Stochastic Beams and Where to Find Them: The Gumbel-Top-k Trick for Sampling Sequences Without Replacement

The well-known Gumbel-Max trick for sampling from a categorical distribution can be extended to sample $k$ elements without replacement. We show how to implicitly apply this 'Gumbel-Top-$k$' trick on a factorized distribution over sequences, allowing to draw exact samples without replacement using a Stochastic Beam Search. Even for exponentially large domains, the number of model evaluations grows only linear in $k$ and the maximum sampled sequence length. The algorithm creates a theoretical connection between sampling and (deterministic) beam search and can be used as a principled intermediate alternative. In a translation task, the proposed method compares favourably against alternatives to obtain diverse yet good quality translations. We show that sequences sampled without replacement can be used to construct low-variance estimators for expected sentence-level BLEU score and model entropy.Comment: ICML 2019 ; 13 pages, 4 figure

Attention, Learn to Solve Routing Problems!

The recently presented idea to learn heuristics for combinatorial optimization problems is promising as it can save costly development. However, to push this idea towards practical implementation, we need better models and better ways of training. We contribute in both directions: we propose a model based on attention layers with benefits over the Pointer Network and we show how to train this model using REINFORCE with a simple baseline based on a deterministic greedy rollout, which we find is more efficient than using a value function. We significantly improve over recent learned heuristics for the Travelling Salesman Problem (TSP), getting close to optimal results for problems up to 100 nodes. With the same hyperparameters, we learn strong heuristics for two variants of the Vehicle Routing Problem (VRP), the Orienteering Problem (OP) and (a stochastic variant of) the Prize Collecting TSP (PCTSP), outperforming a wide range of baselines and getting results close to highly optimized and specialized algorithms.Comment: Accepted at ICLR 2019. 25 pages, 7 figure

Preservation Health Check: Monitoring Threats to Digital Repository Content

Preservation Health Check: Monitoring Threats to Digital Repository Content presents the preliminary findings of Phase 1 of our Preservation Health Check investigation of preservation monitoring and suggests that there is an opportunity to use PREMIS preservation metadata as an evidence base to support a threat assessment exercise based on the Simple Property-Oriented Threat (SPOT) model.Key highlights:There is a need for digital preservation repositories to perform periodic "health checks" as a routine part of preservation activitiesPreservation Health Check activities serve the day-to-day planning and operations of digital repositoriesA certain level of predictability and harmonization is necessary for threat assessment applications that rely on automated data evaluationAnalysis reveals a variety of gaps in current preservation metadata coverage, which might be filled by other metadata schemaFindings suggest an opportunity to use PREMIS preservation metadata as an evidence base to support a threat assessment exerciseThe results of preservation actions (PREMIS Events) represent a crucial part of the information needed for assessment—whether this information is under the direct control of the repository itself, or whether it is created and maintained by parties external to the repository.The flexibility of the PREMIS standard allows for a large diversity in implementations and leaves much room for encoding relevant metadata in other formats and schemas—all of which impedes the implementation of a threat assessment logic that generalizes over many repositories. This report will be of interest to digital repository managers, digital preservation practitioners, and PREMIS implementers

Rudolph Agricola: Six Lives and Erasmus’s Testimonies

Rudolph Agricola: Six Lives and Erasmus’s Testimonies The Frisian humanist Rudolph Agricola (1443-1485) is rightly famous for single-handedly bringing the Italian Renaissance to the North. Owing to his fascinating personality and many talents, he attracted the love and admiration of his contemporaries and the following generations. As a result, six biographies on Agricola have been preserved. The authors of these lives drew their materials from different sources and wrote their texts independently from each other. Differing vastly in rhetorical aims and methods, they provide us with a vivid image of cultural and intellectual life in the 15th century. Erasmus praised Agricola's work throughout his writings. No less than fifty testimonies from Erasmus and his correspondents are presented here. This edition of sources supplements the volume of Agricola's letters (BLN, 2002) and is preceded by an expert survey of all biographical information now at our disposal. Thus it fills a gap in our knowledge of a great man of letters, while correcting a number of persistent misconceptions (concerning the year of Agricola's birth, for instance)

When Does Model-Based Control Pay Off?

Many accounts of decision making and reinforcement learning posit the existence of two distinct systems that control choice: a fast, automatic system and a slow, deliberative system. Recent research formalizes this distinction by mapping these systems to “model-free” and “model-based” strategies in reinforcement learning. Model-free strategies are computationally cheap, but sometimes inaccurate, because action values can be accessed by inspecting a look-up table constructed through trial-and-error. In contrast, model-based strategies compute action values through planning in a causal model of the environment, which is more accurate but also more cognitively demanding. It is assumed that this trade-off between accuracy and computational demand plays an important role in the arbitration between the two strategies, but we show that the hallmark task for dissociating model-free and model-based strategies, as well as several related variants, do not embody such a trade-off. We describe five factors that reduce the effectiveness of the model-based strategy on these tasks by reducing its accuracy in estimating reward outcomes and decreasing the importance of its choices. Based on these observations, we describe a version of the task that formally and empirically obtains an accuracy-demand trade-off between model-free and model-based strategies. Moreover, we show that human participants spontaneously increase their reliance on model-based control on this task, compared to the original paradigm. Our novel task and our computational analyses may prove important in subsequent empirical investigations of how humans balance accuracy and demand

Benchmarking laboratory processes to characterise low-biomass respiratory microbiota

Abstract The low biomass of respiratory samples makes it difficult to accurately characterise the microbial community composition. PCR conditions and contaminating microbial DNA can alter the biological profile. The objective of this study was to benchmark the currently available laboratory protocols to accurately analyse the microbial community of low biomass samples. To study the effect of PCR conditions on the microbial community profile, we amplified the 16S rRNA gene of respiratory samples using various bacterial loads and different number of PCR cycles. Libraries were purified by gel electrophoresis or AMPure XP and sequenced by V2 or V3 MiSeq reagent kits by Illumina sequencing. The positive control was diluted in different solvents. PCR conditions had no significant influence on the microbial community profile of low biomass samples. Purification methods and MiSeq reagent kits provided nearly similar microbiota profiles (paired Bray–Curtis dissimilarity median: 0.03 and 0.05, respectively). While profiles of positive controls were significantly influenced by the type of dilution solvent, the theoretical profile of the Zymo mock was most accurately analysed when the Zymo mock was diluted in elution buffer (difference compared to the theoretical Zymo mock: 21.6% for elution buffer, 29.2% for Milli-Q, and 79.6% for DNA/RNA shield). Microbiota profiles of DNA blanks formed a distinct cluster compared to low biomass samples, demonstrating that low biomass samples can accurately be distinguished from DNA blanks. In summary, to accurately characterise the microbial community composition we recommend 1. amplification of the obtained microbial DNA with 30 PCR cycles, 2. purifying amplicon pools by two consecutive AMPure XP steps and 3. sequence the pooled amplicons by V3 MiSeq reagent kit. The benchmarked standardized laboratory workflow presented here ensures comparability of results within and between low biomass microbiome studies

Maturation of the infant respiratory microbiota, environmental drivers and health consequences: a prospective cohort study

Rationale: Perinatal and postnatal influences are presumed important drivers of the early-life respiratory microbiota composition. We hypothesized that the respiratory microbiota composition and development in infancy is affecting microbiota stability and thereby resistance against respiratory tract infections (RTIs) over time. Objectives: To investigate common environmental drivers, including birth mode, feeding type, antibiotic exposure, and crowding conditions, in relation to respiratory tract microbiota maturation and stability, and consecutive risk of RTIs over the first year of life. Methods: In a prospectively followed cohort of 112 infants, we characterized the nasopharyngeal microbiota longitudinally from birth on (11 consecutive sample moments and the maximum three RTI samples per subject; in total, n = 1,121 samples) by 16S-rRNA gene amplicon sequencing. Measurements and Main Results: Using a microbiota-based machine-learning algorithm, we found that children experiencing a higher number of RTIs in the first year of life already demonstrate an aberrant microbial developmental trajectory from the first month of life on as compared with the reference group (0-2 RTIs/yr). The altered microbiota maturation process coincided with decreased microbial community stability, prolonged reduction of Corynebacterium and Dolosigranulum, enrichment of Moraxella very early in life, followed by later enrichment of Neisseria and Prevotella spp. Independent drivers of these aberrant developmental trajectories of respiratory microbiota members were mode of delivery, infant feeding, crowding, and recent antibiotic use. Conclusions: Our results suggest that environmental drivers impact microbiota development and, consequently, resistance against development of RTIs. This supports the idea that microbiota form the mediator between early-life environmental risk factors for and susceptibility to RTIs over the first year of life

Association of NIPA1 repeat expansions with amyotrophic lateral sclerosis in a large international cohort

NIPA1 (nonimprinted in Prader-Willi/Angelman syndrome 1) mutations are known to cause hereditary spastic paraplegia type 6, a neurodegenerative disease that phenotypically overlaps to some extent with amyotrophic lateral sclerosis (ALS). Previously, a genomewide screen for copy number variants found an association with rare deletions in NIPA1 and ALS, and subsequent genetic analyses revealed that long (or expanded) polyalanine repeats in NIPA1 convey increased ALS susceptibility. We set out to perform a large-scale replication study to further investigate the role of NIPA1 polyalanine expansions with ALS, in which we characterized NIPA1 repeat size in an independent international cohort of 3955 patients with ALS and 2276 unaffected controls and combined our results with previous reports. Meta-analysis on a total of 6245 patients with ALS and 5051 controls showed an overall increased risk of ALS in those with expanded (>8) GCG repeat length (odds ratio = 1.50, p = 3.8×10-5). Together with previous reports, these findings provide evidence for an association of an expanded polyalanine repeat in NIPA1 and ALS