Abstract--T o maximize hardware efficiency and performance accuracy in Compute-In-Memory (CIM)-based neural network accelerators for Artificial Intelligence (AI) applications, co-optimizing both software and hardware design parameters is essential. Manual tuning is impractical due to the vast number of parameters and their complex interdependencies. T o effectively automate the design and optimization of CIM-based neural network accelerators, hardware-aware neural architecture search (HW-NAS) techniques can be applied. This work introduces CIMNAS, a joint model-quantization-hardware optimization framework for CIM architectures. CIMNAS simultaneously searches across software parameters, quantization policies, and a broad range of hardware parameters, incorporating device-, circuit-, and architecture-level co-optimizations. CIMNAS experiments were conducted over a search space of 9.9 10 Evaluated on the ImageNet dataset, CIMNAS achieved a reduction in energy-delay-area product (EDAP) ranging from 90.1 to 104.5, an improvement in TOPS/W between 4.68 and 4.82, and an enhancement in TOPS/mm The adaptability and robustness of CIMNAS are demonstrated by extending the framework to support the SRAM-based ResNet50 architecture, achieving up to an 819.5 reduction in EDAP . Unlike other state-of-the-art methods, CIMNAS achieves EDAP-focused optimization without any accuracy loss, generating diverse software-hardware parameter combinations for high-performance CIMbased neural network designs. The exponential growth of Artificial Intelligence (AI) applications and increasing AI model complexity are raising the energy demands for training and processing AI workloads [1]. This trend has created a demand for more sustainable and energy-efficient hardware solutions for AI applications. Compute-In-Memory (CIM) neural network accelerators have emerged as promising architectures for achieving energy-efficient AI processing [2]-[6]. To maximize the hardware efficiency of CIM accelerators and maintain high performance for neural network workloads, it is essential to co-optimize both neural network model parameters and CIM hardware parameters [7]. Mohammed Fouda is with Compumacy for Artificial Intelligence Solutions, Cairo, Egypt.

Large language models (LLMs) have demonstrated remarkable capabilities in complex reasoning tasks when equipped with external tools. However, current frameworks predominantly rely on sequential processing, leading to inefficient execution particularly for tasks requiring extensive tool interaction. This paper introduces Flash-Searcher, a novel parallel agent reasoning framework that fundamentally reimagines the execution paradigm from sequential chains to directed acyclic graphs (DAGs). Flash-Searcher decomposes complex tasks into subtasks with explicit dependencies, enabling concurrent execution of independent reasoning paths while maintaining logical constraints. Through dynamic workflow optimization, our framework continuously refines the execution graph based on intermediate results, effectively integrating summary module. Comprehensive evaluations across multiple benchmarks demonstrate that Flash-Searcher consistently outperforms existing approaches. Specifically, it achieves 67.7% accuracy on BrowseComp and 83% on xbench-DeepSearch, while reducing agent execution steps by up to 35% compared to current frameworks. Furthermore, when distilling this parallel reasoning pipeline into single models, we observe substantial performance gains across diverse backbone architectures, underscoring the generalizability of our methodology. Our work thus represents a significant advance in agent architecture design, offering a more scalable and efficient paradigm for complex reasoning tasks.

Dynamic resource allocation in heterogeneous wireless networks (HetNets) is challenging for traditional methods under varying user loads and channel conditions. We propose a deep reinforcement learning (DRL) framework that jointly optimises transmit power, bandwidth, and scheduling via a multi-objective reward balancing throughput, energy efficiency, and fairness. Using real base station coordinates, we compare Proximal Policy Optimisation (PPO) and Twin Delayed Deep Deterministic Policy Gradient (TD3) against three heuristic algorithms in multiple network scenarios. Our results show that DRL frameworks outperform heuristic algorithms in optimising resource allocation in dynamic networks. These findings highlight key trade-offs in DRL design for future HetNets.

This paper presents a systematic comparative analysis of Variational Quantum Classifier (VQC) configurations for financial fraud detection, encompassing three distinct quantum encoding techniques and comprehensive architectural variations. Through empirical evaluation across multiple entanglement patterns, circuit depths, and optimization strategies,quantum advantages in fraud classification accuracy are demonstrated, achieving up to 94.3 % accuracy with ZZ encoding schemes. The analysis reveals significant performance variations across entanglement topologies, with circular entanglement consistently outperforming linear (90.7) %) and full connectivity (92.0 %) patterns, achieving optimal performance at 93.3 % accuracy. The study introduces novel visualization methodologies for quantum circuit analysis and provides actionable deployment recommendations for practical quantum machine learning implementations. Notably, systematic entanglement pattern analysis shows that circular connectivity provides superior balance between expressivity and trainability while maintaining computational efficiency. These researches offer initial benchmarks for quantum enhanced fraud detection systems and propose potential benefits of quantum machine learning in financial security applications.

In modern logistics management systems, route planning requires high efficiency. The Open Capacitated Vehicle Routing Problem (OCVRP) deals with finding optimal delivery routes for a fleet of vehicles serving geographically distributed customers, without requiring the vehicles to return to the depot after deliveries. The present study is comparative in nature and speaks of two algorithms for OCVRP solution: Ant Colony Optimization (ACO), a nature-inspired metaheuristic; and Google OR-Tools, an industry-standard toolkit for optimization. Both implementations were developed in Python and using a custom dataset. Performance appraisal was based on routing efficiency, computation time, and scalability. The results show that ACO allows flexibility in routing parameters while OR-Tools runs much faster with more consistency and requires less input. This could help choose among routing strategies for scalable real-time logistics systems.

Danish PM calls for strong answer from EU leaders to Russia's hybrid attacks EU leaders have met in Copenhagen under pressure to boost European defence after a series of Russian incursions into EU airspace, and days after drones targeted Danish airports. Danish Prime Minister Mette Frederiksen told reporters that from a European perspective there is only one country... willing to threaten us and that is Russia, and therefore we need a very strong answer back. The incursions have become most acute for countries on the EU's eastern flank such as Poland and Estonia. A number of member states have already backed plans for a multi-layered drone wall to quickly detect, then track and destroy Russian drones. We meet at a time when Russia have intensified their attacks in Ukraine, where we have seen Russian airspace violations and unwanted drone activity in several European countries, Frederiksen told a news conference after the talks had concluded.

Trump dollar coin design released by Treasury... and it's inspired by an iconic political photo Top plastic surgeons reveal secrets behind Taylor Swift's'changing' face: 'It is looking very full' Shroud of Turin mystery deepens as surgeon spots hidden detail that points to Jesus' resurrection Hollywood A-listers pay me $50,000 to cure their drug addicted nepo-babies because they can't afford for these secrets to go public I'm no longer sleeping with my husband - and never will again, says MOLLY RYDDELL. I love him, but counted down the moments until he climaxed. Then I couldn't bear it any more and the truth spilled out... so many women feel the same Fans erupt at Taylor Swift's'dig' at Travis Kelce's ex Kayla Nicole in wild The Life of a Showgirl track Lori Loughlin's husband Mossimo Giannulli seen with mystery brunette in tiny skirt day after shock split The truth about Keith Urban's guitarist'other woman' Maggie Baugh revealed amid Nicole Kidman divorce Taylor, your album should be'Life of a Callgirl'. KENNEDY's appalled take on Swift's new record... and its ultra-vivid sex shout outs for Travis the Sasquatch I was so happy after trying a trendy new cosmetic procedure. But 10 years later I suffered a devastating side effect... the doctor had lied The'middle-class kinks' saving marriages: Wives reveal the eight buzzy sex trends that revived their lagging libidos - including the fantasy husbands are secretly obsessed with I'm a woman with autism... here are the signs you might be masking, even from yourself Cake-faced 90s sitcom star looks unrecognizable as she ditches the heavy eyeshadow for an LA errand run can you guess who?

Trump dollar coin design released by Treasury... and it's inspired by an iconic political photo Top plastic surgeons reveal secrets behind Taylor Swift's'changing' face: 'It is looking very full' Shroud of Turin mystery deepens as surgeon spots hidden detail that points to Jesus' resurrection Hollywood A-listers pay me $50,000 to cure their drug addicted nepo-babies because they can't afford for these secrets to go public I'm no longer sleeping with my husband - and never will again, says MOLLY RYDDELL. I love him, but counted down the moments until he climaxed. Then I couldn't bear it any more and the truth spilled out... so many women feel the same Fans erupt at Taylor Swift's'dig' at Travis Kelce's ex Kayla Nicole in wild The Life of a Showgirl track Lori Loughlin's husband Mossimo Giannulli seen with mystery brunette in tiny skirt day after shock split The truth about Keith Urban's guitarist'other woman' Maggie Baugh revealed amid Nicole Kidman divorce Taylor, your album should be'Life of a Callgirl'. KENNEDY's appalled take on Swift's new record... and its ultra-vivid sex shout outs for Travis the Sasquatch I was so happy after trying a trendy new cosmetic procedure. But 10 years later I suffered a devastating side effect... the doctor had lied The'middle-class kinks' saving marriages: Wives reveal the eight buzzy sex trends that revived their lagging libidos - including the fantasy husbands are secretly obsessed with I'm a woman with autism... here are the signs you might be masking, even from yourself Cake-faced 90s sitcom star looks unrecognizable as she ditches the heavy eyeshadow for an LA errand run can you guess who?

Environment Animals Wildlife Bats 1,500th discovered bat species is a'god of the island' What better way to kick off Bat Appreciation Month? Breakthroughs, discoveries, and DIY tips sent every weekday. It's official: the world's 1,500th known bat species has been discovered in Equatorial Guinea. And as luck would have it, 's announcement is just in time for Bat Appreciation Month . Still, biologists estimate that bats have existed for at least 55 to 56 million years .

President Trump has announced a 20-point peace plan to end the war in Gaza, showing various lines of Israeli troop withdrawal should President Trump's plan go ahead. BBC Verify has analysed this map alongside the latest satellite imagery and the Israeli military's control of Gaza. Prime Minister Starmer says the move will revive the hope of peace but Israel says it is nothing but a reward for jihadist Hamas. BBC Verify's Merlyn Thomas looks at the latest Israeli strikes on buildings in Gaza City city over the weekend. BBC Verify analyses footage of the suspected drone attacks and images of a device found after the incident.