Next-to-leading order corrections for gg→ZHgg \to ZH with top quark mass dependence

In this Letter, we present for the first time a calculation of the complete next-to-leading order corrections to the $gg \to ZH$ process. We use the method of small mass expansion to tackle the most challenging two-loop virtual amplitude, in which the top quark mass dependence is retained throughout the calculations. We show that our method provides reliable numeric results in all kinematic regions, and present phenomenological predictions for the total and differential cross sections at the Large Hadron Collider and its future upgrades. Our results are necessary ingredients towards reducing the theoretical uncertainties of the $pp \to ZH$ cross sections down to the percent-level, and provide important theoretical inputs for future precision experimental collider programs

Thrust distribution in Higgs decays up to the fifth logarithmic order

In this work, we extend the resummation for the thrust distribution in Higgs decays up to the fifth logarithmic order. We show that one needs the accurate values of the three-loop soft functions for reliable predictions in the back-to-back region. This is especially true in the gluon channel, where the soft function exhibits poor perturbative convergence.Comment: 31 pages, 6 figures, 3 table

Isolation and characterization of an Aux/IAA gene (LaIAA2) from Larix

The phytohormone auxin controls many aspects of plant development. Auxin/indole-3-acetic acid (Aux/IAA)  transcriptional factors are key regulators of auxin responses in plants. To investigate the effects of auxin on  gene expression during the rooting process of Larix cuttings, a subtractive cDNA library was constructed and  272 UniEST were obtained by using suppression subtractive hybridization (SSH). Based on a fragment of 272  UniEST, the full-length cDNA of LaIAA2, an Aux /IAA gene from Larix was isolated. Then, the response  expression of LaIAA2 to auxin was determined by treating with different sources and concentration of auxin and cycloheximide and the expression patterns of LaIAA2 were examined in different tissues. The results show  that LaIAA2 appears to be the first response gene of auxin and LaIAA2 gene was involved in the root  development and auxin signaling. The express pattern of LaIAA2 gene indicated that it might play a central role in root development, specially regulated lateral and adventitious root production.Key words: Aux/IAA gene family, auxin, LaIAA2, Lari

Phenol Derivatives From the Sponge-Derived Fungus Didymellaceae sp. SCSIO F46

Seven new phenol derivatives named coleophomones E and F (1, 2), diorcinols L and M (3, 4), 1-hydroxy-6-methyl-11-methoxy-8-hydroxymethylxanthone (5), porric acid E (6), and 7-(2-hydroxyphenyl) butane-7,8,9-triol (7), were isolated from the EtOAc extract of the marine sponge-derived fungus Didymellaceae sp. SCSIO F46, together with 10 known compounds. Their structures were determined by spectroscopic analyses, including NMR, MS, X-ray diffraction, and theoretical calculations. Each of 1 and 2 contains an unusual spiro [cyclohexane-1,2′-inden] moiety, which is relatively seldom in nature products. Cytotoxic and COX-2 inhibitory activities of all purified compounds were tested and evaluated. Compound 3 displayed obvious cytotoxicities against Huh-7, HeLa, DU145 and HL60 cells (IC50 values 5.7–9.6 μM) and weak activities against other five cell lines, while 8 showed weak cytotoxicities against HeLa and HL7702 cells. Compound 6 displayed COX-2 inhibitory activity with IC50 value of 3.3 μM

A Multi-facet Paradigm to Bridge Large Language Model and Recommendation

Large Language Models (LLMs) have garnered considerable attention in recommender systems. To achieve LLM-based recommendation, item indexing and generation grounding are two essential steps, bridging between recommendation items and natural language. Item indexing assigns a unique identifier to represent each item in natural language, and generation grounding grounds the generated token sequences to in-corpus items. However, previous works suffer from inherent limitations in the two steps. For item indexing, existing ID-based identifiers (e.g., numeric IDs) and description-based identifiers (e.g., titles) often compromise semantic richness or uniqueness. Moreover, generation grounding might inadvertently produce out-of-corpus identifiers. Worse still, autoregressive generation heavily relies on the initial token's quality. To combat these issues, we propose a novel multi-facet paradigm, namely TransRec, to bridge the LLMs to recommendation. Specifically, TransRec employs multi-facet identifiers that incorporate ID, title, and attribute, achieving both distinctiveness and semantics. Additionally, we introduce a specialized data structure for TransRec to guarantee the in-corpus identifier generation and adopt substring indexing to encourage LLMs to generate from any position. We implement TransRec on two backbone LLMs, i.e., BART-large and LLaMA-7B. Empirical results on three real-world datasets under diverse settings (e.g., full training and few-shot training with warm- and cold-start testings) attest to the superiority of TransRec

Eliminating Reasoning via Inferring with Planning: A New Framework to Guide LLMs' Non-linear Thinking

Chain-of-Thought(CoT) prompting and its variants explore equipping large language models (LLMs) with high-level reasoning abilities by emulating human-like linear cognition and logic. However, the human mind is complicated and mixed with both linear and nonlinear thinking. In this work, we propose \textbf{I}nferential \textbf{E}xclusion \textbf{P}rompting (IEP), a novel prompting that combines the principles of elimination and inference in order to guide LLMs to think non-linearly. IEP guides LLMs to plan and then utilize Natural Language Inference (NLI) to deduce each possible solution's entailment relation with context, commonsense, or facts, therefore yielding a broader perspective by thinking back for inferring. This forward planning and backward eliminating process allows IEP to better simulate the complex human thinking processes compared to other CoT-based methods, which only reflect linear cognitive processes. We conducted a series of empirical studies and have corroborated that IEP consistently outperforms CoT across various tasks. Additionally, we observe that integrating IEP and CoT further improves the LLMs' performance on certain tasks, highlighting the necessity of equipping LLMs with mixed logic processes. Moreover, to better evaluate comprehensive features inherent in human logic, we introduce \textbf{M}ental-\textbf{A}bility \textbf{R}easoning \textbf{B}enchmark (MARB). The benchmark comprises six novel subtasks with a total of 9,115 questions, among which 1,685 are developed with hand-crafted rationale references. We believe both \textsc{IEP} and \textsc{MARB} can serve as a promising direction for unveiling LLMs' logic and verbal reasoning abilities and drive further advancements. \textsc{MARB} will be available at ~\texttt{anonymity link} soon

ToxicChat: Unveiling Hidden Challenges of Toxicity Detection in Real-World User-AI Conversation

Despite remarkable advances that large language models have achieved in chatbots, maintaining a non-toxic user-AI interactive environment has become increasingly critical nowadays. However, previous efforts in toxicity detection have been mostly based on benchmarks derived from social media content, leaving the unique challenges inherent to real-world user-AI interactions insufficiently explored. In this work, we introduce ToxicChat, a novel benchmark based on real user queries from an open-source chatbot. This benchmark contains the rich, nuanced phenomena that can be tricky for current toxicity detection models to identify, revealing a significant domain difference compared to social media content. Our systematic evaluation of models trained on existing toxicity datasets has shown their shortcomings when applied to this unique domain of ToxicChat. Our work illuminates the potentially overlooked challenges of toxicity detection in real-world user-AI conversations. In the future, ToxicChat can be a valuable resource to drive further advancements toward building a safe and healthy environment for user-AI interactions

Relation-aware Ensemble Learning for Knowledge Graph Embedding

Knowledge graph (KG) embedding is a fundamental task in natural language processing, and various methods have been proposed to explore semantic patterns in distinctive ways. In this paper, we propose to learn an ensemble by leveraging existing methods in a relation-aware manner. However, exploring these semantics using relation-aware ensemble leads to a much larger search space than general ensemble methods. To address this issue, we propose a divide-search-combine algorithm RelEns-DSC that searches the relation-wise ensemble weights independently. This algorithm has the same computation cost as general ensemble methods but with much better performance. Experimental results on benchmark datasets demonstrate the effectiveness of the proposed method in efficiently searching relation-aware ensemble weights and achieving state-of-the-art embedding performance. The code is public at https://github.com/LARS-research/RelEns.Comment: This short paper has been accepted by EMNLP 202

The Akebia Genus as a Novel Forest Crop: A Review of Its Genetic Resources, Nutritional Components, Biosynthesis, and Biological Studies

The genus Akebia belongs to the Lardizabalaceae family and comprises five species that are primarily distributed in East Asia. Plants of the Akebia genus comprise deciduous and semi-evergreen perennial twining vines that have been used in Chinese herbal medicine for at least 2000 years. The plants of this genus have the potential to form a novel forest crop with high nutritional and economic value because their fruit has a delicious sweet taste and rich nutrient components. In this study, we organized, analyzed, and evaluated the available published scientific literature on the botanical, ecological, and phytochemical characteristics of Akebia plants. Based on these studies, we briefly introduced botanical and ecological characteristics and focused on reviewing the development and utilization of wild genetic resources in the genus Akebia. We further explored the genus' rich nutritional components, such as triterpenes, flavonoids, polyphenols, polysaccharides, and fatty acids, and their potential use in food and health improvement applications. In addition, several papers describing advances in biotechnological research focusing on micropropagation, nutrient biosynthesis, and fruit ripeness were also included. This review provides comprehensive knowledge of the Akebia genus as a new forest crop for food and fruit utilization, and we also discuss future breeding and research prospects