Unsupervised Deep Single-Image Intrinsic Decomposition using Illumination-Varying Image Sequences

Machine learning based Single Image Intrinsic Decomposition (SIID) methods decompose a captured scene into its albedo and shading images by using the knowledge of a large set of known and realistic ground truth decompositions. Collecting and annotating such a dataset is an approach that cannot scale to sufficient variety and realism. We free ourselves from this limitation by training on unannotated images. Our method leverages the observation that two images of the same scene but with different lighting provide useful information on their intrinsic properties: by definition, albedo is invariant to lighting conditions, and cross-combining the estimated albedo of a first image with the estimated shading of a second one should lead back to the second one's input image. We transcribe this relationship into a siamese training scheme for a deep convolutional neural network that decomposes a single image into albedo and shading. The siamese setting allows us to introduce a new loss function including such cross-combinations, and to train solely on (time-lapse) images, discarding the need for any ground truth annotations. As a result, our method has the good properties of i) taking advantage of the time-varying information of image sequences in the (pre-computed) training step, ii) not requiring ground truth data to train on, and iii) being able to decompose single images of unseen scenes at runtime. To demonstrate and evaluate our work, we additionally propose a new rendered dataset containing illumination-varying scenes and a set of quantitative metrics to evaluate SIID algorithms. Despite its unsupervised nature, our results compete with state of the art methods, including supervised and non data-driven methods.Comment: To appear in Pacific Graphics 201

Biological Soil Crusts as Modern Analogues for the Archean Continental Biosphere: Insights from Carbon and Nitrogen Isotopes

Stable isotope signatures of elements related to life such as carbon and nitrogen can be powerful biomarkers that provide key information on the biological origin of organic remains and their paleoenvironments. Marked advances have been achieved in the last decade in our understanding of the coupled evolution of biological carbon and nitrogen cycling and the chemical evolution of the early Earth thanks, in part, to isotopic signatures preserved in fossilized microbial mats and organic matter of marine origin. However, the geologic record of the early continental biosphere, as well as its evolution and biosignatures, is still poorly constrained. Following a recent report of direct fossil evidence of life on land at 3.22 Ga, we compare here the carbon and nitrogen isotopic signals of this continental Archean biosphere with biosignatures of cyanobacteria biological soil crusts (cyanoBSCs) colonizing modern arid environments. We report the first extended δ13C and δ15N data set from modern cyanoBSCs and show that these modern communities harbor specific isotopic biosignatures that compare well with continental Archean organic remains. We therefore suggest that cyanoBSCs are likely relevant analogues for the earliest continental ecosystems. As such, they can provide key information on the timing, extent, and possibly mechanism of colonization of the early Earth's emergent landmasses

Patients’ understandings about cellulitis and views about how best to prevent recurrent episodes: mixed methods study in primary and secondary care

BackgroundCellulitis is a common painful infection of the skin and underlying tissues that recurs in approximately a third of cases. The only proven strategy to reduce the risk of recurrence is long‐term, low‐dose antibiotics. Given current concerns about antibiotic resistance and the pressure to reduce antibiotic prescribing, other prevention strategies are needed.ObjectivesTo explore patients’ views about cellulitis and different ways of preventing recurrent episodes.MethodsAdults aged 18 or over with a history of first episode or recurrent cellulitis were invited through primary care, hospitals and advertising to complete a survey, take part in an interview, or both.ResultsThirty interviews were conducted between August 2016 and July 2017. Two hundred and forty surveys were completed (response rate 17%). Triangulation of quantitative and qualitative data showed that people who have had cellulitis have wide‐ranging beliefs about what can cause cellulitis and are often unaware of risk of recurrence or potential strategies to prevent recurrence. Enhanced foot hygiene, applying emollients daily, exercise and losing weight were more popular potential strategies than use of compression stockings or long‐term antibiotics. Participants expressed caution about long‐term oral antibiotics, particularly those who had experienced only one episode of cellulitis.ConclusionsPeople who have had cellulitis are keen to know about possible ways to prevent further episodes. Enhanced foot hygiene, applying emollients daily, exercise and losing weight were generally viewed to be more acceptable, feasible strategies than compression or antibiotics, but further research is needed to explore uptake and effectiveness in practice

Optimized I-values for the use with the Bragg additivity rule and their impact on proton stopping power and range uncertainty

Purpose: Novel imaging modalities estimate patient elemental compositions for particle treatment planning. The mean excitation energy (I-value) is a main contributor to the proton range uncertainty. To minimize their impact on beam range errors and quantify their uncertainties, the currently used I-values proposed in 1982 are revisited. The study aims at proposing a new set of optimized elemental I-values for use with the Bragg additivity rule (BAR) and establishing uncertainties on optimized I-values and the BAR. // Methods: We optimize elemental I-values for the use in compounds based on measured material I-values. We gain a new set of elemental I-values and corresponding uncertainties, based on the experimental uncertainties and our uncertainty model. We evaluate uncertainties on I-values and relative stopping powers (RSP) of 70 human tissues, taking into account statistical correlations between tissues and water. The effect of new I-values on proton beam ranges is quantified using Monte Carlo simulations. // Results: Our elemental I-values describe measured material I-values with higher accuracy than ICRU-recommended I-values (RMSE: 6.17% (ICRU), 5.19% (this work)). Our uncertainty model estimates an uncertainty component from the BAR to 4.42%. Using our elemental I-values, we calculate the I-value of water as 78.73+/-2.89 eV, being consistent with ICRU 90 (78+/-2 eV). We observe uncertainties on tissue I-values between 1.82-3.38 eV, and RSP uncertainties between 0.002%-0.44%. With transport simulations of a proton beam in human tissues, we observe range uncertainties between 0.31% and 0.47%, as compared to current estimates of 1.5%. // Conclusion: We propose a set of elemental I-values well suited for human tissues in combination with the BAR. Our model establishes uncertainties on elemental I-values and the BAR, enabling to quantify uncertainties on tissue I-values, RSP as well as particle range. This work is particularly relevant for Monte Carlo simulations where the interaction probabilities are reconstructed from elemental compositions

Deep Markov Random Field for Image Modeling

Markov Random Fields (MRFs), a formulation widely used in generative image modeling, have long been plagued by the lack of expressive power. This issue is primarily due to the fact that conventional MRFs formulations tend to use simplistic factors to capture local patterns. In this paper, we move beyond such limitations, and propose a novel MRF model that uses fully-connected neurons to express the complex interactions among pixels. Through theoretical analysis, we reveal an inherent connection between this model and recurrent neural networks, and thereon derive an approximated feed-forward network that couples multiple RNNs along opposite directions. This formulation combines the expressive power of deep neural networks and the cyclic dependency structure of MRF in a unified model, bringing the modeling capability to a new level. The feed-forward approximation also allows it to be efficiently learned from data. Experimental results on a variety of low-level vision tasks show notable improvement over state-of-the-arts.Comment: Accepted at ECCV 201

Authigenic iron oxide proxies for marine zinc over geological time and implications for eukaryotic metallome evolution

Author Posting. © The Author(s), 2012. This is the author's version of the work. It is posted here by permission of John Wiley & Sons for personal use, not for redistribution. The definitive version was published in Geobiology 11 (2013): 295-306, doi:10.1111/gbi.12036.Here we explore enrichments in paleomarine Zn as recorded by authigenic iron oxides including Precambrian iron formations, ironstones and Phanerozoic hydrothermal exhalites. This compilation of new and literature-based iron formation analyses track dissolved Zn abundances and constrain the magnitude of the marine reservoir over geological time. Overall, the iron formation record is characterized by a fairly static range in Zn/Fe ratios throughout the Precambrian, consistent with the shale record (Scott et al., 2013, Nature Geoscience, 6, 125-128). When hypothetical partitioning scenarios are applied to this record, paleomarine Zn concentrations within about an order of magnitude of modern are indicated. We couple this examination with new chemical speciation models used to interpret the iron formation record. We present two scenarios: first, under all but the most sulfidic conditions and with Zn binding organic ligand concentrations similar to modern oceans, the amount of bioavailable Zn remained relatively unchanged through time. Late proliferation of Zn in eukaryotic metallomes has previously been linked to marine Zn biolimitation, but under this scenario, the expansion in eukaryotic Zn metallomes may be better linked to biologically intrinsic evolutionary factors. In this case zinc’s geochemical and biological evolution may be decoupled, and viewed as a function of increasing need for genome regulation and diversification of Zn-binding transcription factors. In the second scenario, we consider Archean organic ligand complexation in such excess that it may render Zn bioavailability low. However, this is dependent on Zn organic ligand complexes not being bioavailable, which remains unclear. In this case, although bioavailability may be low, sphalerite precipitation is prevented, thereby maintaining a constant Zn inventory throughout both ferruginous and euxinic conditions. These results provide new perspectives and constraints 50 on potential couplings between the trajectory of biological and marine geochemical coevolution.This work was supported by a NSERC Discovery Grant to KOK, a NSERC PDF to SVL, a NSERC CGSM to LJR, and an NSF-EAR-PDF to NJP. MAS acknowledges support from the Gordon and Betty Moore Foundation Grant #2724. This work was also supported by grants from the Deutsche Forschungsgemeinschaft (DFG) to A.K. (KA 1736/4-1 and 12-1)

The potential of dual-energy CT to reduce proton beam range uncertainties

PURPOSE: Dual‐energy CT (DECT) promises improvements in estimating stopping power ratios (SPRs) for proton therapy treatment planning. Although several comparable mathematical formalisms have been proposed in literature, the optimal techniques to characterize human tissue SPRs with DECT in a clinical environment are not fully established. The aim of this work is to compare the most robust DECT methods against conventional single‐energy CT (SECT) in conditions reproducing a clinical environment, where CT artifacts and noise play a major role on the accuracy of these techniques.METHODS: Available DECT tissue characterization methods are investigated and their ability to predict SPRs is compared in three contexts: (a) a theoretical environment using the XCOM cross section database; (b) experimental data using a dual‐source CT scanner on a calibration phantom; (c) simulations of a virtual humanoid phantom with the ImaSim software. The latter comparison accounts for uncertainties caused by CT artifacts and noise, but leaves aside other sources of uncertainties such as CT grid size and the I‐values. To evaluate the clinical impact, a beam range calculation model is used to predict errors from the probability distribution functions determined with ImaSim simulations. Range errors caused by SPR errors in soft tissues and bones are investigated. RESULTS: Range error estimations demonstrate that DECT has the potential of reducing proton beam range uncertainties by 0.4% in soft tissues using low noise levels of 12 and 8 HU in DECT, corresponding to 7 HU in SECT. For range uncertainties caused by the transport of protons through bones, the reduction in range uncertainties for the same levels of noise is found to be up to 0.6 to 1.1 mm for bone thicknesses ranging from 1 to 5 cm, respectively. We also show that for double the amount noise, i.e., 14 HU in SECT and 24 and 16 HU for DECT, the advantages of DECT in soft tissues are lost over SECT. In bones however, the reduction in range uncertainties is found to be between 0.5 and 0.9 mm for bone thicknesses ranging from 1 to 5 cm, respectively. CONCLUSION: DECT has a clear potential to improve proton beam range predictions over SECT in proton therapy. However, in the current state high levels of noise remain problematic for DECT characterization methods and do not allow getting the full benefits of this technology. Future work should focus on adapting DECT methods to noise and investigate methods based on raw‐data to reduce CT artifacts

Regulation of Motor Function and Behavior by Atypical Chemokine Receptor 1

The final publication is available at Springer via http://dx.doi.org/10.1007/s10519-014-9665-7Atypical Chemokine Receptor 1 (ACKR1), previously known as the Duffy Antigen Receptor for Chemokines, stands out among chemokine receptors for its high selective expression on Purkinje cells of the cerebellum, consistent with the ability of ACKR1 ligands to activate Purkinje cells in vitro. Nevertheless, evidence for ACKR1 regulation of brain function in vivo has been lacking. Here we demonstrate that Ackr1−/− mice have markedly impaired balance and ataxia when placed on a rotating rod and increased tremor when injected with harmaline, a drug that induces whole-body tremor by activating Purkinje cells. Ackr1−/− mice also exhibited impaired exploratory behavior, increased anxiety-like behavior and frequent episodes of marked hypoactivity under low-stress conditions. The behavioral phenotype of Ackr1−/− mice was the opposite of the phenotype occurring in mice with cerebellar degeneration and the defects persisted when Ackr1 was deficient only on non-hematopoietic cells. We conclude that normal motor function and behavior depend in part on negative regulation of Purkinje cell activity by Ackr1

Birational cobordism invariance of uniruled symplectic manifolds

A symplectic manifold $(M,\omega)$ is called {\em (symplectically) uniruled} if there is a nonzero genus zero GW invariant involving a point constraint. We prove that symplectic uniruledness is invariant under symplectic blow-up and blow-down. This theorem follows from a general Relative/Absolute correspondence for a symplectic manifold together with a symplectic submanifold. A direct consequence is that symplectic uniruledness is a symplectic birational invariant. Here we use Guillemin and Sternberg's notion of cobordism as the symplectic analogue of the birational equivalence.Comment: To appear in Invent. Mat

Animal tissue-based quantitative comparison of dual-energy CT to SPR conversion methods using high-resolution gel dosimetry

Dual-energy computed tomography (DECT) has been shown to allow for more accurate ion therapy treatment planning by improving the estimation of tissue stopping power ratio (SPR) relative to water, among other tissue properties. In this study, we measured and compared the accuracy of SPR values derived using both dual- and single-energy CT (SECT) based on different published conversion algorithms. For this purpose, a phantom setup containing either fresh animal soft tissue samples (beef, pork) and a water reference or tissue equivalent plastic materials was designed and irradiated in a clinical proton therapy facility. Dosimetric polymer gel was positioned downstream of the samples to obtain a three-dimensional proton range distribution with high spatial resolution. The mean proton range in gel for each tissue relative to the water sample was converted to a SPR value. Additionally, the homogeneous samples were probed with a variable water column encompassed by two ionization chambers to benchmark the SPR accuracy of the gel dosimetry. The SPR values measured with both methods were consistent with a mean deviation of 0.2%, but the gel dosimetry captured range variations up to 5 mm within individual samples. Across all fresh tissue samples the SECT approach yielded significantly greater mean absolute deviations from the SPR deduced using gel range measurements, with an average difference of 1.2%, compared to just 0.3% for the most accurate DECT-based algorithm. These results show a significant advantage of DECT over SECT for stopping power prediction in a realistic setting, and for the first time allow to compare a large set of methods under the same conditions