As more young women in China live alone, they navigate entangled privacy, security, and safety (PSS) risks across smart homes, online platforms, and public infrastructures. Drawing on six participatory threat modeling (PTM) workshops (n = 33), we present a human-centered threat model that illustrates how digitally facilitated physical violence, digital harassment and scams, and pervasive surveillance by individuals, companies, and the state are interconnected and mutually reinforcing. We also document four mitigation strategies employed by participants: smart home device configurations, boundary management, sociocultural practices, and social media tactics–each of which can introduce new vulnerabilities and emotional burdens. Based on these insights, we developed a digital PSS guidebook for young women living alone (YWLA) in China. We further propose actionable design implications for smart home devices and social media platforms, along with policy and legal recommendations and directions for educational interventions.

FemHealth apps (e.g., period, fertility, and pregnancy trackers) collect highly sensitive sexual and reproductive health data and have become a focal point for privacy, security, and safety (PSS) concerns, particularly amid changes in reproductive health regulations in the US and growing public scrutiny. While prior work has identified shortcomings in the PSS protections implemented by FemHealth apps, far less is known about how the product teams developing these apps conceptualize and operationalize PSS in practice. We conducted semi-structured interviews with 14 FemHealth app product team members who represented 11 unique apps and held diverse roles, including software engineering and architecture, security and privacy, product management, policy and legal compliance, and UX/UI research and design. We found that data collection decisions are often driven by feature-specific objectives, such as improving prediction accuracy and enabling reliable backups, rather than by explicitly articulated PSS requirements. Teams also frequently rely on legal compliance frameworks and PSS practices designed for general-purpose apps, while expressing confidence that these measures provide adequate protection. In addition, we identified four persistent challenges faced by FemHealth app product teams: disagreement over whether FemHealth apps require distinct PSS protections compared to non-FemHealth apps; difficulty establishing and maintaining user trust; uncertainty arising from cross-border enforcement related to abortion; and technical limitations associated with backups, end-to-end encryption, and coercive local access to data. Based on these findings, we derive a set of requirements and provide actionable technical and policy recommendations to inform the design, engineering, and governance of PSS in FemHealth apps.

Language models (LMs) are capable of conducting in-context learning for multiple choice reasoning tasks, but the options in these tasks are treated equally.As humans often first eliminate wrong options before picking the final correct answer, we argue a similar two-step strategy can make LMs better at these tasks.To this end, we present the Process of Elimination (POE), a two-step scoring method.In the first step, POE scores each option, and eliminates seemingly wrong options.In the second step, POE masks these wrong options, and makes the final prediction from the remaining options.Zero-shot experiments on 8 reasoning tasks illustrate the effectiveness of POE, and a following analysis finds our method to be especially performant on logical reasoning tasks.We further analyze the effect of masks, and show that POE applies to few-shot settings and large language models (LLMs) like ChatGPT. 1

BACKGROUND: Cancer constitutes a momentous health burden in our society. Critical information on cancer may be hidden in its signaling pathways. However, even though a large amount of money has been spent on cancer research, some critical information on cancer-related signaling pathways still remains elusive. Hence, new works towards a complete understanding of cancer-related signaling pathways will greatly benefit the prevention, diagnosis, and treatment of cancer. RESULTS: We propose the node-weighted Steiner tree approach to identify important elements of cancer-related signaling pathways at the level of proteins. This new approach has advantages over previous approaches since it is fast in processing large protein-protein interaction networks. We apply this new approach to identify important elements of two well-known cancer-related signaling pathways: PI3K/Akt and MAPK. First, we generate a node-weighted protein-protein interaction network using protein and signaling pathway data. Second, we modify and use two preprocessing techniques and a state-of-the-art Steiner tree algorithm to identify a subnetwork in the generated network. Third, we propose two new metrics to select important elements from this subnetwork. On a commonly used personal computer, this new approach takes less than 2 s to identify the important elements of PI3K/Akt and MAPK signaling pathways in a large node-weighted protein-protein interaction network with 16,843 vertices and 1,736,922 edges. We further analyze and demonstrate the significance of these identified elements to cancer signal transduction by exploring previously reported experimental evidences. CONCLUSIONS: Our node-weighted Steiner tree approach is shown to be both fast and effective to identify important elements of cancer-related signaling pathways. Furthermore, it may provide new perspectives into the identification of signaling pathways for other human diseases.