Dynamics of polymer chain collapse into compact states

Molecular dynamics simulation methods are used to study the folding of polymer chains into packed cubic states. The polymer model, based on a chain of linked sites moving in the continuum, includes both excluded volume and torsional interactions. Different native-state packing arrangements and chain lengths are explored; the organization of the native state is found to affect both the ability of the chain to fold successfully and the nature of the folding pathway as the system is gradually cooled. An order parameter based on contact counts is used to provide information about the folding process, with contacts additionally classified according to criteria such as core and surface sites or local and distant site pairs. Fully detailed contact maps and their evolution are also examined.Comment: 11 pages, 11 figures (some low resolution

Camera motion estimation through planar deformation determination

In this paper, we propose a global method for estimating the motion of a camera which films a static scene. Our approach is direct, fast and robust, and deals with adjacent frames of a sequence. It is based on a quadratic approximation of the deformation between two images, in the case of a scene with constant depth in the camera coordinate system. This condition is very restrictive but we show that provided translation and depth inverse variations are small enough, the error on optical flow involved by the approximation of depths by a constant is small. In this context, we propose a new model of camera motion, that allows to separate the image deformation in a similarity and a ``purely'' projective application, due to change of optical axis direction. This model leads to a quadratic approximation of image deformation that we estimate with an M-estimator; we can immediatly deduce camera motion parameters.Comment: 21 pages, version modifi\'ee accept\'e le 20 mars 200

Self-compassion and suicide risk in veterans : when the going gets tough, do the tough benefit more from self-kindness?

Objectives: Veterans are at particular risk for suicide due to psychopathological, emotional, and interpersonal risk factors. However, the presence of individual-level protective factors, such as self-compassion, may reduce risk, becoming more salient at increasing levels of distress and psychopathology, per theory. We examined the relation between self-compassion and suicide risk, and the moderating effects of depression, PTSD symptoms, anger, shame, and thwarted interpersonal needs. Methods: Our sample of United States veterans (n=541) in our cross-sectional study were mostly male (69.1%) with an average age of 49.90 (SD=16.78), who completed online self-report measures: Suicidal Behaviors Questionnaire-Revised, Multidimensional Health Profile-Psychosocial Functioning Screening Tool, PTSD Checklist-Military Version, Differential Emotions Scale-IV, and the Interpersonal Needs Questionnaire. Results: The linkage between self-compassion and suicidal behavior in our veteran sample was moderated by distress-evoking risk factors, including depression, anger, shame, and thwarted interpersonal needs, such that, as level of risk severity increases, the inverse association between self-compassion and suicidal behavior is strengthened. Conclusions: Our findings highlight an emergent protective process that may prevent suicide in times of distress. Therapeutically bolstering the ability for self-compassion may provide a proactive coping strategy that can be brought to bear in times of crisis, reducing suicide risk for veterans

The effect of asymmetries on stock index return value-at-risk estimates

It is widely accepted that equity return volatility increases more following negative shocks rather than positive shocks. However, much of value-at-risk (VaR) analysis relies on the assumption that returns are normally distributed (a symmetric distribution). This article considers the effect of asymmetries on the evaluation and accuracy of VaR by comparing estimates based on various models

A study of smoke formation from wood combustion

Aerosol time of flight mass spectrometry (ATOFMS) was used to analyse the particles emitted during the flaming and smouldering phases of the combustion of samples of hard and soft woods. Eugenol and furfural were also burned and using results from previous work of the authors, they have been shown to be useful proxies for initial wood combustion products. The ratios of elementary carbon to total carbon in the particles were similar for both the woods and for eugenol. The ATOFMS spectra of most of the particles were consistent with the presence of soot precursor constituents along with oxygen containing fragments. Most particle diameters were less than 2.5. μm, with the greatest concentration of <. 0.12. μm

Signatures of the slow solar wind streams from active regions in the inner corona

Some of local sources of the slow solar wind can be associated with spectroscopically detected plasma outflows at edges of active regions accompanied with specific signatures in the inner corona. The EUV telescopes (e.g. SPIRIT/CORONAS-F, TESIS/CORONAS-Photon and SWAP/PROBA2) sometimes observed extended ray-like structures seen at the limb above active regions in 1MK iron emission lines and described as "coronal rays". To verify the relationship between coronal rays and plasma outflows, we analyze an isolated active region (AR) adjacent to small coronal hole (CH) observed by different EUV instruments in the end of July - beginning of August 2009. On August 1 EIS revealed in the AR two compact outflows with the Doppler velocities V =10-30 km/s accompanied with fan loops diverging from their regions. At the limb the ARCH interface region produced coronal rays observed by EUVI/STEREO-A on July 31 as well as by TESIS on August 7. The rays were co-aligned with open magnetic field lines expanded to the streamer stalks. Using the DEM analysis, it was found that the fan loops diverged from the outflow regions had the dominant temperature of ~1 MK, which is similar to that of the outgoing plasma streams. Parameters of the solar wind measured by STEREO-B, ACE, WIND, STEREO-A were conformed with identification of the ARCH as a source region at the Wang-Sheeley-Arge map of derived coronal holes for CR 2086. The results of the study support the suggestion that coronal rays can represent signatures of outflows from ARs propagating in the inner corona along open field lines into the heliosphere.Comment: Accepted for publication in Solar Physics; 31 Pages; 13 Figure

Fibromyalgia impact and depressive symptoms: Can perceiving a silver lining make a difference?

Individuals with fibromyalgia are at greater risk for depressive symptoms than the general population, and this may be partially attributable to physical symptoms that impair day‐to‐day functioning. However, individual‐level protective characteristics may buffer risk for psychopathology. For instance, the ability to perceive a “silver lining” in one’s illness may be related to better mental and physical health. We examined perceived silver lining as a potential moderator of the relation between fibromyalgia impact and depressive symptoms. Our sample of persons with fibromyalgia (N = 401) completed self‐report measures including the Fibromyalgia Impact Questionnaire‐Revised, Depression Anxiety Stress Scales, and the Silver Lining Questionnaire. Moderation analyses covaried age, sex, and ethnicity. Supporting hypotheses, increasing impact of disease was related to greater depressive symptoms, and perceptions of a silver lining attenuated that association. Despite the linkage between impairment and depressive symptoms, identifying positive aspects or outcomes of illness may reduce risk for psychopathology. Therapeutically promoting perception of a silver lining, perhaps via signature strengths exercises or a blessings journal, and encouraging cognitive reframing of the illness experience, perhaps via Motivational Interviewing or Cognitive Behavioral Therapy, may reduce depressive symptoms in persons with fibromyalgia

Investigation of conduction band structure, electron scattering mechanisms and phase transitions in indium selenide by means of transport measurements under pressure

In this work we report on Hall effect, resistivity and thermopower measurements in n-type indium selenide at room temperature under either hydrostatic and quasi-hydrostatic pressure. Up to 40 kbar (= 4 GPa), the decrease of carrier concentration as the pressure increases is explained through the existence of a subsidiary minimum in the conduction band. This minimum shifts towards lower energies under pressure, with a pressure coefficient of about -105 meV/GPa, and its related impurity level traps electrons as it reaches the band gap and approaches the Fermi level. The pressure value at which the electron trapping starts is shown to depend on the electron concentration at ambient pressure and the dimensionality of the electron gas. At low pressures the electron mobility increases under pressure for both 3D and 2D electrons, the increase rate being higher for 2D electrons, which is shown to be coherent with previous scattering mechanisms models. The phase transition from the semiconductor layered phase to the metallic sodium cloride phase is observed as a drop in resistivity around 105 kbar, but above 40 kbar a sharp nonreversible increase of the carrier concentration is observed, which is attributed to the formation of donor defects as precursors of the phase transition.Comment: 18 pages, Latex, 10 postscript figure

Transfer learning for galaxy morphology from one survey to another

© 2018 The Author(s). Published by Oxford University Press on behalf of the Royal Astronomical Society.Deep Learning (DL) algorithms for morphological classification of galaxies have proven very successful, mimicking (or even improving) visual classifications. However, these algorithms rely on large training samples of labelled galaxies (typically thousands of them). A key question for using DL classifications in future Big Data surveys is how much of the knowledge acquired from an existing survey can be exported to a new dataset, i.e. if the features learned by the machines are meaningful for different data. We test the performance of DL models, trained with Sloan Digital Sky Survey (SDSS) data, on Dark Energy survey (DES) using images for a sample of $\sim$5000 galaxies with a similar redshift distribution to SDSS. Applying the models directly to DES data provides a reasonable global accuracy ($\sim$ 90%), but small completeness and purity values. A fast domain adaptation step, consisting in a further training with a small DES sample of galaxies ($\sim$500-300), is enough for obtaining an accuracy > 95% and a significant improvement in the completeness and purity values. This demonstrates that, once trained with a particular dataset, machines can quickly adapt to new instrument characteristics (e.g., PSF, seeing, depth), reducing by almost one order of magnitude the necessary training sample for morphological classification. Redshift evolution effects or significant depth differences are not taken into account in this study.Peer reviewedFinal Accepted Versio