Selected Basis Diagonalization (SBD) plays a central role in Sample-based Quantum Diagonalization (SQD), where iterative diagonalization of the Hamiltonian in selected configuration subspaces forms the dominant classical workload. We present a GPU-accelerated implementation of SBD using the Thrust library. By restructuring key components – including configuration processing, excitation generation, and matrix-vector operations – around fine-grained data-parallel primitives and flattened GPU-friendly data layouts, the proposed approach efficiently exploits modern GPU architectures. In our experiments, the Thrust-based SBD achieves up to ∼40× speedup over CPU execution and substantially reduces the total runtime of SQD iterations. These results demonstrate that GPU-native parallel primitives provide a simple, portable, and high-performance foundation for accelerating SQD-based quantum-classical workflows.
Visual impairments create barriers to learning physical activities, since conventional training methods rely on visual demonstrations or often inadequate verbal descriptions. This research explores 3D-printed human body models to enhance movement comprehension for blind individuals. Through a participatory design approach in collaboration with a blind designer, we developed detailed 3D models representing various body movements and incorporated tactile reference elements to enhance spatial understanding. We conducted two user studies with 10 blind participants across different activities: static yoga poses and sequential calisthenic movements. The results demonstrated that 3D models significantly improved understanding speed, reduced questions for clarification, and enhanced movement accuracy compared to conventional teaching methods. Participants consistently rated 3D models higher for ease of understanding, effectiveness, and motivation.
Modeling long-range dependencies remains a central challenge in natural language processing. Transformer architectures achieve strong performance via self-attention but scale quadratically ($O(N^2)$) with sequence length, while State Space Models (SSMs) scale linearly ($O(N)$) but suffer from a selective recall bottleneck, struggling to retrieve precise information from compressed states. This creates a fundamental tradeoff between efficiency and perplexity. To tackle these challenges, we propose the \textit{Parallel Hybrid Architecture (PHA)}, which runs Gated State Spaces (GSS), Grouped Query Attention (GQA), and Feed-Forward Networks (FFNs) as independent parallel branches fused by a learnable mixing mechanism. Instead of forcing SSMs to approximate attention or serializing the two paradigms, PHA allows each branch to specialize: GSS captures global context, while attention performs selective retrieval, with FFN providing complementary processing. On WikiText-103, PHA achieves 16.51 PPL at 125M parameters, outperforming Hedgehog (16.70) and H3-125M (23.70). Scaling to 180M parameters yields 16.42 PPL, which gives comparable results with the pure attention baseline while delivering 24\% higher throughput and up to 40\% lower memory usage at long contexts. On OpenWebText, our 125M model achieves 19.72 PPL, outperforming standard Transformers (20.60) and GSS hybrid baselines (19.80). These results demonstrate that separating sequence modeling paradigms into parallel specialists enables Transformer-level perplexity with substantially improved efficiency for long-context language modeling.
Group interactions are essential to social functioning, yet effective engagement relies on the ability to recognize and interpret visual cues, making such engagement a significant challenge for blind people. In this paper, we investigate how a mobile robot can support group interactions for blind people. We used the scenario of a guided tour with mixed-visual groups involving blind and sighted visitors. Based on insights from an interview study with blind people (n=5) and museum experts (n=5), we designed and prototyped a robotic system that supported blind visitors to join group tours. We conducted a field study in a science museum where each blind participant (n=8) joined a group tour with one guide and two sighted participants (n=8). Findings indicated users' sense of safety from the robot's navigational support, concerns in the group participation, and preferences for obtaining environmental information. We present design implications for future robotic systems to support blind people's mixed-visual group participation.
Efficient numerical computation is foundational to many disciplines, whose computing demands continue to grow dramatically, yet their continued scaling is jeopardized by the slowdown in Moore’s law. Multi-function multi-way analog (MFMWA) architectures represent a promising computing framework that supports in-memory computation of matrix operations and offers the potential for tremendous improvements in computation time and energy consumption, but at the expense of inherent unpredictability and noise which prevents such architectures from supporting reliable numerical computation in general. In this article, we summarize and review a recent novel combination of MFMWA architectures and randomized algorithms tailored to these architectures that seek to mitigate the detrimental impact of imperfect analog computations with the goal of realizing the promising benefits of such advances in analog architectures for numerical speedups and energy reductions across various disciplines. Through a mixture of analysis, physical measurements and simulations, we organize and review recent research results that demonstrate the potential to achieve orders of magnitude reduction in both computation time and energy consumption for broadly applicable core numerical methods with algorithmic solution accuracy similar to digital computers. Together, these results provide understanding and insights into the computational and energy efficiencies of such a combination of MFMWA architectures and tailored randomized algorithms.
