Edge disorder induced Anderson localization and conduction gap in graphene nanoribbons

We study the effect of the edge disorder on the conductance of the graphene nanoribbons (GNRs). We find that only very modest edge disorder is sufficient to induce the conduction energy gap in the otherwise metallic GNRs and to lift any difference in the conductance between nanoribbons of different edge geometry. We relate the formation of the conduction gap to the pronounced edge disorder induced Anderson-type localization which leads to the strongly enhanced density of states at the edges, formation of surface-like states and to blocking of conductive paths through the ribbons

Answering Ambiguous Questions via Iterative Prompting

In open-domain question answering, due to the ambiguity of questions, multiple plausible answers may exist. To provide feasible answers to an ambiguous question, one approach is to directly predict all valid answers, but this can struggle with balancing relevance and diversity. An alternative is to gather candidate answers and aggregate them, but this method can be computationally costly and may neglect dependencies among answers. In this paper, we present AmbigPrompt to address the imperfections of existing approaches to answering ambiguous questions. Specifically, we integrate an answering model with a prompting model in an iterative manner. The prompting model adaptively tracks the reading process and progressively triggers the answering model to compose distinct and relevant answers. Additionally, we develop a task-specific post-pretraining approach for both the answering model and the prompting model, which greatly improves the performance of our framework. Empirical studies on two commonly-used open benchmarks show that AmbigPrompt achieves state-of-the-art or competitive results while using less memory and having a lower inference latency than competing approaches. Additionally, AmbigPrompt also performs well in low-resource settings. The code are available at: https://github.com/sunnweiwei/AmbigPrompt.Comment: To be published in ACL 202

Experimental investigation of mechanical properties and energy features of granite after heat treatment under different loading paths

Povijest temperature i opterećenja dva su glavna čimbenika koji utječu na mikrostrukturu stijene i fizikalna i mehanička svojstva. Da bi se istražio utjecaj toplinske obrade i opterećenja na mehanička svojstva i energetske značajke granita, uzorci granita najprije su toplinski obrađeni na 25 °C, 300 °C, 600 °C i 900° C. Zatim je 12 skupina eksperimenata troosnog sabijanja podvrgnuto opterećenju na tri načina, kako slijedi: jednoosno sabijanje, konvencionalno troosno sabijanje i ograničeno tlačno rasterećenje. Sustavno se uspoređuju i analiziraju mehanička svojstva i značajke energije u procesu deformacije i oštećenja na temelju tih eksperimentalnih rezultata. Rezultati pokazuju da su Youngov modul, jednoosna tlačna čvrstoća i troosna tlačna čvrstoća porasli u temperaturnom području od 25 °C do 300 °C, ali su se smanjili u temperaturnom području od 300 °C do 900 °C. Pod istim načinima opterećenja, razlike između ukupne apsorbirane energije, raspršene energije i elastične energije naprezanja povećale su se s porastom temperature od 25 °C do 900 °C. Pri istoj temperaturi, razlika između energetskih značajki pod rasterećenjem ograničenim tlakom je između jednoosnog i troosnog sabijanja. Zaključci izneseni u ovoj studiji pružaju značajnu referencu za projektiranje i izgradnju u inženjerstvu stijena izloženih visokim temperaturama.Temperature and loading history are the two main factors that influence rock microstructure and physical and mechanical properties. To explore the influence of heat treatment and loading path on mechanical properties and energy features of granite, granite samples were first heat-treated at 25 °C, 300 °C, 600 °C, and 900 °C. Then, 12 groups of triaxial compression experiments were placed under three loading paths, as follows: uniaxial compression, conventional triaxial compression, and confining pressure unloading. Mechanical properties and energy features in the deformation and failure process based on these experimental results were systematically compared and analyzed. Results demonstrate that Young’s modulus, uniaxial compressive strength, and triaxial compressive strength increased in the temperature range of 25 °C to 300 °C, but decreased in the temperature range of 300 °C to 900 °C. Under the same loading path, the gaps among total absorbed energy, dissipated energy, and elastic strain energy widened with increasing temperature from 25 °C to 900 °C. At the same temperature, the energy features’ gap under confining pressure unloading is between uniaxial and triaxial compression. The conclusions drawn in this study provide a significant reference for the design and construction of rock engineering exposed to high temperature

High-yield production of Streptavidin with native C-terminal in Escherichia coli

To increase the production yield of functional recombinant streptavidin in Escherichia coli, the effects of host strains and culture conditions on expression of streptavidin with native C terminal (CNSA, amino acid residues 13 to 159) were investigated. Results show that the CNSA, encoded by the CNSA gene, was produced by E. coli BL21(DE3)pLysS strain in the inclusion body with a high yield up to 46.3% of the total cell protein (about 230 mg/g dry cell weight) after culture condition optimization. The dialysis method was adapted to refold CNSA and the refolding conditions were optimized. More than 90% of inclusion body protein was refolded to mature CNSA under optimized refolding conditions. The purity of the recombinant CNSA achieved 95.0% without using any affinity separation method. Enzyme linked immunosorbent assay (ELISA) analysis indicated that the biotin binding capability of our recombinant CNSA was similar to that of commercial products.Keywords: Streptavidin, Escherichia coli, protein refolding, recombinant protei

Learning from Easy to Complex: Adaptive Multi-curricula Learning for Neural Dialogue Generation

Current state-of-the-art neural dialogue systems are mainly data-driven and are trained on human-generated responses. However, due to the subjectivity and open-ended nature of human conversations, the complexity of training dialogues varies greatly. The noise and uneven complexity of query-response pairs impede the learning efficiency and effects of the neural dialogue generation models. What is more, so far, there are no unified dialogue complexity measurements, and the dialogue complexity embodies multiple aspects of attributes---specificity, repetitiveness, relevance, etc. Inspired by human behaviors of learning to converse, where children learn from easy dialogues to complex ones and dynamically adjust their learning progress, in this paper, we first analyze five dialogue attributes to measure the dialogue complexity in multiple perspectives on three publicly available corpora. Then, we propose an adaptive multi-curricula learning framework to schedule a committee of the organized curricula. The framework is established upon the reinforcement learning paradigm, which automatically chooses different curricula at the evolving learning process according to the learning status of the neural dialogue generation model. Extensive experiments conducted on five state-of-the-art models demonstrate its learning efficiency and effectiveness with respect to 13 automatic evaluation metrics and human judgments.Comment: Accepted to AAAI 202

Karst landslides detection and monitoring with multiple SAR data and multi-dimensional SBAS technique in Shuicheng, Guizhou, China

Shuicheng District is a karst mountain area, located in Guizhou Province, China. Its fragile stratum and frequent underground mining activities makes it prone to landslides. Owning to its wide coverage and frequent revisit, the InSAR technology has advantages in potential landslide identification and deformation monitor. However, affected by dense vegetation and atmospheric delay, it is much difficult to get sufficient effective targets to derive the deformation in this area. Besides, deformation derived from single orbit SAR data can result in the missing identification of some potential landslides and the misinterpreting of the real kinematics process of landslides. In this study, the multi-source SAR data, atmospheric error correction by quadratic tree image segmentation method, and phase-stacking method were selected to derive the surface deformation of this area. Besides, DS-InSAR and MSBAS method were combined to derive the deformation of Pingdi landslide. First, the potential landslides in this area were identified, surface deformation result, optical remote sensing images and geomorphological features were jointly considered. Then, the landslide distribution characteristics was analyzed in terms of slope, elevation and stratum. After that, the deformation along the LOS direction was acquired using the DS-InSAR method. The MSBAS method was used to retrieve the two-dimensional deformation of Pingdi landslide. Finally, the comprehensive analysis of triggering factors and failure process were conducted according to the spatial-temporal deformation characteristics and field investigation. The results indicated that landslides in Shuicheng district were mostly located in the junction of T1 and P3 stratum and mining related. Mining activity was the main cause of the Pingdi landslide deformation, the precipitation was the driving factor of the landslide instability. The research provides an insight into the explore the unstable slope distribution characteristic and the failure process of the landslides

Explainability for Large Language Models: A Survey

Large language models (LLMs) have demonstrated impressive capabilities in natural language processing. However, their internal mechanisms are still unclear and this lack of transparency poses unwanted risks for downstream applications. Therefore, understanding and explaining these models is crucial for elucidating their behaviors, limitations, and social impacts. In this paper, we introduce a taxonomy of explainability techniques and provide a structured overview of methods for explaining Transformer-based language models. We categorize techniques based on the training paradigms of LLMs: traditional fine-tuning-based paradigm and prompting-based paradigm. For each paradigm, we summarize the goals and dominant approaches for generating local explanations of individual predictions and global explanations of overall model knowledge. We also discuss metrics for evaluating generated explanations, and discuss how explanations can be leveraged to debug models and improve performance. Lastly, we examine key challenges and emerging opportunities for explanation techniques in the era of LLMs in comparison to conventional machine learning models

Ultrahigh-sensitivity label-free optical fiber biosensor based on a tapered singlemode- no core-singlemode coupler for Staphylococcus aureus detection

An ultra-high sensitivity label-free optical fiber biosensor for inactivated Staphylococcus aureus (S. aureus) detection is proposed and investigated in this study, with additional advantages of robust and stability compared to traditional tapered fiber structure. The proposed fiber biosensor is based on a tapered singlemode- no core-singlemode fiber coupler (SNSFC) structure, where the no core fiber was tapered to small diameter (taper-waist diameter of about 10 µm) and functionalized with the pig immunoglobulin G (IgG) antibody for detection of S. aureus. The measured maximum wavelength shift of the sensor for an S. aureus concentration of 7 × 101 CFU/ml (colony forming unit per milliliter) is 2.04 nm, which is equivalent to a limit of detection (LOD) of 3.1 CFU/ml (a highest LOD reported so far for optical fiber biosensors), considering the maximum wavelength variation of the sensor in phosphate buffered saline (PBS) is ±0.03 nm over 40 minutes, where 3 times of maximum wavelength variation (3 × 0.03 = 0.09 nm) is defined as measurement limit. The response time of the developed fiber sensor is less than 30 minutes. The ultra-sensitive biosensor has potential to be widely applied to various areas such as disease, medical diagnostics and food safety inspection