Delayed Sampling and Automatic Rao-Blackwellization of Probabilistic Programs

We introduce a dynamic mechanism for the solution of analytically-tractable substructure in probabilistic programs, using conjugate priors and affine transformations to reduce variance in Monte Carlo estimators. For inference with Sequential Monte Carlo, this automatically yields improvements such as locally-optimal proposals and Rao-Blackwellization. The mechanism maintains a directed graph alongside the running program that evolves dynamically as operations are triggered upon it. Nodes of the graph represent random variables, edges the analytically-tractable relationships between them. Random variables remain in the graph for as long as possible, to be sampled only when they are used by the program in a way that cannot be resolved analytically. In the meantime, they are conditioned on as many observations as possible. We demonstrate the mechanism with a few pedagogical examples, as well as a linear-nonlinear state-space model with simulated data, and an epidemiological model with real data of a dengue outbreak in Micronesia. In all cases one or more variables are automatically marginalized out to significantly reduce variance in estimates of the marginal likelihood, in the final case facilitating a random-weight or pseudo-marginal-type importance sampler for parameter estimation. We have implemented the approach in Anglican and a new probabilistic programming language called Birch.Comment: 13 pages, 4 figure

Automatic Alignment in Higher-Order Probabilistic Programming Languages

Probabilistic Programming Languages (PPLs) allow users to encode statistical inference problems and automatically apply an inference algorithm to solve them. Popular inference algorithms for PPLs, such as sequential Monte Carlo (SMC) and Markov chain Monte Carlo (MCMC), are built around checkpoints -- relevant events for the inference algorithm during the execution of a probabilistic program. Deciding the location of checkpoints is, in current PPLs, not done optimally. To solve this problem, we present a static analysis technique that automatically determines checkpoints in programs, relieving PPL users of this task. The analysis identifies a set of checkpoints that execute in the same order in every program run -- they are aligned. We formalize alignment, prove the correctness of the analysis, and implement the analysis as part of the higher-order functional PPL Miking CorePPL. By utilizing the alignment analysis, we design two novel inference algorithm variants: aligned SMC and aligned lightweight MCMC. We show, through real-world experiments, that they significantly improve inference execution time and accuracy compared to standard PPL versions of SMC and MCMC

Conditions of emergence of the Sooty Bark Disease and aerobiology of Cryptostroma corticale in Europe

The sooty bark disease (SBD) is an emerging disease affecting sycamore maple trees (Acer pseudoplatanus) in Europe. Cryptostroma corticale, the causal agent, putatively native to eastern North America, can be also pathogenic for humans causing pneumonitis. It was first detected in 1945 in Europe, with markedly increasing reports since 2000. Pathogen development appears to be linked to heat waves and drought episodes. Here, we analyse the conditions of the SBD emergence in Europe based on a three-decadal time -series data set. We also assess the suitability of aerobiological samples using a species-specific quantitative PCR assay to inform the epidemiology of C. corticale, through a regional study in France comparing two-year aerobiological and epidemiological data, and a continental study including 12 air samplers from six countries (Czechia, France, Italy, Portugal, Sweden and Switzerland). We found that an accumulated water deficit in spring and summer lower than-132 mm correlates with SBD outbreaks. Our results suggest that C. corticale is an efficient airborne pathogen which can dis-perse its conidia as far as 310 km from the site of the closest disease outbreak. Aerobiology of C. corticale followed the SBD distribution in Europe. Pathogen detection was high in countries within the host native area and with longer disease presence, such as France, Switzerland and Czech Republic, and sporadic in Italy, where the pathogen was reported just once. The pathogen was absent in samples from Portugal and Sweden, where the disease has not been reported yet. We conclude that aerobiological surveillance can inform the spatial distribution of the SBD, and contribute to early detection in pathogen-free countries

Conditions of emergence of the Sooty Bark Disease and aerobiology of Cryptostroma corticale in Europe

The sooty bark disease (SBD) is an emerging disease affecting sycamore maple trees (Acer pseudoplatanus) in Europe. Cryptostroma corticale, the causal agent, putatively native to eastern North America, can be also pathogenic for humans causing pneumonitis. It was first detected in 1945 in Europe, with markedly increasing reports since 2000. Pathogen development appears to be linked to heat waves and drought episodes. Here, we analyse the conditions of the SBD emergence in Europe based on a three-decadal time- series data set. We also assess the suitability of aerobiological samples using a species-specific quantitative PCR assay to inform the epidemiology of C. corticale, through a regional study in France comparing two- year aerobiological and epidemiological data, and a continental study including 12 air samplers from six countries (Czechia, France, Italy, Portugal, Sweden and Switzerland). We found that an accumulated water deficit in spring and summer lower than -132 mm correlates with SBD outbreaks. Our results suggest that C. corticale is an efficient airborne pathogen which can dis- perse its conidia as far as 310 km from the site of the closest disease outbreak. Aerobiology of C. corticale followed the SBD distribution in Europe. Pathogen detection was high in countries within the host native area and with longer disease presence, such as France, Switzerland and Czech Republic, and sporadic in Italy, where the pathogen was reported just once. The pathogen was absent in samples from Portugal and Sweden, where the disease has not been reported yet. We conclude that aerobiological surveillance can inform the spatial distribution of the SBD, and contribute to early detection in pathogen-free countriesinfo:eu-repo/semantics/publishedVersio

Preclinical Assessment of the Treatment of Second-Stage African Trypanosomiasis with Cordycepin and Deoxycoformycin

There is an urgent need to substitute the highly toxic arsenic compounds still in use for treatment of the encephalitic stage of African trypanosomiasis, a disease caused by infection with Trypanosoma brucei. We exploited the inability of trypanosomes to engage in de novo purine synthesis as a therapeutic target. Cordycepin was selected from a trypanocidal screen of a 2200-compound library. When administered together with the adenosine deaminase inhibitor deoxycoformycin, cordycepin cured mice inoculated with the human pathogenic subspecies T. brucei rhodesiense or T. brucei gambiense even after parasites had penetrated into the brain. Successful treatment was achieved by intraperitoneal, oral or subcutaneous administration of the compounds. Treatment with the doublet also diminished infection-induced cerebral inflammation. Cordycepin induced programmed cell death of the parasites. Although parasites grown in vitro with low doses of cordycepin gradually developed resistance, the resistant parasites lost virulence and showed no cross-resistance to trypanocidal drugs in clinical use. Our data strongly support testing cordycepin and deoxycoformycin as an alternative for treatment of second-stage and/or melarsoprol-resistant HAT

Reactive probabilistic programming

International audienceSynchronous modeling is at the heart of programming languages like Lustre, Esterel, or SCADE used routinely for implementing safety critical control software, e.g., fly-bywire and engine control in planes. However, to date these languages have had limited modern support for modeling uncertainty-probabilistic aspects of the software's environment or behavior-even though modeling uncertainty is a primary activity when designing a control system. In this paper we present ProbZelus the first synchronous probabilistic programming language. ProbZelus conservatively provides the facilities of a synchronous language to write control software, with probabilistic constructs to model uncertainties and perform inference-in-the-loop. We present the design and implementation of the language. We propose a measure-theoretic semantics of probabilistic stream functions and a simple type discipline to separate deterministic and probabilistic expressions. We demonstrate a semantics-preserving compilation into a first-order functional language that lends itself to a simple presentation of inference algorithms for streaming models. We also redesign the delayed sampling inference algorithm to provide efficient streaming inference. Together with an evaluation on several reactive applications, our results demonstrate that ProbZelus enables the design of reactive probabilistic applications and efficient, bounded memory inference

D4.3 Final Report on Network-Level Solutions

Research activities in METIS reported in this document focus on proposing solutions to the network-level challenges of future wireless communication networks. Thereby, a large variety of scenarios is considered and a set of technical concepts is proposed to serve the needs envisioned for the 2020 and beyond. This document provides the final findings on several network-level aspects and groups of solutions that are considered essential for designing future 5G solutions. Specifically, it elaborates on: -Interference management and resource allocation schemes -Mobility management and robustness enhancements -Context aware approaches -D2D and V2X mechanisms -Technology components focused on clustering -Dynamic reconfiguration enablers These novel network-level technology concepts are evaluated against requirements defined by METIS for future 5G systems. Moreover, functional enablers which can support the solutions mentioned aboveare proposed. We find that the network level solutions and technology components developed during the course of METIS complement the lower layer technology components and thereby effectively contribute to meeting 5G requirements and targets.Aydin, O.; Valentin, S.; Ren, Z.; Botsov, M.; Lakshmana, TR.; Sui, Y.; Sun, W.... (2015). D4.3 Final Report on Network-Level Solutions. http://hdl.handle.net/10251/7675

De osynliga parkeringsautomaterna : En studie om orienterbarhet i en innerstadsmiljö

Det här examensarbetet handlar om hur man med hjälp av ett skyltsystem på tre nivåskillnader, i ögonhöjd, på marknivå och på en förhöjd nivå kan underlätta navigeringen till parkeringsautomater i Stockholms innerstad. Denna studie är skriven inom ämnet Informationsdesign med inriktning mot Rumslig gestaltning. Förstagångsbesökares svårighet med att hitta till parkeringsautomater är det praktiska problem som legat till grund för detta arbete. De metoder som använts i denna studie är rumslig analys, innefattande observation samt platsanalys. Även benchmarking, kundresa, intervju, surveyundersökning samt användartest har använts för empirisk datainsamling. Studien har resulterat i ett designförslag som har i syfte att underlätta besökarnas navigering från det att de har parkerat sitt fordon till att de nått parkeringsautomaten. Detta ska medföra att färre kommer bli försenade eller missa sådant som de planerat och på så sätt kommer det även minska stressnivån för samtliga. Stressen som uppstår när en känner sig vilse ska alltså minimeras med hjälp av mitt skyltsystem.This thesis is about how a three-level sign system; at eye level, at ground level and at an elevated level, can facilitate navigation to parking meters in Stockholm's inner city. This study is written in the field of Information Design with specialization in Spatial Design. The first-time visitor’s difficulty in finding parking meters is the practical problem that underlies this work. The methods used are spatial analysis, including observation as well as site analysis. Benchmarking, customer experience audit, interview, survey and user tests have also been used for empirical data collection. The study has resulted in a design proposal aimed at facilitating visitor’s navigation from the time they have parked their car until they have reached the parking meter. Stress that occurs when one feels lost in a city environment should be minimized with the help of my design proposal. This will result in fewer missed plans for the visitors and an overall better experience of paying for your parking in Stockholm’s inner city

Correct and Efficient Monte Carlo Inference for Universal Probabilistic Programming Languages

Probabilistic programming languages (PPLs) allow users to express statistical inference problems that the PPL implementation then, ideally, solves automatically. In particular, PPL users can focus on encoding their inference problems, and need not concern themselves with the intricacies of inference. Universal PPLs are PPLs with great expressive power, meaning that users can express essentially any inference problem. Consequently, universal PPL implementations often use general-purpose inference algorithms that are compatible with all such inference problems. A problem, however, is that general-purpose inference algorithms can often not efficiently solve complex inference problems. Furthermore, for certain inference algorithms, there are no formal correctness proofs in the context of universal PPLs. This dissertation considers research problems related to Monte Carlo inference algorithms—sampling-based general-purpose inference algorithms that universal PPL implementations often apply. The first research problem concerns the correctness of sequential Monte Carlo (SMC) inference algorithms. A contribution in the dissertation is a proof of correctness for SMC algorithms in the context of universal PPLs. The second research problem concerns execution time improvements when suspending executions—a requirement in many Monte Carlo inference algorithms. The dissertation addresses the problem through two separate approaches. The first approach is a compilation technique targeting high-performance platforms. The second approach is a static suspension analysis guiding a selective continuation-passing style (CPS) transformation, reducing overhead compared to a full CPS transformation. The third research problem concerns inference improvements through alignment—a useful and often overlooked property in PPLs. The dissertation contributions are a formal definition of alignment, a static analysis technique that automatically aligns programs, and aligned versions of SMC and Markov chain Monte Carlo (MCMC) inference algorithms. The final research problem is more practical, and concerns the effective implementation of PPLs. Specifically, the contribution is the Miking CorePPL universal PPL and its compiler. Overall, the contributions in the dissertation significantly improve the efficiency of Monte Carlo algorithms as applied in universal PPLs.Probabilistiska programmeringsspråk (PPL:er) tillåter användare att uttrycka statistiska inferensproblem som PPL-implementationen sedan, i bästa fall, löser automatiskt. I synnerhet kan PPL-användare fokusera på att uttrycka sina inferensproblem utan att behöva bekymra sig om svårigheter tillhörande inferensen. Universella PPL:er är PPL:er med stor uttrycksfullhet, vilket innebär att användare kan uttrycka i princip vilket inferensproblem som helst. Följaktligen använder universella PPL-implementationer ofta inferensalgoritmer för allmänna ändamål som är kompatibla med alla sådana inferensproblem. Ett problem är dock att inferensalgoritmer för allmänna ändamål ofta inte effektivt kan lösa komplexa inferensproblem. Dessutom finns det inga formella korrekthetsbevis för vissa inferensalgoritmer när de används i universella PPL:er. I denna avhandling behandlas forskningsproblem som rör Monte Carlo-inferensalgoritmer—samplingbaserade inferensalgoritmer för allmänna ändamål som universella PPL-implementationer ofta tillämpar. Det första forskningsproblemet rör korrektheten av sekventiella Monte Carlo-inferensalgoritmer (SMC). Ett bidrag i avhandlingen är ett korrekthetsbevis för SMC-algoritmer i universella PPL:er. Det andra forskningsproblemet rör förbättringar av exekveringstid vid exekveringsavbrott—ett krav i många Monte Carlo-inferensalgoritmer. Avhandlingen behandlar problemet genom två separata tillvägagångssätt. Det första tillvägagångssättet är en kompileringsteknik som riktar sig mot högpresterande plattformar. Det andra tillvägagångssättet är en statisk avbrottsanalys som styr en selektiv transformation till fortsättningsskickande stil (CPS), vilket reducerar exekveringstid jämfört med en fullständig CPS-transformation. Det tredje forskningsproblemet rör inferensförbättringar genom samordning—en användbar och ofta förbisedd egenskap i PPL:er. Avhandlingens bidrag är en formell definition av samordning, en statisk analysteknik som automatiskt samordnar program, samt samordnade versioner av SMC- och Markovkedjebaserade Monte Carlo-inferensalgoritmer (MCMC). Det sista forskningsproblemet är mer praktiskt och rör den effektiva implementationen av PPL:er. Konkret är bidraget den universella PPL:en Miking CorePPL och dess kompilator. Sammanfattningsvis förbättrar avhandlingens bidrag avsevärt effektiviteten hos Monte Carlo-algoritmer som tillämpas i universella PPL:er.QC 20230303</p