Roche Lobe Shapes for testing MOND-like Modified Gravity

Dark Matter (DM) theories and mass-tracing-light theories like MOND are by construction nearly degenerate on galactic scales, but not when it comes to the predicted shapes of Roche Lobes of a two-body system (e.g., a globular cluster orbiting a host galaxy). We show that the flattening of the Roche lobe is sensitive to the function mu(g) in modification of the law of gravity. We generalise the analytical results obtained in the deep-MOND limit by Zhao (2005, astro-ph/0511713 and astro-ph/0512425), and consider a binary in the framework of a MOND-like gravity modification function mu(g) or a general non-Keplerian gravity g \propto R^-\zeta. We give analytical expressions for the inner Lagrange point and Robe lobe axis ratios. The Roche lobe volume is proven to scale linearly with the true mass ratio, which applies to any mu(g), hence mass-tracing light models would overpredict the Roche lobe of a DM-poor globular cluster in a DM-rich host galaxy, and underpredict the size of a DM-richer dwarf satellite. The lobes are squashed with the flattening ~ 0.4 in the strong gravity and ~ 0.6 in the weak gravity; a precise measurement of the flattening could be used to verify the anisotropic dilation effect which is generic to MOND-like gravity. We generalise these results for extended mass distribution, and compare predicted Roche radii in different gravity theories with limiting radii of observed globular clusters and dwarf galaxy satellites.Comment: 11p, 7 figs, accepted for Astronomy and Astrophysic

InstructSeq: Unifying Vision Tasks with Instruction-conditioned Multi-modal Sequence Generation

Empowering models to dynamically accomplish tasks specified through natural language instructions represents a promising path toward more capable and general artificial intelligence. In this work, we introduce InstructSeq, an instruction-conditioned multi-modal modeling framework that unifies diverse vision tasks through flexible natural language control and handling of both visual and textual data. InstructSeq employs a multimodal transformer architecture encompassing visual, language, and sequential modeling. We utilize a visual encoder to extract image features and a text encoder to encode instructions. An autoregressive transformer fuses the representations and generates sequential task outputs. By training with LLM-generated natural language instructions, InstructSeq acquires a strong comprehension of free-form instructions for specifying visual tasks. This provides an intuitive interface for directing capabilities using flexible natural instructions. Without any task-specific tuning, InstructSeq achieves compelling performance on semantic segmentation, referring expression segmentation/comprehension, and image captioning. The flexible control and multi-task unification empower the model with more human-like versatility and generalizability for computer vision. The code will be released soon at https://github.com/rongyaofang/InstructSeq.Comment: 10 page

The Relaxin Gene Knockout Mouse: A Model of Progressive Scleroderma

Relaxin is a peptide hormone with anti-fibrotic properties. To investigate the long-term effects of relaxin deficiency on the ageing skin, we compared structural changes in the skin of ageing relaxin-deficient (RLX-/-) and normal (RLX+/+) mice, by biochemical, histological, and magnetic resonance imaging analyses. Skin biopsies from RLX+/+ and RLX-/- mice were obtained at different ages and analyzed for changes in collagen expression and distribution. We demonstrated an age-related progression of dermal fibrosis and thickening in male and female RLX-/- mice, associated with marked increases in types I and III collagen. The increased collagen was observed primarily in the dermis of RLX-/- mice by 1 mo of age, and eventually superseded the hypodermal layer. Additionally, fibroblasts from the dermis of RLX-/- mice were shown to produce increased collagen in vitro. Recombinant human gene-2 (H2) relaxin treatment of RLX-/- mice resulted in the complete reversal of dermal fibrosis, when applied to the early onset of disease, but was ineffective when applied to more established stages of dermal scarring. These combined findings demonstrate that relaxin provides a means to regulate excessive collagen deposition in disease states characterized by dermal fibrosis and with our previously published work demonstrate the relaxin-null mouse as a model of progressive scleroderma

Galaxy Bulges As Tests of CDM vs MOND in Strong Gravity

The tight correlation between galaxy bulges and their central black hole masses likely emerges in a phase of rapid collapse and starburst at high redshift, due to the balance of gravity on gas with the feedback force from starbursts and the wind from the black hole; the average gravity on per unit mass of gas is ~ 2 x 10^-10 m/sec^2 during the star burst phase. This level of gravity could come from the real r^{-1} cusps of Cold Dark Matter (CDM) halos, but the predicted gravity would have a large scatter due to dependence on cosmological parameters and formation histories. Better agreement is found with the gravity from the scalar field in some co-variant versions of MOND, which can create the mirage of a Newtonian effective dark halo of density Pi r^{-1} near the center, where the characteristic surface density Pi=130alpha^{-1} Msun pc^{-2} and alpha is a fundamental constant of order unity fixed by the Lagrangian of the co-variant theory if neglecting environmental effects. We show with a toy analytical model and a hydrodynamical simulation that a constant background gravity due to MOND/TeVeS scalar field implies a critical pressure synchronizing starbursts and the formation of galaxy bulges and ellipticals. A universal threshold for the formation of the brightest regions of galaxies in a MONDian universe suggests that the central BHs, bulges and ellipticals would respect tight correlations like the M_{bulge}-M_{BH}-sigma relations. In general MOND tends to produce tight correlations in galaxy properties because its effective halo has less freedom and scatter than CDM halos.Comment: 30p, 6 figs, expanded. Accpeted for Ap

A proportional hazards model for time-to-event data with epidemiological bias

In hepatitis C virus (HCV) epidemiological studies, the estimation of progression to cirrhosis and prognostic effects of associated risk factors is of particular importance when projecting national disease burden. However, the progression estimates obtained from conventional methods could be distorted due to a referral bias (Fu et al., 2007). In recent years, several approaches have been developed to handle this epidemiological bias in analyzing time-to-event data. This paper proposes a new estimation approach for this problem under a semiparametric proportional hazards framework. The new method uses a martingale approach based on the mean rate function, rather than the traditional hazard rate function, and develops an iterative algorithm to estimate the Cox regression parameter and baseline hazard rate simultaneously. The consistency and asymptotic properties of the proposed estimators are derived theoretically and evaluated via simulation studies. The new method is also applied to a real HCV cohort study