146 research outputs found

    Development of P-I Diagrams for Framed PVB-Laminated Glass Windows

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    Analysis of Kinase Gene Expression in the Frontal Cortex of Suicide Victims: Implications of Fear and Stress†

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    Suicide is a serious public health issue that results from an interaction between multiple risk factors including individual vulnerabilities to complex feelings of hopelessness, fear, and stress. Although kinase genes have been implicated in fear and stress, including the consolidation and extinction of fearful memories, expression profiles of those genes in the brain of suicide victims are less clear. Using gene expression microarray data from the Online Stanley Genomics Database1 and a quantitative PCR, we investigated the expression profiles of multiple kinase genes including the calcium calmodulin-dependent kinase (CAMK), the cyclin-dependent kinase, the mitogen-activated protein kinase (MAPK), and the protein kinase C (PKC) in the prefrontal cortex (PFC) of mood disorder patients died with suicide (N = 45) and without suicide (N = 38). We also investigated the expression pattern of the same genes in the PFC of developing humans ranging in age from birth to 49 year (N = 46). The expression levels of CAMK2B, CDK5, MAPK9, and PRKCI were increased in the PFC of suicide victims as compared to non-suicide controls (false discovery rate, FDR-adjusted p < 0.05, fold change >1.1). Those genes also showed changes in expression pattern during the postnatal development (FDR-adjusted p < 0.05). These results suggest that multiple kinase genes undergo age-dependent changes in normal brains as well as pathological changes in suicide brains. These findings may provide an important link to protein kinases known to be important for the development of fear memory, stress associated neural plasticity, and up-regulation in the PFC of suicide victims. More research is needed to better understand the functional role of these kinase genes that may be associated with the pathophysiology of suicide

    Neutron Energy Spectrum Measurements with a Compact Liquid Scintillation Detector on EAST

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    A neutron detector based on EJ301 liquid scintillator has been employed at EAST to measure the neutron energy spectrum for D-D fusion plasma. The detector was carefully characterized in different quasi-monoenergetic neutron fields generated by a 4.5 MV Van de Graaff accelerator. In recent experimental campaigns, due to the low neutron yield at EAST, a new shielding device was designed and located as close as possible to the tokamak to enhance the count rate of the spectrometer. The fluence of neutrons and gamma-rays was measured with the liquid neutron spectrometer and was consistent with 3He proportional counter and NaI (Tl) gamma-ray spectrometer measurements. Plasma ion temperature values were deduced from the neutron spectrum in discharges with lower hybrid wave injection and ion cyclotron resonance heating. Scattered neutron spectra were simulated by the Monte Carlo transport Code, and they were well verified by the pulse height measurements at low energies.Comment: 19 pages,10 figures, 1 tabl

    Activity of Platinum/Carbon and Palladium/Carbon Catalysts Promoted by Ni2P in Direct Ethanol Fuel Cells

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    Ethanol is an alternative fuel for direct alcohol fuel cells, in which the electrode materials are commonly based on Pt or Pd. Owing to the excellent promotion effect of Ni2P that was found in methanol oxidation, we extended the catalyst system of Pt or Pd modified by Ni2P in direct ethanol fuel cells. The Ni2P-promoted catalysts were compared to commercial catalysts as well as to reference catalysts promoted with only Ni or only P. Among the studied catalysts, Pt/C and Pd/C modified by Ni2P (30 wt%) showed both the highest activity and stability. Upon integration into the anode of a homemade direct ethanol fuel cell, the Pt-Ni2P/C-30% catalyst showed a maximum power density of 21 mWcm<sup>-2</sup>, which is approximately two times higher than that of a commercial Pt/C catalyst. The Pd- Ni2P/C-30% catalyst exhibited a maximum power density of 90 mWcm<sup>-2</sup>. This is approximately 1.5 times higher than that of a commercial Pd/C catalyst. The discharge stability on both two catalysts was also greatly improved over a 12 h discharge operation

    Dual Modality Prompt Tuning for Vision-Language Pre-Trained Model

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    With the emergence of large pre-trained vison-language model like CLIP, transferable representations can be adapted to a wide range of downstream tasks via prompt tuning. Prompt tuning tries to probe the beneficial information for downstream tasks from the general knowledge stored in the pre-trained model. A recently proposed method named Context Optimization (CoOp) introduces a set of learnable vectors as text prompt from the language side. However, tuning the text prompt alone can only adjust the synthesized "classifier", while the computed visual features of the image encoder can not be affected , thus leading to sub-optimal solutions. In this paper, we propose a novel Dual-modality Prompt Tuning (DPT) paradigm through learning text and visual prompts simultaneously. To make the final image feature concentrate more on the target visual concept, a Class-Aware Visual Prompt Tuning (CAVPT) scheme is further proposed in our DPT, where the class-aware visual prompt is generated dynamically by performing the cross attention between text prompts features and image patch token embeddings to encode both the downstream task-related information and visual instance information. Extensive experimental results on 11 datasets demonstrate the effectiveness and generalization ability of the proposed method. Our code is available in https://github.com/fanrena/DPT.Comment: 12 pages, 7 figure

    Ground-to-Aerial Person Search: Benchmark Dataset and Approach

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    In this work, we construct a large-scale dataset for Ground-to-Aerial Person Search, named G2APS, which contains 31,770 images of 260,559 annotated bounding boxes for 2,644 identities appearing in both of the UAVs and ground surveillance cameras. To our knowledge, this is the first dataset for cross-platform intelligent surveillance applications, where the UAVs could work as a powerful complement for the ground surveillance cameras. To more realistically simulate the actual cross-platform Ground-to-Aerial surveillance scenarios, the surveillance cameras are fixed about 2 meters above the ground, while the UAVs capture videos of persons at different location, with a variety of view-angles, flight attitudes and flight modes. Therefore, the dataset has the following unique characteristics: 1) drastic view-angle changes between query and gallery person images from cross-platform cameras; 2) diverse resolutions, poses and views of the person images under 9 rich real-world scenarios. On basis of the G2APS benchmark dataset, we demonstrate detailed analysis about current two-step and end-to-end person search methods, and further propose a simple yet effective knowledge distillation scheme on the head of the ReID network, which achieves state-of-the-art performances on both of the G2APS and the previous two public person search datasets, i.e., PRW and CUHK-SYSU. The dataset and source code available on \url{https://github.com/yqc123456/HKD_for_person_search}.Comment: Accepted by ACM MM 202
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