Pay Attention: Accuracy Versus Interpretability Trade-off in Fine-tuned Diffusion Models

The recent progress of diffusion models in terms of image quality has led to a major shift in research related to generative models. Current approaches often fine-tune pre-trained foundation models using domain-specific text-to-image pairs. This approach is straightforward for X-ray image generation due to the high availability of radiology reports linked to specific images. However, current approaches hardly ever look at attention layers to verify whether the models understand what they are generating. In this paper, we discover an important trade-off between image fidelity and interpretability in generative diffusion models. In particular, we show that fine-tuning text-to-image models with learnable text encoder leads to a lack of interpretability of diffusion models. Finally, we demonstrate the interpretability of diffusion models by showing that keeping the language encoder frozen, enables diffusion models to achieve state-of-the-art phrase grounding performance on certain diseases for a challenging multi-label segmentation task, without any additional training. Code and models will be available at https://github.com/MischaD/chest-distillation

Whole Slide Multiple Instance Learning for Predicting Axillary Lymph Node Metastasis

Breast cancer is a major concern for women's health globally, with axillary lymph node (ALN) metastasis identification being critical for prognosis evaluation and treatment guidance. This paper presents a deep learning (DL) classification pipeline for quantifying clinical information from digital core-needle biopsy (CNB) images, with one step less than existing methods. A publicly available dataset of 1058 patients was used to evaluate the performance of different baseline state-of-the-art (SOTA) DL models in classifying ALN metastatic status based on CNB images. An extensive ablation study of various data augmentation techniques was also conducted. Finally, the manual tumor segmentation and annotation step performed by the pathologists was assessed.Comment: Accepted for MICCAI DEMI Workshop 202

Color Change Effect in an Organic-Inorganic Hybrid Material Based on a Porphyrin Diacid

Porphyrinic materials show a range of interesting and useful optical and electrical properties. The less well-known sub-class of porphyrin diacids has been used in this work to construct an ionic hybrid organic-inorganic material in combination with a halogenidometalate anion. The resulting compound, $[H_6TPyP][BiCl_6]_2$ (1) (TPyP = tetra(4-pyridyl)porphyrin) has been obtained via a facile solution based synthesis in single crystalline form. The material exhibits a broad photoluminescence emission band between 650 and 850 nm at room temperature. Single crystals of $[H_6TPyP][BiCl_6]_2$ show a photocurrent in the fA and a much higher dark current in the nA range. They also display an unexpected reversible color change upon wetting with different liquids. This phenomenon has been investigated with optical spectroscopy, SEM, XPS and NEXAFS techniques, showing that a surface-based structural coloration effect is the source of the color change. This stands in contrast to other materials where structural coloration typically has to be introduced through elaborate, multi-step processes or the use of natural templates. Additionally, it underscores the potential of self-assembly of porphyrinic hybrid compounds in the fabrication of materials with unusual optical properties

Plasma Protein Profiling Reveals Protein Clusters Related to BMI and Insulin Levels in Middle-Aged Overweight Subjects

Biomarkers that allow detection of the onset of disease are of high interest since early detection would allow intervening with lifestyle and nutritional changes before the disease is manifested and pharmacological therapy is required. Our study aimed to improve the phenotypic characterization of overweight but apparently healthy subjects and to identify new candidate profiles for early biomarkers of obesity-related diseases such as cardiovascular disease and type 2 diabetes

Erratum: Sequence data and association statistics from 12,940 type 2 diabetes cases and controls.

This corrects the article DOI: 10.1038/sdata.2017.179

Searching for a Stochastic Background of Gravitational Waves with LIGO

The Laser Interferometer Gravitational-wave Observatory (LIGO) has performed the fourth science run, S4, with significantly improved interferometer sensitivities with respect to previous runs. Using data acquired during this science run, we place a limit on the amplitude of a stochastic background of gravitational waves. For a frequency independent spectrum, the new limit is $\Omega_{\rm GW} < 6.5 \times 10^{-5}$. This is currently the most sensitive result in the frequency range 51-150 Hz, with a factor of 13 improvement over the previous LIGO result. We discuss complementarity of the new result with other constraints on a stochastic background of gravitational waves, and we investigate implications of the new result for different models of this background.Comment: 37 pages, 16 figure

Formation of a Bazooka–Stardust complex is essential for plasma membrane polarity in epithelia

Recruitment of the Crumbs–Stardust polarity complex depends on interactions between Bazooka and the Stardust PDZ domain and is regulated by aPKC-mediated phosphorylation

Attribution of multi-annual to decadal changes in the climate system: The Large Ensemble Single Forcing Model Intercomparison Project (LESFMIP)

Multi-annual to decadal changes in climate are accompanied by changes in extreme events that cause major impacts on society and severe challenges for adaptation. Early warnings of such changes are now potentially possible through operational decadal predictions. However, improved understanding of the causes of regional changes in climate on these timescales is needed both to attribute recent events and to gain further confidence in forecasts. Here we document the Large Ensemble Single Forcing Model Intercomparison Project that will address this need through coordinated model experiments enabling the impacts of different external drivers to be isolated. We highlight the need to account for model errors and propose an attribution approach that exploits differences between models to diagnose the real-world situation and overcomes potential errors in atmospheric circulation changes. The experiments and analysis proposed here will provide substantial improvements to our ability to understand near-term changes in climate and will support the World Climate Research Program Lighthouse Activity on Explaining and Predicting Earth System Change.publishedVersio