Effects of taxes on the after-tax cost of capital: A simulation approach for multi-period models

According to Lund (2011), when depreciation deductions are taken into account, the after-tax cost of capital depends on the specific taxation rules. The commonly used WACC method fails to recognize the effects of taxation on the risk sharing pattern between the firm and the tax authority and thus is misleading. To find out correct asset betas for different tax systems, one needs not only to “unlever” but also “untax” and “unaverage” the observed equity betas. The thesis extends the one-period model for uncertain tax position of Lund (2011) and multi-period model for certain tax position of Lund (2002) to multi-period models under uncertain tax position. It is then possible to consider the more common tax rule, carryforwards. However, it is impossible to find the analytical solutions and the risk neutral valuation method using Monte Carlo simulation, originally developed for derivative pricing in financial markets, can be applied under reasonable assumptions. It is found that the number of periods can increase the risk but still the after-tax betas are significantly lower than the pre-tax cash flows. Under carryforwards tax rule the betas are even lower compared to no-loss-offset rule. Surprisingly, the interest payment from tax authority does not reduce the betas significantly compared to the no interest payment case. The sensitivity test on tax rate also suggests taxation does have a strong effect on the after-tax betas

Perfect bidder collusion through bribe and request

We study collusion in a second-price auction with two bidders in a dynamic environment. One bidder can make a take-it-or-leave-it collusion proposal, which consists of both an offer and a request of bribes, to the opponent. We show that there always exists a robust equilibrium in which the collusion success probability is one. In the equilibrium, for each type of initiator the expected payoff is generally higher than the counterpart in any robust equilibria of the single-option model (Es\"{o} and Schummer (2004)) and any other separating equilibria in our model

Label-Assemble: Leveraging Multiple Datasets with Partial Labels

The success of deep learning relies heavily on large and diverse datasets with extensive labels, but we often only have access to several small datasets associated with partial labels. In this paper, we start a new initiative, "Label-Assemble", that aims to unleash the full potential of partially labeled data from an assembly of public datasets. Specifically, we introduce a new dynamic adapter to encode different visual tasks, which addresses the challenges of incomparable, heterogeneous, or even conflicting labeling protocols. We also employ pseudo-labeling and consistency constraints to harness data with missing labels and to mitigate the domain gap across datasets. From rigorous evaluations on three natural imaging and six medical imaging tasks, we discover that learning from "negative examples" facilitates both classification and segmentation of classes of interest. This sheds new light on the computer-aided diagnosis of rare diseases and emerging pandemics, wherein "positive examples" are hard to collect, yet "negative examples" are relatively easier to assemble. Apart from exceeding prior arts in the ChestXray benchmark, our model is particularly strong in identifying diseases of minority classes, yielding over 3-point improvement on average. Remarkably, when using existing partial labels, our model performance is on-par with that using full labels, eliminating the need for an additional 40% of annotation costs. Code will be made available at https://github.com/MrGiovanni/LabelAssemble

In-painting Radiography Images for Unsupervised Anomaly Detection

We propose space-aware memory queues for in-painting and detecting anomalies from radiography images (abbreviated as SQUID). Radiography imaging protocols focus on particular body regions, therefore producing images of great similarity and yielding recurrent anatomical structures across patients. To exploit this structured information, our SQUID consists of a new Memory Queue and a novel in-painting block in the feature space. We show that SQUID can taxonomize the ingrained anatomical structures into recurrent patterns; and in the inference, SQUID can identify anomalies (unseen/modified patterns) in the image. SQUID surpasses the state of the art in unsupervised anomaly detection by over 5 points on two chest X-ray benchmark datasets. Additionally, we have created a new dataset (DigitAnatomy), which synthesizes the spatial correlation and consistent shape in chest anatomy. We hope DigitAnatomy can prompt the development, evaluation, and interpretability of anomaly detection methods, particularly for radiography imaging.Comment: Main paper with appendi

4,4′-[(2,7-Dibromo­fluorene-9,9-di­yl)dimethyl­ene]dipyridinium bis­(perchlorate)

In the crystal of the title compound, C25H20Br2N2 2+·2ClO4 −, inter­molecular N—H⋯O and C—H⋯O hydrogen bonds, along with C—H⋯π inter­actions, stabilize the crystal structure

CLIP-Driven Universal Model for Organ Segmentation and Tumor Detection

An increasing number of public datasets have shown a marked impact on automated organ segmentation and tumor detection. However, due to the small size and partially labeled problem of each dataset, as well as a limited investigation of diverse types of tumors, the resulting models are often limited to segmenting specific organs/tumors and ignore the semantics of anatomical structures, nor can they be extended to novel domains. To address these issues, we propose the CLIP-Driven Universal Model, which incorporates text embedding learned from Contrastive Language-Image Pre-training (CLIP) to segmentation models. This CLIP-based label encoding captures anatomical relationships, enabling the model to learn a structured feature embedding and segment 25 organs and 6 types of tumors. The proposed model is developed from an assembly of 14 datasets, using a total of 3,410 CT scans for training and then evaluated on 6,162 external CT scans from 3 additional datasets. We rank first on the Medical Segmentation Decathlon (MSD) public leaderboard and achieve state-of-the-art results on Beyond The Cranial Vault (BTCV). Additionally, the Universal Model is computationally more efficient (6x faster) compared with dataset-specific models, generalized better to CT scans from varying sites, and shows stronger transfer learning performance on novel tasks.Comment: Rank first in Medical Segmentation Decathlon (MSD) Competitio

Microbiome-derived bile acids contribute to elevated antigenic response and bone erosion in rheumatoid arthritis

Rheumatoid arthritis (RA) is a chronic, disabling and incurable autoimmune disease. It has been widely recognized that gut microbial dysbiosis is an important contributor to the pathogenesis of RA, although distinct alterations in microbiota have been associated with this disease. Yet, the metabolites that mediate the impacts of the gut microbiome on RA are less well understood. Here, with microbial profiling and non-targeted metabolomics, we revealed profound yet diverse perturbation of the gut microbiome and metabolome in RA patients in a discovery set. In the Bacteroides-dominated RA patients, differentiation of gut microbiome resulted in distinct bile acid profiles compared to healthy subjects. Predominated Bacteroides species expressing BSH and 7a-HSDH increased, leading to elevated secondary bile acid production in this subgroup of RA patients. Reduced serum fibroblast growth factor-19 and dysregulated bile acids were evidence of impaired farnesoid X receptor-mediated signaling in the patients. This gut microbiota-bile acid axis was correlated to ACPA. The patients from the validation sets demonstrated that ACPA-positive patients have more abundant bacteria expressing BSH and 7a-HSDH but less Clostridium scindens expressing 7a-dehydroxylation enzymes, together with dysregulated microbial bile acid metabolism and more severe bone erosion than ACPA-negative ones. Mediation analyses revealed putative causal relationships between the gut microbiome, bile acids, and ACPA-positive RA, supporting a potential causal effect of Bacteroides species in increasing levels of ACPA and bone erosion mediated via disturbing bile acid metabolism. These results provide insights into the role of gut dysbiosis in RA in a manifestation-specific manner, as well as the functions of bile acids in this gut-joint axis, which may be a potential intervention target for precisely controlling RA conditions.Comment: 38 pages, 6 figure