Failure Mode and Stability of Excavation Face on Shield Tunnel Undercrossing Existing Tunnel

The supporting pressure value of excavation face directly determines the stable state of excavation face, and its value will directly lead to instability of excavation face if the value is too small. When the shield is underneath the existing tunnel, special attention should be paid to the support pressure setting of the shield working face. When setting support pressure, the rigidity constraint of existing tunnel on surrounding soil should be fully considered. In this paper, we used ABAQUS software to analyse the failure mode of the soil around the existing tunnel due to the instability of the excavation surface caused by the small pressure setting of the excavation face, which is caused by the small pressure setting of the excavation face. By using the method of theoretical analysis, we optimized the prism in the traditional wedge model to chamfer platform with different opening angles to make it closer to the actual situation, and calculated the critical support pressure of shield tunnel face when it passes through the built tunnel. The research results can provide a reference for the effective value of support force of shield excavation face when the shield tunnel passes under the existing tunnel at a short distance

Optimal information disclosure and optimal learning

This dissertation addresses the effect of information on firm and individual behavior. The first chapter examines the design of an optimal feedback mechanism by an informed principal and uses the results to explain why firms tend to assign coarse subjective ratings to their employees. When a firm has private information about an employee's ability, it can communicate this information through a subjective evaluation mechanism. I characterize the firm's optimal disclosure policy as a function of the worker's ability distribution and provide an algorithm to compute it. Further, I show that with some reasonable restrictions on the ability distribution, the firm's optimal strategy is always to reward the best workers, fire the worst ones, and assign one central rating to the rest. The second chapter investigates an informed principal's optimal feedback strategy in a dynamic setting. I first consider the case where both parties have non-binding outside options. In this case, if the principal ever wants to reveal any information, she will do so at the earliest possible stage. Moreover, the optimal disclosure policy can be characterized in the same way as in the static case. The same conclusion holds for the case where both parties have binding and constant outside options. I also discuss the case where both parties have binding and time-variant outside options. After incorporating firms' need to promote and/or to retain workers, the model is used to explain wage dynamics. The third chapter models a decision maker who "rationally" distorts his own belief to avoid the feeling of regret. People often suffer from regret when they realize that their previous choices were suboptimal. As a result, in a dynamic setting where information is revealed gradually, people are tempted to deny new negative information in order to avoid regret. At the same time, they are also aware of the economic cost of such belief distortions. A "rational" decision maker will optimally trade off these two concerns and choose his own belief accordingly. This tradeoff makes the past affect current decisions and hence can explain the sunk cost fallacy

Enhanced Multimodal Representation Learning with Cross-modal KD

This paper explores the tasks of leveraging auxiliary modalities which are only available at training to enhance multimodal representation learning through cross-modal Knowledge Distillation (KD). The widely adopted mutual information maximization-based objective leads to a short-cut solution of the weak teacher, i.e., achieving the maximum mutual information by simply making the teacher model as weak as the student model. To prevent such a weak solution, we introduce an additional objective term, i.e., the mutual information between the teacher and the auxiliary modality model. Besides, to narrow down the information gap between the student and teacher, we further propose to minimize the conditional entropy of the teacher given the student. Novel training schemes based on contrastive learning and adversarial learning are designed to optimize the mutual information and the conditional entropy, respectively. Experimental results on three popular multimodal benchmark datasets have shown that the proposed method outperforms a range of state-of-the-art approaches for video recognition, video retrieval and emotion classification.Comment: Accepted by CVPR202

Develop a Hazard Index Using Machine Learning Approach for the Hazard Identification of Chemical Logistic Warehouses

PresentationWith the rapid development of chemical process plants, the safe storage of hazardous chemicals become an essential topic. Several chemical warehouse incidents related to fire and explosion have been reported recently. Therefore, an accurate hazard identification method for the logistic warehouse is needed not only for the facility to develop a proper emergency response plan but also for the residents who live near the facility to have an effective hazard communication. Furthermore, the government can better allocate the resources for first responders to make fire protection strategies, and the stakeholders can lead to improved risk management. Hazard index is a helpful tool to identify and quantify the hazard in a facility or a process unit. The challenge for this research is to improve the current method with the novel technique to implement our purpose. The first objective of this research is to develop a “Storage Hazard Factor” (SHF) to evaluate and rank the inherent hazards of chemicals stored in logistic warehouses. In the factor calculation, the inherent hazard of chemicals is determined by various parameters (e.g., the NFPA rating, the flammability limit, and the protective action criteria values, etc.) and validated by the comparison with other indices. The current criteria for flammable hazard ratings are based on flash point, which is proved to be insufficient. Two machine learning based methods will be used for the classification of liquid flammability considering aerosolization based on DIPPR 801 database. Subsequently, SHF and other warehouse safety penalty factors (e.g., the quantity of the chemicals, the distance to the nearest fire department, etc.) are utilized to identify the Logistic Warehouse Hazard Index (LWHI) of the facilities. In the last chapter, this method is applied to real-time data from Houston Chronicle, and several statistical analyses are used to prove the hazard index is helpful for hazard identification to emergency responders and hazard communication to the public

Learning Disentangled Representation Implicitly via Transformer for Occluded Person Re-Identification

Person re-identification (re-ID) under various occlusions has been a long-standing challenge as person images with different types of occlusions often suffer from misalignment in image matching and ranking. Most existing methods tackle this challenge by aligning spatial features of body parts according to external semantic cues or feature similarities but this alignment approach is complicated and sensitive to noises. We design DRL-Net, a disentangled representation learning network that handles occluded re-ID without requiring strict person image alignment or any additional supervision. Leveraging transformer architectures, DRL-Net achieves alignment-free re-ID via global reasoning of local features of occluded person images. It measures image similarity by automatically disentangling the representation of undefined semantic components, e.g., human body parts or obstacles, under the guidance of semantic preference object queries in the transformer. In addition, we design a decorrelation constraint in the transformer decoder and impose it over object queries for better focus on different semantic components. To better eliminate interference from occlusions, we design a contrast feature learning technique (CFL) for better separation of occlusion features and discriminative ID features. Extensive experiments over occluded and holistic re-ID benchmarks (Occluded-DukeMTMC, Market1501 and DukeMTMC) show that the DRL-Net achieves superior re-ID performance consistently and outperforms the state-of-the-art by large margins for Occluded-DukeMTMC

Optimal Remedies for Patent Infringement

This paper derives optimal remedies for patent infringement, examining damages awards and injunctions. The fundamental optimality condition that applies to both awards and injunctions equates the marginal static cost of intellectual property protection with the marginal “dynamic” benefit from the innovation thereby induced. We find that the optimal damages award may be greater than (or less than) the standard “lost profits” measure, depending on the social value of the innovation. When the social value of the patent is sufficiently high, the optimal award induces socially efficient investment by giving the innovator the entire social value of her investment