Instant Photorealistic Style Transfer: A Lightweight and Adaptive Approach

In this paper, we propose an Instant Photorealistic Style Transfer (IPST) approach, designed to achieve instant photorealistic style transfer on super-resolution inputs without the need for pre-training on pair-wise datasets or imposing extra constraints. Our method utilizes a lightweight StyleNet to enable style transfer from a style image to a content image while preserving non-color information. To further enhance the style transfer process, we introduce an instance-adaptive optimization to prioritize the photorealism of outputs and accelerate the convergence of the style network, leading to a rapid training completion within seconds. Moreover, IPST is well-suited for multi-frame style transfer tasks, as it retains temporal and multi-view consistency of the multi-frame inputs such as video and Neural Radiance Field (NeRF). Experimental results demonstrate that IPST requires less GPU memory usage, offers faster multi-frame transfer speed, and generates photorealistic outputs, making it a promising solution for various photorealistic transfer applications.Comment: 8 pages (reference excluded), 6 figures, 4 table

Tensorized NeuroEvolution of Augmenting Topologies for GPU Acceleration

The NeuroEvolution of Augmenting Topologies (NEAT) algorithm has received considerable recognition in the field of neuroevolution. Its effectiveness is derived from initiating with simple networks and incrementally evolving both their topologies and weights. Although its capability across various challenges is evident, the algorithm's computational efficiency remains an impediment, limiting its scalability potential. In response, this paper introduces a tensorization method for the NEAT algorithm, enabling the transformation of its diverse network topologies and associated operations into uniformly shaped tensors for computation. This advancement facilitates the execution of the NEAT algorithm in a parallelized manner across the entire population. Furthermore, we develop TensorNEAT, a library that implements the tensorized NEAT algorithm and its variants, such as CPPN and HyperNEAT. Building upon JAX, TensorNEAT promotes efficient parallel computations via automated function vectorization and hardware acceleration. Moreover, the TensorNEAT library supports various benchmark environments including Gym, Brax, and gymnax. Through evaluations across a spectrum of robotics control environments in Brax, TensorNEAT achieves up to 500x speedups compared to the existing implementations such as NEAT-Python. Source codes are available at: https://github.com/EMI-Group/tensorneat.Comment: Genetic and Evolutionary Computation Conference (GECCO '24

StepCoder: Improve Code Generation with Reinforcement Learning from Compiler Feedback

The advancement of large language models (LLMs) has significantly propelled the field of code generation. Previous work integrated reinforcement learning (RL) with compiler feedback for exploring the output space of LLMs to enhance code generation quality. However, the lengthy code generated by LLMs in response to complex human requirements makes RL exploration a challenge. Also, since the unit tests may not cover the complicated code, optimizing LLMs by using these unexecuted code snippets is ineffective. To tackle these challenges, we introduce StepCoder, a novel RL framework for code generation, consisting of two main components: CCCS addresses the exploration challenge by breaking the long sequences code generation task into a Curriculum of Code Completion Subtasks, while FGO only optimizes the model by masking the unexecuted code segments to provide Fine-Grained Optimization. In addition, we furthermore construct the APPS+ dataset for RL training, which is manually verified to ensure the correctness of unit tests. Experimental results show that our method improves the ability to explore the output space and outperforms state-of-the-art approaches in corresponding benchmarks. Our dataset APPS+ and StepCoder are available online.Comment: 13 pages, 5 figure

TIGIT Is the Central Player in T-Cell Suppression Associated With CAR T-Cell Relapse in Mantle Cell Lymphoma

BACKGROUND: Chimeric antigen receptor (CAR) T-cell therapy using brexucabtagene autoleucel (BA) induces remission in many patients with mantle cell lymphoma (MCL), and BA is the only CAR T-cell therapy approved by the FDA for MCL. However, development of relapses to BA is recognized with poor patient outcomes. Multiple CAR T-cell therapies have been approved for other lymphomas and the resistance mechanisms have been investigated. However, the mechanisms underlying BA relapse in MCL have not been investigated and whether any previously reported resistance mechanisms apply to BA-relapsed patients with MCL is unknown. METHODS: To interrogate BA resistance mechanisms in MCL, we performed single-cell RNA sequencing on 39 longitudinally collected samples from 15 BA-treated patients, and multiplex cytokine profiling on 80 serial samples from 20 patients. RESULTS: We demonstrate that after BA relapse, the proportion of T cells, especially cytotoxic T cells (CTLs), decreased among non-tumor cells, while the proportion of myeloid cells correspondingly increased. TIGIT, LAG3, and CD96 were the predominant checkpoint molecules expressed on exhausted T cells and CTLs; only TIGIT was significantly increased after relapse. CTLs expanded during remission, and then contracted during relapse with upregulated TIGIT expression. Tumor cells also acquired TIGIT expression after relapse, leading to the enhanced interaction of tumor cell TIGIT with monocyte CD155/PVR. In myeloid cells, post-relapse HLA-II expression was reduced relative to pretreatment and during remission. Myeloid-derived suppressor cells (MDSCs) were enriched after relapse with elevated expression of activation markers, including CLU (clusterin) and VCAN (versican). Extracellular chemokines (CCL4, CXCL9, CXCL13), soluble checkpoint inhibitors (sPD-L1, sTIM3, s4-1BB), and soluble receptors (sIL-2R, sTNFRII) were decreased during remission but elevated after relapse. CONCLUSIONS: Our data demonstrate that multiple tumor-intrinsic and -extrinsic factors are associated with T-cell suppression and BA relapse. Among these, TIGIT appears to be the central player given its elevated expression after BA relapse in not only CTLs but also MCL cells. The acquisition of TIGIT expression on tumor cells is MCL-specific and has not been reported in other CAR T-treated diseases. Together, our data suggest that co-targeting TIGIT may prevent CAR T relapses and thus promote long-term progression-free survival in MCL patients

Secrets of RLHF in Large Language Models Part II: Reward Modeling

Reinforcement Learning from Human Feedback (RLHF) has become a crucial technology for aligning language models with human values and intentions, enabling models to produce more helpful and harmless responses. Reward models are trained as proxies for human preferences to drive reinforcement learning optimization. While reward models are often considered central to achieving high performance, they face the following challenges in practical applications: (1) Incorrect and ambiguous preference pairs in the dataset may hinder the reward model from accurately capturing human intent. (2) Reward models trained on data from a specific distribution often struggle to generalize to examples outside that distribution and are not suitable for iterative RLHF training. In this report, we attempt to address these two issues. (1) From a data perspective, we propose a method to measure the strength of preferences within the data, based on a voting mechanism of multiple reward models. Experimental results confirm that data with varying preference strengths have different impacts on reward model performance. We introduce a series of novel methods to mitigate the influence of incorrect and ambiguous preferences in the dataset and fully leverage high-quality preference data. (2) From an algorithmic standpoint, we introduce contrastive learning to enhance the ability of reward models to distinguish between chosen and rejected responses, thereby improving model generalization. Furthermore, we employ meta-learning to enable the reward model to maintain the ability to differentiate subtle differences in out-of-distribution samples, and this approach can be utilized for iterative RLHF optimization

METI: Deep Profiling of Tumor Ecosystems by Integrating Cell Morphology and Spatial Transcriptomics

Recent advances in spatial transcriptomics (ST) techniques provide valuable insights into cellular interactions within the tumor microenvironment (TME). However, most analytical tools lack consideration of histological features and rely on matched single-cell RNA sequencing data, limiting their effectiveness in TME studies. To address this, we introduce the Morphology-Enhanced Spatial Transcriptome Analysis Integrator (METI), an end-to-end framework that maps cancer cells and TME components, stratifies cell types and states, and analyzes cell co-localization. By integrating spatial transcriptomics, cell morphology, and curated gene signatures, METI enhances our understanding of the molecular landscape and cellular interactions within the tissue. We evaluate the performance of METI on ST data generated from various tumor tissues, including gastric, lung, and bladder cancers, as well as premalignant tissues. We also conduct a quantitative comparison of METI with existing clustering and cell deconvolution tools, demonstrating METI\u27s robust and consistent performance

An Atlas of Epithelial Cell States and Plasticity in Lung Adenocarcinoma

Understanding the cellular processes that underlie early lung adenocarcinoma (LUAD) development is needed to devise intervention strategies1. Here we studied 246,102 single epithelial cells from 16 early-stage LUADs and 47 matched normal lung samples. Epithelial cells comprised diverse normal and cancer cell states, and diversity among cancer cells was strongly linked to LUAD-specific oncogenic drivers. KRAS mutant cancer cells showed distinct transcriptional features, reduced differentiation and low levels of aneuploidy. Non-malignant areas surrounding human LUAD samples were enriched with alveolar intermediate cells that displayed elevated KRT8 expression (termed KRT8+ alveolar intermediate cells (KACs) here), reduced differentiation, increased plasticity and driver KRAS mutations. Expression profiles of KACs were enriched in lung precancer cells and in LUAD cells and signified poor survival. In mice exposed to tobacco carcinogen, KACs emerged before lung tumours and persisted for months after cessation of carcinogen exposure. Moreover, they acquired Kras mutations and conveyed sensitivity to targeted KRAS inhibition in KAC-enriched organoids derived from alveolar type 2 (AT2) cells. Last, lineage-labelling of AT2 cells or KRT8+ cells following carcinogen exposure showed that KACs are possible intermediates in AT2-to-tumour cell transformation. This study provides new insights into epithelial cell states at the root of LUAD development, and such states could harbour potential targets for prevention or intervention

TSC1/2 Signaling Complex Is Essential for Peripheral Naïve CD8+ T Cell Survival and Homeostasis in Mice

The PI3K-Akt-mTOR pathway plays crucial roles in regulating both innate and adaptive immunity. However, the role of TSC1, a critical negative regulator of mTOR, in peripheral T cell homeostasis remains elusive. With T cell-specific Tsc1 conditional knockout (Tsc1 KO) mice, we found that peripheral naïve CD8+ T cells but not CD4+ T cells were severely reduced. Tsc1 KO naïve CD8+ T cells showed profound survival defect in an adoptive transfer model and in culture with either stimulation of IL-7 or IL-15, despite comparable CD122 and CD127 expression between control and KO CD8+ T cells. IL-7 stimulated phosphorylation of Akt(S473) was diminished in Tsc1 KO naïve CD8+T cells due to hyperactive mTOR-mediated feedback suppression on PI3K-AKT signaling. Furthermore, impaired Foxo1/Foxo3a phosphorylation and increased pro-apoptotic Bim expression in Tsc1 KO naïve CD8+T cells were observed upon stimulation of IL-7. Collectively, our study suggests that TSC1 plays an essential role in regulating peripheral naïve CD8+ T cell homeostasis, possible via an mTOR-Akt-FoxO-Bim signaling pathway

CONTINUOUS CAPACITIVE DEIONIZATION (CDI) PROCESSES TREATING HIGH-CASO4 FEEDS FOR POTENTIAL INDUSTRIAL BRINE MANAGEMENT – IMPACTS, MECHANISMS AND MITIGATIONS

Ph.DDOCTOR OF PHILOSOPHY (FOE

A WEIGHTED INVERSE MINIMUM CUT PROBLEM UNDER THE BOTTLENECK TYPE HAMMING DISTANCE

An inverse optimization problem is defined as follows. Let S denote the set of feasible solutions of an optimization problem P, let c be a specified cost (capacity) vector, and x0 ∈ S. We want to perturb the cost (capacity) vector c to d so that x0 is an optimal solution of P with respect to the cost (capacity) vector d, and to minimize some objective function. In this paper, we consider the weighted inverse minimum cut problem under the bottleneck type Hamming distance. For the general case, we present a combinatorial algorithm that runs in strongly polynomial time.Minimum cut, inverse problem, hamming distance, strongly polynomial algorithm