 1 trillion clean and deduplicated Arabic, English and Code tokens. Fanar Prime is a 9B parameter model continually trained on the Gemma-2 9B base model on the same 1 trillion token set. Both models are concurrently deployed and designed to address different types of prompts transparently routed through a custom-built orchestrator. The Fanar platform provides many other capabilities including a customized Islamic Retrieval Augmented Generation (RAG) system for handling religious prompts, a Recency RAG for summarizing information about current or recent events that have occurred after the pre-training data cut-off date. The platform provides additional cognitive capabilities including in-house bilingual speech recognition that supports multiple Arabic dialects, voice and image generation that is fine-tuned to better reflect regional characteristics. Finally, Fanar provides an attribution service that can be used to verify the authenticity of fact based generated content. The design, development, and implementation of Fanar was entirely undertaken at Hamad Bin Khalifa University's Qatar Computing Research Institute (QCRI) and was sponsored by Qatar's Ministry of Communications and Information Technology to enable sovereign AI technology development.

Quantum security improves cryptographic protocols by applying quantum mechanics principles, assuring resistance to both quantum and conventional computer attacks. This work addresses these issues by integrating Quantum Key Distribution (QKD) utilizing the E91 method with Multi-Layer Chaotic Encryption, which employs a variety of patterns to detect eavesdropping, resulting in a highly secure image-transmission architecture. The method leverages entropy calculations to determine the unpredictability and integrity of encrypted and decrypted pictures, guaranteeing strong security. Extensive statistical scenarios illustrate the framework's effectiveness in image encryption while preserving high entropy and sensitivity to the original visuals. The findings indicate significant improvement in encryption and decryption performance, demonstrating the framework's potential as a robust response to weaknesses introduced by advances in quantum computing. Several metrics, such as Peak Signal-to-Noise Ratio (PSNR), Structural Similarity Index (SSIM), Normalized Cross-Correlation (NCC), Bit Error Rate (BER), entropy values for original, encrypted, and decrypted images, and the correlation between original and decrypted images, validate the framework's effectiveness. The combination of QKD with Multi-Layer Chaotic Encryption provides a scalable and resilient technique to secure image communication. As quantum computing advances, this framework offers a future-proof approach for defining secure communication protocols in crucial sectors such as medical treatment, forensic computing, and national security, where information confidentiality is valuable., Comment: no

We construct new classes of the dynamical black hole solutions in five or higher dimensional Einstein-Maxwell theory, coupled to a dilaton field, in the presence of arbitrary cosmological constant. The dilaton field interacts non-trivially with the Maxwell field, as well as the cosmological constant, with two arbitrary coupling constants. The solutions are non-stationary, and almost conformally regular everywhere. To construct the solutions, we use the four-dimensional Bianchi type IX geometry, as the base space. We find three different classes of solutions, based on the values of the coupling constants. We notice that our solutions could be asymptotically de-Sitter, anti-de-Sitter or flat. We find the relevant quantities of the solutions, and discuss the properties of the solutions., Comment: 26 pages, 13 figures

The Yamada-Watanabe theory provides a robust framework for understanding stochastic equations driven by Wiener processes. Despite its comprehensive treatment in the literature, the applicability of the theory to SPDEs driven by Poisson random measures or, more generally, L\'evy processes remains significantly less explored, with only a handful of results addressing this context. In this work, we leverage a result by Kurtz to demonstrate that the existence of a martingale solution combined with pathwise uniqueness implies the existence of a unique strong solution for SPDEs driven by both a Wiener process and a Poisson random measure. Our discussion is set within the variational framework, where the SPDE under consideration may be nonlinear. This work is influenced by earlier research conducted by the second author alongside de Bouard and Ondrej\'at.

We find the non-extremal charged rotating black holes in quadratic $f(T)$ gravity are holographically dual to two different hidden conformal field theories. The two conformal field theories can be merged to find a very general hidden conformal field theory, which is generated by the $SL(2,\mathbb{Z})$ modular group. We also carry out the the calculation to the extremal limit of the black holes, and find the corresponding dual quantities. Contrary to the existence of two different dual conformal field theories for the extremal charged rotating black holes in Einstein gravity, we find only one dual theory exists for the extremal charged rotating black holes in quadratic $f(T)$ gravity., Comment: 31 pages, 8 figures