Structural Discovery with Partial Ordering Information for Time-Dependent Data with Convergence Guarantees

Structural discovery amongst a set of variables is of interest in both static and dynamic settings. In the presence of lead-lag dependencies in the data, the dynamics of the system can be represented through a structural equation model (SEM) that simultaneously captures the contemporaneous and temporal relationships amongst the variables, with the former encoded through a directed acyclic graph (DAG) for model identification. In many real applications, a partial ordering amongst the nodes of the DAG is available, which makes it either beneficial or imperative to incorporate it as a constraint in the problem formulation. This paper develops an algorithm that can seamlessly incorporate a priori partial ordering information for solving a linear SEM (also known as Structural Vector Autoregression) under a high-dimensional setting. The proposed algorithm is provably convergent to a stationary point, and exhibits competitive performance on both synthetic and real data sets.Comment: Accepted by the Journal of Computational and Graphical Statistic

MMHQA-ICL: Multimodal In-context Learning for Hybrid Question Answering over Text, Tables and Images

In the real world, knowledge often exists in a multimodal and heterogeneous form. Addressing the task of question answering with hybrid data types, including text, tables, and images, is a challenging task (MMHQA). Recently, with the rise of large language models (LLM), in-context learning (ICL) has become the most popular way to solve QA problems. We propose MMHQA-ICL framework for addressing this problems, which includes stronger heterogeneous data retriever and an image caption module. Most importantly, we propose a Type-specific In-context Learning Strategy for MMHQA, enabling LLMs to leverage their powerful performance in this task. We are the first to use end-to-end LLM prompting method for this task. Experimental results demonstrate that our framework outperforms all baselines and methods trained on the full dataset, achieving state-of-the-art results under the few-shot setting on the MultimodalQA dataset

MoELoRA: Contrastive Learning Guided Mixture of Experts on Parameter-Efficient Fine-Tuning for Large Language Models

Fine-tuning is often necessary to enhance the adaptability of Large Language Models (LLM) to downstream tasks. Nonetheless, the process of updating billions of parameters demands significant computational resources and training time, which poses a substantial obstacle to the widespread application of large-scale models in various scenarios. To address this issue, Parameter-Efficient Fine-Tuning (PEFT) has emerged as a prominent paradigm in recent research. However, current PEFT approaches that employ a limited set of global parameters (such as LoRA, which adds low-rank approximation matrices to all weights) face challenges in flexibly combining different computational modules in downstream tasks. In this work, we introduce a novel PEFT method: MoELoRA. We consider LoRA as Mixture of Experts (MoE), and to mitigate the random routing phenomenon observed in MoE, we propose the utilization of contrastive learning to encourage experts to learn distinct features. We conducted experiments on 11 tasks in math reasoning and common-sense reasoning benchmarks. With the same number of parameters, our approach outperforms LoRA significantly. In math reasoning, MoELoRA achieved an average performance that was 4.2% higher than LoRA, and demonstrated competitive performance compared to the 175B GPT-3.5 on several benchmarks

HRoT: Hybrid prompt strategy and Retrieval of Thought for Table-Text Hybrid Question Answering

Answering numerical questions over hybrid contents from the given tables and text(TextTableQA) is a challenging task. Recently, Large Language Models (LLMs) have gained significant attention in the NLP community. With the emergence of large language models, In-Context Learning and Chain-of-Thought prompting have become two particularly popular research topics in this field. In this paper, we introduce a new prompting strategy called Hybrid prompt strategy and Retrieval of Thought for TextTableQA. Through In-Context Learning, we prompt the model to develop the ability of retrieval thinking when dealing with hybrid data. Our method achieves superior performance compared to the fully-supervised SOTA on the MultiHiertt dataset in the few-shot setting

Robust Synthetic-to-Real Transfer for Stereo Matching

With advancements in domain generalized stereo matching networks, models pre-trained on synthetic data demonstrate strong robustness to unseen domains. However, few studies have investigated the robustness after fine-tuning them in real-world scenarios, during which the domain generalization ability can be seriously degraded. In this paper, we explore fine-tuning stereo matching networks without compromising their robustness to unseen domains. Our motivation stems from comparing Ground Truth (GT) versus Pseudo Label (PL) for fine-tuning: GT degrades, but PL preserves the domain generalization ability. Empirically, we find the difference between GT and PL implies valuable information that can regularize networks during fine-tuning. We also propose a framework to utilize this difference for fine-tuning, consisting of a frozen Teacher, an exponential moving average (EMA) Teacher, and a Student network. The core idea is to utilize the EMA Teacher to measure what the Student has learned and dynamically improve GT and PL for fine-tuning. We integrate our framework with state-of-the-art networks and evaluate its effectiveness on several real-world datasets. Extensive experiments show that our method effectively preserves the domain generalization ability during fine-tuning.Comment: Accepted at CVPR 202

Nasopharyngeal carcinoma with non-squamous phenotype may be a variant of nasopharyngeal squamous cell carcinoma after inhibition of EGFR/PI3K/AKT/mTOR pathway

Nasopharyngeal carcinoma (NPC) is a cancerous tumor that develops in the nasopharynx epithelium and typically has squamous differentiation. The squamous phenotype is evident in immunohisto-chemistry, with diffuse nuclear positivity for p63 and p40. Nonetheless, a few NPCs have been identified by clinicopathological diagnosis that do not exhibit the squamous phenotype; these NPCs are currently referred to as non-squamous immuno-phenotype nasopharyngeal carcinomas (NSNPCs). In a previous work, we have revealed similarities between the histological appearance, etiology, and gene alterations of NSNPC and conventional NPC. According to ultrastructural findings, NSNPC still falls under the category of non-keratinized squamous cell carcinoma that is undifferentiated. NSNPC has an excellent prognosis and a low level of malignancy, according to a retrospective investigation. Based on prior research, we investigated the molecular mechanism of NSNPC not expressing the squamous phenotype and its biological behavior. IHC was used to determine the expression of EGFR, PI3K, AKT, p-AKT, mTOR, p-mTOR, Notch, STAT3 and p-STAT3 in a total of 20 NSNPC tissue samples and 20 classic NPC tissue samples. We obtained human NPC cell lines (CNE-2,5-8F) and used EGFR overexpression plasmid and shRNAs to transfect them. To find out whether mRNA and proteins were expressed in the cells, we used Western blotting and qRT-PCR. Cell biological behavior was discovered using the CCK-8 assay, cell migration assay, and cell invasion assay. EGFR, PI3K, p-AKT and p-mTOR proteins were lowly expressed in NSNPC tissues by immunohistochemistry, compared with classical NPC. In the classical NPC cell lines CNE-2 and 5-8F, overexpression EGFR can up-regulate the expression of p63 through the PI3K/AKT/mTOR pathway, and promote the proliferation, migration, and invasion of nasopharyngeal carcinoma cells. At the same time, knockout of EGFR can down-regulate p63 expression through the PI3K/AKT/mTOR pathway, and inhibit the proliferation, migration, and invasion of nasopharyngeal carcinoma cells. The lack of p63 expression in NSNPC was linked with the inhibition of the EGFR/PI3K/AKT/mTOR pathway, and NSNPC may be a variant of classical NPC

TableQAKit: A Comprehensive and Practical Toolkit for Table-based Question Answering

Table-based question answering (TableQA) is an important task in natural language processing, which requires comprehending tables and employing various reasoning ways to answer the questions. This paper introduces TableQAKit, the first comprehensive toolkit designed specifically for TableQA. The toolkit designs a unified platform that includes plentiful TableQA datasets and integrates popular methods of this task as well as large language models (LLMs). Users can add their datasets and methods according to the friendly interface. Also, pleasantly surprised using the modules in this toolkit achieves new SOTA on some datasets. Finally, \tableqakit{} also provides an LLM-based TableQA Benchmark for evaluating the role of LLMs in TableQA. TableQAKit is open-source with an interactive interface that includes visual operations, and comprehensive data for ease of use.Comment: Work in progres

Cotton WATs Modulate SA Biosynthesis and Local Lignin Deposition Participating in Plant Resistance Against Verticillium dahliae

Verticillium wilt, caused by Verticillium dahliae, seriously limits cotton production. It is difficult to control this pathogen damage mainly due to the complexity of the molecular mechanism of plant resistance to V. dahliae. Here, we identified three homologous cotton Walls Are Thin (WAT) genes, which were designated as GhWAT1, GhWAT2, and GhWAT3. The GhWATs were predominantly expressed in the roots, internodes, and hypocotyls and induced by infection with V. dahliae and treatment with indole-3-acetic acid (IAA) and salicylic acid (SA). GhWAT1-, GhWAT2-, or GhWAT3-silenced plants showed a comparable phenotype and level of resistance with control plants, but simultaneously silenced three GhWATs (GhWAT123-silenced), inhibited plant growth and increased plant resistance to V. dahliae, indicating that these genes were functionally redundant. In the GhWAT123-silenced plants, the expression of SA related genes was significantly upregulated compared with the control, resulting in an increase of SA level. Moreover, the histochemical analysis showed that xylem development was inhibited in GhWAT123-silenced plants compared with the control. However, lignin deposition increased in the xylem of the GhWAT123-silenced plants compared to the control, and there were higher expression levels of lignin synthesis- and lignifications-related genes in the GhWAT123-silenced plants. Collectively, the results showed that GhWATs in triple-silenced plants acts as negative regulators of plant resistance against V. dahliae. The potential mechanism of the WATs functioning in the plant defence can modulate the SA biosynthesis and lignin deposition in the xylem

Global urban environmental change drives adaptation in white clover

Urbanization transforms environments in ways that alter biological evolution. We examined whether urban environmental change drives parallel evolution by sampling 110,019 white clover plants from 6169 populations in 160 cities globally. Plants were assayed for a Mendelian antiherbivore defense that also affects tolerance to abiotic stressors. Urban-rural gradients were associated with the evolution of clines in defense in 47% of cities throughout the world. Variation in the strength of clines was explained by environmental changes in drought stress and vegetation cover that varied among cities. Sequencing 2074 genomes from 26 cities revealed that the evolution of urban-rural clines was best explained by adaptive evolution, but the degree of parallel adaptation varied among cities. Our results demonstrate that urbanization leads to adaptation at a global scale