37 research outputs found
Stylistic Dialogue Generation Based on Character Personality in Narrative Films.
Traditional narrative systems consist of two steps of process, story generation and discourse generation. However, many interactive systems make more effort on story generation rather than discourse generation. For discourse generation, dialogue is an important way used to unfold story and reveal characters in stories, and it is reasonable to expand the capability of narrative system by exploring the potential of dialogue generation in narratives. Also, Recent research in conditional dialogue generation is mostly focusing on the context of natural conversation generation with speakersâ profile information. While incorporating the styles that relevant to narratives is yet to be widely investigated.
According to the research made, in this document, we propose an approach using a pre-trained language model, in order to explore the potential of generating dialogues with embedded narrative-related features within the context of narrative films. In this approach, three different embedding methods are leveraged to incorporate Big-Five personalities of characters into transformer-based neural networks, training on a new corpus, which is created and well-parsed from screenplays.
We conduct experiments using both automatic metrics and human evaluation to measure the quality of the generated dialogue and personality identification accuracy. All the dialogues for evaluation and analysis are generated with settings of the perspectives of embedding method, personality trait, personality level, and film genre, which is to explore the impact of different setting on dialogue generation with additional narrative-related styles.
According to the automatic experimental results, we demonstrate that our approach is able to generate dialogues with increased variety. Also overall, the generated dialogues are able to correctly reflect the given target personality.
We also conduct three user studies for evaluate dialogues with human judgements. In the first and the second user study, we evaluate the dialogues generated with film- level personality using CTE (Combined Textual Embedding) embedding method. The results show that human participants are inclined to perceive one extreme end of each personality trait. In the third user study, we evaluate generated dialogues with all setting combinations synthetically. Overall, the results show that target personalities can be identified with various degrees of accuracy. Also, a negative correlation between personality identification accuracy and dialogue quality is observed.
In this thesis, we propose a new approach for stylistic dialogue generation and demonstrate its effectiveness. We believe the observations and discoveries could be a start and a tryout to apply deep learning technique and big data to boost narrative dialogue generation. And we also believe that our research can be applied in plenty of potential scenarios, such as helping the authors creating huge amount of conversations between different characters by popping utterance options corresponding to the character settings
Influence of Personality-based Features for Dialogue Generation in Computational Narratives
In this paper, we present an approach for generating dialogues for characters within the context of computational narratives
using personality-based features for deep neural networks. The approach integrates the requirements of both narrative genres and personality traits for the definition of character-based stylistic models.
The modelling of charactersâ features from existing datasets of complete stories permits the generation of personality-rich character dialogues. We present early results from an evaluation based on a sample of charactersâ personality traits across different narrative genres,
demonstrating variability in the resulting dialogue
Faster Ray Tracing through Hierarchy Cut Code
We propose a novel ray reordering technique to accelerate the ray tracing
process by encoding and sorting rays prior to traversal. Instead of spatial
coordinates, our method encodes rays according to the cuts of the hierarchical
acceleration structure, which is called the hierarchy cut code. This approach
can better adapt to the acceleration structure and obtain a more reliable
encoding result. We also propose a compression scheme to decrease the sorting
overhead by a shorter sorting key. In addition, based on the phenomenon of
boundary drift, we theoretically explain the reason why existing reordering
methods cannot achieve better performance by using longer sorting keys. The
experiment demonstrates that our method can accelerate secondary ray tracing by
up to 1.81 times, outperforming the existing methods. Such result proves the
effectiveness of hierarchy cut code, and indicate that the reordering technique
can achieve greater performance improvement, which worth further research
Anaplastic large-cell lymphoma with atypical chromosomal translocation t(2;5) and hypophyseal tumor
Biomimetic Z-scheme photocatalyst with a tandem solid-state electron flow catalyzing H_2 evolution
Similar to natural photosynthetic systems, artificial photosynthetic systems require synergistic cooperation between light harvesting, charge separation and redox catalysis. Herein, a three-dimensional (3D) hierarchical photocatalyst is designed with a novel Z-scheme two-photon excitation, defined by the complementary absorption of higher energy and lower energy photons by cadmium sulfide nanowires (CdS NWs) and cobaltâbenzimidazole (Co-bIm) coordination polymers (CBPs), respectively. Without any noble-metal co-catalyst, the microscopically integrated CdSâCBP photocatalysts demonstrated dramatically enhanced photocatalytic activities of H_2 evolution, which were up to 10.6 folds higher than those of pristine CdS NWs. Structurally, the intimate interfacial contact between the 3D CdS NW scaffold and the discrete CBP microstructures benefits their strong electronic interaction and efficient charge separation. Upon simultaneous light excitation, a tandem solid-state electron flow from CdS to CBP and then from metal (Co) to ligand (bIm) precisely catalyzes the reduction of pre-activated H atoms on the bIm ligands for efficient H_2 evolution
Biomimetic Z-scheme photocatalyst with a tandem solid-state electron flow catalyzing H_2 evolution
Similar to natural photosynthetic systems, artificial photosynthetic systems require synergistic cooperation between light harvesting, charge separation and redox catalysis. Herein, a three-dimensional (3D) hierarchical photocatalyst is designed with a novel Z-scheme two-photon excitation, defined by the complementary absorption of higher energy and lower energy photons by cadmium sulfide nanowires (CdS NWs) and cobaltâbenzimidazole (Co-bIm) coordination polymers (CBPs), respectively. Without any noble-metal co-catalyst, the microscopically integrated CdSâCBP photocatalysts demonstrated dramatically enhanced photocatalytic activities of H_2 evolution, which were up to 10.6 folds higher than those of pristine CdS NWs. Structurally, the intimate interfacial contact between the 3D CdS NW scaffold and the discrete CBP microstructures benefits their strong electronic interaction and efficient charge separation. Upon simultaneous light excitation, a tandem solid-state electron flow from CdS to CBP and then from metal (Co) to ligand (bIm) precisely catalyzes the reduction of pre-activated H atoms on the bIm ligands for efficient H_2 evolution
Application of the ultrasonic technology for quantitative measurements of elastic properties of materials
Presently, ultrasonic non-destructive testing technique is widely and confidently used in many different fields. In this project, ultrasonic technology is used to determine the physical properties of materials. In the first part of the project, the V(z) technique is presented to determine the YoungâsModulus nad Raileigh velocity of isotropic materials.Master of Science (Mechanics & Processing of Materials
Downregulation of hTERT: an important As2O3 induced mechanism of apoptosis in myelodysplastic syndrome.
Two myelodysplastic syndrome (MDS) cell lines, MUTZ-1 and SKM-1 cells, were used to study the effect of arsenic trioxide (As2O3) on hematological malignant cells. As2O3 induced this two cell lines apoptosis via activation of caspase-3/8 and cleavage of poly (ADP-ribose) polymerase (PARP), a DNA repair enzyme. As2O3 reduced NF-ÎșB activity, which was important for inducing MUTZ-1 and SKM-1 cells apoptosis. As2O3 also inhibited the activities of hTERT in MUTZ-1 and SKM-1 cells. Moreover, the NF-ÎșB inhibitor, pyrrolidine dithiocarbamate (PDTC), had no effect on caspase-8 activation, although PDTC did inhibit MUTZ-1 and SKM-1 cells proliferation. Incubation of MUTZ-1 cells with a caspase-8 inhibitor failed to block As2O3-induced inhibition of NF-ÎșB activity. Our findings suggest that As2O3 may induce apoptosis in MUTZ-1 and SKM-1 cells by two independent pathways: first, by activation of caspase-3/8 and PARP; and second, by inhibition of NF-ÎșB activity, which results in downregulation of hTERT expression. We conclude that hTERT and NF-ÎșB are important molecular targets in As2O3-induced apoptosis
The complete mitochondrial genome of Amur ide (Leuciscus waleckii waleckii)
In this study, the complete mitochondrial genome of Leuciscus waleckii waleckii was sequenced and got a whole length of 16605âbp. This genome was contain 2 rRNA, 22 tRNA, 13 protein-coding genes, 1 control region (D-loop) and 1 replication origin. And the nucleotide composition of this mitochondrial genome is 27.72% for A, 26.28% for T, 27.23% for C and 18.77% for G. To clarify the phylogenetic relationship of the Leuciscus waleckii waleckii, we concluded the phylogenetic tree using 12 PCGs (except ND6) of mitochondrial genome in Leuciscus waleckii waleckii and 16 other cyprinid fish by Bayesian inference (BI) methods and maximum-likelihood (ML). And the result show that Leuciscus waleckii waleckii was close to other Leuciscus species, especially Leuciscus baicalensis
A DeepâLearning Approach for LowâSpatialâCoherence Imaging in ComputerâGenerated Holography
The lowâspatialâcoherence imaging capability of computerâgenerated holography (CGH) is a key to highâresolution displays, virtual reality, augmented reality, and holographic microscopy. The low spatial coherence caused by complex disturbances can damage the image quality irreversibly. The optical field with low spatial coherence has large fluctuations, making it difficult to be quantified and modeled directly. To tackle these challenges, a deep neural networkâbased model Uâresidual dense network (UâRDN) is proposed, which obtains the optimal solution under the lowâspatialâcoherence condition. The largeâscale images are generated using optical experiments, with analysis and restoration by deep learning. Extensive experiments demonstrate the strong outâofâdistribution robustness of UâRDN, which is generalizable to unseen classes in unseen domains. The learningâbased approach and lowâspatialâcoherence dataset open a new path toward the next generation of CGH