Combined translational-rotational jumps in solid (alpha)-CO and CO(,2)

Combined translational-rotational jumps in two orientationally ordered solids, (alpha)-CO and CO(,2), have been measured using C(\u2713) NMR. In the Pa3 structure of these solids, a molecule which jumps to a neighboring (presumably vacant) site will reorient, due to the orientationally ordered structure.;The rates of translation and rotation have been measured independently by using different NMR techniques. The rotations were detected at high field (14.7 MHz) through the modulation of the chemical shift anisotropy; spin echoes and stimulated echoes were used. The translational jumps modulate the dipolar interactions and were studied at low fields (1.256 MHz) with line narrowing and Slichter-Ailion slow motion (T(,1D)) experiments. The rates of translations and rotations agree, indicating that they are two aspects of one combined motion.;The shift anisotropies of (alpha)-phase C(\u2713)O and C(\u2713)O(,2) were found to be 350 (+OR-) 15 ppm and 325 (+OR-) 15ppm, respectively; both values are in good agreement with previous NMR measurements. The jump rates of the combined motion in both materials obey the thermally activated expression: (omega)(,j) = (tau)(,j)(\u27-1) = (omega)(,0) exp(-E(,a)/kT). The activation parameters are E(,a)/k = 2100 K and (omega)(,0) = 2 x 10(\u2718) s(\u27-1) for (alpha)-CO and E(,a)/k = 6600 K and (omega)(,0) = 2 x 10(\u2717) s(\u27-1) for CO(,2). The activation energies of (alpha)-CO and CO(,2) from this study agree by corresponding states analysis with that found previously for the same motion in N(,2)O. All three molecular solids belong to the family of solids composed of small, linear molecules with Pa3 crystal structure. Unusually high frequency prefactors ((omega)(,0)) are seen in all three solids and are not understood. The high prefactors are also shown to appear in other molecular solids such as benzene and ammonia

BOURNE: Bootstrapped Self-supervised Learning Framework for Unified Graph Anomaly Detection

Graph anomaly detection (GAD) has gained increasing attention in recent years due to its critical application in a wide range of domains, such as social networks, financial risk management, and traffic analysis. Existing GAD methods can be categorized into node and edge anomaly detection models based on the type of graph objects being detected. However, these methods typically treat node and edge anomalies as separate tasks, overlooking their associations and frequent co-occurrences in real-world graphs. As a result, they fail to leverage the complementary information provided by node and edge anomalies for mutual detection. Additionally, state-of-the-art GAD methods, such as CoLA and SL-GAD, heavily rely on negative pair sampling in contrastive learning, which incurs high computational costs, hindering their scalability to large graphs. To address these limitations, we propose a novel unified graph anomaly detection framework based on bootstrapped self-supervised learning (named BOURNE). We extract a subgraph (graph view) centered on each target node as node context and transform it into a dual hypergraph (hypergraph view) as edge context. These views are encoded using graph and hypergraph neural networks to capture the representations of nodes, edges, and their associated contexts. By swapping the context embeddings between nodes and edges and measuring the agreement in the embedding space, we enable the mutual detection of node and edge anomalies. Furthermore, we adopt a bootstrapped training strategy that eliminates the need for negative sampling, enabling BOURNE to handle large graphs efficiently. Extensive experiments conducted on six benchmark datasets demonstrate the superior effectiveness and efficiency of BOURNE in detecting both node and edge anomalies

SnarkFold: Efficient SNARK Proof Aggregation from Split Incrementally Verifiable Computation

The succinct non-interactive argument of knowledge (SNARK) technique is widely used in blockchain systems to replace the costly on-chain computation with the verification of a succinct proof. However, when dealing with multiple proofs, most existing applications require each proof to be independently verified, resulting in a heavy load on nodes and high transaction fees for users. To improve the efficiency of verifying multiple proofs, we introduce SnarkFold, a universal SNARK-proof aggregation scheme based on incrementally verifiable computation (IVC). Unlike previous proof aggregation approaches based on inner product arguments, which have a logarithmic proof size and verification cost, SnarkFold achieves constant verification time and proof size. One core technical advance in SnarkFold, of independent interest, is the ``split IVC\u27\u27: rather than using one running instance to fold/accumulate the computation, we employ two (or more) running instances of different types in the recursive circuit to avoid transferring into the same structure. This distinguishing feature is particularly well-suited for proof aggregation scenarios, as constructing arithmetic circuits for pairings can be expensive. We further demonstrate how to fold Groth16 proofs with our SnarkFold. With some further optimizations, SnarkFold achieves the highest efficiency among all approaches

A novel double-chain silver(I) coordination polymer: catena-poly[[[μ-aqua-aqua­disilver(I)]-bis­(μ3-5-methyl­pyrazine-2-carboxyl­ato)] dihydrate]

In the title silver(I) coordination polymer, {[Ag2(C6H5N2O2)2(H2O)2]·2H2O}n, the [Ag2(μ2-H2O)(H2O)] cores are extended by anti­parallel 5-methyl­pyrazine-2-carboxyl­ate (L) ligands, forming a novel double-chain structure. Both Ag+ cations show a distorted square-pyramidal coordination. Ag1 is bonded to two water molecules, one L N atom, one N atom and one carboxylate O atom from a neighbouring L, whereas Ag2 is surrounded by two L N atoms, two L carboxylate O atoms and one bridging water molecule. O—H⋯O hydrogen-bonding inter­actions involving water clusters and carboxyl­ate O atoms link the mol­ecules into a three-dimensional supra­molecular architecture, which is further consolidated by weak C—H⋯O inter­actions and π–π stacking inter­actions [centroid–centroid distance 3.643 (5) Å]

From Indeterminacy to Determinacy: Augmenting Logical Reasoning Capabilities with Large Language Models

Recent advances in LLMs have revolutionized the landscape of reasoning tasks. To enhance the capabilities of LLMs to emulate human reasoning, prior works focus on modeling reasoning steps using specific thought structures like chains, trees, or graphs. However, LLM-based reasoning continues to encounter three challenges: 1) Selecting appropriate reasoning structures for various tasks; 2) Exploiting known conditions sufficiently and efficiently to deduce new insights; 3) Considering the impact of historical reasoning experience. To address these challenges, we propose DetermLR, a novel reasoning framework that formulates the reasoning process as a transformational journey from indeterminate premises to determinate ones. This process is marked by the incremental accumulation of determinate premises, making the conclusion progressively closer to clarity. DetermLR includes three essential components: 1) Premise identification: We categorize premises into two distinct types: determinate and indeterminate. This empowers LLMs to customize reasoning structures to match the specific task complexities. 2) Premise prioritization and exploration: We leverage quantitative measurements to assess the relevance of each premise to the target, prioritizing more relevant premises for exploring new insights. 3) Iterative process with reasoning memory: We introduce a reasoning memory module to automate storage and extraction of available premises and reasoning paths, preserving historical reasoning details for more accurate premise prioritization. Comprehensive experimental results show that DetermLR outperforms all baselines on four challenging logical reasoning tasks: LogiQA, ProofWriter, FOLIO, and LogicalDeduction. DetermLR can achieve better reasoning performance while requiring fewer visited states, highlighting its superior efficiency and effectiveness in tackling logical reasoning tasks.Comment: Code repo: https://github.com/XiaoMi/DetermL

Molecular understanding of the catalytic consequence of ketene intermediates under confinement

[Image: see text] Neutral ketene is a crucial intermediate during zeolite carbonylation reactions. In this work, the roles of ketene and its derivates (viz., acylium ion and surface acetyl) associated with direct C–C bond coupling during the carbonylation reaction have been theoretically investigated under realistic reaction conditions and further validated by synchrotron radiation X-ray diffraction (SR-XRD) and Fourier transformed infrared (FT-IR) studies. It has been demonstrated that the zeolite confinement effect has significant influence on the formation, stability, and further transformation of ketene. Thus, the evolution and the role of reactive and inhibitive intermediates depend strongly on the framework structure and pore architecture of the zeolite catalysts. Inside side pockets of mordenite (MOR), rapid protonation of ketene occurs to form a metastable acylium ion exclusively, which is favorable toward methyl acetate (MA) and acetic acid (AcOH) formation. By contrast, in 12MR channels of MOR, a relatively longer lifetime was observed for ketene, which tends to accelerate deactivation of zeolite due to coke formation by the dimerization of ketene and further dissociation to diene and alkyne. Thus, we resolve, for the first time, a long-standing debate regarding the genuine role of ketene in zeolite catalysis. It is a paradigm to demonstrate the confinement effect on the formation, fate, and catalytic consequence of the active intermediates in zeolite catalysis