Variational Sequential Monte Carlo

Many recent advances in large scale probabilistic inference rely on variational methods. The success of variational approaches depends on (i) formulating a flexible parametric family of distributions, and (ii) optimizing the parameters to find the member of this family that most closely approximates the exact posterior. In this paper we present a new approximating family of distributions, the variational sequential Monte Carlo (VSMC) family, and show how to optimize it in variational inference. VSMC melds variational inference (VI) and sequential Monte Carlo (SMC), providing practitioners with flexible, accurate, and powerful Bayesian inference. The VSMC family is a variational family that can approximate the posterior arbitrarily well, while still allowing for efficient optimization of its parameters. We demonstrate its utility on state space models, stochastic volatility models for financial data, and deep Markov models of brain neural circuits

Comparison of the Effect of Caffeine Ingestion on Time to Exhaustion between Endurance Trained and Untrained Men

This study compared the ergogenic effects of caffeine on men who were endurance trained to those who were untrained. The study was a double-blind, placebo-controlled crossover experimental design. Ten endurance trained men (mean age 24.4 ± 2.0 yrs, weight 79.4 ± 8.5 kg, predicted VO2 max 46.3 ± 1.8 mL·kg-1·min-1) and 10 untrained men (mean age 22.8 ± 1.9 yrs, weight 88.9 ± 9.9 kg, predicted VO2 max 37.6 ± 2.7 mL·kg-1·min-1) completed two cycle ergometer trials to exhaustion at 80% of their predicted workload max 30 min after ingesting either 5 mg·kg-1 of body weight of caffeine or a placebo. Neither group displayed significant increases in time to exhaustion (Trained Group: 786.4 ± 251.5 sec for the placebo trial and 810.7 ± 209.4 sec for the caffeine trial and the Untrained Group: 514.6 ± 107.8 sec for the placebo trial and 567.3 ± 140.5 sec for the caffeine trial) after ingesting caffeine. When compared statistically between groups, the difference was not significant. When the groups were combined, the difference was caffeine and the placebo was not significant. The findings indicate that there was no ergogenic effect of caffeine on time to exhaustion in either endurance trained or untrained men

Why do Process Improvement Projects Fail in Organizations? A Review and Future Research Agenda

Purpose: The purpose of this article is to examine the Critical Failure Factors (CFFs) linked to various types of Process improvement (PI) projects such as Kaizen, Lean, Six Sigma, Lean Six Sigma and Agile. Proposing a mitigation framework accordingly is also an aim of this study. Design/ Methodology/ Approach: This research undertakes a systematic literature review of 49 articles that were relevant to the scope of our study and that were published in four prominent databases including Google Scholar, Scopus, Web of Science and EBSCO. Findings: Further analysis identifies 39 factors that contribute to the failure of PI projects. Among these factors, significant emphasis is placed on issues such as "resistance to cultural change," "insufficient support from top management," "inadequate training and education," "poor communication," and "lack of resources", as primary causes of PI project failures. To address and overcome the PI project failures, we propose a framework for failure mitigation based on change management models. We present future research directions that aim to enhance both the theoretical understanding and practical aspects of PI project failures. Practical Implications: Through this study researchers and project managers can benefit from well structured guidelines and invaluable insights that will help them identify and address potential failures, leading to successful implementation and sustainable improvements within organizations. Originality: This paper is the first study of its kind that examine the CFFs of five PI methodologies and introduces a novel approach derived from change management theory as a solution to minimize the risk associated with PI failure

Psychological and behavioural impact of returning personal results from whole-genome sequencing: the HealthSeq project

Providing ostensibly healthy individuals with personal results from whole-genome sequencing could lead to improved health and well-being via enhanced disease risk prediction, prevention, and diagnosis, but also poses practical and ethical challenges. Understanding how individuals react psychologically and behaviourally will be key in assessing the potential utility of personal whole-genome sequencing. We conducted an exploratory longitudinal cohort study in which quantitative surveys and in-depth qualitative interviews were conducted before and after personal results were returned to individuals who underwent whole-genome sequencing. The participants were offered a range of interpreted results, including Alzheimer’s disease, type 2 diabetes, pharmacogenomics, rare disease-associated variants, and ancestry. They were also offered their raw data. Of the 35 participants at baseline, 29 (82.9%) completed the 6-month follow-up. In the quantitative surveys, test-related distress was low, although it was higher at 1-week than 6-month follow-up (Z=2.68, P=0.007). In the 6-month qualitative interviews, most participants felt happy or relieved about their results. A few were concerned, particularly about rare disease-associated variants and Alzheimer’s disease results. Two of the 29 participants had sought clinical follow-up as a direct or indirect consequence of rare disease-associated variants results. Several had mentioned their results to their doctors. Some participants felt having their raw data might be medically useful to them in the future. The majority reported positive reactions to having their genomes sequenced, but there were notable exceptions to this. The impact and value of returning personal results from whole-genome sequencing when implemented on a larger scale remains to be seen

Montecarlo simulation of the role of defects as the melting mechanism

We study in this paper the melting transition of a crystal of fcc structure with the Lennard-Jones potential, by using isobaric-isothermal Monte Carlo simulations. Local and collective updates are sequentially used to optimize the convergence. We show the important role played by defects in the melting mechanism in favor of modern melting theories.Comment: 6 page, 10 figures included. Corrected version to appear in Phys. Rev.

Neuronal Variability during Handwriting: Lognormal Distribution

We examined time-dependent statistical properties of electromyographic (EMG) signals recorded from intrinsic hand muscles during handwriting. Our analysis showed that trial-to-trial neuronal variability of EMG signals is well described by the lognormal distribution clearly distinguished from the Gaussian (normal) distribution. This finding indicates that EMG formation cannot be described by a conventional model where the signal is normally distributed because it is composed by summation of many random sources. We found that the variability of temporal parameters of handwriting - handwriting duration and response time - is also well described by a lognormal distribution. Although, the exact mechanism of lognormal statistics remains an open question, the results obtained should significantly impact experimental research, theoretical modeling and bioengineering applications of motor networks. In particular, our results suggest that accounting for lognormal distribution of EMGs can improve biomimetic systems that strive to reproduce EMG signals in artificial actuators

Fluoroquinolone Efficacy against Tuberculosis Is Driven by Penetration into Lesions and Activity against Resident Bacterial Populations.

Fluoroquinolones represent the pillar of multidrug-resistant tuberculosis (MDR-TB) treatment, with moxifloxacin, levofloxacin, or gatifloxacin being prescribed to MDR-TB patients. Recently, several clinical trials of "universal" drug regimens, aiming to treat drug-susceptible and drug-resistant TB, have included a fluoroquinolone. In the absence of clinical data comparing their side-by-side efficacies in controlled MDR-TB trials, a pharmacological rationale is needed to guide the selection of the most efficacious fluoroquinolone. The present studies were designed to test the hypothesis that fluoroquinolone concentrations (pharmacokinetics) and activity (pharmacodynamics) at the site of infection are better predictors of efficacy than the plasma concentrations and potency measured in standard growth inhibition assays and are better suited to determinations of whether one of the fluoroquinolones outperforms the others in rabbits with active TB. We first measured the penetration of these fluoroquinolones in lung lesion compartments, and their potency against bacterial populations that reside in each compartment, to compute lesion-centric pharmacokinetic-pharmacodynamic (PK/PD) parameters. PK modeling methods were used to quantify drug penetration from plasma to tissues at human-equivalent doses. On the basis of these metrics, moxifloxacin emerged with a clear advantage, whereas plasma-based PK/PD favored levofloxacin (the ranges of the plasma AUC/MIC ratio [i.e., the area under the concentration-time curve over 24 h in the steady state divided by the MIC] are 46 to 86 for moxifloxacin and 74 to 258 for levofloxacin). A comparative efficacy trial in the rabbit model of active TB demonstrated the superiority of moxifloxacin in reducing bacterial burden at the lesion level and in sterilizing cellular and necrotic lesions. Collectively, these results show that PK/PD data obtained at the site of infection represent an adequate predictor of drug efficacy against TB and constitute the baseline required to explore synergies, antagonism, and drug-drug interactions in fluoroquinolone-containing regimens

Shear Lag Sutures: Improved Suture Repair through the use of Adhesives

Conventional surgical suture is mechanically limited by the ability of the suture to transfer load to tissue at suture anchor points. Sutures coated with adhesives can improve mechanical load transfer beyond the range of performance of existing suture methods, thereby strengthening orthopaedic repairs and decreasing the risk of failure. The mechanical properties of suitable adhesives were identified using a shear lag model. Examination of the design space for an optimal adhesive demonstrated requirements for strong adhesion and low stiffness to maximize strength. As a proof of concept, cyanoacrylate-coated sutures were used to perform a clinically relevant flexor digitorum profundus tendon repair in cadaver tissue. Even with this non-ideal adhesive, the maximum load resisted by repaired cadaveric canine flexor tendon increased by ∼ 17.0% compared to standard repairs without adhesive. To rapidly assess adhesive binding to tendon, we additionally developed a lap shear test method using bovine deep digital flexor tendons as the adherends. Further study is needed to develop a strongly adherent, compliant adhesive within the optimal design space described by the model

Heavy-tailed kernels reveal a finer cluster structure in t-SNE visualisations

T-distributed stochastic neighbour embedding (t-SNE) is a widely used data visualisation technique. It differs from its predecessor SNE by the low-dimensional similarity kernel: the Gaussian kernel was replaced by the heavy-tailed Cauchy kernel, solving the "crowding problem" of SNE. Here, we develop an efficient implementation of t-SNE for a $t$-distribution kernel with an arbitrary degree of freedom $\nu$, with $\nu\to\infty$ corresponding to SNE and $\nu=1$ corresponding to the standard t-SNE. Using theoretical analysis and toy examples, we show that $\nu<1$ can further reduce the crowding problem and reveal finer cluster structure that is invisible in standard t-SNE. We further demonstrate the striking effect of heavier-tailed kernels on large real-life data sets such as MNIST, single-cell RNA-sequencing data, and the HathiTrust library. We use domain knowledge to confirm that the revealed clusters are meaningful. Overall, we argue that modifying the tail heaviness of the t-SNE kernel can yield additional insight into the cluster structure of the data