Many machine learning problems, including similarity learning, ranking, and clustering, rely on empirical pairwise loss functions whose quadratic computational cost quickly becomes prohibitive at scale. We demonstrate how a frugal approach that retains only a fraction of the available information on pairs can achieve estimation or optimization performance comparable to that obtained by using all pairs, by leveraging survey sampling techniques. A central finding, supported by both theory and experiments, is that such sampling plans must target pairs directly rather than individual observations. In particular, for pairwise losses between high-dimensional vectors such as embeddings in vision or graph learning, assigning higher inclusion probabilities to informative pairs using suitable auxiliary information yields performance close to full pairwise evaluation, providing a principled and theoretically grounded trade-off between accuracy and computational cost.
Gender equality has been a pivotal focus in development agendas globally since the 1980s. Despite significant progress having been made thus far, patriarchal backlash remains a critical challenge in many regions across the globe, including Kenya. This study conducted in Kenya by Advocates for Social Change-Kenya (ADSOCK), explores the multifaceted dimensions of patriarchal backlash and resistance to gender equality initiatives, identifying key areas of concern and suggesting strategic interventions through which resistance to gender equality can be mitigated. The research has four main objectives: a) to understand the nature and extent of patriarchal backlash against gender equality in Kenya; b) to identify the socio-cultural, economic, and political factors contributing to patriarchal resistance and backlash; c) to assess the impact of patriarchal resistance on gender equality initiatives within communities and within institutions; and d) to recommend actionable solutions through which patriarchal backlash can be challenged and gender equality can be promoted.
Background Drawing on the Fleming Fund's work to strengthen antimicrobial resistance (AMR) surveillance across Africa and Asia during 2018-2026, this article provides one of the first syntheses of AMR surveillance data use in low- and middle-income countries.Methods A participatory synthesis for narrative review was conducted to summarize experience across administrative levels (facility, subnational, national, and regional).Results Data use was most evident at the facility level, in human health systems where resistance patterns, antimicrobial use, and clinical data increasingly informed antimicrobial stewardship, infection prevention and control, and other quality improvement initiatives. Data use in other sectors required different approaches. At the national level, Fleming Fund investments were associated with revisions of treatment guidelines, updates of essential medicines lists, and regulatory measures, primarily through improved governance and policy communities. Regional platforms supported member country data use at the national level. Subnational data use was undertaken in countries with decentralized health systems. Across levels, data use pathways driven by local priorities helped surveillance inform action.Conclusions Fleming Fund experience suggests that surveillance is more likely to inform action when data use is built into system design. Continued investment will be needed if AMR surveillance is to improve policy and practice in low- and middle-income countries.
This research briefing identifies how frontline workers delivering health, education, and social protection services in fragile and conflict-affected settings (FCAS) can be better supported. It synthesises evidence from six major research programmes that undertook studies across Africa, Asia, and the Middle East. The briefing’s core message is that resilient services depend on resilient frontline workforces, and that support for these workers during fragility and conflict must go beyond short-term incentives or fragmented projects. This review’s conclusion is that policymakers, donors, and practitioners need to take a more systemic, long-term and gender-transformative approach to supporting frontline workers if they want service delivery systems in FCAS to endure and strengthen more ambitious accountability and stability goals. The briefing also shows that evidence remains uneven and quite limited. There is emerging strong qualitative insight into recurrent problems and promising practices, but there is much more limited systematic evaluation of which support interventions for frontline workers are the most effective, affordable, and sustainable over time in different places and settings.
The human dentition follows a remarkably precise developmental sequence; however, deviations from this pattern are not uncommon. Among these, tooth agenesis represents one of the most prevalent developmental anomalies and continues to challenge clinicians involved in pediatric oral healthcare. Defined as the congenital absence of one or more teeth, agenesis ranges from hypodontia to the complete absence of dentition, with hypodontia being the most frequently encountered form. Premolars, after third molars, are among the teeth most commonly affected, making premolar agenesis a clinically significant finding during the transitional years of dental development. [1–5] Premolar agenesis often remains “silent,” escaping clinical detection until the mixed dentition stage, when delayed exfoliation of primary molars or routine radiographic evaluation reveals the absence of permanent successors. This quiet absence can have profound implications, influencing arch integrity, occlusal harmony, alveolar bone development, and future esthetic outcomes. The etiology is complex and multifactorial, with genetic determinants such as mutations in odontogenesis-related genes interacting with environmental influences. While premolar agenesis may be associated with syndromic conditions, the majority of cases present as non-syndromic, isolated findings in otherwise healthy children. [1,2,6,7]
