New Clarification About Observation Billing May Improve Care for Behavioral Health Patients

Emergency Physicians provide ongoing care to psychiatric patients beyond the confines of a standard emergency room visit.  Often, when we identify patients who need specialty psychiatric care, patients board in the emergency department awaiting acceptance and transfer to an outside facility.  Even in cases where it has taken multiple days to complete the transfer, it has been unclear how to properly obtain reimbursement for this care.  We discuss a new coding clarification that may provide a pathway to improve part of this situation

Catholicity and the university: in search of the whole amidst fragments

Thesis advisor: Brian DunkleThesis (STL) — Boston College, 2019.Submitted to: Boston College. School of Theology and Ministry.Discipline: Sacred Theology

Foreground-Background Separation through Concept Distillation from Generative Image Foundation Models

Curating datasets for object segmentation is a difficult task. With the advent of large-scale pre-trained generative models, conditional image generation has been given a significant boost in result quality and ease of use. In this paper, we present a novel method that enables the generation of general foreground-background segmentation models from simple textual descriptions, without requiring segmentation labels. We leverage and explore pre-trained latent diffusion models, to automatically generate weak segmentation masks for concepts and objects. The masks are then used to fine-tune the diffusion model on an inpainting task, which enables fine-grained removal of the object, while at the same time providing a synthetic foreground and background dataset. We demonstrate that using this method beats previous methods in both discriminative and generative performance and closes the gap with fully supervised training while requiring no pixel-wise object labels. We show results on the task of segmenting four different objects (humans, dogs, cars, birds) and a use case scenario in medical image analysis. The code is available at https://github.com/MischaD/fobadiffusion.Comment: Accepted at ICCV202

Auditor style and common disclosure deficiencies : evidence from SEC comment letters

Prior literature documents that auditor style increases financial statement comparability, implicitly making financial statements more useful to outsiders. Auditor style results from policies and procedures that centralize decision-making within the audit firm. A potential hazard of centralized decision-making is the propagation of decision errors throughout the entity. I predict, and find evidence to suggest, that auditor style is associated with a set of common disclosure deficiencies among clientele as measured by receipt of similar SEC comment letters. Clients also converge in both style and disclosure deficiencies as auditor tenure increases. Further, after changing auditors, clients appear to assume the style and disclosure deficiencies of the subsequent auditor. These results provide the first evidence that auditor style, while potentially a net benefit to users of the financial statements, has potential costs as well.Includes biblographical reference

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

The 2dF Galaxy Redshift Survey: the power spectrum and the matter content of the Universe

The 2dF Galaxy Redshift Survey has now measured in excess of 160000 galaxy redshifts. This paper presents the power spectrum of the galaxy distribution, calculated using a direct Fourier transform based technique. We argue that, within the k-space region 0.02<~k<~0.15hMpc-1, the shape of this spectrum should be close to that of the linear density perturbations convolved with the window function of the survey. This window function and its convolving effect on the power spectrum estimate are analysed in detail. By convolving model spectra, we are able to fit the power-spectrum data and provide a measure of the matter content of the Universe. Our results show that models containing baryon oscillations are mildly preferred over featureless power spectra. Analysis of the data yields 68 per cent confidence limits on the total matter density times the Hubble parameter Ωmh=0.20+/-0.03, and the baryon fraction Ωb/Ωm=0.15+/-0.07, assuming scale-invariant primordial fluctuations

The 2dF Galaxy Redshift Survey: a targeted study of catalogued clusters of galaxies

We have carried out a study of known clusters within the 2dF Galaxy Redshift Survey (2dFGRS) observed areas and have identified 431 Abell, 173APM and 343 EDCC clusters. Precise redshifts, velocity dispersions and new centroids have been measured for the majority of these objects, and this information is used to study the completeness of these catalogues, the level of contamination from foreground and background structures along the cluster's line of sight, the space density of the clusters as a function of redshift, and their velocity dispersion distributions. We find that the Abell and EDCC catalogues are contaminated at the level of about 10 per cent, whereas the APM catalogue suffers only 5 per cent contamination. If we use the original catalogue centroids, the level of contamination rises to approximately 15 per cent for the Abell and EDCC catalogues, showing that the presence of foreground and background groups may alter the richness of clusters in these catalogues. There is a deficiency of clusters at z~0.05 that may correspond to a large underdensity in the Southern hemisphere. From the cumulative distribution of velocity dispersions for these clusters, we derive a space density of σ>1000kms-1 clusters of 3.6×10-6h3Mpc-3. This result is used to constrain models for structure formation; our data favour low-density cosmologies, subject to the usual assumptions concerning the shape and normalization of the power spectrum

The 2dF galaxy redshift survey: near-infrared galaxy luminosity functions

We combine the Two Micron All Sky Survey (2MASS) Extended Source Catalogue and the 2dF Galaxy Redshift Survey to produce an infrared selected galaxy catalogue with 17173 measured redshifts. We use this extensive data set to estimate the galaxy luminosity functions in the J- and KS-bands. The luminosity functions are fairly well fitted by Schechter functions with parameters MJ*-5logh=-22.36+/-0.02, αJ=-0.93+/-0.04, ΦJ*=0.0104+/-0.0016h3Mpc-3 in the J-band and MKS*-5logh=-23.44+/-0.03, αKS=-0.96+/-0.05, ΦKS*=0.0108+/-0.0016h3Mpc-3 in the KS-band (2MASS Kron magnitudes). These parameters are derived assuming a cosmological model with Ω0=0.3 and Λ0=0.7. With data sets of this size, systematic rather than random errors are the dominant source of uncertainty in the determination of the luminosity function. We carry out a careful investigation of possible systematic effects in our data. The surface brightness distribution of the sample shows no evidence that significant numbers of low surface brightness or compact galaxies are missed by the survey. We estimate the present-day distributions of bJ-KS and J-KS colours as a function of the absolute magnitude and use models of the galaxy stellar populations, constrained by the observed optical and infrared colours, to infer the galaxy stellar mass function. Integrated over all galaxy masses, this yields a total mass fraction in stars (in units of the critical mass density) of Ωstarsh=(1.6+/-0.24)×10-3 for a Kennicutt initial mass function (IMF) and Ωstarsh=(2.9+/-0.43)×10-3 for a Salpeter IMF. These values are consistent with those inferred from observational estimates of the total star formation history of the Universe provided that dust extinction corrections are modest