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Simple ways of preparing qudit Dicke states
Noah B. Kerzner, Federico Galeazzi, Rafael I. Nepomechie
https://arxiv.org/abs/2507.13308 https://

Simple ways of preparing qudit Dicke states
Dicke states are permutation-invariant superpositions of qubit computational basis states, which play a prominent role in quantum information science. We consider here two higher-dimensional generalizations of these states: $SU(2)$ spin-$s$ Dicke states and $SU(d)$ Dicke states. We present various ways of preparing both types of qudit Dicke states on a qudit quantum computer, using two main approaches: a deterministic approach, based on exact canonical matrix product state representations; and …

#Blakes7 Series D, Episode 11 - Orbit
VILA: Hideaway? Avon, if I got my hands on that kind of money, I'd have gone somewhere I could enjoy it. What is the point of having money if you have to exist on a hole like that!
AVON: You are forgetting, there is a big difference between you and Egrorian. HE has a brain.

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Companion site to the Blakes7Bot Twitter, Mastodon, and Bluesky accounts. Automated lines of Blakes 7 dialog. Includes a script search facility.

Leveraging Transfer Learning and User-Specific Updates for Rapid Training of BCI Decoders
Ziheng Chen, Po T. Wang, Mina Ibrahim, Shivali Baveja, Rong Mu, An H. Do, Zoran Nenadic
https://arxiv.org/abs/2506.14120

Leveraging Transfer Learning and User-Specific Updates for Rapid Training of BCI Decoders
Lengthy subject- or session-specific data acquisition and calibration remain a key barrier to deploying electroencephalography (EEG)-based brain-computer interfaces (BCIs) outside the laboratory. Previous work has shown that cross subject, cross-session invariant features exist in EEG. We propose a transfer learning pipeline based on a two-layer convolutional neural network (CNN) that leverages these invariants to reduce the burden of data acquisition and calibration. A baseline model is traine…

Voter model on heterogeneous directed networks
Luca Avena, Federico Capannoli, Rajat Subhra Hazra, Diego Garlaschelli
https://arxiv.org/abs/2506.12169 http…

Voter model on heterogeneous directed networks
We investigate the consensus dynamics of the voter model on large random graphs with heterogeneous and directed features, focusing in particular on networks with power-law degree distributions. By extending recent results on sparse directed graphs, we derive exact first-order asymptotics for the expected consensus time in directed configuration models with i.i.d. Pareto-distributed in- and out-degrees. For any tail exponent α>0, we derive the mean consensus time scaling depending on the networ…

The Software Landscape for the Density Matrix Renormalization Group
Per Sehlstedt, Jan Brandejs, Paolo Bientinesi, Lars Karlsson
https://arxiv.org/abs/2506.12629

The Software Landscape for the Density Matrix Renormalization Group
The density matrix renormalization group (DMRG) algorithm is a cornerstone computational method for studying quantum many-body systems, renowned for its accuracy and adaptability. Despite DMRG's broad applicability across fields such as materials science, quantum chemistry, and quantum computing, numerous independent implementations have been developed. This survey maps the rapidly expanding DMRG software landscape, providing a comprehensive comparison of features among 35 existing packages. We…

This https://arxiv.org/abs/2410.00425 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csRO_…

ManiSkill3: GPU Parallelized Robotics Simulation and Rendering for Generalizable Embodied AI
Simulation has enabled unprecedented compute-scalable approaches to robot learning. However, many existing simulation frameworks typically support a narrow range of scenes/tasks and lack features critical for scaling generalizable robotics and sim2real. We introduce and open source ManiSkill3, the fastest state-visual GPU parallelized robotics simulator with contact-rich physics targeting generalizable manipulation. ManiSkill3 supports GPU parallelization of many aspects including simulation+re…

Generating and Understanding Tests via Path-Aware Symbolic Execution with LLMs
Yaoxuan Wu, Xiaojie Zhou, Ahmad Humayun, Muhammad Ali Gulzar, Miryung Kim
https://arxiv.org/abs/2506.19287

Generating and Understanding Tests via Path-Aware Symbolic Execution with LLMs
Symbolic execution is a widely used technique for test generation, offering systematic exploration of program paths through constraint solving. However, it is fundamentally constrained by the capability to model the target code including library functions in terms of symbolic constraint and the capability of underlying constraint solvers. As a result, many paths involving complex features remain unanalyzed or insufficiently modeled. Recent advances in large language models (LLMs) have shown pro…

Adaptive Multipath-Based SLAM for Distributed MIMO Systems
Xuhong Li, Benjamin J. B. Deutschmann, Erik Leitinger, Florian Meyer
https://arxiv.org/abs/2506.21798

Adaptive Multipath-Based SLAM for Distributed MIMO Systems
Localizing users and mapping the environment using radio signals is a key task in emerging applications such as reliable communications, location-aware security, and safety critical navigation. Recently introduced multipath-based simultaneous localization and mapping (MP-SLAM) can jointly localize a mobile agent and the reflective surfaces in radio frequency (RF) environments. Most existing MP-SLAM methods assume that map features and their corresponding RF propagation paths are statistically i…

SurGSplat: Progressive Geometry-Constrained Gaussian Splatting for Surgical Scene Reconstruction
Yuchao Zheng, Jianing Zhang, Guochen Ning, Hongen Liao
https://arxiv.org/abs/2506.05935

SurGSplat: Progressive Geometry-Constrained Gaussian Splatting for Surgical Scene Reconstruction
Intraoperative navigation relies heavily on precise 3D reconstruction to ensure accuracy and safety during surgical procedures. However, endoscopic scenarios present unique challenges, including sparse features and inconsistent lighting, which render many existing Structure-from-Motion (SfM)-based methods inadequate and prone to reconstruction failure. To mitigate these constraints, we propose SurGSplat, a novel paradigm designed to progressively refine 3D Gaussian Splatting (3DGS) through the …

Near-Field Propagation and Spatial Non-Stationarity Channel Model for 6-24 GHz (FR3) Extremely Large-Scale MIMO: Adopted by 3GPP for 6G
Huixin Xu, Jianhua Zhang, Pan Tang, Hongbo Xing, Haiyang Miao, Nan Zhang, Jian Li, Jianming Wu, Wenfei Yang, Zhening Zhang, Wei Jiang, Zijian He, Afshin Haghighat, Qixing Wang, Guangyi Liu
https://

Near-Field Propagation and Spatial Non-Stationarity Channel Model for 6-24 GHz (FR3) Extremely Large-Scale MIMO: Adopted by 3GPP for 6G
Next generation cellular deployments are expected to exploit the 6-24 GHz frequency range 3 (FR3) and extremely large-scale multiple-input multiple-output (XL-MIMO) to enable ultra-high data rates and reliability. However, the significantly enlarged antenna apertures and higher carrier frequencies render the far-field and spatial stationarity assumptions in the existing 3rd generation partnership project (3GPP) channel models invalid, giving rise to new features such as near-field propagation a…