Research on the TNT Equivalence of Aluminized Explosive

AbstractAluminum as an important fuel component has been widely used in the field of arms and ammunition. The Determination of TNT equivalent of aluminized explosive, focused on the experimental study, is still lack of numerical calculation study. It is one of the main factors on safety research, so that effective measures should be taken to determine the TNT equivalence of aluminized explosive. In previous studies, the determination of TNT equivalence of aluminized explosive is mainly based on experimental study. But the affection to its explosive heat due to different ratio of aluminum powder is neglected in experiment researches. Based on the minimum free energy method, this paper programmed composition with Matlab. The equilibrium products of aluminized explosive detonation were calculated. The TNT equivalence of aluminized explosive with different ratio was determined. The results show that for the same mass of aluminized explosive, the higher mass fraction of aluminum powder was, the higher thermal damage to the environment was

Dynamic sentiment asset pricing model

Conventional wisdom suggests that the equilibrium stock price is not affected by investor sentiment, and the equilibrium price at an early time is higher than the one at a later time. In contrast to this wisdom, we present a dynamic asset pricing model with investor sentiment and we find that investor sentiment has a significant impact on the equilibrium stock price. The equilibrium stock price, which is affected by pessimistic sentiment at time 0, may be lower than the one at time 1. Moreover, consistent with the reality stock market, our model shows that time varying sentiments can lead to various price changes. Finally, the model could offer a partial explanation for the financial anomaly of high volatility

The candidate tumor suppressor gene ECRG4 inhibits cancer cells migration and invasion in esophageal carcinoma

<p>Abstract</p> <p>Background</p> <p>The esophageal cancer related gene 4 (ECRG4) was initially identified and cloned in our laboratory from human normal esophageal epithelium (GenBank accession no.<ext-link ext-link-id="AF325503" ext-link-type="gen">AF325503</ext-link>). ECRG4 was a new tumor suppressor gene in esophageal squamous cell carcinoma (ESCC) associated with prognosis. In this study, we investigated the novel tumor-suppressing function of ECRG4 in cancer cell migration, invasion, adhesion and cell cycle regulation in ESCC.</p> <p>Methods</p> <p>Transwell and Boyden chamber experiments were utilized to examined the effects of ECRG4 expression on ESCC cells migration, invasion and adhesion. And flow cytometric analysis was used to observe the impact of ECRG4 expression on cell cycle regulation. Finally, the expression levels of cell cycle regulating proteins p53 and p21 in human ESCC cells transfected with ECRG4 gene were evaluated by Western blotting.</p> <p>Results</p> <p>The restoration of ECRG4 expression in ESCC cells inhibited cancer cells migration and invasion (<it>P </it>< 0.05), which did not affect cell adhesion capacity (<it>P </it>> 0.05). Furthermore, ECRG4 could cause cell cycle G1 phase arrest in ESCC (<it>P </it>< 0.05), through inducing the increased expression of p53 and p21 proteins.</p> <p>Conclusion</p> <p>ECRG4 is a candidate tumor suppressor gene which suppressed tumor cells migration and invasion without affecting cell adhesion ability in ESCC. Furthermore, ECRG4 might cause cell cycle G1 phase block possibly through inducing the increased expression of p53 and p21 proteins in ESCC.</p

The Geochemical Data Imaging and Application in Geoscience: Taking the Northern Daxinganling Metallogenic Belt as an Example

Geochemical data were predominantly expressed by vector format, the research on geochemical data visualization, i.e., raster data format, was not paid proper attention. A total of 39 geochemical elements in 1:200,000 regional geochemical exploration data were rasterized to form images, and then a geochemical image database was generated. This article has carried out the study on geochemical imaging within Daxinganling metallogenic belt. The metallogenic belt had once carried out the regional geochemical survey, the sampling density was 1 site/4 km2, and 39 geochemistry elements including the microelement and trace element have been analyzed. Quintic polynomial method was used to implement the geochemical data interpolation, and the cell size of formed geochemical elemental image is 1 km. The images of the geochemical elements were processed by image enhancement methods, and then hyperspectral remote sensing data processing method was used for prospecting target selection, lithology mapping, and so on. The interpreted results have been verified in practice. All the abovementioned suggested a good development prospect for the rasterized geochemical images. Finally the author puts forward using rasterize geochemical images in combination with other geological, geophysical, and remote sensing data to make better use of the geochemical data and be more extensively applied in the geoscience

Regularized Training and Tight Certification for Randomized Smoothed Classifier with Provable Robustness

Recently smoothing deep neural network based classifiers via isotropic Gaussian perturbation is shown to be an effective and scalable way to provide state-of-the-art probabilistic robustness guarantee against $\ell_2$ norm bounded adversarial perturbations. However, how to train a good base classifier that is accurate and robust when smoothed has not been fully investigated. In this work, we derive a new regularized risk, in which the regularizer can adaptively encourage the accuracy and robustness of the smoothed counterpart when training the base classifier. It is computationally efficient and can be implemented in parallel with other empirical defense methods. We discuss how to implement it under both standard (non-adversarial) and adversarial training scheme. At the same time, we also design a new certification algorithm, which can leverage the regularization effect to provide tighter robustness lower bound that holds with high probability. Our extensive experimentation demonstrates the effectiveness of the proposed training and certification approaches on CIFAR-10 and ImageNet datasets.Comment: AAAI202

水中における二酸化チタンナノ粒子の分散安定性と凝集・膜ろ過プロセスによる除去

学位の種別: 課程博士審査委員会委員 : （主査）東京大学教授 滝沢 智, 東京大学准教授 中島 典之, 東京大学准教授 小熊 久美子, 東京大学講師 春日 郁朗, 東京工業大学准教授 吉村 千洋University of Tokyo(東京大学

A survey on deep geometry learning: from a representation perspective

Researchers have achieved great success in dealing with 2D images using deep learning. In recent years, 3D computer vision and geometry deep learning have gained ever more attention. Many advanced techniques for 3D shapes have been proposed for different applications. Unlike 2D images, which can be uniformly represented by a regular grid of pixels, 3D shapes have various representations, such as depth images, multi-view images, voxels, point clouds, meshes, implicit surfaces, etc. The performance achieved in different applications largely depends on the representation used, and there is no unique representation that works well for all applications. Therefore, in this survey, we review recent developments in deep learning for 3D geometry from a representation perspective, summarizing the advantages and disadvantages of different representations for different applications. We also present existing datasets in these representations and further discuss future research directions

Improving the Robustness of Transformer-based Large Language Models with Dynamic Attention

Transformer-based models, such as BERT and GPT, have been widely adopted in natural language processing (NLP) due to their exceptional performance. However, recent studies show their vulnerability to textual adversarial attacks where the model's output can be misled by intentionally manipulating the text inputs. Despite various methods that have been proposed to enhance the model's robustness and mitigate this vulnerability, many require heavy consumption resources (e.g., adversarial training) or only provide limited protection (e.g., defensive dropout). In this paper, we propose a novel method called dynamic attention, tailored for the transformer architecture, to enhance the inherent robustness of the model itself against various adversarial attacks. Our method requires no downstream task knowledge and does not incur additional costs. The proposed dynamic attention consists of two modules: (I) attention rectification, which masks or weakens the attention value of the chosen tokens, and (ii) dynamic modeling, which dynamically builds the set of candidate tokens. Extensive experiments demonstrate that dynamic attention significantly mitigates the impact of adversarial attacks, improving up to 33\% better performance than previous methods against widely-used adversarial attacks. The model-level design of dynamic attention enables it to be easily combined with other defense methods (e.g., adversarial training) to further enhance the model's robustness. Furthermore, we demonstrate that dynamic attention preserves the state-of-the-art robustness space of the original model compared to other dynamic modeling methods