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Turkana County Annual Development Plan 2025/2026
No full textThe Annual Development Plan (ADP) for the Financial Year 2025/2026 has been meticulously crafted in compliance with Section 126 of the Public Finance Management Act, 2012. This plan aims to strategically align the priorities outlined in the County Integrated Development Plan (CIDP) with the short-term development agenda, thereby strengthening the critical linkage between planning and budgeting as envisioned in the Constitution of Kenya, 2010. The priority programs and projects outlined in this ADP are carefully selected to build upon the successes of CIDP III while steering development forward, in accordance with Article 220 (2) of the Constitution.
Drawing inspiration from the Third Generation CIDP and guided by the Governor’s 9-Point Agenda, the ADP FY 2025/2026 focuses on key sectoral priorities, including Water, Health Services and Sanitation, Education and Child Protection, Food Security, Trade, Industry and Enterprise Development, Peace Building and Conflict Management, Land, Mineral, and Natural Resource Management, Wealth Creation, and Strategic Partnerships and Collaborations. The preparation of this Plan was a collaborative effort involving comprehensive consultations with a diverse range of stakeholders, including County Departments, Non-Governmental Organizations, and the general public. Public participation forums were conducted across all county administrative units to ensure that the voices of the citizens were captured and incorporated into the ADP
National Policy for the Prevention, Management and Control of Alcohol, Drugs and Substance Abuse 2025
No full textThis policy is the culmination of an extensive, inclusive, and collaborative effort involving diverse stakeholders across Kenya—from national and
county governments to civil society, faith-based groups, the private sector, and the general public. Together, we have forged a unified approach to prevent, mitigate, and control the devastating impact of alcohol, drugs, and substance abuse in our nation. Acknowledging the grave threat these substances pose to human health, societal well-being, and national development, this policy delivers a sustainable, multi-sectoral framework. It strategically balances demand reduction—through robust prevention, treatment, and rehabilitation programs—with supply reduction, via effective regulation and enforcement. Grounded in the principles of the United Nations drug control conventions, it ensures alignment with global best practices while directly addressing Kenya’s unique challenges. This policy serves as our strategic blueprint for coordinating all stakeholders, especially county governments, in the fight against alcohol and drug abuse. It establishes a united front to combat this escalating crisis, particularly safeguarding our children, youth, and women, whose vulnerability directly threatens Kenya’s future productivity and development.
The alarming rise in substance potency and variety, coupled with surging illicit drug trafficking, demands urgent, decisive action. All stakeholders
must act, leveraging this policy’s provisions to forge strong, synchronized partnerships with the government and one another. The moment is now to restore order, protect our communities, and build a brighter future. Through collective action, we can ensure Kenya’s youth and children thrive in an alcohol- and drug-free environment, laying the foundation for a healthier, more prosperous nation for generations to come
Nyeri County Fiscal Strategy Paper 2025
Get PDFThe 2025 County Fiscal Strategy Paper (CFSP), which is the third to be prepared under the Kenya Kwanza Administration, highlights the progress made in the implementation of the strategic interventions articulated in the County Integrated Development Plan (CIDP) 2023-2027 which are aligned with the National and Regional Development Policies. The CIDP focuses on investing the limited available resources where it will generate wealth and improve livelihoods at the grassroot level. It is a commitment to invest in smallholder agriculture and the informal sector and end socio-economic exclusion by levelling the playing field for all investors. Great progress has been realized in the implementation of the planned project and programmes. Going forward and over the medium term, the Government will consolidate the gains realized under the in the implementation of the County Development Agenda across the county sectors as outlined in the Annual Development Plan 2025/2026 and whose programmes will be firmed in this document. Emphasis will be placed on enhancing the enablers and harnessing implementation of the targeted interventions through deliberate approach. This will improve production, promote value addition and market access, and attract investments
Study of light-meson resonances decaying to in the channels
No full textInternational audienceA study is presented of and decays based on the analysis of proton-proton collision data collected with the LHCb detector at centre-of-mass energies of 7, 8 and 13 TeV, corresponding to an integrated luminosity of . The invariant-mass distributions of both decay modes show, in the GeV mass region, a rich spectrum of light-meson resonances, resolved using an amplitude analysis. A complex mixture of and resonances is observed, dominated by , , , , and resonances. The Dalitz plots are dominated by asymmetric crossing bands which are different for the two decay modes. This is due to a different interference pattern between the and amplitudes in the two channels. Branching fractions are measured for each resonant contribution
Kernel density estimation for stationary random fields with values in a finite-dimensional Riemannian manifold
No full textThis paper investigates some asymptotic properties of the kernel spatial density estimation for stationary α-mixing process on a finite-dimensional Riemannian manifold without boundary. The results extend beyond the classical independently and identically distributed (i.i.d.) data, focusing on the case where the manifold is known and extending the classical theory to random fields.</div
Nonparametric Regression on Riemannian manifold under α-Mixing process
No full textThe main focus of our paper is to investigate the behavior of the kernel estimator for the regression function between a real-valued random variable Y and a random variable X, where X takes values in a Riemannian submanifold. The estimator is adapted from the article of Pelletier (2006). Additionally, we study data that adheres to the α-mixing condition, which imposes valuable constraints on the dependence structure of the observations. Specifically, we provide the rate of convergence in mean square error, enabling us to assess the precision and efficiency of the estimator.</div
Enhancing Metric Privacy With a Shuffler
No full textInternational audienceDifferential Privacy (DP) is one of the most successful privacy-preserving frameworks. In the central model of DP a trusted server adds controlled noise as it acts as an interface between the data providers (users) and the data consumers (analysts). To overcome the strong trust assumption of having a trusted server, Local Differential Privacy (LDP) has been proposed, where the individual data are obfuscated directly at the end of the data provider. To improve LDP, in recent years researchers have proposed to combine it with a shuffler which is supposed to mix the data at the time of collection, enhancing the privacy of LDP without affecting utility. The shuffler is assumed to be trusted, but this is also an arguably strong assumption that cannot always be guaranteed. Metric privacy (aka d-privacy) is a variant of DP that can be applied in domains provided with a notion of distance and it is particularly used in location privacy, where it takes the name of geo-indistinguihability. In contrast to DP, metric privacy allows calibrating the noise so that data points closer to the true one are more likely to be reported. In this work we study how metric privacy can be improved by combining it with a shuffler. More specifically, we consider the combination of the shuffler with three mechanisms, Randomized Response, Geometric and an optimal protocol, in the context of the sum and average queries. In all cases, we formally derive the relations that express the privacy amplification due to the shuffler, in terms of metric privacy. Moreover, we formally study the privacy guarantees of each protocol if the shuffler is compromised. Finally we conduct experiments using synthetic data as well as real-world location data, showing that the proposed mechanisms achieve a better privacy-utility trade-off compared to the baseline of the standard geometric mechanism
Difference-in-Differences for Continuous Treatments and Instruments with Stayers
No full textThe first version of this paper (date: Jan 18th, 2022) circulated under the title "Difference-in-Differences Estimators for Treatments Continuously Distributed at Every Period".We propose difference-in-differences estimators in designs where the treatment is continuously distributed at every period, as is often the case when one studies the effects of taxes, tariffs, or prices. We assume that between consecutive periods, the treatment of some units, the switchers, changes, while the treatment of other units remains constant. We show that under a placebo-testable parallel-trends assumption, averages of the slopes of switchers’ potential outcomes can be nonparametrically estimated. We generalize our estimators to the instrumental-variable case. We use our estimators to estimate the price-elasticity of gasoline consumption
Climate model downscaling in central Asia: a dynamical and a neural network approach
No full textInternational audienceAbstract. High-resolution climate projections are essential for estimating future climate change impacts. Statistical and dynamical downscaling methods, or a hybrid of both, are commonly employed to generate input datasets for impact modelling. In this study, we employ COSMO-CLM (CCLM) version 6.0, a regional climate model, to explore the benefits of dynamically downscaling a general circulation model (GCM) from the Coupled Model Intercomparison Project Phase 6 (CMIP6), focusing on climate change projections for central Asia (CA). The CCLM, at 0.22° horizontal resolution, is driven by the MPI-ESM1-2-HR GCM (at 1° spatial resolution) for the historical period of 1985–2014 and the projection period of 2019–2100 under three Shared Socioeconomic Pathways (SSPs), namely the SSP1-2.6, SSP3-7.0, and SSP5-8.5 scenarios. Using the Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS) gridded observation dataset as a reference, we evaluate the performance of CCLM driven by ERA-Interim reanalysis over the historical period. The added value of CCLM, compared to its driving GCM, is evident over mountainous areas in CA, which are at a higher risk of extreme precipitation events. The mean absolute error and bias of climatological precipitation (mm d−1) are reduced by 5 mm d−1 for summer and 3 mm d−1 for annual values. For winter, there was no error reduction achieved. However, the frequency of extreme precipitation values improved in the CCLM simulations. Additionally, we employ CCLM to refine future climate projections. We present high-resolution maps of heavy precipitation changes based on CCLM and compare them with the CMIP6 GCM ensemble. Our analysis indicates an increase in the intensity and frequency of heavy precipitation events over CA areas already at risk of extreme climatic events by the end of the century. The number of days with precipitation exceeding 20 mm increases by more than 90 by the end of the century, compared to the historical reference period, under the SSP3-7.0 and SSP5-8.5 scenarios. The annual 99th percentile of total precipitation increases by more than 9 mm d−1 over mountainous areas of central Asia by the end of the century, relative to the 1985–2014 reference period, under the SSP3-7.0 and SSP5-8.5 scenarios. Finally, we train a convolutional neural network (CNN) to map a GCM simulation to its dynamically downscaled CCLM counterpart. The CNN successfully emulates the GCM–CCLM model chain over large areas of CA but shows reduced skill when applied to a different GCM–CCLM model chain. The scientific community interested in downscaling CMIP6 models could use our downscaling data, and the CNN architecture offers an alternative to traditional dynamical and statistical methods
Fast interpolation of multivariate polynomials with sparse exponents
No full textInternational audienceConsider a sparse multivariate polynomial f with integer coefficients. Assume that f is represented as a "modular black box polynomial", e.g. via an algorithm to evaluate f at arbitrary integer points, modulo arbitrary positive integers. The problem of sparse interpolation is to recover f in its usual sparse representation, as a sum of coefficients times monomials. For the first time we present a quasi-optimal algorithm for this task