Query: Policy Search in Continuous Action Domains: an Overview

Relevant passages:
FeUdal Networks for Hierarchical Reinforcement Learning <=SEP=> We introduce FeUdal Networks (FuNs): a novel architecture for hierarchical reinforcement learning. Our approach is inspired by the feudal reinforcement learning proposal of Dayan and Hinton, and gains power and efficacy by decoupling end-to-end learning across multiple levels – allowing it to utilise different resolutions of time. Our framework employs a Manager module and a Worker module. The Manager operates at a lower temporal resolution and sets abstract goals which are conveyed to and enacted by the Worker. The Worker generates primitive actions at every tick of the environment. The decoupled structure of FuN conveys several benefits – in addition to facilitating very long timescale credit assignment it also encourages the emergence of sub-policies associated with different goals set by the Manager. These properties allow FuN to dramatically outperform a strong baseline agent on tasks that involve long-term credit assignment or memorisation. We demonstrate the performance of our proposed system on a range of tasks from the ATARI suite and also from a 3D DeepMind Lab environment.

Parameter Space Noise for Exploration <=SEP=> Deep reinforcement learning (RL) methods generally engage in exploratory behavior through noise injection in the action space. An alternative is to add noise directly to the agent’s parameters, which can lead to more consistent exploration and a richer set of behaviors. Methods such as evolutionary strategies use parameter perturbations, but discard all temporal structure in the process and require significantly more samples. Combining parameter noise with traditional RL methods allows to combine the best of both worlds. We demonstrate that both offand on-policy methods benefit from this approach through experimental comparison of DQN, DDPG, and TRPO on high-dimensional discrete action environments as well as continuous control tasks. Our results show that RL with parameter noise learns more efficiently than traditional RL with action space noise and evolutionary strategies individually.

BOA: the Bayesian optimization algorithm <=SEP=> In this paper, an algorithm based on the concepts of genetic algorithms that uses an estimation of a probability distribution of promising solutions in order to generate new candidate solutions is proposed. To estimate the distribution, techniques for modeling multivariate data by Bayesian networks are used. The proposed algorithm identifies, reproduces and mixes building blocks up to a specified order. It is independent of the ordering of the variables in the strings representing the solutions. Moreover, prior information about the problem can be incorporated into the algorithm. However, prior information is not essential. Preliminary experiments show that the BOA outperforms the simple genetic algorithm even on decomposable functions with tight building blocks as a problem size grows.

Prioritized Experience Replay <=SEP=> Experience replay lets online reinforcement learning agents remember and reuse experiences from the past. In prior work, experience transitions were uniformly sampled from a replay memory. However, this approach simply replays transitions at the same frequency that they were originally experienced, regardless of their significance. In this paper we develop a framework for prioritizing experience, so as to replay important transitions more frequently, and therefore learn more efficiently. We use prioritized experience replay in the Deep Q-Network (DQN) algorithm, which achieved human-level performance in Atari games. DQN with prioritized experience replay achieves a new state-of-the-art, outperforming DQN with uniform replay on 42 out of 57 games.

Hierarchical Deep Reinforcement Learning: Integrating Temporal Abstraction and Intrinsic Motivation <=SEP=> Learning goal-directed behavior in environments with sparse feedback is a major challenge for reinforcement learning algorithms. One of the key difficulties is insufficient exploration, resulting in an agent being unable to learn robust policies. Intrinsically motivated agents can explore new behavior for their own sake rather than to directly solve external goals. Such intrinsic behaviors could eventually help the agent solve tasks posed by the environment. We present hierarchicalDQN (h-DQN), a framework to integrate hierarchical action-value functions, operating at different temporal scales, with goal-driven intrinsically motivated deep reinforcement learning. A top-level q-value function learns a policy over intrinsic goals, while a lower-level function learns a policy over atomic actions to satisfy the given goals. h-DQN allows for flexible goal specifications, such as functions over entities and relations. This provides an efficient space for exploration in complicated environments. We demonstrate the strength of our approach on two problems with very sparse and delayed feedback: (1) a complex discrete stochastic decision process with stochastic transitions, and (2) the classic ATARI game – ‘Montezuma’s Revenge’.

Hybrid sampling Bayesian Occupancy Filter <=SEP=> Modeling and monitoring dynamic environments is a complex task but is crucial in the field of intelligent vehicle. A traditional way of addressing these issues is the modeling of moving objects, through Detection And Tracking of Moving Objects (DATMO) methods. An alternative to a classic object model framework is the occupancy grid filtering domain. Instead of segmenting the scene into objects and track them, the environment is represented as a regular grid of occupancy, in which each cell is tracked at a sub-object level. The Bayesian Occupancy Filter [1] is a generic occupancy grid framework which predicts the spread of spatial occupancy by estimating cell velocity distributions. However its velocity model, corresponding to a transition histogram per cell, leads to huge data management which in practice makes it hardly compatible to severe computational and hardware constraints, like in many embedded systems. In this paper, we present a new representation for the BOF, describing the environment through a mix of static and dynamic occupancy. This differentiation enables the use of a model adapted to the considered nature: static occupancy is described in a classic occupancy grid, while dynamic occupancy is modeled by a set of moving particles. Both static and dynamic parts are jointly generated and evaluated, their distribution over the cells being adjusted. This approach leads to a more compact model and to drastically improve the accuracy of the results, in particular in term of velocities. Experimental results show that the number of values required to model the velocities have been reduced from a typical 900 per cell (for a 30×30 neighborhood) to less than 2 per cell in average. The massive data compression allows to plan dedicated embedded devices.

Compression of multiple depth maps for IBR <=SEP=> Image-based rendering techniques include those with geometry and those without. Geometric information in the form of a depth map aligned with the image holds a lot of promise for IBR due to the several methods available to capture it. It can improve the quality of generated views using a limited number of views. Compression of light fields or multiple images has attracted a lot of attention in the past. Compression of multiple depth maps of the same scene has not been explored much in the literature. We propose a method for compressing multiple depth maps in this paper using geometric proxy. Different quality of rendering and compression ratio can be achieved by varying different parameters. Experiments show the effectiveness of the compression technique on several model data.

Cell biology of protein misfolding: The examples of Alzheimer's and Parkinson's diseases <=SEP=> The salutary intersection of fundamental cell biology with the study of disease is well illustrated by the emerging elucidation of neurodegenerative disorders. Novel mechanisms in cell biology have been uncovered through disease-orientated research; for example, the discovery of presenilin as an intramembrane aspartyl protease that processes many diverse proteins within the lipid bilayer. A common theme has arisen in this field: normally-soluble proteins accumulate, misfold and oligomerize, inducing cytotoxic effects that are particularly devastating in the post-mitotic milieu of the neuron.

Topological Data Analysis of Biological Aggregation Models <=SEP=> We apply tools from topological data analysis to two mathematical models inspired by biological aggregations such as bird flocks, fish schools, and insect swarms. Our data consists of numerical simulation output from the models of Vicsek and D'Orsogna. These models are dynamical systems describing the movement of agents who interact via alignment, attraction, and/or repulsion. Each simulation time frame is a point cloud in position-velocity space. We analyze the topological structure of these point clouds, interpreting the persistent homology by calculating the first few Betti numbers. These Betti numbers count connected components, topological circles, and trapped volumes present in the data. To interpret our results, we introduce a visualization that displays Betti numbers over simulation time and topological persistence scale. We compare our topological results to order parameters typically used to quantify the global behavior of aggregations, such as polarization and angular momentum. The topological calculations reveal events and structure not captured by the order parameters.

The Sound of APALM Clapping: Faster Nonsmooth Nonconvex Optimization with Stochastic Asynchronous PALM <=SEP=> We introduce the Stochastic Asynchronous Proximal Alternating Linearized Minimization (SAPALM) method, a block coordinate stochastic proximal-gradient method for solving nonconvex, nonsmooth optimization problems. SAPALM is the first asynchronous parallel optimization method that provably converges on a large class of nonconvex, nonsmooth problems. We prove that SAPALM matches the best known rates of convergence — among synchronous or asynchronous methods — on this problem class. We provide upper bounds on the number of workers for which we can expect to see a linear speedup, which match the best bounds known for less complex problems, and show that in practice SAPALM achieves this linear speedup. We demonstrate state-of-the-art performance on several matrix factorization problems.

Characterization of Silicided Polysilicon Fuse Implemented in 65nm Logic CMOS Technology <=SEP=> NiSi electrically programmable fuses (eFUSE) were fabricated and investigated using 65 nm logic CMOS technology. The optimization of fuse program was achieved by analyzing electrical and physical responses of fuse bits for various conditions. Controlled electromigration of Ni during fuse program was identified as a key factor in achieving reliably high post-program fuse resistance.

An anatomically-constrained local deformation model for monocular face capture <=SEP=> We present a new anatomically-constrained local face model and fitting approach for tracking 3D faces from 2D motion data in very high quality. In contrast to traditional global face models, often built from a large set of blendshapes, we propose a local deformation model composed of many small subspaces spatially distributed over the face. Our local model offers far more flexibility and expressiveness than global blendshape models, even with a much smaller model size. This flexibility would typically come at the cost of reduced robustness, in particular during the under-constrained task of monocular reconstruction. However, a key contribution of this work is that we consider the face anatomy and introduce subspace skin thickness constraints into our model, which constrain the face to only valid expressions and helps counteract depth ambiguities in monocular tracking. Given our new model, we present a novel fitting optimization that allows 3D facial performance reconstruction from a single view at extremely high quality, far beyond previous fitting approaches. Our model is flexible, and can be applied also when only sparse motion data is available, for example with marker-based motion capture or even face posing from artistic sketches. Furthermore, by incorporating anatomical constraints we can automatically estimate the rigid motion of the skull, obtaining a rigid stabilization of the performance for free. We demonstrate our model and single-view fitting method on a number of examples, including, for the first time, extreme local skin deformation caused by external forces such as wind, captured from a single high-speed camera.

Prediction of Microwave Absorption Behavior of Grading Honeycomb Composites Based on Effective Permittivity Formulas <=SEP=> Microwave absorbing honeycomb composites with grading coating thickness are prepared by impregnation of aramid paper frame within water solutions of conductive carbon blacks. Based on effective medium method, grading impedance in the composites can be theoretically expressed by a linear approximation of the inversely tapered effective permittivity at wave propagation direction, which provides a feasible way to optimize the design of microwave absorbing honeycomb structures. The consistent result of calculations and experimental measurements for positive impedance grading design confirms the broadband absorption effect in high frequencies.

New support vector algorithms with parametric insensitive/margin model <=SEP=> In this paper, a modification of v-support vector machines (v-SVM) for regression and classification is described, and the use of a parametric insensitive/margin model with an arbitrary shape is demonstrated. This can be useful in many cases, especially when the noise is heteroscedastic, that is, the noise strongly depends on the input value x. Like the previous v-SVM, the proposed support vector algorithms have the advantage of using the parameter 0 < or = v < or = 1 for controlling the number of support vectors. To be more precise, v is an upper bound on the fraction of training errors and a lower bound on the fraction of support vectors. The algorithms are analyzed theoretically and experimentally.

Moving beyond linearity and independence in top-N recommender systems <=SEP=> This paper suggests a number of research directions in which the recommender systems can improve their quality, by moving beyond the assumptions of linearity and independence that are traditionally made. These assumptions, while producing effective and meaningful results, can be suboptimal, as in lots of cases they do not represent the real datasets. In this paper, we discuss three different ways to address some of the previous constraints. More specifically, we focus on the development of methods capturing higher-order relations between the items, cross-feature interactions and intra-set dependencies which can potentially lead to a considerable enhancement of the recommendation accuracy.

The feeling of effort during mental activity <=SEP=> The feeling of effort is familiar to most, if not all, humans. Prior research shows that the feeling of effort shapes judgments (e.g., of agency) and decisions (e.g., to quit the current task) in various ways, but the proximal causes of the feeling of effort are not well understood. In this research, I address these proximal causes. In particular, I conducted two preregistered experiments in which participants performed a difficult vs. easy cognitive task, while I measured effort-related phenomenology (feeling of effort) and physiology (pupil dilation) on a moment-to-moment basis. In both experiments, difficult tasks increased the feeling of effort; however, this effect could not be explained by concurrent increases in physiological effort. To explain these findings, I suggest that the feeling of effort during mental activity stems from the decision to exert physiological effort, rather than from physiological effort itself.

Temporal and Real-Time Databases: A Survey <=SEP=> • a short biography of John Doe. – born on April 3rd, 1975 in Smallville. His birth was registered on April 4th, 1975. – He went to live on his own in Bigtown. Although he moved out on August 26th, 1994, he forgot to register the change of address officially. – He updated his record on December 27, 1994. – John Doe was accidentally hit by a truck on April 1st, 2001. The coroner reported his date of death on the next day.

Conflict-Aware Event-Participant Arrangement and Its Variant for Online Setting <=SEP=> With the rapid development of Web 2.0 and Online To Offline (O2O) marketing model, various <italic>online <underline> e</underline>vent-<underline>b</underline>ased <underline>s</underline>ocial <underline>n</underline>etwork<underline>s </underline></italic> (EBSNs) are getting popular. An important task of EBSNs is to facilitate the most satisfactory event-participant arrangement for both sides, i.e., events enroll more participants and participants are arranged with personally interesting events. Existing approaches usually focus on the arrangement of each single event to a set of potential users, or ignore the conflicts between different events, which leads to infeasible or redundant arrangements. In this paper, to address the shortcomings of existing approaches, we first identify a more general and useful event-participant arrangement problem, called <italic><underline>G</underline>lobal <underline>E</underline> vent-participant <underline>A</underline>rrangement with <underline>C</underline>onflict and <underline>C</underline> apacity</italic> (<inline-formula><tex-math notation="LaTeX">$GEACC$</tex-math><alternatives> <inline-graphic xlink:type="simple" xlink:href="chen-ieq1-2565468.gif"/></alternatives></inline-formula>) problem, focusing on the conflicts of different events and making event-participant arrangements in a global view. We find that the GEACC problem is NP-hard due to the conflicts among events. Thus, we design two approximation algorithms with provable approximation ratios and an exact algorithm with pruning technique to address this problem. In addition, we propose an online setting of GEACC, called OnlineGEACC, which is also practical in real-world scenarios. We further design an online algorithm with provable performance guarantee. Finally, we verify the effectiveness and efficiency of the proposed methods through extensive experiments on real and synthetic datasets.

Interobserver variation in the assessment of pelvic organ prolapse <=SEP=> The aim of this study was to determine the interobserver agreement of two grading systems for pelvic organ prolapse: the vaginal profile and the International Continence Society (ICS) draft proposal. Forty-nine consecutive women referred for evaluation of urinary incontinence and/or pelvic organ prolapse were studied. Patients were first examined by a physician and a nurse clinician using the vaginal profile, followed by an examination according to the technique described in the ICS draft proposal for standardization of terminology (1994). κ statistic and Pearson's correlation coefficient were used to determine interobserver variability for the ICS system by overall stage, by stage-specific comparison, and by specific anatomic location. The vaginal profile was evaluated by obtaining a κ for overall degree of prolapse, stage-specific comparison and by anatomic area. The κ for the ICS stage was 0.79 (P<0.001), and the κ for the vaginal profile by area of greatest prolapse was 0.68 (P<0.001), indicating substantial interobserver agreement for both systems. The ICS system was noted to have substantial interobserver agreement by a stage-specific comparison. All anatomic locations of the ICS staging system were found to correlate significantly, and a high degree of interobserver precision was found. The vaginal profile also showed significant interobserver agreement by overall degree of prolapse, by specific degree of prolapse, and by anatomic area. It was concluded that both the proposed ICS staging system and the traditional vaginal profile show significant interobserver agreement both by overall stage, stage-specific analysis and specific location. The registered nurse examination correlated well with the physican examination, indicating that the most important factor in obtaining reproducible results may be definition and close attention to examination technique.

"Good job, you're so smart": The effects of inconsistency of praise type on young children's motivation. <=SEP=> Previous research has demonstrated that generic praise ("good drawer") is related to children giving up after failure because failure implies the lack of a critical trait (e.g., drawing ability). Conversely, nongeneric praise ("good job drawing") is related to mastery motivation because it implies that success is related to effort. Yet children may receive a mixture of these praise types (i.e., inconsistent praise), the effects of which are unclear. We tested how inconsistent praise influenced two components of motivation: self-evaluation and persistence. Kindergarteners (N=135) were randomly assigned to one of five conditions in which consistency of praise type was varied. After two failure scenarios, children reported self-evaluations and persistence. Results indicated that more nongeneric praise related linearly to greater motivation, yet self-evaluation and persistence were impacted differently by inconsistent praise types. Hearing even a small amount of generic praise reduced persistence, whereas hearing a small amount of nongeneric praise preserved self-evaluation.

Duration prediction using multi-level model for GPR-based speech synthesis <=SEP=> This paper introduces frame-based Gaussian process regression (GPR) into phone/syllable duration modeling for Thai speech synthesis. The GPR model is designed for predicting framelevel acoustic features using corresponding frame information, which includes relative position in each unit of utterance structure and linguistic information such as tone type and part of speech. Although the GPR-based prediction can be applied to a phone duration model, the use of phone duration model only is not always sufficient to generate natural sounding speech. Specifically, in some languages including Thai, syllable durations affect the perception of sentence structure. In this paper, we propose a duration prediction technique using a multi-level model which includes syllable and phone levels for prediction. In the technique, first, syllable durations are predicted, and then they are used as additional contexts in phone-level model to generate phone duration for synthesizing. Objective and subjective evaluation results show that GPR-based modeling with multi-level model for duration prediction outperforms the conventional HMM-based speech synthesis.

The effect of glycolic acid on the treatment of acne in Asian skin. <=SEP=> BACKGROUND
Glycolic acid has become important and popular for treating acne.


OBJECTIVE
To evaluate the efficacy and safety of serial glycolic acid peels with glycolic acid home care products on facial acne lesions and other associated skin problems.


METHODS
We collected 40 Asian candidates with moderate to moderately severe acne. They were divided into two groups according to the degree of greasiness of their facial skin. The two groups' members were treated with four series of 35% and 50% glycolic acid peels, respectively. They also used 15% glycolic acid home care products during this study period. The improvement of acne as well as other associated problems were assessed by both the physicians and the patient themselves.


RESULTS
Significant resolution of comedones, papules, and pustules was found. The skin texture of each candidate was dramatically rejuvenated. Consistent and repetitive treatment with glycolic acid was needed for the apparent improvement of acne scars and cystic lesions. The follicular pores also became comparatively smaller. Furthermore, most of the candidates had much brighter and lighter looking skin. Only small percentage of patients (5.6%) developed side effects.


CONCLUSION
Glycolic acid has considerable therapeutic value for acne with minimal side effects even in Asian skin. It may be an ideal adjunctive treatment of acne.

Calibration of Ultrawide Fisheye Lens Cameras by Eigenvalue Minimization <=SEP=> We present a new technique for calibrating ultrawide fisheye lens cameras by imposing the constraint that collinear points be rectified to be collinear, parallel lines to be parallel, and orthogonal lines to be orthogonal. Exploiting the fact that line fitting reduces to an eigenvalue problem in 3D, we do a rigorous perturbation analysis to obtain a practical calibration procedure. Doing experiments, we point out that spurious solutions exist if collinearity and parallelism alone are imposed. Our technique has many desirable properties. For example, no metric information is required about the reference pattern or the camera position, and separate stripe patterns can be displayed on a video screen to generate a virtual grid, eliminating the grid point extraction processing.

Aspect Based Recommendations: Recommending Items with the Most Valuable Aspects Based on User Reviews <=SEP=> In this paper, we propose a recommendation technique that not only can recommend items of interest to the user as traditional recommendation systems do but also specific aspects of consumption of the items to further enhance the user experience with those items. For example, it can recommend the user to go to a specific restaurant (item) and also order some specific foods there, e.g., seafood (an aspect of consumption). Our method is called Sentiment Utility Logistic Model (SULM). As its name suggests, SULM uses sentiment analysis of user reviews. It first predicts the sentiment that the user may have about the item based on what he/she might express about the aspects of the item and then identifies the most valuable aspects of the user's potential experience with that item. Furthermore, the method can recommend items together with those most important aspects over which the user has control and can potentially select them, such as the time to go to a restaurant, e.g. lunch vs. dinner, and what to order there, e.g., seafood. We tested the proposed method on three applications (restaurant, hotel, and beauty & spa) and experimentally showed that those users who followed our recommendations of the most valuable aspects while consuming the items, had better experiences, as defined by the overall rating.

VisComplete: Automating Suggestions for Visualization Pipelines <=SEP=> Building visualization and analysis pipelines is a large hurdle in the adoption of visualization and workflow systems by domain scientists. In this paper, we propose techniques to help users construct pipelines by consensus-automatically suggesting completions based on a database of previously created pipelines. In particular, we compute correspondences between existing pipeline subgraphs from the database, and use these to predict sets of likely pipeline additions to a given partial pipeline. By presenting these predictions in a carefully designed interface, users can create visualizations and other data products more efficiently because they can augment their normal work patterns with the suggested completions. We present an implementation of our technique in a publicly-available, open-source scientific workflow system and demonstrate efficiency gains in real-world situations.

Deep motion features for visual tracking <=SEP=> Robust visual tracking is a challenging computer vision problem, with many real-world applications. Most existing approaches employ hand-crafted appearance features, such as HOG or Color Names. Recently, deep RGB features extracted from convolutional neural networks have been successfully applied for tracking. Despite their success, these features only capture appearance information. On the other hand, motion cues provide discriminative and complementary information that can improve tracking performance. Contrary to visual tracking, deep motion features have been successfully applied for action recognition and video classification tasks. Typically, the motion features are learned by training a CNN on optical flow images extracted from large amounts of labeled videos. This paper presents an investigation of the impact of deep motion features in a tracking-by-detection framework. We further show that hand-crafted, deep RGB, and deep motion features contain complementary information. To the best of our knowledge, we are the first to propose fusing appearance information with deep motion features for visual tracking. Comprehensive experiments clearly suggest that our fusion approach with deep motion features outperforms standard methods relying on appearance information alone.

A New Local Distance-Based Outlier Detection Approach for Scattered Real-World Data <=SEP=> Detecting outliers which are grossly different from or inconsistent with the remaining dataset is a major challenge in real-world KDD applications. Existing outlier detection methods are ineffective on scattered real-world datasets due to implicit data patterns and parameter setting issues. We define a novel Local Distance-based Outlier Factor (LDOF) to measure the outlier-ness of objects in scattered datasets which addresses these issues. LDOF uses the relative location of an object to its neighbours to determine the degree to which the object deviates from its neighbourhood. Properties of LDOF are theoretically analysed including LDOF’s lower bound and its false-detection probability, as well as parameter settings. In order to facilitate parameter settings in real-world applications, we employ a top-n technique in our outlier detection approach, where only the objects with the highest LDOF values are regarded as outliers. Compared to conventional approaches (such as top-n KNN and top-n LOF), our method top-n LDOF is more effective at detecting outliers in scattered data. It is also easier to set parameters, since its performance is relatively stable over a large range of parameter values, as illustrated by experimental results on both real-world and synthetic datasets.

Unified Model for Contrast Enhancement and Denoising <=SEP=> In this paper, we attempt a challenging task to unify two important complementary operations, i.e. contrast enhancement and denoising, that is required in most image processing applications. The proposed method is implemented using practical analog circuit configurations that can lead to near real-time processing capabilities useful to be integrated with vision sensors. Metrics used for performance includes estimation of Residual Noise Level (RNL), Structural Similarity Index Measure (SSIM), Output-to-Input Contrast Ratio (CRo_i), and its combined score (SCD). The class of contrast stretching methods has resulted in higher noise levels (RNL ≥ 7) along with increased contrast measures (CRo-i ≥ eight times than that of the input image) and SSIM ≤ 0.52. Denoising methods generates images with lesser noise levels (RNL ≤ 0.2308), poor contrast enhancements (CRo-i ≤ 1.31) and with best structural similarity (SSIM ≥ 0.85). In contrast, the proposed model offers best contrast stretching (CRo-i = 5.83), least noise (RNL = 0.02), a descent structural similarity (SSIM = 0.6453) and the highest combined score (SCD = 169).

A Process Mining Driven Framework for Clinical Guideline Improvement in Critical Care <=SEP=> This paper presents a framework for process mining in critical care. The framework uses the CRISP-DM model, extended to incorporate temporal and multidimensional aspects (CRISP-TDM), combined with the Patient Journey Modeling Architecture (PaJMa), to provide a structured approach to knowledge discovery of new condition onset pathophysiologies in physiological data streams. The approach is based on temporal abstraction and mining of physiological data streams to develop process flow mappings that can be used to update patient journeys; instantiated in critical care within clinical practice guidelines. We demonstrate the framework within the neonatal intensive care setting, where we are performing clinical research in relation to pathophysiology within physiological streams of patients diagnosed with late onset neonatal sepsis. We present an instantiation of the framework for late onset neonatal sepsis, using CRISP-TDM for the process mining model and PaJMa for the knowledge representation.

Second to fourth digit ratio: a predictor of adult penile length. <=SEP=> The second to fourth digit ratio (2D:4D) has been proposed as a putative biomarker for prenatal testosterone and covaries with the sensitivity of the androgen receptor (AR). Both prenatal testosterone and the AR play a central role in penile growth. In this study, we investigated the relationship between digit ratio and penile length. Korean men who were hospitalized for urological surgery at a single tertiary academic centre were examined in this study, and 144 men aged 20 years or older who gave informed consent were prospectively enrolled. Right-hand second- and fourth-digit lengths were measured by a single investigator prior to measurement of penile length. Under anaesthesia, flaccid and stretched penile lengths were measured by another investigator who did not measure nor have any the information regarding the digit lengths. Univariate and multivariate analysis using linear regression models showed that only height was a significant predictive factor for flaccid penile length (univariate analysis: r=0.185, P=0.026; multivariate analysis: r=0.172, P=0.038) and that only digit ratio was a significant predictive factor for stretched penile length (univariate analysis:r=-0.216, P=0.009; multivariate analysis: r=-0.201, P=0.024; stretched penile length=-9.201×digit ratio + 20.577). Based on this evidence, we suggest that the digit ratio can predict adult penile size and that the effects of prenatal testosterone may in part explain the differences in adult penile length.