Unifying and Merging Well-trained Deep Neural Networks for Inference Stage

We propose a novel method to merge convolutional neural-nets for the inference stage. Given two well-trained networks that may have different architectures that handle different tasks, our method aligns the layers of the original networks and merges them into a unified model by sharing the representative codes of weights. The shared weights are further re-trained to fine-tune the performance of the merged model. The proposed method effectively produces a compact model that may run original tasks simultaneously on resource-limited devices. As it preserves the general architectures and leverages the co-used weights of well-trained networks, a substantial training overhead can be reduced to shorten the system development time. Experimental results demonstrate a satisfactory performance and validate the effectiveness of the method.Comment: To appear in the 27th International Joint Conference on Artificial Intelligence and the 23rd European Conference on Artificial Intelligence, 2018. (IJCAI-ECAI 2018

Putative cell adhesion membrane protein Vstm5 regulates neuronal morphology and migration in the central nervous system

During brain development, dynamic changes in neuronal membranes perform critical roles in neuronal morphogenesis and migration to create functional neural circuits. Among the proteins that induce membrane dynamics, cell adhesion molecules are important in neuronal membrane plasticity. Here, we report that V-set and transmembrane domain-containing protein 5 (Vstm5), a cell-adhesion-like molecule belonging to the Ig superfamily, was found in mouse brain. Knock-down of Vstm5 in cultured hippocampal neurons markedly reduced the complexity of dendritic structures, as well as the number of dendritic filopodia. Vstm5 also regulates neuronal morphology by promoting dendritic protrusions that later develop into dendritic spines. Using electroporationin utero, we found that Vstm5 overexpression delayed neuronal migration and induced multiple branches in leading processes during corticogenesis. These results indicate that Vstm5 is a new cell-adhesion-like molecule and is critically involved in synaptogenesis and corticogenesis by promoting neuronal membrane dynamics.SIGNIFICANCE STATEMENTNeuronal migration and morphogenesis play critical roles in brain development and function. In this study, we demonstrate for the first time that V-set and transmembrane domain-containing protein 5 (Vstm5), a putative cell adhesion membrane protein, modulates both the position and complexity of central neurons by altering their membrane morphology and dynamics. Vstm5 is also one of the target genes responsible for variations in patient responses to treatments for major depressive disorder. Our results provide the first evidence that Vstm5 is a novel factor involved in the modulation of the neuronal membrane and a critical element in normal neural circuit formation during mammalian brain development.</jats:p

Learning to Terminate in Object Navigation

This paper tackles the critical challenge of object navigation in autonomous navigation systems, particularly focusing on the problem of target approach and episode termination in environments with long optimal episode length in Deep Reinforcement Learning (DRL) based methods. While effective in environment exploration and object localization, conventional DRL methods often struggle with optimal path planning and termination recognition due to a lack of depth information. To overcome these limitations, we propose a novel approach, namely the Depth-Inference Termination Agent (DITA), which incorporates a supervised model called the Judge Model to implicitly infer object-wise depth and decide termination jointly with reinforcement learning. We train our judge model along with reinforcement learning in parallel and supervise the former efficiently by reward signal. Our evaluation shows the method is demonstrating superior performance, we achieve a 9.3% gain on success rate than our baseline method across all room types and gain 51.2% improvements on long episodes environment while maintaining slightly better Success Weighted by Path Length (SPL). Code and resources, visualization are available at: https://github.com/HuskyKingdom/DITA_acml2023Comment: 16 page

Exponential fall-off Behavior of Regge Scatterings in Compactified Open String Theory

We calculate massive string scattering amplitudes of compactified open string in the Regge regime. We extract the complete infinite ratios among high-energy amplitudes of different string states in the fixed angle regime from these Regge string scattering amplitudes. The complete ratios calculated by this indirect method include and extend the subset of ratios calculated previously (Lee and Yang, 2007, and Lee, Takimi, and Yang, 2008) by the more difficult direct fixed angle calculation. In this calculation of compactified open string scattering, we discover a realization of arbitrary real values L in the identity Eq.(4.18), rather than integer value only in all previous high-energy string scattering amplitude calculations. The identity in Eq.(4.18) was explicitly proved recently in Lee, Yan, and Yang to link fixed angle and Regge string scattering amplitudes. In addition, we discover a kinematic regime with stringy highly winding modes, which shows the unusual exponential fall-off behavior in the Regge string scattering. This is in complementary with a kenematic regime discovered previously (Lee, Takimi, and Yang, 2008), which shows the unusual power-law behavior in the high-energy fixed angle compactified string scatterings. Key words: Regge string scatterings; High-energy StringComment: 17 pages. v2:18 pages,corrected Eqs.(3.4),(4.24)-(4.26). v3:19 pages,Eqs.(3.11),(3.12) added, more on conclusion. arXiv admin note: text overlap with arXiv:0805.3168, arXiv:1101.1228, arXiv:0811.4502, arXiv:0812.4190, arXiv:1001.539