Fine-Tuning InstructPix2Pix for Advanced Image Colorization

This paper presents a novel approach to human image colorization by fine-tuning the InstructPix2Pix model, which integrates a language model (GPT-3) with a text-to-image model (Stable Diffusion). Despite the original InstructPix2Pix model's proficiency in editing images based on textual instructions, it exhibits limitations in the focused domain of colorization. To address this, we fine-tuned the model using the IMDB-WIKI dataset, pairing black-and-white images with a diverse set of colorization prompts generated by ChatGPT. This paper contributes by (1) applying fine-tuning techniques to stable diffusion models specifically for colorization tasks, and (2) employing generative models to create varied conditioning prompts. After finetuning, our model outperforms the original InstructPix2Pix model on multiple metrics quantitatively, and we produce more realistically colored images qualitatively. The code for this project is provided on the GitHub Repository https://github.com/AllenAnZifeng/DeepLearning282

Detailed simulation of LOX/GCH4 flame-vortex interaction in supercritical Taylor-Green flows with machine learning

Accurate and affordable simulation of supercritical reacting flow is of practical importance for developing advanced engine systems for liquid rockets, heavy-duty powertrains, and next-generation gas turbines. In this work, we present detailed numerical simulations of LOX/GCH4 flame-vortex interaction under supercritical conditions. The well-established benchmark configuration of three-dimensional Taylor-Green vortex (TGV) embedded with a diffusion flame is modified for real fluid simulations. Both ideal gas and Peng-Robinson (PR) cubic equation of states are studied to reveal the real fluid effects on the TGV evolution and flame-vortex interaction. The results show intensified flame stretching and quenching arising from the intrinsic large density gradients of real gases, as compared to that for the idea gases. Furthermore, to reduce the computational cost associated with real fluid thermophysical property calculations, a machine learning-based strategy utilising deep neural networks (DNNs) is developed and then assessed using the three-dimensional reactive TGV. Generally good prediction accuracy is achieved by the DNN, meanwhile providing a computational speed-up of 13 times over the convectional approach. The profound physics involved in flame-vortex interaction under supercritical conditions demonstrated by this study provides a benchmark for future related studies, and the machine learning modelling approach proposed is promising for practical high-fidelity simulation of supercritical combustion

CEIL: Generalized Contextual Imitation Learning

In this paper, we present \textbf{C}ont\textbf{E}xtual \textbf{I}mitation \textbf{L}earning~(CEIL), a general and broadly applicable algorithm for imitation learning (IL). Inspired by the formulation of hindsight information matching, we derive CEIL by explicitly learning a hindsight embedding function together with a contextual policy using the hindsight embeddings. To achieve the expert matching objective for IL, we advocate for optimizing a contextual variable such that it biases the contextual policy towards mimicking expert behaviors. Beyond the typical learning from demonstrations (LfD) setting, CEIL is a generalist that can be effectively applied to multiple settings including: 1)~learning from observations (LfO), 2)~offline IL, 3)~cross-domain IL (mismatched experts), and 4) one-shot IL settings. Empirically, we evaluate CEIL on the popular MuJoCo tasks (online) and the D4RL dataset (offline). Compared to prior state-of-the-art baselines, we show that CEIL is more sample-efficient in most online IL tasks and achieves better or competitive performances in offline tasks.Comment: NeurIPS 202

Tracking Ionic Rearrangements and Interpreting Dynamic Volumetric Changes in Two-Dimensional Metal Carbide Supercapacitors: A Molecular Dynamics Simulation Study

We present a molecular dynamics simulation study achieved on two‐dimensional (2D) Ti3C2Tx MXenes in the ionic liquid 1‐ethyl‐3‐methylimidazolium bis(trifluoromethylsulfonyl)imide ([EMIM]+[TFSI]−) electrolyte. Our simulations reproduce the different patterns of volumetric change observed experimentally for both the negative and positive electrodes. The analysis of ionic fluxes and structure rearrangements in the 2D material provide an atomic scale insight into the charge and discharge processes in the layer pore and confirm the existence of two different charge‐storage mechanisms at the negative and positive electrodes. The ionic number variation and the structure rearrangement contribute to the dynamic volumetric changes of both electrodes: negative electrode expansion and positive electrode contraction

Comment to the letter to the editor from Costentin et al. Entitled “Ohmic drop correction in electrochemical techniques. Multiple potential step chrono-amperometry at the test bench”

Comment to the letter to the editor from Costentin et al. Entitled “Ohmic drop correction in electrochemical techniques. Multiple potential step chrono-amperometry at the test bench

MXenes as High-Rate Electrodes for Energy Storage

Two-dimensional transition-metal carbides/nitrides, namely MXenes, are gaining increasing interest in many research fields, including electrochemical energy storage. This short review article emphasizes some recent breakthroughs achieved in MXene chemistry and electrochemical performance when used as high-rate electrodes, especially in nonaqueous electrolytes. Lastly, the current limitations and future perspectives are highlighted

Electrochemical study of pseudocapacitive behavior of Ti3C2Tx MXene material in aqueous electrolytes

In this paper, a multiple potential step chronoamperometry (MUSCA) technique is used to analyze the electrochemical behavior of pseudocapacitive Ti3C2Tx MXene material. MUSCA allows for reconstruction of cyclic voltammograms with considerably lower ohmic drop contribution. As such, the voltammogram current responses from the surface and bulk processes can be precisely deconvoluted at any given potentials, especially at high scan rates. An electrochemical kinetic analysis of the Ti3C2Tx electrode using the calculated voltammograms showed that the surface process dominates at higher scan rate while the bulk process takes over at the low scan rate in both acidic and alkaline electrolytes. By minimizing the ohmic drops, the MUSCA method is presented to be a useful tool to study the natural electrochemical behavior of pseudocapacitive electrodes and to help designing better energy storage systems

Effect of thermal treatment with methylolurea impregnated on poplar wood

The aim of this research was to study the physical and chemical performance of poplar wood treated by chemi-thermal modification. A thermal treatment was used to catalyze the effect of the methylolurea impregnated pre-treatment by curing the poplar wood at 160 °C under atmospheric conditions. The results showed that the thermal treatment played an important role in the chemical and mechanical performance. Such an approach not only can significantly reduce the hygroscopicity, but also can increase the bending strength and compressive strength parallel to grain. The positions of the XRD peaks did not change, which indicated that the structure of cellulose was not noticeably affected by the thermal treatment. The FT-IR analysis showed that the intensity of hydroxyl and carbonyl absorption peaks decreased significantly, which indicated that the NH-CH2-OH of methylolurea reacted with the wood carboxyl (C=O) and hydroxyl (-OH). The TGA showed that the thermal stability of treated wood improved. The SEM showed that the cell wall and vessels were filled with impregnated chemicals

Effects of three different probiotics of Tibetan sheep origin and their complex probiotics on intestinal damage, immunity, and immune signaling pathways of mice infected with Clostridium perfringens type C

Tibetan sheep have unique intestinal microorganisms in their intestines that are adapted to the highland alpine and anoxic environment. To further clarify the probiotic properties of Tibetan sheep-derived probiotics, we selected three Tibetan sheep-derived probiotic isolates (Enterococcus faecalis EF1-mh, Bacillus subtilis BS1-ql, and Lactobacillus sakei LS-ql) to investigate the protective mechanisms of monocultures and their complex strains against Clostridium perfringens type C infection in mice. We established a model of C. perfringens type C infection and used histology and molecular biology to analyze the effects and mechanisms of different probiotic treatments on mice after C. perfringens type C infection. After supplementation with either probiotics or complex probiotics, mice were improved in terms of weight reduction and reduced the levels of cytokines in serum and increased the levels of intestinal sIgA, and supplementation with complex probiotics was effective. In addition, both probiotic and complex probiotic supplementation effectively improved the damage of intestinal mucosa and spleen tissue. The relative expressions of Muc 2, Claudin-1, and Occludin genes were increased in the ileum. The three probiotics and the compound probiotics treatment significantly reduced the relative mRNA expression of toll-like/MyD88/NF-κB/MAPK. The effect of probiotic treatment was similar to the results of engramycin treatment, but the effect of engramycin treatment on intestinal sIgA was not significant. Our results clarify the immunomodulatory effects of the three probiotic isolates and the complex probiotics on C. perfringens infection, and the repair of the intestinal mucosal barrier