Active Semi-Supervised Learning Using Sampling Theory for Graph Signals

We consider the problem of offline, pool-based active semi-supervised learning on graphs. This problem is important when the labeled data is scarce and expensive whereas unlabeled data is easily available. The data points are represented by the vertices of an undirected graph with the similarity between them captured by the edge weights. Given a target number of nodes to label, the goal is to choose those nodes that are most informative and then predict the unknown labels. We propose a novel framework for this problem based on our recent results on sampling theory for graph signals. A graph signal is a real-valued function defined on each node of the graph. A notion of frequency for such signals can be defined using the spectrum of the graph Laplacian matrix. The sampling theory for graph signals aims to extend the traditional Nyquist-Shannon sampling theory by allowing us to identify the class of graph signals that can be reconstructed from their values on a subset of vertices. This approach allows us to define a criterion for active learning based on sampling set selection which aims at maximizing the frequency of the signals that can be reconstructed from their samples on the set. Experiments show the effectiveness of our method.Comment: 10 pages, 6 figures, To appear in KDD'1

Pulsed beams as field probes for precision measurement

We describe a technique for mapping the spatial variation of static electric, static magnetic, and rf magnetic fields using a pulsed atomic or molecular beam. The method is demonstrated using a beam designed to measure the electric dipole moment of the electron. We present maps of the interaction region, showing sensitivity to (i) electric field variation of 1.5 V/cm at 3.3 kV/cm with a spatial resolution of 15 mm; (ii) magnetic field variation of 5 nT with 25 mm resolution; (iii) radio-frequency magnetic field amplitude with 15 mm resolution. This new diagnostic technique is very powerful in the context of high-precision atomic and molecular physics experiments, where pulsed beams have not hitherto found widespread application.Comment: 6 pages, 12 figures. Figures heavily compressed to comply with arxiv's antediluvian file-size polic

Fluctuations and stability in front propagation

Propagating fronts arising from bistable reaction-diffusion equations are a purely deterministic effect. Stochastic reaction-diffusion processes also show front propagation which coincides with the deterministic effect in the limit of small fluctuations (usually, large populations). However, for larger fluctuations propagation can be affected. We give an example, based on the classic spruce-budworm model, where the direction of wave propagation, i.e., the relative stability of two phases, can be reversed by fluctuations.Comment: 5 pages, 5 figure

Functional Relaxation and Guided Imagery as Complementary Therapy in Asthma: A Randomized Controlled Clinical Trial

Background: Asthma is a frequently disabling and almost invariably distressing disease that has a high overall prevalence. Although relaxation techniques and hypnotherapeutic interventions have proven their effectiveness in numerous trials, relaxation therapies are still not recommended in treatment guidelines due to a lack of methodological quality in many of the trials. Therefore, this study aims to investigate the efficacy of the brief relaxation technique of functional relaxation (FR) and guided imagery (GI) in adult asthmatics in a randomized controlled trial. Methods: 64 patients with extrinsic bronchial asthma were treated over a 4-week period and assessed at baseline, after treatment and after 4 months, for follow-up. 16 patients completed FR, 14 GI, 15 both FR and GI (FR/GI) and 13 received a placebo relaxation technique as the control intervention (CI). The forced expiratory volume in the first second (FEV 1) as well as the specific airway resistance (sR(aw)) were employed as primary outcome measures. Results: Participation in FR, GI and FR/GI led to increases in FEV 1 (% predicted) of 7.6 +/- 13.2, 3.3 +/- 9.8, and 8.3 +/- 21.0, respectively, as compared to -1.8 +/- 11.1 in the CI group at the end of the therapy. After follow-up, the increases in FEV 1 were 6.9 +/- 10.3 in the FR group, 4.4 +/- 7.3 in the GI and 4.5 +/- 8.1 in the FR/GI, compared to -2.8 +/- 9.2 in the CI. Improvements in sR(aw) (% predicted) were in keeping with the changes in FEV 1 in all groups. Conclusions: Our study confirms a positive effect of FR on respiratory parameters and suggests a clinically relevant long-term benefit from FR as a nonpharmacological and complementary therapy treatment option. Copyright (C) 2009 S. Karger AG, Base

Autonomy in Weapons Systems. The Military Application of Artificial Intelligence as a Litmus Test for Germany’s New Foreign and Security Policy

The future international security landscape will be critically impacted by the military use of artificial intelligence (AI) and robotics. With the advent of autonomous weapon systems (AWS) and a currently unfolding transformation of warfare, we have reached a turning point and are facing a number of grave new legal, ethical and political concerns. In light of this, the Task Force on Disruptive Technologies and 21st Century Warfare, deployed by the Heinrich Böll Foundation, argues that meaningful human control over weapon systems and the use of force must be retained. In their report, the task force authors offer recommendations to the German government and the German armed forces to that effect

Measurement of the electron electric dipole moment using YbF molecules

The most sensitive measurements of the electron electric dipole moment d_e have previously been made using heavy atoms. Heavy polar molecules offer a greater sensitivity to d_e because the interaction energy to be measured is typically 10^3 times larger than in a heavy atom. We report the first measurement of this kind, for which we have used the molecule YbF. Together, the large interaction energy and the strong tensor polarizability of the molecule make our experiment essentially free of the systematic errors that currently limit d_e measurements in atoms. Our first result d_e = (- 0.2 \pm 3.2) x 10^-26 e.cm is less sensitive than the best atom measurement, but is limited only by counting statistics and demonstrates the power of the method.Comment: 4 pages, 4 figures. v2. Minor corrections and clarifications made in response to referee comment

Antipersistent binary time series

Completely antipersistent binary time series are sequences in which every time that an $N$-bit string $\mu$ appears, the sequence is continued with a different bit than at the last occurrence of $\mu$. This dynamics is phrased in terms of a walk on a DeBruijn graph, and properties of transients and cycles are studied. The predictability of the generated time series for an observer who sees a longer or shorter time window is investigated also for sequences that are not completely antipersistent.Comment: 6 pages, 6 figure

Protein sets define disease states and predict in vivo effects of drug treatment

Gaining understanding of common complex diseases and their treatments are the main drivers for life sciences. As we show here, comprehensive protein set analyses offer new opportunities to decipher functional molecular networks of diseases and assess the efficacy and side-effects of treatments in vivo. Using mass spectrometry, we quantitatively detected several thousand proteins and observed significant changes in protein pathway (dys-) regulated in diet-induced obesity mice. Analysis of the expression and posttranslational modifications of proteins in various peripheral metabolic target tissues including adipose, heart and liver tissue generated functional insights in the regulation of cell and tissue homeostasis during high fat diet and medication with two anti-diabetic compounds. Protein set analyses singled out pathways for functional characterization, and indicated for example early on potential cardiovascular complication of the diabetes drug rosiglitazone. In vivo protein set detection can provide new avenues for monitoring complex disease processes, and for evaluating preclinical drug candidates

Dimension of interaction dynamics

A method allowing to distinguish interacting from non-interacting systems based on available time series is proposed and investigated. Some facts concerning generalized Renyi dimensions that form the basis of our method are proved. We show that one can find the dimension of the part of the attractor of the system connected with interaction between its parts. We use our method to distinguish interacting from non-interacting systems on the examples of logistic and H\'enon maps. A classification of all possible interaction schemes is given.Comment: 15 pages, 14 (36) figures, submitted to PR