Polymorphic Aβ42 fibrils adopt similar secondary structure but differ in cross-strand side chain stacking interactions within the same β-sheet.

Formation of polymorphic amyloid fibrils is a common feature in neurodegenerative diseases involving protein aggregation. In Alzheimer's disease, different fibril structures may be associated with different clinical sub-types. Structural basis of fibril polymorphism is thus important for understanding the role of amyloid fibrils in the pathogenesis and progression of these diseases. Here we studied two types of Aβ42 fibrils prepared under quiescent and agitated conditions. Quiescent Aβ42 fibrils adopt a long and twisted morphology, while agitated fibrils are short and straight, forming large bundles via lateral association. EPR studies of these two types of Aβ42 fibrils show that the secondary structure is similar in both fibril polymorphs. At the same time, agitated Aβ42 fibrils show stronger interactions between spin labels across the full range of the Aβ42 sequence, suggesting a more tightly packed structure. Our data suggest that cross-strand side chain packing interactions within the same β-sheet may play a critical role in the formation of polymorphic fibrils

Metamagnetic transitions and anomalous magnetoresistance in EuAg4_4As2_2 single crystal

In this paper, the magnetic and transport properties were systematically studied for EuAg$_4$As$_2$ single crystals, crystallizing in a centrosymmetric trigonal CaCu$_4$P$_2$ type structure. It was confirmed that two magnetic transitions occur at $\textit{T}$$_{N1}$ = 10 K and $\textit{T}$$_{N2}$ = 15 K, respectively. With the increasing field, the two transitions are noticeably driven to lower temperature. At low temperatures, applying a magnetic field in the $\textit{ab}$ plane induces two successive metamagnetic transitions. For both $\textit{H}$ $\parallel$ $\textit{ab}$ and $\textit{H}$ $\parallel$ $\textit{c}$, EuAg$_4$As$_2$ shows a positive, unexpected large magnetoresistance (up to 202\%) at low fields below 10 K, and a large negative magnetoresistance (up to -78\%) at high fields/intermediate temperatures. Such anomalous field dependence of magnetoresistance may have potential application in the future magnetic sensors. Finally, the magnetic phase diagrams of EuAg$_{4}$As$_{2}$ were constructed for both $\textit{H}$ $\parallel$ $\textit{ab}$ and $\textit{H}$ $\parallel$ $\textit{c}$

High-order Joint Constituency and Dependency Parsing

This work revisits the topic of jointly parsing constituency and dependency trees, i.e., to produce compatible constituency and dependency trees simultaneously for input sentences, which is attractive considering that the two types of trees are complementary in representing syntax. The original work of Zhou and Zhao (2019) performs joint parsing only at the inference phase. They train two separate parsers under the multi-task learning framework (i.e., one shared encoder and two independent decoders). They design an ad-hoc dynamic programming-based decoding algorithm of $O(n^5)$ time complexity for finding optimal compatible tree pairs. Compared to their work, we make progress in three aspects: (1) adopting a much more efficient decoding algorithm of $O(n^4)$ time complexity, (2) exploring joint modeling at the training phase, instead of only at the inference phase, (3) proposing high-order scoring components to promote constituent-dependency interaction. We conduct experiments and analysis on seven languages, covering both rich-resource and low-resource scenarios. Results and analysis show that joint modeling leads to a modest overall performance boost over separate modeling, but substantially improves the complete matching ratio of whole trees, thanks to the explicit modeling of tree compatibility.Comment: LREC-COLING 202

Reference Matters: Benchmarking Factual Error Correction for Dialogue Summarization with Fine-grained Evaluation Framework

Factuality is important to dialogue summarization. Factual error correction (FEC) of model-generated summaries is one way to improve factuality. Current FEC evaluation that relies on factuality metrics is not reliable and detailed enough. To address this problem, we are the first to manually annotate a FEC dataset for dialogue summarization containing 4000 items and propose FERRANTI, a fine-grained evaluation framework based on reference correction that automatically evaluates the performance of FEC models on different error categories. Using this evaluation framework, we conduct sufficient experiments with FEC approaches under a variety of settings and find the best training modes and significant differences in the performance of the existing approaches on different factual error categories.Comment: Accepted to ACL 2023 Main Conferenc

How Well Do Large Language Models Understand Syntax? An Evaluation by Asking Natural Language Questions

While recent advancements in large language models (LLMs) bring us closer to achieving artificial general intelligence, the question persists: Do LLMs truly understand language, or do they merely mimic comprehension through pattern recognition? This study seeks to explore this question through the lens of syntax, a crucial component of sentence comprehension. Adopting a natural language question-answering (Q&A) scheme, we craft questions targeting nine syntactic knowledge points that are most closely related to sentence comprehension. Experiments conducted on 24 LLMs suggest that most have a limited grasp of syntactic knowledge, exhibiting notable discrepancies across different syntactic knowledge points. In particular, questions involving prepositional phrase attachment pose the greatest challenge, whereas those concerning adjectival modifier and indirect object are relatively easier for LLMs to handle. Furthermore, a case study on the training dynamics of the LLMs reveals that the majority of syntactic knowledge is learned during the initial stages of training, hinting that simply increasing the number of training tokens may not be the `silver bullet' for improving the comprehension ability of LLMs.Comment: 20 pages, 6 figure

Mining Density Contrast Subgraphs

Dense subgraph discovery is a key primitive in many graph mining applications, such as detecting communities in social networks and mining gene correlation from biological data. Most studies on dense subgraph mining only deal with one graph. However, in many applications, we have more than one graph describing relations among a same group of entities. In this paper, given two graphs sharing the same set of vertices, we investigate the problem of detecting subgraphs that contrast the most with respect to density. We call such subgraphs Density Contrast Subgraphs, or DCS in short. Two widely used graph density measures, average degree and graph affinity, are considered. For both density measures, mining DCS is equivalent to mining the densest subgraph from a "difference" graph, which may have both positive and negative edge weights. Due to the existence of negative edge weights, existing dense subgraph detection algorithms cannot identify the subgraph we need. We prove the computational hardness of mining DCS under the two graph density measures and develop efficient algorithms to find DCS. We also conduct extensive experiments on several real-world datasets to evaluate our algorithms. The experimental results show that our algorithms are both effective and efficient.Comment: Full version of an ICDE'18 pape