Research on Relationship between Knowledge Diffusion Rate and Enterprise Knowledge Search Breadth

From the perspective of patent, this paper constructs a theoretical model between knowledge diffusion rate and enterprise knowledge search breadth, and probes into the relationship between them. At the same time, this paper explores the adjustment functions of these variables including the technical difficulties and the market expectation .Through the analysis of the patent data，we came to the follow conclusions: the diffusion rate of knowledge has a positive effect on the breadth of enterprise knowledge search. The difficulty of technology and the degree of decentralization of innovation sources positively modulate this promotion effect. The market expects this role to be negatively regulated

Dual-space Hierarchical Learning for Goal-guided Conversational Recommendation

Proactively and naturally guiding the dialog from the non-recommendation context (e.g., Chit-chat) to the recommendation scenario (e.g., Music) is crucial for the Conversational Recommender System (CRS). Prior studies mainly focus on planning the next dialog goal~(e.g., chat on a movie star) conditioned on the previous dialog. However, we find the dialog goals can be simultaneously observed at different levels, which can be utilized to improve CRS. In this paper, we propose Dual-space Hierarchical Learning (DHL) to leverage multi-level goal sequences and their hierarchical relationships for conversational recommendation. Specifically, we exploit multi-level goal sequences from both the representation space and the optimization space. In the representation space, we propose the hierarchical representation learning where a cross attention module derives mutually enhanced multi-level goal representations. In the optimization space, we devise the hierarchical weight learning to reweight lower-level goal sequences, and introduce bi-level optimization for stable update. Additionally, we propose a soft labeling strategy to guide optimization gradually. Experiments on two real-world datasets verify the effectiveness of our approach. Code and data are available here.Comment: Accepted by Neurocomputin

Analysis and Optimization of TrueType Font Bytecode

TrueType is one of the most widely used vectorized font formats. It can be optimally rendered on screens with different resolutions and different font sizes thanks to hints expressed as bytecode programs. Font engines execute the bytecode programs to adjust the outlines of the glyphs. TrueType font bytecode is a highly-dynamic stack-based bytecode language. It manipulates data with a global stack, and it uses hardware related information, such as screen resolutions and font sizes, which are unknown at compile time. Thus, it is hard to perform static analysis and optimizations on this bytecode. Fonts are sometimes subsetted to only include the glyphs that appear in a webpage before sending to the client. Existing font manipulation techniques do not touch the bytecode, so subsetted fonts contain un-optimized bytecode programs. TrueType bytecode analysis can help reduce bandwidth demands for serving webpages. This thesis presents improvements to COI, a tool for manipulating TrueType bytecode. New features include enhanced abstract execution as well as basic optimizations on COI, such as tree shaking, no-effect instruction removal, and dead block elimination. Finally, it completes the cycle by translating the COI back to TrueType bytecode. We tested our tool on fonts from different font families, including Microsoft Core TrueType font Arial, and NotoSansTibetan-Bold. Our experiments show that our optimizations can reduce the size of bytecode by 0.37\% to 18.82\% of the test fonts in our benchmarks. On average, we can reduce the size of bytecode of our test fonts by 7.10\%. Our optimized fonts yield the same bitmaps as the original font

Collective flow and the fluid behavior in p/d/3^3He+Au collisions at sNN=200\sqrt{s_{NN}} = 200 GeV

By varying the intrinsic initial geometry, the p/d/$^3$He+Au collisions at the Relativistic Heavy Ion Collider (RHIC) provide a unique opportunity to understand the collective behavior in the small systems. In this paper, we employ the hybrid model iEBE-VISHNU with TRENTO initial conditions to study the collective flow and the fluid behavior in p/d/$^3$He+Au collisions. With fine-tuned parameters, iEBE-VISHNU can describe the $v_2(p_T)$ and $v_3(p_T)$ data from the PHENIX and STAR collaborations. However, for these parameter sets tuned to fit the STAR data, the hydrodynamic simulations have already beyond their limits with the average Knudsen number $\langle K_n \rangle$ obviously larger than one. Our calculations demonstrate that, for a meaningful evaluation of the fluid behavior in the small systems, model simulations should also pay attention to the validity range of hydrodynamics