Statistical Embedding: Beyond Principal Components

There has been an intense recent activity in embedding of very high-dimensional and nonlinear data structures, much of it in the data science and machine learning literature. We survey this activity in four parts. In the first part, we cover nonlinear methods such as principal curves, multidimensional scaling, local linear methods, ISOMAP, graph-based methods and diffusion mapping, kernel based methods and random projections. The second part is concerned with topological embedding methods, in particular mapping topological properties into persistence diagrams and the Mapper algorithm. Another type of data sets with a tremendous growth is very high-dimensional network data. The task considered in part three is how to embed such data in a vector space of moderate dimension to make the data amenable to traditional techniques such as cluster and classification techniques. Arguably, this is the part where the contrast between algorithmic machine learning methods and statistical modeling, represented by the so-called stochastic block model, is at its greatest. In the paper, we discuss the pros and cons for the two approaches. The final part of the survey deals with embedding in R2, that is, visualization. Three methods are presented: t-SNE, UMAP and LargeVis based on methods in parts one, two and three, respectively. The methods are illustrated and compared on two simulated data sets; one consisting of a triplet of noisy Ranunculoid curves, and one consisting of networks of increasing complexity generated with stochastic block models and with two types of nodes.acceptedVersio

Effects of Anacetrapib in Patients with Atherosclerotic Vascular Disease

BACKGROUND: Patients with atherosclerotic vascular disease remain at high risk for cardiovascular events despite effective statin-based treatment of low-density lipoprotein (LDL) cholesterol levels. The inhibition of cholesteryl ester transfer protein (CETP) by anacetrapib reduces LDL cholesterol levels and increases high-density lipoprotein (HDL) cholesterol levels. However, trials of other CETP inhibitors have shown neutral or adverse effects on cardiovascular outcomes. METHODS: We conducted a randomized, double-blind, placebo-controlled trial involving 30,449 adults with atherosclerotic vascular disease who were receiving intensive atorvastatin therapy and who had a mean LDL cholesterol level of 61 mg per deciliter (1.58 mmol per liter), a mean non-HDL cholesterol level of 92 mg per deciliter (2.38 mmol per liter), and a mean HDL cholesterol level of 40 mg per deciliter (1.03 mmol per liter). The patients were assigned to receive either 100 mg of anacetrapib once daily (15,225 patients) or matching placebo (15,224 patients). The primary outcome was the first major coronary event, a composite of coronary death, myocardial infarction, or coronary revascularization. RESULTS: During the median follow-up period of 4.1 years, the primary outcome occurred in significantly fewer patients in the anacetrapib group than in the placebo group (1640 of 15,225 patients [10.8%] vs. 1803 of 15,224 patients [11.8%]; rate ratio, 0.91; 95% confidence interval, 0.85 to 0.97; P=0.004). The relative difference in risk was similar across multiple prespecified subgroups. At the trial midpoint, the mean level of HDL cholesterol was higher by 43 mg per deciliter (1.12 mmol per liter) in the anacetrapib group than in the placebo group (a relative difference of 104%), and the mean level of non-HDL cholesterol was lower by 17 mg per deciliter (0.44 mmol per liter), a relative difference of -18%. There were no significant between-group differences in the risk of death, cancer, or other serious adverse events. CONCLUSIONS: Among patients with atherosclerotic vascular disease who were receiving intensive statin therapy, the use of anacetrapib resulted in a lower incidence of major coronary events than the use of placebo. (Funded by Merck and others; Current Controlled Trials number, ISRCTN48678192 ; ClinicalTrials.gov number, NCT01252953 ; and EudraCT number, 2010-023467-18 .)

An Easier Predictive Display Based on Image Transformation for Low Cost Teleoperation of Vehicles With Time Delay

A python package for robot communication and a new predictive display based on image transformation has been developed. An experiment was performed and this thesis present the results

Passive Fingerprinting of Known Operating Systems using Deep Learning Techniques

Passive fingerprinting with a Deep Learning approach. The approach is compared to three well established machine learning algorithms; SVM, KNN and Random Forest. A never before used value for fingerprinting, the TCP Congestion Control Algorithm, was evaluated as a feature

A low-cost predictive display for teleoperation: Investigating effects on human performance and workload

Teleoperation in an environment with latency is difficult and highly stressful for human operators, resulting in high cognitive workload and decreased human performance. This work investigates if a simple predictive display can increase performance and lower subjective workload for the human operator when teleoperating a remotely operated vehicle (ROV). A predictive display based on image transformation was developed by applying positional and scale transformations to the video feed and tested. An experiment was designed, consisting of a simple navigational task (peg-in-hole game) with a ground ROV, in three distinct conditions: C1. Latency, C2. Latency with predictive display (PD) and C3. Baseline (no added latency). Findings from N = 57 participants show a statistically significant increase of 20% in human performance with the aid of the predictive display. Although differences in subjective workload was not statistically significant, both subjective performance and actual game performance did increase significantly by using the predictive display. In fact, the latter almost doubled for participants defining themselves as regular gamers. Lastly, A principle component analysis (PCA) was conducted investigating confounding factors with confirmatory results

Gitek Register

Gitek Register går ut på å lage en modernisert implementasjon av et eldre system brukt av Det norske Skogfrøverk. Den nye løsningen skal være en webløsning som håndterer flere aspekter ved registrering og bestilling av frø på ulike treslag fra innland og utland. Systemet skal effektivisere hele prosessen med å sanke, teste, analysere, registrere og selge frø til kjøpere fra hele verden. Løsningen er fullt integrert inn i Gitek sine systemer og kjører på en av deres servere. Gitek Register er utviklet med HTML, CSS, JavaScript og jQuery

Advanced passive operating system fingerprinting using machine learning and deep learning

Securing and managing large, complex enterprise network infrastructure requires capturing and analyzing network traffic traces in real-time. An accurate passive Operating System (OS) fingerprinting plays a critical role in effective network management and cybersecurity protection. Passive fingerprinting doesn't send probes that introduce extra load to the network and hence it has a clear advantage over active fingerprinting since it also reduces the risk of triggering false alarms. This paper proposes and evaluates an advanced classification approach to passive OS fingerprinting by leveraging state-of-the-art classical machine learning and deep learning techniques. Our controlled experiments on benchmark data, emulated and realistic traffic is performed using two approaches. Through an Oracle-based machine learning approach, we found that the underlying TCP variant is an important feature for predicting the remote OS. Based on this observation, we develop a sophisticated tool for OS fingerprinting that first predicts the TCP flavor using passive traffic traces and then uses this prediction as an input feature for another machine learning algorithm for predicting the remote OS from passive measurements. This paper takes the passive fingerprinting problem one step further by introducing the underlying predicted TCP variant as a distinguishing feature. In terms of accuracy, we empirically demonstrate that accurately predicting the TCP variant has the potential to boost the evaluation performance from 84% to 94% on average across all our validation scenarios and across different types of traffic sources. We also demonstrate a practical example of this potential, by increasing the performance to 91.3% on average using a tool for TCP variant prediction in an emulated setting. To the best of our knowledge, this is the first study that explores the potential for using the knowledge of the TCP variant to significantly boost the accuracy of passive OS fingerprinting

Design of a study evaluating the effects, health economics, and stakeholder perspectives of a multi-component occupational rehabilitation program with an added workplace intervention - A study protocol

Background Recent research has suggested that interventions at the workplace might be the most potent ingredient in return to work interventions, but few studies have investigated the different effects of workplace interventions as part of occupational rehabilitation programs. The comprehensive design described in this article includes effect (on return to work and health outcomes), and health economic evaluations of a workplace intervention added to a multicomponent rehabilitation program. Qualitative and mixed method studies will investigate sick-listed persons’, rehabilitation therapists’ and employers’ perspectives on the usability and outcomes of the rehabilitation program and the workplace intervention. The program and intervention are provided to patients with musculoskeletal, psychological or general and unspecified diagnoses. The program is multi-component and includes Acceptance and Commitment Therapy, physical exercise, patient education and creating a plan for increased work participation. Methods Persons who are employed, aged from 18 to 60 years, with a current sick leave status of 50% or more and a diagnosis within the musculoskeletal, psychological or general and unspecified chapters of International Classification of Primary Care-2 (ICPC-2) will be recruited to a researcher-blinded parallel-group randomized controlled trial. All participants take part in an in-patient occupational rehabilitation program, while the intervention group also takes part in an intervention at the workplace. The effect and economic evaluation will investigate the effect of the added workplace intervention. The primary outcome measures will be time until full sustainable return to work and total number of sickness absence days in the 12 months after inclusion. Health economic evaluations will investigate the cost-effectiveness and cost-utility. Qualitative studies will investigate rehabilitation therapists’ experiences with working towards return to work within an ACT-approach and stakeholders’ experiences with the workplace intervention. A mixed methods study will combine quantitative and qualitative findings on the participants’ expectations and motivation for return to work. Discussion The outline of this comprehensive study could represent an important addition to the standard designs of return to work evaluation. The mixed methods design, with qualitative approaches as well as a rigorous randomized controlled trial, might prove useful to shed light on contextual factors.publishedVersion© The Author(s). 2018 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License ( http://creativecommons.org/licenses/by/4.0/ ), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/ ) applies to the data made available in this article, unless otherwise stated