A Quasi-PTAS for Unsplittable Flow on Line Graphs

We study the Unsplittable Flow Problem (UFP) on a line graph, focusing on the long-standing open question of whether the problem is APX-hard. We describe a deterministic quasi-polynomial time approximation scheme for UFP on line graphs, thereby ruling out an APX-hardness result, unless NP is contained in DTIME(2^polylog(n)). Our result requires a quasi-polynomial bound on all edge capacities and demands in the input instance. Earlier results on this problem included a polynomial time (2+epsilon)-approximation under the assumption that no demand exceeds any edge capacity (the no-bottleneck assumption ) and a super-constant integrality gap if this assumption did not hold. Unlike most earlier work on UFP, our results do not require a no-bottleneck assumption

A Quasi-PTAS for Unsplittable Flow on Line Graphs

We study the Unsplittable Flow Problem (UFP) on a line graph, focusing on the long-standing open question of whether the problem is APX-hard. We describe a deterministic quasi-polynomial time approximation scheme for UFP on line graphs, thereby ruling out an APX-hardness result, unless NP is contained in DTIME(2^polylog(n)). Our result requires a quasi-polynomial bound on all edge capacities and demands in the input instance. Earlier results on this problem included a polynomial time (2+epsilon)-approximation under the assumption that no demand exceeds any edge capacity (the no-bottleneck assumption ) and a super-constant integrality gap if this assumption did not hold. Unlike most earlier work on UFP, our results do not require a no-bottleneck assumption

Derandomized Novelty Detection with FDR Control via Conformal E-values

Conformal prediction and other randomized model-free inference techniques are gaining increasing attention as general solutions to rigorously calibrate the output of any machine learning algorithm for novelty detection. This paper contributes to the field by developing a novel method for mitigating their algorithmic randomness, leading to an even more interpretable and reliable framework for powerful novelty detection under false discovery rate control. The idea is to leverage suitable conformal e-values instead of p-values to quantify the significance of each finding, which allows the evidence gathered from multiple mutually dependent analyses of the same data to be seamlessly aggregated. Further, the proposed method can reduce randomness without much loss of power, partly thanks to an innovative way of weighting conformal e-values based on additional side information carefully extracted from the same data. Simulations with synthetic and real data confirm this solution can be effective at eliminating random noise in the inferences obtained with state-of-the-art alternative techniques, sometimes also leading to higher power.Comment: 19 pages, 11 figure

Improving the Network Scalability of Erlang

As the number of cores grows in commodity architectures so does the likelihood of failures. A distributed actor model potentially facilitates the development of reliable and scalable software on these architectures. Key components include lightweight processes which ‘share nothing’ and hence can fail independently. Erlang is not only increasingly widely used, but the underlying actor model has been a beacon for programming language design, influencing for example Scala, Clojure and Cloud Haskell. While the Erlang distributed actor model is inherently scalable, we demonstrate that it is limited by some pragmatic factors. We address two network scalability issues here: globally registered process names must be updated on every node (virtual machine) in the system, and any Erlang nodes that communicate maintain an active connection. That is, there is a fully connected O(n2) network of n nodes. We present the design, implementation, and initial evaluation of a conservative extension of Erlang — Scalable Distributed (SD) Erlang. SD Erlang partitions the global namespace and connection network using s_groups. An s_group is a set of nodes with its own process namespace and with a fully connected network within the s_group, but only individual connections outside it. As a node may belong to more than one s_group it is possible to construct arbitrary connection topologies like trees or rings. We present an operational semantics for the s_group functions, and outline the validation of conformance between the implementation and the semantics using the QuickCheck automatic testing tool. Our preliminary evaluation in comparison with distributed Erlang shows that SD Erlang dramatically improves network scalability even if the number of global operations is tiny (0.01%). Moreover, even in the absence of global operations the reduced connection maintenance overheads mean that SD Erlang scales better beyond 80 nodes (1920 cores)

Psychometric properties of the Quality of Life Inventory-Disability (QI-Disability) measure

PURPOSE: Children with intellectual disability encounter daily challenges beyond those captured in current quality of life measures. This study evaluated a new parent-report measure for children with intellectual disability, the Quality of Life Inventory-Disability (QI-Disability). METHODS: QI-Disability was administered to 253 primary caregivers of children (aged 5-18 years) with intellectual disability across four diagnostic groups: Rett syndrome, Down syndrome, cerebral palsy or autism spectrum disorder. Exploratory and confirmatory factor analyses were conducted and goodness of fit of the factor structure assessed. Associations between QI-Disability scores, and diagnostic and age groups were examined with linear regression. RESULTS: Six domains were identified: physical health, positive emotions, negative emotions, social interaction, leisure and the outdoors, and independence. Goodness-of-fit statistics were satisfactory and similar for the whole sample and when the sample was split by ability to walk or talk. On 100 point scales and compared to Rett syndrome, children with Down syndrome had higher leisure and the outdoors (coefficient 10.6, 95% CI 3.4,17.8) and independence (coefficient 29.7, 95% CI 22.9, 36.5) scores, whereas children with autism spectrum disorder had lower social interaction scores (coefficient -?12.8, 95% CI -?19.3, -?6.4). Scores for positive emotions (coefficient -?6.1, 95% CI -?10.7, -?1.6) and leisure and the outdoors (coefficient 5.4, 95% CI -?10.6, -?0.1) were lower for adolescents compared with children. CONCLUSIONS: Initial evaluation suggests that QI-Disability is a reliable and valid measure of quality of life across the spectrum of intellectual disability. It has the potential to allow clearer identification of support needs and measure responsiveness to interventions

PH wave-front propagation in the urea-urease reaction

The urease-catalyzed hydrolysis of urea displays feedback that results in a switch from acid (pH ∼3) to base (pH ∼9) after a controllable period of time (from 10 to \u3e5000 s). Here we show that the spatially distributed reaction can support pH wave fronts propagating with a speed of the order of 0.1-1 mm min-1. The experimental results were reproduced qualitatively in reaction-diffusion simulations including a Michaelis-Menten expression for the urease reaction with a bell-shaped rate-pH dependence. However, this model fails to predict that at lower enzyme concentrations, the unstirred reaction does not always support fronts when the well-stirred reaction still rapidly switches to high pH. © 2012 by the Biophysical Society

The Effect of Prolonged Physical Activity Performed during Extreme Caloric Deprivation on Cardiac Function

Background: Endurance exercise may induce transient cardiac dysfunction. Data regarding the effect of caloric restriction on cardiac function is limited. We studied the effect of physical activity performed during extreme caloric deprivation on cardiac function. Methods: Thirty-nine healthy male soldiers (mean age 2060.3 years) were studied during a field training exercise lasted 85– 103 hours, with negligible food intake and unlimited water supply. Anthropometric measurements, echocardiographic examinations and blood and urine tests were performed before and after the training exercise. Results: Baseline VO2 max was 5965.5 ml/kg/min. Participants ’ mean weight reduction was 5.760.9 kg. There was an increase in plasma urea (11.662.6 to 15.863.8 mmol/L, p,0.001) and urine osmolarity (6926212 to 10946140 mmol/kg, p,0.001) and a decrease in sodium levels (140.561.0 to 136.662.1 mmol/L, p,0.001) at the end of the study. Significant alterations in diastolic parameters included a decrease in mitral E wave (93.6 to 83.5 cm/s; p = 0.003), without change in E/A and E/E9 ratios, and an increase in iso-volumic relaxation time (73.9 to 82.9 ms, p = 0.006). There was no change in left or right ventricular systolic function, or pulmonary arterial pressure. Brain natriuretic peptide (BNP) levels were significantly reduced post-training (median 9 to 0 pg/ml, p,0.001). There was no elevation in Troponin T or CRP levels. On multivariate analysis, BNP reduction correlated with sodium levels and weight reduction (R = 0.8, p,0.001)

TIAToolbox as an end-to-end library for advanced tissue image analytics

Background: Computational pathology has seen rapid growth in recent years, driven by advanced deep-learning algorithms. Due to the sheer size and complexity of multi-gigapixel whole-slide images, to the best of our knowledge, there is no open-source software library providing a generic end-to-end API for pathology image analysis using best practices. Most researchers have designed custom pipelines from the bottom up, restricting the development of advanced algorithms to specialist users. To help overcome this bottleneck, we present TIAToolbox, a Python toolbox designed to make computational pathology accessible to computational, biomedical, and clinical researchers. Methods: By creating modular and configurable components, we enable the implementation of computational pathology algorithms in a way that is easy to use, flexible and extensible. We consider common sub-tasks including reading whole slide image data, patch extraction, stain normalization and augmentation, model inference, and visualization. For each of these steps, we provide a user-friendly application programming interface for commonly used methods and models. Results: We demonstrate the use of the interface to construct a full computational pathology deep-learning pipeline. We show, with the help of examples, how state-of-the-art deep-learning algorithms can be reimplemented in a streamlined manner using our library with minimal effort. Conclusions: We provide a usable and adaptable library with efficient, cutting-edge, and unit-tested tools for data loading, pre-processing, model inference, post-processing, and visualization. This enables a range of users to easily build upon recent deep-learning developments in the computational pathology literature