Compositional Chain-of-Thought Prompting for Large Multimodal Models

The combination of strong visual backbones and Large Language Model (LLM) reasoning has led to Large Multimodal Models (LMMs) becoming the current standard for a wide range of vision and language (VL) tasks. However, recent research has shown that even the most advanced LMMs still struggle to capture aspects of compositional visual reasoning, such as attributes and relationships between objects. One solution is to utilize scene graphs (SGs)--a formalization of objects and their relations and attributes that has been extensively used as a bridge between the visual and textual domains. Yet, scene graph data requires scene graph annotations, which are expensive to collect and thus not easily scalable. Moreover, finetuning an LMM based on SG data can lead to catastrophic forgetting of the pretraining objective. To overcome this, inspired by chain-of-thought methods, we propose Compositional Chain-of-Thought (CCoT), a novel zero-shot Chain-of-Thought prompting method that utilizes SG representations in order to extract compositional knowledge from an LMM. Specifically, we first generate an SG using the LMM, and then use that SG in the prompt to produce a response. Through extensive experiments, we find that the proposed CCoT approach not only improves LMM performance on several vision and language VL compositional benchmarks but also improves the performance of several popular LMMs on general multimodal benchmarks, without the need for fine-tuning or annotated ground-truth SGs. Code: https://github.com/chancharikmitra/CCo

Hybrid-PIC Modeling of a High-Voltage, High-Specific-Impulse Hall Thruster

The primary life-limiting mechanism of Hall thrusters is the sputter erosion of the discharge channel walls by high-energy propellant ions. Because of the difficulty involved in characterizing this erosion experimentally, many past efforts have focused on numerical modeling to predict erosion rates and thruster lifespan, but those analyses were limited to Hall thrusters operating in the 200-400V discharge voltage range. Thrusters operating at higher discharge voltages (V(sub d) >= 500 V) present an erosion environment that may differ greatly from that of the lower-voltage thrusters modeled in the past. In this work, HPHall, a well-established hybrid-PIC code, is used to simulate NASA's High-Voltage Hall Accelerator (HiVHAc) at discharge voltages of 300, 400, and 500V as a first step towards modeling the discharge channel erosion. It is found that the model accurately predicts the thruster performance at all operating conditions to within 6%. The model predicts a normalized plasma potential profile that is consistent between all three operating points, with the acceleration zone appearing in the same approximate location. The expected trend of increasing electron temperature with increasing discharge voltage is observed. An analysis of the discharge current oscillations shows that the model predicts oscillations that are much greater in amplitude than those measured experimentally at all operating points, suggesting that the differences in oscillation amplitude are not strongly associated with discharge voltage

Hybrid-PIC modeling of a high-voltage, high-specific-impulse Hall thruster

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/106478/1/AIAA2013-3887.pd

Seeing the World through Your Eyes

The reflective nature of the human eye is an underappreciated source of information about what the world around us looks like. By imaging the eyes of a moving person, we can collect multiple views of a scene outside the camera's direct line of sight through the reflections in the eyes. In this paper, we reconstruct a 3D scene beyond the camera's line of sight using portrait images containing eye reflections. This task is challenging due to 1) the difficulty of accurately estimating eye poses and 2) the entangled appearance of the eye iris and the scene reflections. Our method jointly refines the cornea poses, the radiance field depicting the scene, and the observer's eye iris texture. We further propose a simple regularization prior on the iris texture pattern to improve reconstruction quality. Through various experiments on synthetic and real-world captures featuring people with varied eye colors, we demonstrate the feasibility of our approach to recover 3D scenes using eye reflections.Comment: CVPR 2024. First two authors contributed equally. Project page: https://world-from-eyes.github.io

Landscape Surrogate: Learning Decision Losses for Mathematical Optimization Under Partial Information

Recent works in learning-integrated optimization have shown promise in settings where the optimization problem is only partially observed or where general-purpose optimizers perform poorly without expert tuning. By learning an optimizer $\mathbf{g}$ to tackle these challenging problems with $f$ as the objective, the optimization process can be substantially accelerated by leveraging past experience. The optimizer can be trained with supervision from known optimal solutions or implicitly by optimizing the compound function $f\circ \mathbf{g}$. The implicit approach may not require optimal solutions as labels and is capable of handling problem uncertainty; however, it is slow to train and deploy due to frequent calls to optimizer $\mathbf{g}$ during both training and testing. The training is further challenged by sparse gradients of $\mathbf{g}$, especially for combinatorial solvers. To address these challenges, we propose using a smooth and learnable Landscape Surrogate $M$ as a replacement for $f\circ \mathbf{g}$. This surrogate, learnable by neural networks, can be computed faster than the solver $\mathbf{g}$, provides dense and smooth gradients during training, can generalize to unseen optimization problems, and is efficiently learned via alternating optimization. We test our approach on both synthetic problems, including shortest path and multidimensional knapsack, and real-world problems such as portfolio optimization, achieving comparable or superior objective values compared to state-of-the-art baselines while reducing the number of calls to $\mathbf{g}$. Notably, our approach outperforms existing methods for computationally expensive high-dimensional problems

The Performance of Syngas-Fueled SOFCs Predicted by a Reduced Order Model (ROM): Temperature and Fuel Composition Effects

An electrochemical reduced order model (ROM) has been developed in this study to simulate the performance of syngas-fueled anode-supported SOFCs with coupled bulk chemical reactions and multi-species gas diffusion in the electrodes. Experimental V-I curves with syngas fuel were used to validate the model to ensure its high fidelity. The model was used to investigate the effects of fuel composition and temperature on the electrochemical performance of the cell, chemical reaction rate and concentration distributions of gaseous species across the anode. The results show that H2 electro-oxidation dominates the overall cell performance, and that CO contributes to the performance indirectly via water gas shift (WGS) reaction, especially at low CO:H2 ratio and low current densities. Increasing the temperature enhances the performance of syngas-fueled SOFCs by increasing the rates of total electrochemical oxidation and the WGS reaction. The present work provides fundamental knowledge and framework for future performance simulations of large-scale and more complex syngas-fueled SOFC systems. Solid oxide fuel cells (SOFCs) offer high efficiency pathways to producing electricity from fuels.1–4 Systems are being developed for a variety of applications, operating on a range of fuels from hydrogen to natural gas to syngas.5–9 Computational models that can accurately describe the gas phase reactions, electrochemistry, and the heat and mass transfer within SOFC cells and modules are an invaluable tool for the design of efficient and cost-effective systems.10–17 Models described in the open literature can be generally grouped into three categories - classical semi-empirical models, full-order models (FOM) and reduced-order models (ROM). The categories differ in how they manage the trade-off between accuracy and computational effort. Semi-empirical models estimate the cell voltage by subtracting the overpotentials resulting from activation, ohmic and concentration polarizations from the Nernst potential.18 Three major simplifications are typically used: 1) the Butler-Volmer equations are approximated by either linear or Tafel equations; 2) the concentration overpotential is correlated to gas diffusion using empirical or semi-empirical relationships; and 3) the model ignores cell geometry. These approximations simplify the analysis, but have several drawbacks: 1) coupling between the gas diffusion and activation/concentration losses is ignored, which is particularly problematic for systems using syngas fuel; 2) the exchange current density at the triple-phase-boundary (TPB) is more complicated for multi-step elementary reactions, also problematic for systems in which chemical reactions such as reforming or water-gas-shift are occurring; and 3) the limiting current density is obtained by an empirical relation, which means the effects of cell and stack design are not always captured accurately. Full order models were first introduced in the 1990s.19 These approaches include all the relevant physical and chemical processes in the cell, including gas diffusion through the porous electrodes, mass and momentum conservation in the channels, charge transport within electrodes and the electrolyte as described by Ohm\u27s law, and charge-transfer kinetics as described by the Butler-Volmer equation. Early versions described the H2 electro-oxidation reaction using global reactions by a finite volume method.19,20 More recent FOMs have incorporated the microscale elementary reactions occurring near TPBs with cell performance.21–23 FOM approaches offer the highest resolution and accuracy (short of complete 3-D models), but are more computationally expensive than semi-empirical approaches, which could be an issue when applied to 3D SOFC stack simulations. Reduced order models attempt to retain much of the accuracy of FOMs while reducing the computational burden. This is accomplished in several ways. A common approach is to simplify the physics, such as the gas diffusion or the electrochemical reactions. Specifically, the diffusion could be simplified to a single dimension, typically in the flow direction24–27 or anode-thickness direction.28–31 This captures some of the physics due to 1D gas diffusion and heterogeneous reactions at the solid/gas interfaces, while significantly reducing the computational effort required. Another more widely used approach to reducing the model order is by projection-based mathematical reduction, in which a set of data is mapped into sub-set with certain accuracy. One interesting ROM developed in this way by PNNL32 uses a sub-model to predict the performance and response of a SOFC stack. The sub-model was constructed using a simple empirical relationship generated from sampling a limited number of input parameters, ranking of input parameters, constructing relations between inputs and outputs, and studying sensitivity of inputs in different regions. Such an approach can be used to rapidly explore performance under specific scenarios to aid in the design process. Here, we use the first approach to developing ROMs, but instead of simplifying the diffusion procedure, we lowered the order of model by reducing the electronic/ionic charge transfer and the electrochemical reactions from the 3D electrode domains to the 2D electrode/electrolyte interface. Meanwhile, the electrolyte is treated as an interface between anode and cathode by a pure ionic resistor. Since the concentration of gas species varies significantly along the direction of gas flow and thickness, the 3D diffusion feature in the electrode domains is kept in this study for further development of stack model. ROMs have been used successfully to explore the competition between different physical processes. Friedrich et al.33,34 developed a ROM that includes detailed H2-oxidation elementary reactions for coupled charge-transfer and surface chemistry in the anode, and gas diffusion in the flow direction and cell thickness direction were decoupled and calculated separately. Another ROM developed by Campanari et al.35 simulated the combined electrochemical oxidation of CO and H2 (relevant to this work) with the assumption that exchange current density for CO oxidation is 0.4 times the H2 oxidation without validation and the diffusion through the thickness was significantly simplified. Further progress can be made in several areas to increase the utility of ROMs, particularly for hydrocarbon or syngas fuels. First, additional experimental validation is needed to further demonstrate the usefulness of ROMs. Second, ROMs can be extended to explore the competition between direct electrochemical oxidation of fuel and indirect oxidation of fuels through chemical conversion to form hydrogen. This second issue is of particular interest in practical systems where the relative importance of internal reforming or water-gas-shift reactions can vary through the stack. In this paper, we address these issues by developing a ROM for anode-supported SOFCs. We begin with a derivation of the ROM, and validate it using experimental data from the literature. We then explore the impact of syngas composition and temperature on the relative importance of direct and indirect oxidation modes. This paper is the first of a series of papers, aiming to lay the ground for systematically investigating the effects of pressure, temperature-field coupling and flow patterns on the performance of commercial-size planar SOFC stacks operated on syngas fuel

Advanced process monitoring and feedback control to enhance cell culture process production and robustness

It is common practice in biotherapeutic manufacturing to define a fixed-volume feed strategy for nutrient feeds based on historical cell demand. However, once the feed volumes are defined, they are inflexible to batch-to-batch variations in cell growth and physiology and can lead to inconsistent productivity and product quality. In an effort to control critical quality attributes and to apply Process Analytical Technology (PAT), we demonstrated three different and novel approaches for implementing online monitoring and feedback control to improve the performance and/or robustness of cell culture processes. First, we describe the first reported fed-batch process utilizing online amino acid measurements (glutamate) to trigger automatic feedback control delivering complex nutrient feed. More importantly, the resulting feed strategy was translated into a manufacturing-friendly manual feed strategy without impact on product quality. Second, we increase the complexity of the control strategy by designing multiple feedback control loops for all feed solutions based on varied inputs (bio-capacitance for cell mass, Nova-Flex for glucose), resulting in a truly fully automatic cell culture process. We then demonstrate the utility of the feedback control system to rescue a batch without manual intervention by automatically adjusting the feed in response to an excursion that was intentionally introduced. Finally, we describe the implementation of a new online monitoring instrument in combination with a logic control module to simultaneously monitor and control glucose and lactate with high frequency, resulting in cell culture process improvement. Together, the three cases presented here illustrate an advanced process control toolbox which can be readily applied to various cell lines, media systems, and processes to significantly increase productivity and improve robustness in manufacturing, with the goal of ensuring process performance and product quality consistenc

Redox and Peroxidase Activities of the Hemoglobin Superfamily: Relevance to Health and Disease

Significance: Erythrocyte hemoglobin (Hb) and myocyte myoglobin, although primarily oxygen-carrying proteins, have the capacity to do redox chemistry. Such redox activity in the wider family of globins now appears to have important associations with the mechanisms of cell stress response. In turn, an understanding of such mechanisms in vivo may have a potential in the understanding of cancer therapy resistance and neurodegenerative disorders such as Alzheimer’s. Recent Advances: There has been an enhanced understanding of the redox chemistry of the globin superfamily in recent years, leading to advances in development of Hb-based blood substitutes and in hypotheses relating to specific disease mechanisms. Neuroglobin (Ngb) and cytoglobin (Cygb) have been linked to cell protection mechanisms against hypoxia and oxidative stress, with implications in the onset and progression of neurodegenerative diseases for Ngb and cancer for Cygb. Critical Issues: Despite advances in the understanding of redox chemistry of globins, the physiological roles of many of these proteins still remain ambiguous at best. Confusion over potential physiological roles may relate to multifunctional roles for globins, which may be modulated by surface-exposed cysteine pairs in some globins. Such roles may be critical in deciphering the relationships of these globins in human diseases. Future Directions: Further studies are required to connect the considerable knowledge on the mechanisms of globin redox chemistry in vitro with the physiological and pathological roles of globins in vivo. In doing so, new therapies for neurodegenerative disorders and cancer therapy resistance may be targeted