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It is well known that changing the intervention package while a trial is ongoing does not lead to valid inference using standard statistical methods. However, it is often necessary to adapt, tailor, or tweak a complex intervention package in public health implementation trials, especially when the intervention package does not have the desired effect. This article presents conditions under which the resulting analyses remain valid even when the intervention package is adapted while a trial is ongoing. Our results on such Learn-As-you-GO (LAGO) studies extend the theory of LAGO for binary outcomes following a logistic regression model (Nevo, Lok and Spiegelman, 2021) to LAGO for continuous outcomes under flexible conditional mean model. We derive point and interval estimators of the intervention effects and ensure the validity of hypothesis tests for an overall intervention effect. We develop a confidence set for the optimal intervention package, which achieves a pre-specified mean outcome while minimizing cost, and confidence bands for the mean outcome under all intervention package compositions. This work will be useful for the design and analysis of large-scale intervention trials where the intervention package is adapted, tailored, or tweaked while the trial is ongoing., Comment: 65 pages, 15 tables

We present the results of modeling and simulating the Hamamatsu R5912 photomultiplier tube that is used in most of the sites of the Latin American Giant Observatory (LAGO). The model was compared with data of in-operation water Cherenkov detectors (WCD) installed at Bucaramanga-Colombia and Bariloche-Argentina. The LAGO project is an international experiment that spans across Latin America at different altitudes joining more than 35 institutions of 11 countries. It is mainly oriented to basic research on gamma-ray bursts and space weather phenomena. The LAGO network consists of single or small arrays of WCDs composed mainly by a photomultiplier tube and a readout electronics that acquires single-particle or extensive air shower events triggered by the interaction of cosmic rays with the Earth atmosphere.

LAGO, the Latin American Giant Observatory, is an extended cosmic ray observatory, consisting of a wide network of water Cherenkov detectors located in 10 countries. With different altitudes and geomagnetic rigidity cutoffs, their geographic distribution, combined with the new electronics for control, atmospheric sensing and data acquisition, allows the realisation of diverse astrophysics studies at a regional scale. It is an observatory designed, built and operated by the LAGO Collaboration, a non-centralised alliance of 30 institutions from 11 countries. While LAGO has access to different computational frameworks, it lacks standardised computational mechanisms to fully grasp its cooperative approach. The European Commission is fostering initiatives aligned to LAGO objectives, especially to enable Open Science and its long-term sustainability. This work introduces the adaptation of LAGO to this paradigm within the EOSC-Synergy project, focusing on the simulations of the expected astrophysical signatures at detectors deployed at the LAGO sites around the World., Comment: 10 pages, 3 figures, Invited Talk at the Winter Simulation Conference WSC2021, Phoenix, AZ, USA

The Latin American Giant Observatory (LAGO) is a distributed cosmic ray observatory at a regional scale in Latin America, by deploying a large network of Water Cherenkov detectors (WCD) and other astroparticle detectors in a wide range of latitudes from Antarctica to M\'exico, and altitudes from sea level to more than 5500 m a.s.l. Detectors telemetry, atmospherics conditions and flux of secondary particles at the ground are measured with extreme detail at each LAGO site by using our own-designed hardware and firmware (ACQUA). To combine and analyse all these data, LAGO developed ANNA, our data analysis framework. Additionally, ARTI, a complete framework of simulations designed to simulate the expected signals at our detectors coming from primary cosmic rays entering the Earth atmosphere, allowing a precise characterization of the sites in realistic atmospheric, geomagnetic and detector conditions. As the measured and synthetic data started to flow, we are facing challenging scenarios given a large amount of data emerging, performed on a diversity of detectors and computing architectures and e-infrastructures. These data need to be transferred, analyzed, catalogued, preserved, and provided for internal and public access and data-mining under an open e-science environment. In this work, we present the implementation of ARTI at the EOSC-Synergy cloud-based services as the first example of LAGO' frameworks that will follow the FAIR principles for provenance, data curation and re-using of data. For this, we calculate the flux of secondary particles expected in up to 1 week at detector level for all the 26 LAGO, and the 1-year flux of high energy secondaries expected at the ANDES Underground Laboratory and other sites. Therefore, we show how this development can help not only LAGO but other data-intensive cosmic rays observatories, muography experiments and underground laboratories., Comment: [30Dic21. No changes in the paper. Corrected mistake in ArXiv' metadata: swapping given name order: Juan Antonio Rubio-Montero -> Antonio Juan Rubio-Montero]

The LAGO (Latin American Giant Observatory) observatory is an experiment that spans over Latin America in a wide range of latitudes that gives different rigidity cut offs for the enter of cosmic rays in the atmosphere. The motivation of the Observatory is to study atmospheric radiation and space weather through the measurement of the secondary emission of low energy cosmic rays at ground level using Water Cherenkov Detectors (WCD). This work presents the contributions of the LAGO collaboration to the 2019 36th ICRC., Comment: International Cosmic Ray Conference 2019

In learn-as-you-go (LAGO) adaptive studies, the intervention is a complex package consisting of multiple components, and is adapted in stages during the study based on past outcome data. This design formalizes standard practice, and desires for practice, in public health intervention studies. An effective intervention package is sought, while minimizing intervention package cost. When analyzing data from a learn-as-you-go study, the interventions in later stages depend upon the outcomes in the previous stages, violating standard statistical theory. We develop methods for estimating the intervention effects in a LAGO study. We prove consistency and asymptotic normality using a novel coupling argument, ensuring the validity of the test for the hypothesis of no overall intervention effect. We develop a confidence set for the optimal intervention package and confidence bands for the success probabilities under alternative package compositions. We illustrate our methods in the BetterBirth Study, which aimed to improve maternal and neonatal outcomes among 157,689 births in Uttar Pradesh, India through a complex, multi-component intervention package.

The Latin American Giant Observatory (LAGO) is an extended astroparticle observatory with the goal of studying Gamma Ray Bursts (among other extreme universe phenomena), space weather and atmospheric radiation at ground level. It consists of a network of several Water Cherenkov Detectors (WCD) located at different sites and different latitudes along the American Continent (from Mexico up to the Antarctic region). Another interest of LAGO is to encourage and support the development of experimental basic research in Latin America, mainly with low cost equipment. In the case of Chiapas, Mexico, the experimental astroparticle physics activity was limited, up to now, to data analysis from other detectors located far away from the region. Thanks to the collaboration within LAGO, the deployment of one WCD is ongoing at the Universidad Aut\'onoma de Chiapas (UNACH). This will allow, for the first time in the region, to train students and researchers in the deployment processes. Till now the setup of the signal-processing electronics has been performed and the characterization of the photomultiplier tube is currently being done. The main, short-term goal is to install one WCD on top of the Tacan\'a volcano in Chiapas in a short term. The status of the work is presented., Comment: International Cosmic Ray Conference, Busan, Korea, 10-20 July, 2017 https://pos.sissa.it/301/

The Latin American Giant Observatory (LAGO) is an extended cosmic ray observatory composed of a network of water-Cherenkov detectors (WCD) spanning over different sites located at significantly different altitudes (from sea level up to more than $5000$\,m a.s.l.) and latitudes across Latin America, covering a wide range of geomagnetic rigidity cut-offs and atmospheric absorption/reaction levels. The LAGO WCD is simple and robust, and incorporates several integrated devices to allow time synchronization, autonomous operation, on board data analysis, as well as remote control and automated data transfer. This detection network is designed to make detailed measurements of the temporal evolution of the radiation flux coming from outer space at ground level. LAGO is mainly oriented to perform basic research in three areas: high energy phenomena, space weather and atmospheric radiation at ground level. It is an observatory designed, built and operated by the LAGO Collaboration, a non-centralized collaborative union of more than 30 institutions from ten countries. In this paper we describe the scientific and academic goals of the LAGO project - illustrating its present status with some recent results - and outline its future perspectives., Comment: 4 pages, 2 figures, Proceedings of the 9th International Workshop on Ring Imaging Cherenkov Detectors (RICH 2016), Lake Bled, Slovenia

We simulate the expected variations of background flux at two particular sites of the Latin American Giant Observatory (LAGO) and found that these fluxes are sensible to the latitude and that neutrons and muons components, of cosmic rays, are affected due to the variation of the geomagnetic field (GF)., Comment: Proceedings XV Latin American Regional IAU Meeting LARIM

We describe a set of tools, services and strategies of the Latin American Giant Observatory (LAGO) data repository network, to implement Data Accessibility, Reproducibility and Trustworthiness.

Direct measurements of VEM using scintillating plastic plates in water Cherenkov prototype detectors and a muon flux model were previously used to reproduce the charge histogram of a detector. In these measurements the VEM has a significantly lower value than would be obtained by locating the charge histogram maximum. In this technical note we will use the inverse process, being known the charge histogram we will use a simple model to estimate the load distribution of the VEM. We will then use this VEM distribution and a one-hour secondary cosmic ray flux simulation to estimate the total muon component of the histogram., Comment: in Spanish

The ability to distribute heralded entanglement between distant matter nodes is a primitive for the implementation of large-scale quantum networks. Some of the most crucial requirements for future applications include high heralding rates at telecom wavelengths, multiplexed operation and on-demand retrieval of stored excitations for synchronization of separate quantum links. Despite tremendous progress in various physical systems, the demonstration of telecom-heralded entanglement between quantum nodes featuring both multiplexed operation and on-demand retrieval remains elusive. In this work, we combine narrowband parametric photon-pair sources and solid-state quantum memories based on rare-earth doped crystals to demonstrate telecom heralded entanglement between spatially separated spin-wave quantum memories with fully adjustable recall time and temporal multiplexing of 15 modes. In a first experiment, the storage in the spin-state is conditioned on the entanglement heralding. We take advantage of the control over readout pulse phase to achieve feed-forward conditional phase-shifts on the stored photons depending on which heralding detector clicked. We exploit this effect to double the entanglement heralding rate for a given quantum state up to \SI{500}{\cps}, with a measured positive concurrence by up to 6 standard deviations. In a second experiment, we simulate the communication time of a long-distance link by implementing an unconditional storage scheme with a dead-time of \SI{100}{\micro\second}, leading to an entanglement rate of \SI{22}{\cps} per heralding detector, increased by a factor of 15 thanks to temporal multiplexing. These results establish our architecture as a prime candidate for the implementation of scalable high-rate quantum network links., Comment: 14 pages, 10 figures

We propose a methodology that combines several advanced techniques in Large Language Model (LLM) retrieval to support the development of robust, multi-source question-answer systems. This methodology is designed to integrate information from diverse data sources, including unstructured documents (PDFs) and structured databases, through a coordinated multi-agent orchestration and dynamic retrieval approach. Our methodology leverages specialized agents-such as SQL agents, Retrieval-Augmented Generation (RAG) agents, and router agents - that dynamically select the most appropriate retrieval strategy based on the nature of each query. To further improve accuracy and contextual relevance, we employ dynamic prompt engineering, which adapts in real time to query-specific contexts. The methodology's effectiveness is demonstrated within the domain of Contract Management, where complex queries often require seamless interaction between unstructured and structured data. Our results indicate that this approach enhances response accuracy and relevance, offering a versatile and scalable framework for developing question-answer systems that can operate across various domains and data sources., Comment: International Conference on NLP, AI, Computer Science & Engineering (NLAICSE 2024)

We present a question-and-answer (Q\&A) application designed to support the contract management process by leveraging combined information from contract documents (PDFs) and data retrieved from contract management systems (database). This data is processed by a large language model (LLM) to provide precise and relevant answers. The accuracy of these responses is further enhanced through the use of Retrieval-Augmented Generation (RAG), text-to-SQL techniques, and agents that dynamically orchestrate the workflow. These techniques eliminate the need to retrain the language model. Additionally, we employed Prompt Engineering to fine-tune the focus of responses. Our findings demonstrate that this multi-agent orchestration and combination of techniques significantly improve the relevance and accuracy of the answers, offering a promising direction for future information systems., Comment: KDIR 2024 - Knowledge Discovery and Information Retrieval

Background. The rapid growth of Language Models (LMs), particularly in code generation, requires substantial computational resources, raising concerns about energy consumption and environmental impact. Optimizing LMs inference for energy efficiency is crucial, and Small Language Models (SLMs) offer a promising solution to reduce resource demands. Aim. Our goal is to analyze the impact of deep learning runtime engines and execution providers on energy consumption, execution time, and computing-resource utilization from the point of view of software engineers conducting inference in the context of code SLMs. Method. We conducted a technology-oriented, multi-stage experimental pipeline using twelve code generation SLMs to investigate energy consumption, execution time, and computing-resource utilization across the configurations. Results. Significant differences emerged across configurations. CUDA execution provider configurations outperformed CPU execution provider configurations in both energy consumption and execution time. Among the configurations, TORCH paired with CUDA demonstrated the greatest energy efficiency, achieving energy savings from 37.99% up to 89.16% compared to other serving configurations. Similarly, optimized runtime engines like ONNX with the CPU execution provider achieved from 8.98% up to 72.04% energy savings within CPU-based configurations. Also, TORCH paired with CUDA exhibited efficient computing-resource utilization. Conclusions. Serving configuration choice significantly impacts energy efficiency. While further research is needed, we recommend the above configurations best suited to software engineers' requirements for enhancing serving efficiency in energy and performance., Comment: 26 pages, submitted to journal

Accurate and timely regional weather prediction is vital for sectors dependent on weather-related decisions. Traditional prediction methods, based on atmospheric equations, often struggle with coarse temporal resolutions and inaccuracies. This paper presents a novel machine learning (ML) model, called MiMa (short for Micro-Macro), that integrates both near-surface observational data from Kentucky Mesonet stations (collected every five minutes, known as Micro data) and hourly atmospheric numerical outputs (termed as Macro data) for fine-resolution weather forecasting. The MiMa model employs an encoder-decoder transformer structure, with two encoders for processing multivariate data from both datasets and a decoder for forecasting weather variables over short time horizons. Each instance of the MiMa model, called a modelet, predicts the values of a specific weather parameter at an individual Mesonet station. The approach is extended with Re-MiMa modelets, which are designed to predict weather variables at ungauged locations by training on multivariate data from a few representative stations in a region, tagged with their elevations. Re-MiMa (short for Regional-MiMa) can provide highly accurate predictions across an entire region, even in areas without observational stations. Experimental results show that MiMa significantly outperforms current models, with Re-MiMa offering precise short-term forecasts for ungauged locations, marking a significant advancement in weather forecasting accuracy and applicability.

To characterize the signals registered by the different types of water Cherenkov detectors (WCD) used by the Latin American Giant Observatory (LAGO) Project, it is necessary to develop detailed simulations of the detector response to the flux of secondary particles at the detector level. These particles are originated during the interaction of cosmic rays with the atmosphere. In this context, the LAGO project aims to study the high energy component of gamma rays bursts (GRBs) and space weather phenomena by looking for the solar modulation of galactic cosmic rays (GCRs). Focus in this, a complete and complex chain of simulations is being developed that account for geomagnetic effects, atmospheric reaction and detector response at each LAGO site. In this work we shown the first steps of a GEANT4 based simulation for the LAGO WCD, with emphasis on the induced effects of the detector internal diffusive coating., Comment: 5 pages, 4 figures, Proceedings X SILAFAE Medellin-2014. To appear in Nuclear Physics B - Proceedings Supplements

Since their appearance in the 1950s, computational models capable of performing probabilistic choices have received wide attention and are nowadays pervasive in almost every areas of computer science. Their development was also inextricably linked with inquiries about computation power and resource issues. Although most crucial notions in the field are well-known, the related terminology is sometimes imprecise or misleading. The present work aims to clarify the core features and main differences between machines and classes developed in relation to randomized computation. To do so, we compare the modern definitions with original ones, recalling the context in which they first appeared, and investigate the relations linking probabilistic and counting models.

Multiparty Session Types (MPSTs) offer a structured way of specifying communication protocols and guarantee relevant communication properties, such as deadlock-freedom. In this paper, we extend a minimal MPST system with quantum data and operations, enabling the specification of quantum protocols. Quantum MPSTs (QMPSTs) provide a formal notation to describe quantum protocols, both at the abstract level of global types, describing which communications can take place in the system and their dependencies, and at the concrete level of local types and quantum processes, describing the expected behavior of each participant in the protocol. Type-checking relates these two levels formally, ensuring that processes behave as prescribed by the global type. Beyond usual communication properties, QMPSTs also allow us to prove that qubits are owned by a single process at any time, capturing the quantum no-cloning and no-deleting theorems. We use our approach to verify four quantum protocols from the literature, respectively Teleportation, Secret Sharing, Bit-Commitment, and Key Distribution., Comment: To appear at SEFM 2024

This is an index of the contributions by the Giant Radio Array for Neutrino Detection (GRAND) Collaboration to the 10th International Workshop on Acoustic and Radio EeV Neutrino Detection Activities (ARENA 2024, University of Chicago, June 11-14, 2024). The contributions include an overview of GRAND in its present and future incarnations, methods of radio-detection that are being developed for them, and ongoing joint work between the GRAND and BEACON experiments., Comment: Note: To access the list of contributions, please follow the "HTML" link that can be found on the arXiv page

A $\lambda$-calculus is introduced in which all programs can be evaluated in probabilistic polynomial time and in which there is sufficient structure to represent sequential cryptographic constructions and adversaries for them, even when the latter are oracle-based. A notion of observational equivalence capturing computational indistinguishability and a class of approximate logical relations are then presented, showing that the latter represent a sound proof technique for the former. The work concludes with the presentation of an example of a security proof in which the encryption scheme induced by a pseudorandom function is proven secure against active adversaries in a purely equational style., Comment: APLAS 2024 conference

This paper presents the global analysis of two extended decreases of the galactic cosmic ray intensity observed by world-wide networks of ground-based detectors in 2012. This analysis is capable of separately deriving the cosmic ray density (or omnidirectional intensity) and anisotropy each as a function of time and rigidity. A simple diffusion model along the spiral field line between Earth and a cosmic-ray barrier indicates the long duration of these events resulting from about 190$^\circ$ eastern extension of a barrier such as an IP-shock followed by the sheath region and/or the corotating interaction region (CIR). It is suggested that the coronal mass ejection merging and compressing the preexisting CIR at its flank can produce such the extended barrier. The derived rigidity spectra of the density and anisotropy both vary in time during each event period. In particular we find that the temporal feature of the ``phantom Forbush decrease'' reported in an analyzed period is dependent on rigidity, looking quite different at different rigidities. From these rigidity spectra of the density and anisotropy, we derive the rigidity spectrum of the average parallel mean-free-path of pitch angle scattering along the spiral field line and infer the power spectrum of the magnetic fluctuation and its temporal variation. Possible physical cause of the strong rigidity dependence of the ``phantom Forbush decrease'' is also discussed. These results demonstrate the high-energy cosmic rays observed at Earth responding to remote space weather., Comment: Accepted for publication in ApJ

The operation of upcoming ultra-high-energy cosmic-ray, gamma-ray, and neutrino radio-detection experiments, like the Giant Radio Array for Neutrino Detection (GRAND), poses significant computational challenges involving the production of numerous simulations of particle showers and their detection, and a high data throughput. GRANDlib is an open-source software tool designed to meet these challenges. Its primary goal is to perform end-to-end simulations of the detector operation, from the interaction of ultra-high-energy particles, through -- by interfacing with external air-shower simulations -- the ensuing particle shower development and its radio emission, to its detection by antenna arrays and its processing by data-acquisition systems. Additionally, GRANDlib manages the visualization, storage, and retrieval of experimental and simulated data. We present an overview of GRANDlib to serve as the basis of future GRAND analyses., Comment: 11 pages, 9 figures, plus appendices; Matches published version

Results are presented for the measurement of large-scale anisotropies in the arrival directions of ultra-high-energy cosmic rays detected at the Pierre Auger Observatory during 19 years of operation, prior to AugerPrime, the upgrade of the Observatory. The 3D dipole amplitude and direction are reconstructed above $4\,$EeV in four energy bins. Besides the established dipolar anisotropy in right ascension above $8\,$EeV, the Fourier amplitude of the $8$ to $16\,$EeV energy bin is now also above the $5\sigma$ discovery level. No time variation of the dipole moment above $8\,$EeV is found, setting an upper limit to the rate of change of such variations of $0.3\%$ per year at the $95\%$ confidence level. Additionally, the results for the angular power spectrum are shown, demonstrating no other statistically significant multipoles. The results for the equatorial dipole component down to $0.03\,$EeV are presented, using for the first time a data set obtained with a trigger that has been optimized for lower energies. Finally, model predictions are discussed and compared with observations, based on two source emission scenarios obtained in the combined fit of spectrum and composition above $0.6\,$EeV., Comment: Minor modifications, accepted for publication in Astrophysical Journal

We introduce a type system for the Quipper language designed to derive upper bounds on the size of the circuits produced by the typed program. This size can be measured according to various metrics, including width, depth and gate count, but also variations thereof obtained by considering only some wire types or some gate kinds. The key ingredients for achieving this level of flexibility are effects and refinement types, both relying on indices, that is, generic arithmetic expressions whose operators are interpreted differently depending on the target metric. The approach is shown to be correct through logical predicates, under reasonable assumptions about the chosen resource metric. This approach is empirically evaluated through the QuRA tool, showing that, in many cases, inferring tight bounds is possible in a fully automatic way., Comment: 26 pages, 13 figures

Development of artificial intelligence (AI) techniques in medical imaging requires access to large-scale and diverse datasets for training and evaluation. In dermatology, obtaining such datasets remains challenging due to significant variations in patient populations, illumination conditions, and acquisition system characteristics. In this work, we propose S-SYNTH, the first knowledge-based, adaptable open-source skin simulation framework to rapidly generate synthetic skin, 3D models and digitally rendered images, using an anatomically inspired multi-layer, multi-component skin and growing lesion model. The skin model allows for controlled variation in skin appearance, such as skin color, presence of hair, lesion shape, and blood fraction among other parameters. We use this framework to study the effect of possible variations on the development and evaluation of AI models for skin lesion segmentation, and show that results obtained using synthetic data follow similar comparative trends as real dermatologic images, while mitigating biases and limitations from existing datasets including small dataset size, lack of diversity, and underrepresentation., Comment: Accepted to the International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI) 2024

In this work, we introduce Gemma 2, a new addition to the Gemma family of lightweight, state-of-the-art open models, ranging in scale from 2 billion to 27 billion parameters. In this new version, we apply several known technical modifications to the Transformer architecture, such as interleaving local-global attentions (Beltagy et al., 2020a) and group-query attention (Ainslie et al., 2023). We also train the 2B and 9B models with knowledge distillation (Hinton et al., 2015) instead of next token prediction. The resulting models deliver the best performance for their size, and even offer competitive alternatives to models that are 2-3 times bigger. We release all our models to the community.