The Role of Chaos in One-Dimensional Heat Conductivity

We investigate the heat conduction in a quasi 1-D gas model with various degree of chaos. Our calculations indicate that the heat conductivity $\kappa$ is independent of system size when the chaos of the channel is strong enough. The different diffusion behaviors for the cases of chaotic and non-chaotic channels are also studied. The numerical results of divergent exponent $\alpha$ of heat conduction and diffusion exponent $\beta$ are in consistent with the formula $\alpha=2-2/\beta$. We explore the temperature profiles numerically and analytically, which show that the temperature jump is primarily attributed to superdiffusion for both non-chaotic and chaotic cases, and for the latter case of superdiffusion the finite-size affects the value of $\beta$ remarkably.Comment: 6 pages, 7 figure

Electron-nuclear entanglement in the cold lithium gas

We study the ground-state entanglement and thermal entanglement in the hyperfine interaction of the lithium atom. We give the relationship between the entanglement and both temperature and external magnetic fields.Comment: 7 pages, 3 figure

Heat conductivity in the presence of a quantized degree of freedom

We propose a model with a quantized degree of freedom to study the heat transport in quasi-one dimensional system. Our simulations reveal three distinct temperature regimes. In particular, the intermediate regime is characterized by heat conductivity with a temperature exponent $\gamma$ much greater than 1/2 that was generally found in systems with point-like particles. A dynamical investigation indicates the occurrence of non-equipartition behavior in this regime. Moreover, the corresponding Poincar\'e section also shows remarkably characteristic patterns, completely different from the cases of point-like particles.Comment: 7 pages, 4 figure

Inductive Relation Prediction from Relational Paths and Context with Hierarchical Transformers

Relation prediction on knowledge graphs (KGs) is a key research topic. Dominant embedding-based methods mainly focus on the transductive setting and lack the inductive ability to generalize to new entities for inference. Existing methods for inductive reasoning mostly mine the connections between entities, i.e., relational paths, without considering the nature of head and tail entities contained in the relational context. This paper proposes a novel method that captures both connections between entities and the intrinsic nature of entities, by simultaneously aggregating RElational Paths and cOntext with a unified hieRarchical Transformer framework, namely REPORT. REPORT relies solely on relation semantics and can naturally generalize to the fully-inductive setting, where KGs for training and inference have no common entities. In the experiments, REPORT performs consistently better than all baselines on almost all the eight version subsets of two fully-inductive datasets. Moreover. REPORT is interpretable by providing each element's contribution to the prediction results.Comment: Accepted by ICASSP 2023 (Oral

Medium effects on the selection of sequences folding into stable proteins in a simple model

We study the medium effects on the selection of sequences in protein folding by taking account of the surface potential in HP-model. Our analysis on the proportion of H and P monomers in the sequences gives a direct interpretation that the lowly designable structures possess small average gap. The numerical calculation by means of our model exhibits that the surface potential enhances the average gap of highly designable structures. It also shows that a most stable structure may be no longer the most stable one if the medium parameters changed.Comment: 4 pages, 4 figure

Random Entity Quantization for Parameter-Efficient Compositional Knowledge Graph Representation

Representation Learning on Knowledge Graphs (KGs) is essential for downstream tasks. The dominant approach, KG Embedding (KGE), represents entities with independent vectors and faces the scalability challenge. Recent studies propose an alternative way for parameter efficiency, which represents entities by composing entity-corresponding codewords matched from predefined small-scale codebooks. We refer to the process of obtaining corresponding codewords of each entity as entity quantization, for which previous works have designed complicated strategies. Surprisingly, this paper shows that simple random entity quantization can achieve similar results to current strategies. We analyze this phenomenon and reveal that entity codes, the quantization outcomes for expressing entities, have higher entropy at the code level and Jaccard distance at the codeword level under random entity quantization. Therefore, different entities become more easily distinguished, facilitating effective KG representation. The above results show that current quantization strategies are not critical for KG representation, and there is still room for improvement in entity distinguishability beyond current strategies. The code to reproduce our results is available at https://github.com/JiaangL/RandomQuantization.Comment: Accepted to EMNLP 202

Imaging of vertical seismic profiling data using weighted generalized radon transform migration in dip-angle domain

The vertical seismic profiling (VSP) acquisition technique deploys sensors down a borehole, which often goes deep into the target area. It is easy to produce serious “smile” migration artifacts near the sensors during the migration imaging process of VSP data, which contaminates the imaging profile and affects the subsequent geological interpretation. In this paper, we analyze in detail the formation of these migration artifacts during the migration of VSP data. We extend the generalized Radon transform (GRT) migration method to the dip-angle domain and then generate dip-angle domain common image gathers (DDCIGs). Meanwhile, a variant sigmoid weighting function is applied to the DDCIGs to suppress the migration artifacts. The numerical results of synthetic and field data demonstrate high-quality DDCIG images and the significant artifact suppression capability of the proposed method

Preparation of Tradescantia pallida-mediated zinc oxide nanoparticles and their activity against cervical cancer cell lines

Purpose: To synthesize zinc oxide nanoparticles (ZnO NPs) using Tradescantia pallida. (Commelinaceae) and determine their fluorescent and cytotoxic properties.Methods: ZnO NPs were synthesized according to a simple protocol using T. pallida aqueous leaf extract (TPALE). Scanning electron microscopy (SEM) and  transmission electron microscopy (TEM) were used to analyze the morphology of the ZnO NPs. X-ray diffraction (XRD) and Fourier transforminfrared spectroscopy (FTIR) measurements were performed to determine their crystalline nature and functional groups, respectively. Fluorescence spectroscopy was used to assess the  photoluminescence properties of ZnO NPs. Upon confirmation of ZnO NP synthesis, cytotoxicity tests were carried out against HeLa cell line by 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide (MTT) assay.Results: The agglomerated ZnO NPs were rod-shaped and had a mean particle size of 25 ± 2 nm. Further, they exhibited good photoluminescence with correlation to ZnO crystals. MTT assay results indicated significant cytotoxicity against HeLa cervical cancer cell line.Conclusion: A simple approach for ZnO NP synthesis based on TPALE has been developed successfully. The synthesized ZnO NPs demonstrate good luminescence properties and cytotoxicity against cervical cancer line.Keywords: Commelinaceae, Cytotoxicity, Photoluminescence, Setcreasea pallida, Setcreasea purpurea, Tradescantia pallida, ZnO nanoparticle

StreamingDialogue: Prolonged Dialogue Learning via Long Context Compression with Minimal Losses

Standard Large Language Models (LLMs) struggle with handling dialogues with long contexts due to efficiency and consistency issues. According to our observation, dialogue contexts are highly structured, and the special token of \textit{End-of-Utterance} (EoU) in dialogues has the potential to aggregate information. We refer to the EoU tokens as ``conversational attention sinks'' (conv-attn sinks). Accordingly, we introduce StreamingDialogue, which compresses long dialogue history into conv-attn sinks with minimal losses, and thus reduces computational complexity quadratically with the number of sinks (i.e., the number of utterances). Current LLMs already demonstrate the ability to handle long context window, e.g., a window size of 200k or more. To this end, by compressing utterances into EoUs, our method has the potential to handle more than 200k of utterances, resulting in a prolonged dialogue learning. In order to minimize information losses from reconstruction after compression, we design two learning strategies of short-memory reconstruction (SMR) and long-memory reactivation (LMR). Our method outperforms strong baselines in dialogue tasks and achieves a 4 $\times$ speedup while reducing memory usage by 18 $\times$ compared to dense attention recomputation