24 papers · ranked by Valyu relevance
Minshuo Chen, Song Mei, Jianqing Fan, Mengdi Wang
Diffusion models, a powerful and universal generative artificial intelligence technology, have achieved tremendous success and opened up new possibilities in diverse applications. In these applications, diffusion models provide flexible high-dimensional data modeling, and act as a sampler for generating new samples…
Cécile Monthus, Alain Mazzolo
When the unconditioned process is a diffusion living on the half-line x ∈] − ∞, a[ in the presence of an absorbing boundary condition at position x = a, we construct various conditioned processes corresponding to finite or infinite horizon. When the time horizon is finite T < +∞, the conditioning consists in imposing…
Alexander Scheinker
Advanced accelerator-based light sources such as free electron lasers (FEL) accelerate highly relativistic electron beams to generate incredibly short (10s of femtoseconds) coherent flashes of light for dynamic imaging, whose brightness exceeds that of traditional synchrotron-based light sources by orders of magnitude.…
Meenu Ajith, Vince D. Calhoun
The development of diffusion models, such as Glide, DALLE 2, Imagen, and Stable Diffusion, marks a significant advancement in generative AI for image synthesis. In this paper, we introduce a novel framework for synthesizing intrinsic connectivity networks (ICNs) by utilizing the nonlinear capabilities of denoising…
Binze Shi, Jie Liu, Tong Pan, Yi Hao + 5 more
Proteolysis-targeting chimeras (PROTACs) enable targeted protein degradation through ternary complex formation with E3 ubiquitin ligase. However, the rational design of PROTACs remains highly challenging due to limited structure–activity relationship data and the vast conformational diversity of linkers. Existing…
Christian Bayer, John Schoenmakers
In this paper we derive stochastic representations for the finite dimensional distributions of a multidimensional diffusion on a fixed time interval, conditioned on the terminal state. The conditioning can be with respect to a fixed point or more generally with respect to some subset. The representations rely on a…
Haotian Chen, Yiting Shen, Jichun Li, Weizhong Zhao
Fragment-based molecular generation has emerged as a promising paradigm in structure-based drug design (SBDD), deriving effective compounds with advanced properties, including chemical validity, synthetic feasibility, pharmacological relevance, etc. However, existing approaches often struggle with generating molecules…
Alain Mazzolo, Cécile Monthus
We consider two independent identical diffusion processes that annihilate upon meeting in order to study their conditioning with respect to their first-encounter properties. For the case of finite horizon T < +∞, the maximum conditioning consists in imposing the probability P ∗ (x, y, T) that the two particles are…
Authors not listed
Goal: To accurately detect infections in Diabetic Foot Ulcers (DFUs) using photographs taken at the Point of Care (POC). Achieving high performance is critical for preventing complications and amputations, as well as minimizing unnecessary emergency department visits and referrals. Methods: This paper proposes the…
Kangfu Mei, Mauricio Delbracio, Hossein Talebi, Zhengzhong Tu + 2 more
'Vishal M. Patel' 'Peyman Milanfar'] Large generative diffusion models have revolutionized text-to-image generation and offer immense potential for conditional generation tasks such as image enhancement, restoration, editing, and compositing. However, their widespread adoption is hindered by the high computational…
Benjamin Kaufman, Edward C. Williams, Ryan Pederson, Carl Underkoffler + 8 more
Designing a small molecule therapeutic is a challenging multi-parameter optimization problem. Key properties, such as potency, selectivity, bioavailability, and safety must be jointly optimized to deliver an effective clinical candidate. We present COATI-LDM, a novel application of latent diffusion models to the…
Kabir Ramola, Satya N. Majumdar, Grégory Schehr
We study analytically the order and gap statistics of particles at time t for the one dimensional branching Brownian motion, conditioned to have a fixed number of particles at t. The dynamics of the process proceeds in continuous time where at each time step, every particle in the system either diffuses (with diffusion…
Yuichi Itto, Vladimir Aristov
A conditional entropic approach is discussed for nonequilibrium complex systems with a weak correlation between spatiotemporally fluctuating quantities on a large time scale. The weak correlation is found to constitute the fluctuation distribution that maximizes the entropy associated with the conditional fluctuations.…
Xingnan Li, Priyanka Rana, Tuba N Gide, Nurudeen A Adegoke + 3 more
Stain imputation in multiplex immunofluorescence (mIF) imaging addresses the challenge of missing or damaged biomarker channels by reconstructing target biomarker images from a limited set of available stains. This approach offers a faster and more efficient alternative to full-panel staining, enabling detailed…
Junkil Park, Aseem Partap Singh Gill, Seyed Mohamad Moosavi, JIHAN KIM
The success of diffusion models in the field of image processing has propelled the creation of software such as Dall-E, Midjourney and Stable Diffusion, which are tools used for text-to-image generations. Mapping this workflow onto materials discovery, a new diffusion model was developed for the generation of pure…
Vishesh Kumar, J. Shepard Bryan, Alex Rojewski, Carlo Manzo + 1 more
Diffusion coefficients often vary across regions, such as cellular membranes, and quantifying their variation can provide valuable insight into local membrane properties such as composition and stiffness. Toward quantifying diffusion coefficient spatial maps and uncertainties from particle tracks, we use a Bayesian…
Jixin Chen
Predicting the reaction kinetics, that is, how fast a reaction can happen in a solution, is essential information for many processes, such as industrial chemical manufacturing, refining, synthesis and separation of petroleum products, environmental processes in air and water, biological reactions in cells, biosensing…
Francesco Coghi, Juan P. Garrahan
We connect self-interacting processes, that is, stochastic processes where transitions depend on the time spent by a trajectory in each configuration, to Doob conditioning. In this way we demonstrate that Markov processes with constrained occupation measures are realised optimally by selfinteracting dynamics. We use a…
Authors not listed
Classical molecular dynamics (MD) simulation is the most computationally efficient way to model large molecular systems atomistically for extended periods; however, due to fixed forcefield parameters, incorporating on-the-fly quantum reactions is not straightforward. Reactive Step-Based Molecular Dynamics (RSMD) is a…
Jixin Chen
Predicting the reaction kinetics, that is, how fast a reaction can happen in a solution, is essential information for many processes, such as industrial chemical manufacturing, refining, synthesis and separation of petroleum products, environmental processes in air and water, biological reactions in cells, biosensing…
Jixin Chen
Predicting the reaction kinetics, that is, how fast a reaction can happen in a solution, is essential information for many processes, such as industrial chemical manufacturing, refining, synthesis and separation of petroleum products, environmental processes in air and water, biological reactions in cells, biosensing…
Sarthak Patnaik
We know that the diffusion is defined as the movement of molecules under a potential gradient. It is characterized by the diffusion coefficient. Diffusion coefficient determination can be done via various techniques, but in this paper, we have had a broad look at interferometry based optical methods for diffusion…
José Augusto Fontenele Magalhães, Muhammad Fuady Emzir, Francesco Corona
In order to characterise the dynamics of a biochemical system such as the chemostat, we consider a differential description of the evolution of its state under environmental fluctuations. We present solutions to the filtering problem for a chemostat subjected to geometric Brownian motion. Under this modelling…
Shangying Wang, Sara Capponi, Simone Bianco
Biological systems are inherently noisy so that two genetically identical cells in the exact same environment will sometimes behave in dramatically different ways. This imposes a big challenge in building traditional supervised machine learning models that can only predict determined phenotypic variables or categories…