As mixed-ability collaboration has become increasingly focal within accessibility research, managing varied, and sometimes conflicting, access needs has become a key consideration in designing for access. When an accessibility feature or practice benefits some people while constraining others, how should designers navigate these trade-offs? This paper responds to this question by analyzing how a mixed-ability nonprofit worked to make communication accessible to its members as it grew from a small blind-focused athletic group to a larger cross-disability organization. Based on a six-month study that combines interviews and field observations, we show that working with conflicting access needs is not just a technical ‘problem’ but a generative process that sparks reflection on technical constraints and preferences, diverse roles and communication norms, and organizational demands. We therefore argue for rethinking “conflicts” in access as key sites for revealing power structures and creating opportunities for accountability and repair.

Meeting growing demands for low latency and cost efficiency in production-grade large language model (LLM) serving systems requires integrating advanced optimization techniques. However, dynamic and unpredictable input-output lengths of LLM, compounded by these optimizations, exacerbate the issues of workload variability, making it difficult to maintain high efficiency on AI accelerators, especially DSAs with tile-based programming models. To address this challenge, we introduce XY-Serve, a versatile, Ascend NPU native, end-to-end production LLM-serving system. The core idea is an abstraction mechanism that smooths out the workload variability by decomposing computations into unified, hardware-friendly, fine-grained meta primitives. Then, kernels can efficiently execute without concerning the irregularity of workload. After this abstraction mechanism, for Attention, we propose a meta-kernel that computes the basic pattern of GEMM-Softmax-GEMM with architectural-aware tile sizes. For Linear, we introduce a virtual padding scheme that adapts to dynamic shape changes while using highly efficient GEMM primitives with assorted fixed tile sizes. XY-Serve sits harmoniously with vLLM. Experimental results show up to 95% end-to-end throughput improvement compared with current publicly available baselines on Ascend NPUs. We also set a new performance record for Linear (average 14.6% faster) and Attention (average 21.5% faster) kernels relative to existing libraries. Lastly, we demonstrate the generality of our technologies on GPU platform.

Vision-Language Models (VLMs) are increasingly used by blind and low-vision (BLV) people to identify and understand products in their everyday lives, such as food, personal care items, and household goods. Despite their prevalence, we lack an empirical understanding of how common image quality issues—such as blur, misframing, and rotation—affect the accuracy of VLM-generated captions and whether the resulting captions meet BLV people’s information needs. Based on a survey of 86 BLV participants, we develop an annotated dataset of 1,859 product images from BLV people to systematically evaluate how image quality issues affect VLM-generated captions. While the best VLM achieves 98% accuracy on images with no quality issues, accuracy drops to 75% overall when quality issues are present, worsening considerably as issues compound. We discuss the need for model evaluations that center on disabled people’s experiences throughout the process and offer concrete recommendations for HCI and ML researchers to make VLMs more reliable for BLV people.

Despite efforts to increase the representation of disabled people in AI datasets, accessibility datasets are often annotated by crowdworkers without disability-specific expertise, leading to inconsistent or inaccurate labels. This paper examines these annotation challenges through a case study of annotating speech data from people who stutter (PWS). Given the variability of stuttering and differing views on how it manifests, annotating and transcribing stuttered speech remains difficult, even for trained professionals. Through interviews and co-design workshops with PWS and domain experts, we identify challenges in stuttered speech annotation and develop practices that integrate the lived experiences of PWS into the annotation process. Our findings highlight the value of embodied knowledge in improving dataset quality, while revealing tensions between the complexity of disability experiences and the rigidity of static labels. We conclude with implications for disability-first and multiplicity-aware approaches to data interpretation across the AI pipeline.

While sign language translation systems promise to enhance deaf people’s access to information and communication, they have been met with strong skepticism from deaf communities due to risks of misrepresenting and oversimplifying the richness of signed communication in technologies. This article provides empirical evidence of the complexity of translation work involved in deaf communication through interviews with 13 deaf Chinese content creators who actively produce and share sign language content on video sharing platforms with both deaf and hearing audiences. By studying this unique group of content creators, our findings highlight the nuances of sign language translation, showing how deaf creators create content with multilingualism and multiculturalism in mind, support meaning making across languages and cultures, and navigate politics involved in their translation work. Grounded in these deaf-led translation practices, we draw on the sociolinguistic concept of (trans)languaging to re-conceptualize and reimagine the design of sign language translation systems.

The HCI research community has witnessed a growing body of research on accessibility and disability driven by efforts to improve access. Yet, the concept of access reveals its limitations when examined within broader ableist structures. Drawing on an autoethnographic method, this study shares the co-first author Zhang’s experiences at two higher-education institutions in China, including a specialized program exclusively for blind and low-vision students and a mainstream university where he was the first blind student admitted. Our analysis revealed tensions around access in both institutions: they either marginalized blind students within society at large or imposed pressures to conform to sighted norms. Both institutions were further constrained by systemic issues, including limited accessible resources, pervasive ableist cultures, and the lack of formalized policies. In response to these tensions, we conceptualize access as a contradictory construct and argue for understanding accessibility as an ongoing, exploratory practice within ableist structures.

As large language models (LLMs) continue to scale up, mixture-of-experts (MoE) has become a common technology in SOTA models. MoE models rely on expert parallelism (EP) to alleviate memory bottleneck, which introduces all-to-all communication to dispatch and combine tokens across devices. However, in widely-adopted GPU clusters, high-overhead crossnode communication makes all-to-all expensive, hindering the adoption of EP. Recently, wafer-scale chips (WSCs) have emerged as a platform integrating numerous devices on a wafer-sized interposer. WSCs provide a unified high-performance network connecting all devices, presenting a promising potential for hosting MoE models. Yet, their network is restricted to a mesh topology, causing imbalanced communication pressure and performance loss. Moreover, the lack of on-wafer disk leads to high-overhead expert migration on the critical path. To fully unleash this potential, we first propose Entwined Ring Mapping (ER-Mapping), which co-designs the mapping of attention and MoE layers to balance communication pressure and achieve better performance. We find that under ER-Mapping, the distribution of cold and hot links in the attention and MoE layers is complementary. Therefore, to hide the migration overhead, we propose the Non-invasive Balancer (NI-Balancer), which splits a complete expert migration into multiple steps and alternately utilizes the cold links of both layers. Evaluation shows ER-Mapping achieves communication reduction up to 62 %. NIBalancer further delivers 54 % and 22 % improvements in MoE computation and communication, respectively. Compared with the SOTA NVL72 supernode, the WSC platform delivers an average 39 % higher per-device MoE performance owing to its scalability to larger EP.

Large language models (LLMs) rely on self–attention for contextual understanding, demanding high-throughput inference and large–scale token parallelism (LTPP). Existing dynamic sparsity accelerators falter under LTPP scenarios due to stage-isolated optimizations. Revisiting the end-to-end sparsity acceleration flow, we identify an overlooked opportunity: crossstage coordination can substantially reduce redundant computation and memory access. We propose STAR, a cross-stage computetation and memory–efficient algorithm–hardware co-design tailored for Transformer inference under LTPP. STAR introduces a leading-zero-based sparsity prediction using log-domain add only operations to minimize prediction overhead. It further employs distributed sorting and a sorted updating FlashAttention mechanism, guided by a coordinated tiling strategy that enables fine-grained stage interaction for improved memory efficiency and latency. These optimizations are supported by a dedicated STAR accelerator architecture, achieving up to 9.2× speedup and 71.2× energy efficiency over A100, and surpassing SOTA accelerators by up to 16.1× energy and 27.1× area efficiency gains. Further, we deploy STAR onto a multi-core spatial architecture, optimizing dataflow and execution orchestration for ultra-long sequence processing. Architectural evaluation shows that, compared to the baseline design, Spatial-STAR achieves a 20.1× throughput improvement.

HCI and CSCW research has witnessed increasing efforts to address diversity and inclusion in research and design practice, as evidenced by the growing body of research with populations deemed as vulnerable, marginalized, or underserved. However, this work has been largely limited to a population-specific approach, i.e., identifying certain populations as vulnerable and gathering their individual experiences. Drawing primarily from human-centered security and privacy research, we identify three key challenges faced by this population-specific approach: (1) It is limited in addressing user diversity within the target population; (2) It may fail to capture the complex social reality of vulnerability; and (3) It runs the risk of perpetuating othering and stereotypes. To address these limitations, we propose a socio-ecological perspective on vulnerability adapted from the Ecological System Theory (EST). We argue that a socio-ecological perspective of vulnerability can guide researchers to look beyond static and stigmatizing definitions of vulnerability --- instead, focus on the situations, relations, and structures that lead to vulnerability, eventually enabling transferable knowledge of vulnerability across populations. We demonstrate how the socio-ecological lens maps onto existing work and generates new insights in the case of older adults' security and privacy, as well as its potential for being applied to other contexts such as reproductive privacy and responsible artificial intelligence. We end by providing concrete recommendations on how HCI and CSCW research can better operationalize vulnerability in scholarship and design practice.

Spatial architecture is a high-performance paradigm that employs control flow graphs and data flow graphs as computation model, and producer/consumer models as execution model. However, existing spatial architectures struggle with control flow handling challenges. Upon thoroughly characterizing their PE execution models, we observe that they lack autonomous, peer-to-peer, and temporally loosely-coupled control flow handling capability. This degrades its performance in intensive control programs. To tackle the existing control flow handling challenges, Marionette, a spatial architecture with an explicit-designed control flow plane, is proposed. We elaborately develop a full stack of Marionette architecture, from ISA, compiler, simulator to RTL. Marionette's flexible Control Flow Plane enables autonomous, peer-to-peer, and temporally loosely-coupled control flow management. Its Proactive PE Configuration ensures computation-overlapped and timely configuration to promote Branch Divergence handling capability. Besides, Marionette's Agile PE Assignment improves pipeline performance of imperfect loops. Compared to state-of-the-art spatial architectures, the experimental results demonstrate that Marionette outperforms Softbrain, TIA, REVEL, and RipTide by geomean 2.88$\mathbf{\times}$, 3.38$\mathbf{\times}$, 1.55$\mathbf{\times}$, and 2.66$\mathbf{\times}$in a variety of challenging intensive control programs.

Benefiting from the self-attention mechanism, Transformer models have attained impressive contextual comprehension capabilities for lengthy texts. The requirements of high-throughput inference arise as the large language models (LLMs) become increasingly prevalent, which calls for large-scale token parallel processing (LTPP). However, existing dynamic sparse accelerators struggle to effectively handle LTPP, as they solely focus on separate stage optimization, and with most efforts confined to computational enhancements. By re-examining the end-to-end flow of dynamic sparse acceleration, we pinpoint an ever-overlooked opportunity that the LTPP can exploit the intrinsic coordination among stages to avoid excessive memory access and redundant computation. Motivated by our observation, we present SOFA, a cross-stage compute-memory efficient algorithm-hardware co-design, which is tailored to tackle the challenges posed by LTPP of Transformer inference effectively. We first propose a novel leading zero computing paradigm, which predicts attention sparsity by using log-based add-only operations to avoid the significant overhead of prediction. Then, a distributed sorting and a sorted updating FlashAttention mechanism are proposed with cross-stage coordinated tiling principle, which enables fine-grained and lightweight coordination among stages, helping optimize memory access and latency. Further, we propose a SOFA accelerator to support these optimizations efficiently. Extensive experiments on 20 benchmarks show that SOFA achieves$9.5\times$speed up and$71.5\times$higher energy efficiency than Nvidia A100 GPU. Compared to eight SOTA accelerators, SOFA achieves an average$15.8\times$energy efficiency,$10.3\times$area efficiency and$9.3\times$speed up, respectively.

Information access is one of the most significant challenges faced by d/Deaf signers due to a lack of sign language information. Given the challenges in machine-driven solutions, we seek to understand how d/Deaf communities can support the growth of sign language content. Based on interviews with 12 d/Deaf people in China, we found that d/Deaf videos, i.e., sign language videos created by and for d/Deaf people, can be crucial information sources and educational materials. Combining content analysis of 360 d/Deaf videos to better understand this type of video, we show how d/Deaf communities co-create information accessibility through collaboration in content creation online. We uncover two major challenges that creators need to address, e.g., difficulties in interpretation and inconsistent content qualities. We propose potential design opportunities and future research directions to support d/Deaf people’s needs for sign language content through collaboration within d/Deaf communities.

Spatial architecture is a high-performance architecture that uses control flow graphs and data flow graphs as the computational model and producer/consumer models as the execution models. However, existing spatial architectures suffer from control flow handling challenges. Upon categorizing their PE execution models, we find that they lack autonomous, peer-to-peer, and temporally loosely-coupled control flow handling capability. This leads to limited performance in intensive control programs.

In today's society, the relationship between younger and older generations is increasingly characterized by tension and conflict, with each generation holding deep-seated biases and stereotypes against each other. Fostering meaningful communication among generations is one way to avoid age segregation and dismantle associated biases. This paper presents a case analysis of an age-integrated online community (r/AskOldPeople) with an in-depth analysis of the most popular types of questions as well as the types of discussions within this community. Using content analysis of the top 999 posts, we describe the exchanges between different generations in this community, with people sharing experiences and perspectives, seeking advice, reminiscing about the past, and engaging in community building. Using thematic analysis of the same corpus, we also identified four themes characterizing the types of discussions prompted by the questions. We find that younger and older generations use the community to learn about experiences of the past, co-navigate psychosocial challenges across the lifespan, understand and commiserate about aging, and collectively make sense of crises and changes in the pandemic era. We discuss the meanings behind the themes and how r/AskOldPeople, as an age-integrated online community, supports intergenerational communication.

Short-form video sharing platforms (SVSPs) have seen a significant surge in older adult content creators in recent years. This emerging trend adds evidence to challenge the conventional perception of older adults as later technology adopters and passive online recipients. To investigate the reason behind the trend, we conducted semi-structured interviews with 13 older adult content creators on two of the most popular SVSPs in China, DouYin and KuaiShou. We found that our participants were initially attracted to SVSPs because of perceived ease of participation and the enjoyment they found. However, what kept them engaged was the attention and support they received there. SVSPs offered a low-barrier and equitable platform through their near-automatic use and relatively equal opportunities for recommendations, allowing everyone to reach audiences. Motivated by their passion for performance and the viewers' support, our participants actively acquired new skills for better performance and became more deeply involved on the platforms. Based on the findings, we reflect on how SVSPs' technical affordances support older adults in the transition from lurkers to contributors. We advocate for participation equity and supportive environments to promote more inclusive social media platforms.

Older adults' technology learning is a long-term process, during which family members often play significant roles. Although much research has emphasized how family support is important, little research has dove into the evolution of family dynamics when older adults are learning to use new technology. Drawing on the results from a qualitative study that performed semi-structured interviews with 20 older adults and 18 younger adults in China, we unpack how family members were involved in technology learning over time. Our findings suggest that younger family members play transformative roles throughout older adults' learning stages, i.e., as influencers, supporters, protectors, and monitors. Younger family members' roles co-evolve with not only older adults' changing needs but also their perceptions of older adults' learning abilities and online behaviors. They may struggle to adjust their teaching strategies to accommodate older adults' needs and abilities during the process. They may also worry about older adults' online benefits and safety as many older adults become far more active online than anticipated. Challenges while teaching and tensions regarding protection may thus emerge during the support process. With these findings, we suggest that older adults' technology learning should be treated as a collaborative activity with family members rather than an activity they pursue alone. We also highlight older adults' technology learning as a recurrent, dynamic, and evolving process, and call attention to the unique culture of "xiaoshun" in China that acts as a buffer to the burdens and tensions found with family support.

MOTIVATION: In recent years, a growing number of studies have proved that microRNAs (miRNAs) play significant roles in the development of human complex diseases. Discovering the associations between miRNAs and diseases has become an important part of the discovery and treatment of disease. Since uncovering associations via traditional experimental methods is complicated and time-consuming, many computational methods have been proposed to identify the potential associations. However, there are still challenges in accurately determining potential associations between miRNA and disease by using multisource data. RESULTS: In this study, we develop a Multi-view Multichannel Attention Graph Convolutional Network (MMGCN) to predict potential miRNA-disease associations. Different from simple multisource information integration, MMGCN employs GCN encoder to obtain the features of miRNA and disease in different similarity views, respectively. Moreover, our MMGCN can enhance the learned latent representations for association prediction by utilizing multichannel attention, which adaptively learns the importance of different features. Empirical results on two datasets demonstrate that MMGCN model can achieve superior performance compared with nine state-of-the-art methods on most of the metrics. Furthermore, we prove the effectiveness of multichannel attention mechanism and the validity of multisource data in miRNA and disease association prediction. Case studies also indicate the ability of the method for discovering new associations.