Bayesian Hypothesis Testing in Latent Variable Models

Hypothesis testing using Bayes factors (BFs) is known not to be well defined under the improper prior. In the context of latent variable models, an additional problem with BFs is that they are difficult to compute. In this paper, a new Bayesian method, based on decision theory and the EM algorithm, is introduced to test a point hypothesis in latent variable models. The new statistic is a by-product of the Bayesian MCMC output and, hence, easy to compute. It is shown that the new statistic is easy to interpret and appropriately defined under improper priors because the method employs a continuous loss function. The method is illustrated using a one-factor asset pricing model and a stochastic volatility model with jumps

LES of non-Newtonian physiological blood flow in a model of arterial stenosis

Large Eddy Simulation (LES) is performed to study the physiological pulsatile transition-to-turbulent non-Newtonian blood flow through a 3D model of arterial stenosis by using five different blood viscosity models: (i) Power-law, (ii) Carreau, (iii) Quemada, (iv) Cross and (v) modified-Casson. The computational domain has been chosen is a simple channel with a biological type stenosis formed eccentrically on the top wall. The physiological pulsation is generated at the inlet of the model using the first four harmonic series of the physiological pressure pulse (Loudon and Tordesillas [1]). The effects of the various viscosity models are investigated in terms of the global maximum shear rate, post-stenotic re-circulation zone, mean shear stress, mean pressure, and turbulent kinetic energy. We find that the non-Newtonian viscosity models enlarge the length of the post-stenotic re-circulation region by moving the reattachment point of the shear layer separating from the upper wall further downstream. But the turbulent kinetic energy at the immediate post-lip of the stenosis drops due to the effects of the non-Newtonian viscosity. The importance of using LES in modelling the non-Newtonian physiological pulsatile blood flow is also assessed for the different viscosity models in terms of the results of the dynamic subgrid-scale (SGS) stress Smagorinsky model constant, C<sub>s</sub>, and the corresponding SGS normalised viscosity

Evidence for the photospheric excitation of incompressible chromospheric waves

Observing the excitation mechanisms of incompressible transverse waves is vital for determining how energy propagates through the lower solar atmosphere. We aim to show the connection between convectively driven photospheric flows and incompressible chromospheric waves. The observations presented here show the propagation of incompressible motion through the quiet lower solar atmosphere, from the photosphere to the chromosphere. We determine photospheric flow vectors to search for signatures of vortex motion and compare results to photospheric flows present in convective simulations. Further, we search for the chromospheric response to vortex motions. Evidence is presented that suggests incompressible waves can be excited by the vortex motions of a strong magnetic flux concentration in the photosphere. A chromospheric counterpart to the photospheric vortex motion is also observed, presenting itself as a quasi-periodic torsional motion. Fine-scale, fibril structures that emanate from the chromospheric counterpart support transverse waves that are driven by the observed torsional motion. A new technique for obtaining details of transverse waves from time-distance diagrams is presented and the properties of transverse waves (e.g., amplitudes and periods) excited by the chromospheric torsional motion are measured

Investigating Interactions of Biomembranes and Alcohols: A Multiscale Approach

We study the interaction of lipid bilayers with short chain alcohols using molecular dynamics on different length scales. We use detailed atomistic modeling and modeling on the length scale where an alcohol is just an amphiphilic dimer. Our strategy is to calibrate a coarse--grained model against the detailed model at selected state points at low alcohol concentration and then perform a wider range of simulations using the coarse--grained model. We get semiquantitative agreement with experiment for the major observables such as order parameter and area per molecule. We find a linear increase of area per molecule with alcohol concentration. The alcohol molecules in both system descriptions are in close contact with the glycerol backbone. Butanol molecules can enter the bilayer to some extent in contrast to the behavior of shorter alcohols. At very high alcohol concentrations we find clearly increased interdigitation between leaflets.Comment: 14 pages, 6 figure

Holistic Transfer: Towards Non-Disruptive Fine-Tuning with Partial Target Data

We propose a learning problem involving adapting a pre-trained source model to the target domain for classifying all classes that appeared in the source data, using target data that covers only a partial label space. This problem is practical, as it is unrealistic for the target end-users to collect data for all classes prior to adaptation. However, it has received limited attention in the literature. To shed light on this issue, we construct benchmark datasets and conduct extensive experiments to uncover the inherent challenges. We found a dilemma -- on the one hand, adapting to the new target domain is important to claim better performance; on the other hand, we observe that preserving the classification accuracy of classes missing in the target adaptation data is highly challenging, let alone improving them. To tackle this, we identify two key directions: 1) disentangling domain gradients from classification gradients, and 2) preserving class relationships. We present several effective solutions that maintain the accuracy of the missing classes and enhance the overall performance, establishing solid baselines for holistic transfer of pre-trained models with partial target data.Comment: Accepted to NeurIPS 2023 main trac

The Interplay between the Escherichia coli Rho Guanine Nucleotide Exchange Factor Effectors and the Mammalian RhoGEF Inhibitor EspH

Rho GTPases are important regulators of many cellular processes. Subversion of Rho GTPases is a common infection strategy employed by many important human pathogens. Enteropathogenic Escherichia coli and enterohemorrhagic Escherichia coli (EPEC and EHEC) translocate the effector EspH, which inactivates mammalian Rho guanine exchange factors (GEFs), as well as Map, EspT, and EspM2, which, by mimicking mammalian RhoGEFs, activate Rho GTPases. In this study we found that EspH induces focal adhesion disassembly, triggers cell detachment, activates caspase-3, and induces cytotoxicity. EspH-induced cell detachment and caspase-3 activation can be offset by EspT, EspM2, and the Salmonella Cdc42/Rac1 GEF effector SopE, which remain active in the presence of EspH. EPEC and EHEC therefore use a novel strategy of controlling Rho GTPase activity by translocating one effector to inactivate mammalian RhoGEFs, replacing them with bacterial RhoGEFs. This study also expands the functional range of bacterial RhoGEFs to include cell adhesion and survival

Enhancing Information Retrieval Through Concept-Based Language Modeling and Semantic Smoothing.

Traditionally, many information retrieval models assume that terms occur in documents independently. Although these models have already shown good performance, the word independency assumption seems to be unrealistic from a natural language point of view, which considers that terms are related to each other. Therefore, such an assumption leads to two well‐known problems in information retrieval (IR), namely, polysemy, or term mismatch, and synonymy. In language models, these issues have been addressed by considering dependencies such as bigrams, phrasal‐concepts, or word relationships, but such models are estimated using simple n‐grams or concept counting. In this paper, we address polysemy and synonymy mismatch with a concept‐based language modeling approach that combines ontological concepts from external resources with frequently found collocations from the document collection. In addition, the concept‐based model is enriched with subconcepts and semantic relationships through a semantic smoothing technique so as to perform semantic matching. Experiments carried out on TREC collections show that our model achieves significant improvements over a single word‐based model and the Markov Random Field model (using a Markov classifier)