Hierarchical Pointer Net Parsing

Transition-based top-down parsing with pointer networks has achieved state-of-the-art results in multiple parsing tasks, while having a linear time complexity. However, the decoder of these parsers has a sequential structure, which does not yield the most appropriate inductive bias for deriving tree structures. In this paper, we propose hierarchical pointer network parsers, and apply them to dependency and sentence-level discourse parsing tasks. Our results on standard benchmark datasets demonstrate the effectiveness of our approach, outperforming existing methods and setting a new state-of-the-art.Comment: Accepted by EMNLP 201

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

Interfacial Li⁺ Diffusion Booster Accelerated by Enhanced Metal‐Organic Framework Sieving and Wettability for High‐Voltage Solid‐State Lithium Metal Batteries

Solid-state lithium metal batteries (SSLMBs) are promising for realizing higher energy density. However, the poor interfacial Li+ transport kinetics and Li dendrite growth inhibit SSLMBs, leading to sluggish interfacial ion diffusion and depressive lifespan, which is attributed to high barriers blocked by anions or interface space in solid-state electrolytes. Herein, a flexible solid-state polymer skeleton employed with ionic liquid and metal-organic frameworks (PIM) electrolyte is proposed to strengthen interfacial Li ion exchange by improving the Li+ sieving effect and interfacial wettability. Thanks to the immobilization effect of TFSI− anions affected by positive metal atom centers and pore morphology, the PIM electrolyte exhibits exceptional properties, i.e., a high ionic conductivity up to 3.1 mS cm−1 at 60 °C and an improved Li+ transference number of 0.65, enabling symmetric cells of Li metal to run steadily for over 1000 h with lower voltage hysteresis (25 mV). Meanwhile, matching with high-voltage electrodes, the solid-state PIM electrolyte exhibits good compatibility and stability toward LiNi0.6Co0.2Mn0.2O2 and LiFePO4 electrodes, showing the capacity retentions of 85.5% and 96.5% after 120 and 400 cycles, respectively. This work suggests low interfacial diffusion resistances and high compatibility for make it a promising candidate for future solid-state battery

Optimizing Language Model's Reasoning Abilities with Weak Supervision

While Large Language Models (LLMs) have demonstrated proficiency in handling complex queries, much of the past work has depended on extensively annotated datasets by human experts. However, this reliance on fully-supervised annotations poses scalability challenges, particularly as models and data requirements grow. To mitigate this, we explore the potential of enhancing LLMs' reasoning abilities with minimal human supervision. In this work, we introduce self-reinforcement, which begins with Supervised Fine-Tuning (SFT) of the model using a small collection of annotated questions. Then it iteratively improves LLMs by learning from the differences in responses from the SFT and unfinetuned models on unlabeled questions. Our approach provides an efficient approach without relying heavily on extensive human-annotated explanations. However, current reasoning benchmarks typically only include golden-reference answers or rationales. Therefore, we present \textsc{PuzzleBen}, a weakly supervised benchmark that comprises 25,147 complex questions, answers, and human-generated rationales across various domains, such as brainteasers, puzzles, riddles, parajumbles, and critical reasoning tasks. A unique aspect of our dataset is the inclusion of 10,000 unannotated questions, enabling us to explore utilizing fewer supersized data to boost LLMs' inference capabilities. Our experiments underscore the significance of \textsc{PuzzleBen}, as well as the effectiveness of our methodology as a promising direction in future endeavors. Our dataset and code will be published soon on \texttt{Anonymity Link}

Anti-TNF Biologicals Enhance the Anti-Inflammatory Properties of IgG N-Glycome in Crohn's Disease

Crohn’s disease (CD) is a chronic inflammation of the digestive tract that significantly impairs patients’ quality of life and well-being. Anti-TNF biologicals revolutionised the treatment of CD,yet many patients do not adequately respond to such therapy. Previous studies have demonstrated apro-inflammatory pattern in the composition of CD patients’ immunoglobulin G (IgG) N-glycomecompared to healthy individuals. Here, we utilised the high-throughput UHPLC method for N-glycan analysis to explore the longitudinal effect of the anti-TNF drugs infliximab and adalimumabon N-glycome composition of total serum IgG in 198 patients, as well as the predictive potential ofIgG N-glycans at baseline to detect primary non-responders to anti-TNF therapy in 1315 patients. Wediscovered a significant decrease in IgG agalactosylation and an increase in monogalactosylation,digalactosylation and sialylation during the 14 weeks of anti-TNF treatment, regardless of therapyresponse, all of which suggested a diminished inflammatory environment in CD patients treated withanti-TNF therapy. Furthermore, we observed that IgG N-glycome might contain certain informationregarding the anti-TNF therapy outcome before initiating the treatment. However, it is impossible to predict future primary non-responders to anti-TNF therapy based solely on IgG N-glycomecomposition at baseline

Anti-TNF Biologicals Enhance the Anti-Inflammatory Properties of IgG N-Glycome in Crohn's Disease

Crohn’s disease (CD) is a chronic inflammation of the digestive tract that significantly impairs patients’ quality of life and well-being. Anti-TNF biologicals revolutionised the treatment of CD,yet many patients do not adequately respond to such therapy. Previous studies have demonstrated apro-inflammatory pattern in the composition of CD patients’ immunoglobulin G (IgG) N-glycomecompared to healthy individuals. Here, we utilised the high-throughput UHPLC method for N-glycan analysis to explore the longitudinal effect of the anti-TNF drugs infliximab and adalimumabon N-glycome composition of total serum IgG in 198 patients, as well as the predictive potential ofIgG N-glycans at baseline to detect primary non-responders to anti-TNF therapy in 1315 patients. Wediscovered a significant decrease in IgG agalactosylation and an increase in monogalactosylation,digalactosylation and sialylation during the 14 weeks of anti-TNF treatment, regardless of therapyresponse, all of which suggested a diminished inflammatory environment in CD patients treated withanti-TNF therapy. Furthermore, we observed that IgG N-glycome might contain certain informationregarding the anti-TNF therapy outcome before initiating the treatment. However, it is impossible to predict future primary non-responders to anti-TNF therapy based solely on IgG N-glycomecomposition at baseline

Comparison of support strategies for grid construction after failure of renewable energy power generation system

As the proportion of renewable energy generation continues to rise, the study of voltage source converter (VSC) control has become a focal point of research. The concepts of emulating the characteristics of synchronous machines have led to the proposals of droop control and virtual synchronous control (VSG). However, a deeper comparison of the control characteristics of these two methods is still needed, particularly in terms of their ability to support the system when partial power sources experience fault conditions. This paper analyzes and compares the two in terms of control principles and small-signal modeling, and finally, based on a nine-bus system with 100% renewable energy generation, two scenarios are designed: a sudden load increase and a partial power source disconnection. The differences in control characteristics between the two are compared and analyzed. The results indicate that the VSG exhibits greater damping compared to droop control and is capable of providing inertial support to the system, making its frequency and voltage less susceptible to change

Hyperprogression of Cutaneous T Cell Lymphoma After Anti-PD-1 Treatment

BACKGROUND Immune checkpoint blockade is an emerging treatment for T cell non-Hodgkin’s lymphoma (T-NHL), but some patients with T-NHL have experienced hyperprogression with undetermined mechanisms upon anti–PD-1 therapy. METHODS Single-cell RNA-Seq, whole-genome sequencing, whole-exome sequencing, and functional assays were performed on primary malignant T cells from a patient with advanced cutaneous T cell lymphoma who experienced hyperprogression upon anti–PD-1 treatment. RESULTS The patient was enrolled in a clinical trial of anti–PD-1 therapy and experienced disease hyperprogression. Single-cell RNA-Seq revealed that PD-1 blockade elicited a remarkable activation and proliferation of the CD4+ malignant T cells, which showed functional PD-1 expression and an exhausted status. Further analyses identified somatic amplification of PRKCQ in the malignant T cells. PRKCQ encodes PKCθ; PKCθ is a key player in the T cell activation/NF-κB pathway. PRKCQ amplification led to high expressions of PKCθ and p-PKCθ (T538) on the malignant T cells, resulting in an oncogenic activation of the T cell receptor (TCR) signaling pathway. PD-1 blockade in this patient released this signaling, derepressed the proliferation of malignant T cells, and resulted in disease hyperprogression. CONCLUSION Our study provides real-world clinical evidence that PD-1 acts as a tumor suppressor for malignant T cells with oncogenic TCR activation. TRIAL REGISTRATION ClinicalTrials.gov ( (NCT03809767)). FUNDING The National Natural Science Foundation of China (81922058), the National Science Fund for Distinguished Young Scholars (T2125002), the National Science and Technology Major Project (2019YFC1315702), the National Youth Top-Notch Talent Support Program (283812), and the Peking University Clinical Medicine plus X Youth Project (PKU2019LCXQ012) supported this work