Learning Personalized End-to-End Goal-Oriented Dialog

Most existing works on dialog systems only consider conversation content while neglecting the personality of the user the bot is interacting with, which begets several unsolved issues. In this paper, we present a personalized end-to-end model in an attempt to leverage personalization in goal-oriented dialogs. We first introduce a Profile Model which encodes user profiles into distributed embeddings and refers to conversation history from other similar users. Then a Preference Model captures user preferences over knowledge base entities to handle the ambiguity in user requests. The two models are combined into the Personalized MemN2N. Experiments show that the proposed model achieves qualitative performance improvements over state-of-the-art methods. As for human evaluation, it also outperforms other approaches in terms of task completion rate and user satisfaction.Comment: Accepted by AAAI 201

Spin Fluctuation Induced Linear Magnetoresistance in Ultrathin Superconducting FeSe Films

The discovery of high-temperature superconductivity in FeSe/STO has trigged great research interest to reveal a range of exotic physical phenomena in this novel material. Here we present a temperature dependent magnetotransport measurement for ultrathin FeSe/STO films with different thickness and protection layers. Remarkably, a surprising linear magnetoresistance (LMR) is observed around the superconducting transition temperatures but absent otherwise. The experimental LMR can be reproduced by magnetotransport calculations based on a model of magnetic field dependent disorder induced by spin fluctuation. Thus, the observed LMR in coexistence with superconductivity provides the first magnetotransport signature for spin fluctuation around the superconducting transition region in ultrathin FeSe/STO films

Linking ethylene to nitrogen-dependent leaf longevity of grass species in a temperate steppe

Author's manuscript made available in accordance with the publisher's policy.Background and Aims Leaf longevity is an important plant functional trait that often varies with soil nitrogen supply. Ethylene is a classical plant hormone involved in the control of senescence and abscission, but its role in nitrogen-dependent leaf longevity is largely unknown. Methods Pot and field experiments were performed to examine the effects of nitrogen addition on leaf longevity and ethylene production in two dominant plant species, Agropyron cristatum and Stipa krylovii, in a temperate steppe in northern China. Key Results Nitrogen addition increased leaf ethylene production and nitrogen concentration but shortened leaf longevity; the addition of cobalt chloride, an ethylene biosynthesis inhibitor, reduced leaf nitrogen concentration and increased leaf longevity. Path analysis indicated that nitrogen addition reduced leaf longevity mainly through altering leaf ethylene production. Conclusions These findings provide the first experimental evidence in support of the involvement of ethylene in nitrogen-induced decrease in leaf longevity

Anti-collision Optimization Design Technology of Large Infill Cluster Well Group in Bohai Sea Artificial Island B

In view of the difficulty and low efficiency in the design of collision prevention and obstacle avoidance in the large-scale infilling adjustment of the cluster well group in Bohai Sea artificial island B, based on the analysis of the overall situation and characteristics of the infilling well, accurate calibration of already drilled data, and using industry standard principles for cluster well platform optimization and anti-collision design, develop specific procedures for designing anti-collision tracks for large-scale infill wells, form anti-collision optimization design methods, optimize drilling sequence, slot matching relationship and anti-collision track design parameters. The track design and anti-collision analysis of 45 infill wells have been completed in the limited platform space through multiple slot adjustment and track optimization.The minimum value of the separation coefficient of each well meets the requirements of the industry standard minimum limit of 1.5, and 70% of them are concentrated above 1.7, which reduces the risk of anti-collision as a whole.The results show that the anti-collision optimization design technology of large-scale infill cluster well group can effectively solve the anti-collision design problem of large-scale infill adjustment of cluster well group, and help to improve the design quality and efficiency

Identification and Characterization of Signals Recognized by Bacterial Extracellular Sensory Domains

Bacteria are ubiquitous and essential components of all ecosystems, not merely existing as solitary entities but rather within complex multispecies communities. The stability of these communities is maintained primarily through the complicated microbial interactions mediated by signal transduction systems. Comprehending these signaling mechanisms is crucial, as they play a role in various physiological processes such as motility, metabolism, biofilm formation, and antibiotic resistance. Although their conserved signaling core is well-characterized, signal recognition by bacterial sensors remains largely unknown. To bridge this gap, we aim to establish a systematic strategy to broadly explore the signals recognized by sensory receptors in different bacterial species, leveraging recent advances in experimental techniques, bioinformatics, and genomic analysis. In the first paper, we employed Pseudomonas aeruginosa PAO1 as a model organism to develop a screening strategy for identifying chemoreceptor specificities. Although Escherichia coli has the simplest and best-studied chemotaxis system, it lacks the diversity of sensory domains found in other organisms. In contrast, P. aeruginosa PAO1 harbors four different chemosensory pathways and diverse sensory domains that are crucial for detecting diverse stimuli. Given that accessing nutrients is the primary benefit of chemotaxis, this chemoreceptor-targeted strategy focused on metabolites with metabolic value. We first identified novel attractants of varying strength from these metabolites by performing capillary chemotaxis assays. Subsequently, a rational combination of various in vivo and in vitro methods enabled us to assign several new attractants to previously characterized chemoreceptors and to annotate a novel purine-specific receptor PctP. Overall, these findings suggested that our screening strategy can be applied to the systematic characterization of unknown chemoreceptors in a wide range of bacterial species. Recent advances in genome sequencing and analysis have revealed an extremely large repertoire of unknown sensory receptors, including not only chemoreceptors but also sensor histidine kinases and transcriptional regulators. This discovery provides a rich resource for extensively identifying sensory receptors in many understudied organisms. The human gut, in particular, is an ideal target for this field of research. Our second paper has developed a framework that enables the identification of sensory domains, prediction of the signals they recognize, and experimental verification of these predictions. This framework began with a bioinformatic approach for identifying sensory domains from both fully sequenced and metagenome-assembled genomes and further predicted their signals based on conserved binding motifs. Subsequently, we validated the accuracy of these predictions through various experimental approaches, as performed in our first study. This pipeline can be used in a variety of situations and is particularly suitable for complex research systems such as microbiota. Building upon our second paper, we successfully expanded our research scope from chemoreceptors in one organism to various sensory receptors in human gut microbiota and generated a curated list containing thousands of unknown sensory domains. In the third paper, we delved into approximately one hundred representative sensory domains selected from this curated list. Using high-throughput thermal shift assays, we characterized several novel bacterial sensory receptors that recognize physiologically relevant signaling molecules in the human gut, further elucidating their underlying molecular mechanisms. Our studies highlighted the differences in stimulus spectrum between two functional classes, with nutrient compounds primarily sensed by chemoreceptors but not by histidine kinases. Moreover, we defined two novel sensor subclasses that bind uracil and short-chain carboxylates, respectively. Ultimately, for the first time, we utilized several conserved binding motifs to predict the abundance of corresponding ligand-specific sensors at the human gut microbiome level. In conclusion, this thesis presents a genomics-guided and interdisciplinary strategy for identifying signals recognized by bacterial sensory receptors, applicable across a wide range of research areas from single bacteria to complex microbiota. These findings pave the way for a deeper understanding of bacterial chemotactic and metabolic preferences and provide novel insights into interspecies communication within the human gut

Tenfold Magnetoconductance in a Non-Magnetic Metal Film

We present magnetoconductance (MC) measurements of homogeneously disordered Be films whose zero field sheet conductance (G) is described by the Efros-Shklovskii hopping law $G(T)=(2e^2/h)\exp{-(T_o/T)^{1/2}}$. The low field MC of the films is negative with G decreasing 200% below 1 T. In contrast the MC above 1 T is strongly positive. At 8 T, G increases 1000% in perpendicular field and 500% in parallel field. In the simpler parallel case, we observe {\em field enhanced} variable range hopping characterized by an attenuation of $T_o$ via the Zeeman interaction.Comment: 9 pages including 5 figure

The Path and Enlightenment of Data-Driven Digital Transformation of Organizational Learning ——A Case Study of the Practice of China Telecom

This paper took China Telecom as a case. It has analyzed data-driven digital transformation in organizational learning, and summarized the methods and enlightenments of digital transformation