Bayesian Approach to Spectral Function Reconstruction for Euclidean Quantum Field Theories

We present a novel approach to the inference of spectral functions from Euclidean time correlator data that makes close contact with modern Bayesian concepts. Our method differs significantly from the maximum entropy method (MEM). A new set of axioms is postulated for the prior probability, leading to an improved expression, which is devoid of the asymptotically flat directions present in the Shanon-Jaynes entropy. Hyperparameters are integrated out explicitly, liberating us from the Gaussian approximations underlying the evidence approach of the maximum entropy method. We present a realistic test of our method in the context of the nonperturbative extraction of the heavy quark potential. Based on hard-thermal-loop correlator mock data, we establish firm requirements in the number of data points and their accuracy for a successful extraction of the potential from lattice QCD. Finally we reinvestigate quenched lattice QCD correlators from a previous study and provide an improved potential estimation at T2.33TC.

Hard thermal loop benchmark for the extraction of the nonperturbative QQ[over ¯] potential

The extraction of the finite temperature heavy quark potential from lattice QCD relies on a spectral analysis of the Wilson loop. General arguments tell us that the lowest lying spectral peak encodes, through its position and shape, the real and imaginary parts of this complex potential. Here we benchmark this extraction strategy using leading order hard-thermal loop (HTL) calculations. In other words, we analytically calculate the Wilson loop and determine the corresponding spectrum. By fitting its lowest lying peak we obtain the real and imaginary parts and confirm that the knowledge of the lowest peak alone is sufficient for obtaining the potential. Access to the full spectrum allows an investigation of spectral features that do not contribute to the potential but can pose a challenge to numerical attempts of an analytic continuation from imaginary time data. Differences in these contributions between the Wilson loop and gauge fixed Wilson line correlators are discussed. To better understand the difficulties in a numerical extraction we deploy the maximum entropy method with extended search space to HTL correlators in Euclidean time and observe how well the known spectral function and values for the real and imaginary parts are reproduced. Possible venues for improvement of the extraction strategy are discussed.

A universal description of ultraslow glass dynamics

The dynamics of glass is of importance in materials science but its nature has not yet been fully understood. Here we report that a verification of the temperature dependencies of the primary relaxation time or viscosity in the ultraslowing/ultraviscous domain of glass-forming systems can be carried out via the analysis of the inverse of the Dyre-Olsen temperature index. The subsequent analysis of experimental data indicates the possibility of the self-consistent description of glass-forming low-molecular-weight liquids, polymers, liquid crystals, orientationally disordered crystals and Ising spin-glass-like systems, as well as the prevalence of equations associated with the 'finite temperature divergence'. All these lead to a new formula for the configurational entropy in glass-forming systems. Furthermore, a link to the dominated local symmetry for a given glass former is identified here. Results obtained show a new relationship between the glass transition and critical phenomena.

Personalized Tuning of a Reinforcement Learning Control Algorithm for Glucose Regulation

Artificial pancreas is in the forefront of research towards the automatic insulin infusion for patients with type 1 diabetes. Due to the high inter- and intra-variability of the diabetic population, the need for personalized approaches has been raised. This study presents an adaptive, patient-specific control strategy for glucose regulation based on reinforcement learning and more specifically on the Actor-Critic (AC) learning approach. The control algorithm provides daily updates of the basal rate and insulin-to-carbohydrate (IC) ratio in order to optimize glucose regulation. A method for the automatic and personalized initialization of the control algorithm is designed based on the estimation of the transfer entropy (TE) between insulin and glucose signals. The algorithm has been evaluated in silico in adults, adolescents and children for 10 days. Three scenarios of initialization to i) zero values, ii) random values and iii) TE-based values have been comparatively assessed. The results have shown that when the TE-based initialization is used, the algorithm achieves faster learning with 98%, 90% and 73% in the A+B zones of the Control Variability Grid Analysis for adults, adolescents and children respectively after five days compared to 95%, 78%, 41% for random initialization and 93%, 88%, 41% for zero initial values. Furthermore, in the case of children, the daily Low Blood Glucose Index reduces much faster when the TE-based tuning is applied. The results imply that automatic and personalized tuning based on TE reduces the learning period and improves the overall performance of the AC algorithm.

Detecting Functional Hubs of Ictogenic Networks

Quantitative EEG (qEEG) has modified our understanding of epileptic seizures, shifting our view from the traditionally accepted hyper-synchrony paradigm toward more complex models based on re-organization of functional networks. However, qEEG measurements are so far rarely considered during the clinical decision-making process. To better understand the dynamics of intracranial EEG signals, we examine a functional network derived from the quantification of information flow between intracranial EEG signals. Using transfer entropy, we analyzed 198 seizures from 27 patients undergoing pre-surgical evaluation for pharmaco-resistant epilepsy. During each seizure we considered for each network the in-, out- and total "hubs", defined respectively as the time and the EEG channels with the maximal incoming, outgoing or total (bidirectional) information flow. In the majority of cases we found that the hubs occur around the middle of seizures, and interestingly not at the beginning or end, where the most dramatic EEG signal changes are found by visual inspection. For the patients who then underwent surgery, good postoperative clinical outcome was on average associated with a higher percentage of out- or total-hubs located in the resected area (for out-hubs p = 0.01, for total-hubs p = 0.04). The location of in-hubs showed no clear predictive value. We conclude that the study of functional networks based on qEEG measurements may help to identify brain areas that are critical for seizure generation and are thus potential targets for focused therapeutic interventions.

A new Bayesian approach to the reconstruction of spectral functions

We present a novel approach for the reconstruction of spectra from Euclidean correlator data that makes close contact to modern Bayesian concepts. It is based upon an axiomatically justified dimensionless prior distribution, which in the case of constant prior function m(ω) only imprints smoothness on the reconstructed spectrum. In addition we are able to analytically integrate out the only relevant overall hyper-parameter α in the prior, removing the necessity for Gaussian approximations found e.g. in the Maximum Entropy Method. Using a quasi-Newton minimizer and high-precision arithmetic, we are then able to find the unique global extremum of P[ρ|D] in the full Nω » Nτ dimensional search space. The method actually yields gradually improving reconstruction results if the quality of the supplied input data increases, without introducing artificial peak structures, often encountered in the MEM. To support these statements we present mock data analyses for the case of zero width delta peaks and more realistic scenarios, based on the perturbative Euclidean Wilson Loop as well as the Wilson Line correlator in Coulomb gauge.

(Non)-Dissipative Hydrodynamics on Embedded Surfaces

We construct the theory of dissipative hydrodynamics of uncharged fluids living on embedded space-time surfaces to first order in a derivative expansion in the case of codimension-1 surfaces (including fluid membranes) and the theory of non-dissipative hydrodynamics to second order in a derivative expansion in the case of codimension higher than one under the assumption of no angular momenta in transverse directions to the surface. This construction includes the elastic degrees of freedom, and hence the corresponding transport coefficients, that take into account transverse fluctuations of the geometry where the fluid lives. Requiring the second law of thermodynamics to be satisfied leads us to conclude that in the case of codimension-1 surfaces the stress-energy tensor is characterized by 2 hydrodynamic and 1 elastic independent transport coefficient to first order in the expansion while for codimension higher than one, and for non-dissipative flows, the stress-energy tensor is characterized by 7 hydrodynamic and 3 elastic independent transport coefficients to second order in the expansion. Furthermore, the constraints imposed between the stress-energy tensor, the bending moment and the entropy current of the fluid by these extra non-dissipative contributions are fully captured by equilibrium partition functions. This analysis constrains the Young modulus which can be measured from gravity by elastically perturbing black branes.

Signal Processing for the Measurement of the Deuterium/Hydrogen Ratio in the Local Interstellar Medium

We report on a comprehensive signal processing procedure for very low signal levels for the measurement of neutral deuterium in the local interstellar medium from a spacecraft in Earth orbit. The deuterium measurements were performed with the IBEX-Lo camera on NASA’s Interstellar Boundary Explorer (IBEX) satellite. Our analysis technique for these data consists of creating a mass relation in three-dimensional time of flight space to accurately determine the position of the predicted D events, to precisely model the tail of the H events in the region where the H tail events are near the expected D events, and then to separate the H tail from the observations to extract the very faint D signal. This interstellar D signal, which is expected to be a few counts per year, is extracted from a strong terrestrial background signal, consisting of sputter products from the sensor’s conversion surface. As reference we accurately measure the terrestrial D/H ratio in these sputtered products and then discriminate this terrestrial background source. During the three years of the mission time when the deuterium signal was visible to IBEX, the observation geometry and orbit allowed for a total observation time of 115.3 days. Because of the spinning of the spacecraft and the stepping through eight energy channels the actual observing time of the interstellar wind was only 1.44 days. With the optimised data analysis we found three counts that could be attributed to interstellar deuterium. These results update our earlier work.

Unified detection and tracking of instruments during retinal microsurgery

Methods for tracking an object have generally fallen into two groups: tracking by detection and tracking through local optimization. The advantage of detection-based tracking is its ability to deal with target appearance and disappearance, but it does not naturally take advantage of target motion continuity during detection. The advantage of local optimization is efficiency and accuracy, but it requires additional algorithms to initialize tracking when the target is lost. To bridge these two approaches, we propose a framework for unified detection and tracking as a time-series Bayesian estimation problem. The basis of our approach is to treat both detection and tracking as a sequential entropy minimization problem, where the goal is to determine the parameters describing a target in each frame. To do this we integrate the Active Testing (AT) paradigm with Bayesian filtering, and this results in a framework capable of both detecting and tracking robustly in situations where the target object enters and leaves the field of view regularly. We demonstrate our approach on a retinal tool tracking problem and show through extensive experiments that our method provides an efficient and robust tracking solution.

Prognostic and diagnostic value of EEG signal coupling measures in coma.

OBJECTIVE Our aim was to assess the diagnostic and predictive value of several quantitative EEG (qEEG) analysis methods in comatose patients. METHODS In 79 patients, coupling between EEG signals on the left-right (inter-hemispheric) axis and on the anterior-posterior (intra-hemispheric) axis was measured with four synchronization measures: relative delta power asymmetry, cross-correlation, symbolic mutual information and transfer entropy directionality. Results were compared with etiology of coma and clinical outcome. Using cross-validation, the predictive value of measure combinations was assessed with a Bayes classifier with mixture of Gaussians. RESULTS Five of eight measures showed a statistically significant difference between patients grouped according to outcome; one measure revealed differences in patients grouped according to the etiology. Interestingly, a high level of synchrony between the left and right hemisphere was associated with mortality on intensive care unit, whereas higher synchrony between anterior and posterior brain regions was associated with survival. The combination with the best predictive value reached an area-under the curve of 0.875 (for patients with post anoxic encephalopathy: 0.946). CONCLUSIONS EEG synchronization measures can contribute to clinical assessment, and provide new approaches for understanding the pathophysiology of coma. SIGNIFICANCE Prognostication in coma remains a challenging task. qEEG could improve current multi-modal approaches.

Time-varying signal analysis to detect high-altitude periodic breathing in climbers ascending to extreme altitude.

This work investigates the performance of cardiorespiratory analysis detecting periodic breathing (PB) in chest wall recordings in mountaineers climbing to extreme altitude. The breathing patterns of 34 mountaineers were monitored unobtrusively by inductance plethysmography, ECG and pulse oximetry using a portable recorder during climbs at altitudes between 4497 and 7546 m on Mt. Muztagh Ata. The minute ventilation (VE) and heart rate (HR) signals were studied, to identify visually scored PB, applying time-varying spectral, coherence and entropy analysis. In 411 climbing periods, 30-120 min in duration, high values of mean power (MP(VE)) and slope (MSlope(VE)) of the modulation frequency band of VE, accurately identified PB, with an area under the ROC curve of 88 and 89%, respectively. Prolonged stay at altitude was associated with an increase in PB. During PB episodes, higher peak power of ventilatory (MP(VE)) and cardiac (MP(LF)(HR) ) oscillations and cardiorespiratory coherence (MP(LF)(Coher)), but reduced ventilation entropy (SampEn(VE)), was observed. Therefore, the characterization of cardiorespiratory dynamics by the analysis of VE and HR signals accurately identifies PB and effects of altitude acclimatization, providing promising tools for investigating physiologic effects of environmental exposures and diseases.

Gas-phase fragmentation and conformation of the sugar-modified antisense oligonucleotide tcDNA

Tricyclo-DNA (tcDNA) is a sugar- and backbone-modified analogue of DNA that is currently tested as antisense oligonucleotide for the treatment of Duchenne muscular dystrophy. The name tricyclo-DNA is derived from the modified sugar-moiety: the deoxyribose is extended to a three-membered ring system. This modification is designed to limit the flexibility of the structure, thus giving rise to entropically stabilized hybrid duplexes formed between tcDNA and complementary DNA or RNA oligonucleotides. While the structural modifications increase the biostability of the therapeutic agent, they also render the oligonucleotide inaccessible to enzyme-based sequencing methods. Tandem mass spectrometry constitutes an alternative sequencing technique for partially and fully modified oligonucleotides. For reliable sequencing, the fragmentation mechanism of the structure in question must be understood. Therefore, the presented work evaluates the effect of the modified sugar-moiety on the gas-phase dissociation of single stranded tcDNA. Moreover, our experiments reflect the exceptional gas-phase stability of hybrid duplexes that is most noticeable in the formation of truncated duplex ions upon collision-induced dissociation. The stability of the duplex arises from the modified sugar-moiety, as the rigid structure of the tcDNA single strand minimizes the change of the entropy for the annealing. Moreover, the tc-modification gives rise to extended conformations of the nucleic acids in the gas-phase, which was studied by ion mobility spectrometry-mass spectrometry.

Shallow Dialogue Processing Using Machine Learning Algorithms (or not)

This paper presents a shallow dialogue analysis model, aimed at human-human dialogues in the context of staff or business meetings. Four components of the model are defined, and several machine learning techniques are used to extract features from dialogue transcripts: maximum entropy classifiers for dialogue acts, latent semantic analysis for topic segmentation, or decision tree classifiers for discourse markers. A rule-based approach is proposed for solving cross-modal references to meeting documents. The methods are trained and evaluated thanks to a common data set and annotation format. The integration of the components into an automated shallow dialogue parser opens the way to multimodal meeting processing and retrieval applications.

Contribution of activity and skin temperature to the risk of developing pressure ulcers in nursing facility residents

Purpose. The central concepts in pressure ulcer risk are exposure to external pressure caused by inactivity and tissue tolerance to pressure, a factor closely related to blood flow. Inactivity measures are effective in predicting pressure ulcer risk. The purpose of the study is to evaluate whether a physiological measure of skin blood flow improves pressure ulcer risk prediction. Skin temperature regularity and self-similarity, as proxy measures of blood flow, and not previously described, may be undefined pressure ulcer risk factors. The specific aims were to determine whether a sample of nursing facility residents at high risk of pressure ulcers classified using the Braden Scale for Pressure Sore Risk© differ from a sample of low risk residents according to (1) exposure to external pressure as measured by resident activity, (2) tissue tolerance to external pressure as measured by skin temperature, and (3) skin temperature fluctuations and recovery in response to a commonly occurring stressor, bathing and additionally whether (4) scores on the Braden Scale mobility subscale score are related to entropy and the spectral exponent. ^ Methods. A two group observational time series design was used to describe activity and skin temperature regularity and self-similarity, calculating entropy and the spectral exponent using detrended fluctuation analysis respectively. Twenty nursing facility residents wore activity and skin temperature monitors for one week. One bathing episode was observed as a commonly occurring stressor for skin temperature.^ Results. Skin temperature multiscale entropy (MSE), F(1, 17) = 5.55, p = .031, the skin temperature spectral exponent, F(1, 17) = 6.19, p = .023, and the activity mean MSE, F(1, 18) = 4.52, p = .048 differentiated the risk groups. The change in skin temperature entropy during bathing was significant, t(16) = 2.55, p = .021, (95% CI, .04-.40). Multiscale entropy for skin temperature was lowest in those who developed pressure ulcers, F(1, 18) = 35.14, p < .001.^ Conclusions. This study supports the tissue tolerance component of the Braden and Bergstrom conceptual framework and shows differences in skin temperature multiscale entropy between pressure ulcer risk categories, pressure ulcer outcome, and during a commonly occurring stressor. ^

Diversificación con frambuesa : : el impacto de su introducción en sistemas hortícolas de Coronda, Santa Fe, Argentina : análisis de un caso de estudio

El objetivo del presente trabajo fue analizar el impacto micro-económico que produce la introducción del cultivo de frambuesa en una pyme hortícola de Coronda, dedicada esencialmente a la producción de frutillas.El patrón de diversificación actual es escaso, debido principalmente a las limitaciones financieras existentes a nivel predial. Se combina el cultivo de frutilla con otras hortalizas, a contra-ciclo, tales como zapallito, berenjena, choclo, sandía y melón. Se analizaron los resultados económicos del ejercicio 2009/2010 de una empresa pyme, estudiada como caso, con una Superficie Agrícola Útil de 5,5 ha, que implantó en el ejercicio evaluado 2 ha de frutilla e introdujo 0,5 ha de frambuesa al esquema productivo, en reemplazo de alguno de los cultivos hortícolas que realizaba. Se obtuvo un Ingreso al capital equivalente a $ 59.786, una rentabilidad sobre el capital total de 12,95 % y sobre el capital sin tierra de 28,79 %. Estos tres indicadores mostraron un incremento de 258%, 248% y 236% respecto de la situación sin frambuesa, respectivamente. Se mejora el grado de diversificación analizado a través del Índice de Entropía y exhibe una excelente complementariedad tanto en el aspecto financiero como laboral.