CMBS Issuance and Collateral Analysis Review

This project reviews the structure of Commercial Mortgage-Backed Securities at issuance as well as their underlying collateral analysis and deal level analysis. Through the comparison of CMBS 1.0 and CMBS 2.0 (CMBS 1.0 is the Commercial Mortgage Backed Securities issued pre-crisis and CMBS 2.0 is the CMBS that was issued after the 2008 Global Financial Crisis), we investigate how the underwriting standards evolved as a response to the crisis of 2008. This paper looks at several different categories with convergent informational outcomes. This paper identifies how underwriting standards becomes stricter, for example the cutoff LTV for CMBS 2.0 is lower and cutoff DSCR for CMBS 2.0 is higher. The new CMBS issuance starts to take off after 2008, though slowly. Moreover, this paper provides estimates of expected loss by vintage through the use of its own collateral model. Bloomberg identifies the deals we modeled as falling into one of the following categories: Conduit, Portfolio, SASB (single asset/single borrower), or Small Balance deals. In total, we modeled 617 American CMBS deals, which originated at various years between 2000 and 2011. Approximately 4,000 bonds were included in our model. The underlying property and loan information that we used in our model came exclusively from the Bloomberg database. We modeled the change in property values by the time-dependent Moody’s CPPI index. We also considered stressed property values. The discount factor on the value of a particular stressed property is influenced by relative location of the target property to stressed properties. We subtracted the current balance of the underlying loan by the current estimated value of the property in our model to get the expected loss for each loan. Then we summed each of the individual losses to get the expected total loss for each deal. This algorithm influenced our decision to investigate the change in value of the collateral underwriting the loans

Beyond Intra-modality: A Survey of Heterogeneous Person Re-identification

An efficient and effective person re-identification (ReID) system relieves the users from painful and boring video watching and accelerates the process of video analysis. Recently, with the explosive demands of practical applications, a lot of research efforts have been dedicated to heterogeneous person re-identification (Hetero-ReID). In this paper, we provide a comprehensive review of state-of-the-art Hetero-ReID methods that address the challenge of inter-modality discrepancies. According to the application scenario, we classify the methods into four categories -- low-resolution, infrared, sketch, and text. We begin with an introduction of ReID, and make a comparison between Homogeneous ReID (Homo-ReID) and Hetero-ReID tasks. Then, we describe and compare existing datasets for performing evaluations, and survey the models that have been widely employed in Hetero-ReID. We also summarize and compare the representative approaches from two perspectives, i.e., the application scenario and the learning pipeline. We conclude by a discussion of some future research directions. Follow-up updates are avaible at: https://github.com/lightChaserX/Awesome-Hetero-reIDComment: Accepted by IJCAI 2020. Project url: https://github.com/lightChaserX/Awesome-Hetero-reI

Effects of Perioperative Psychological Intervention on Rehabilitation Process of Patients with Total Knee Arthroplasty

Background: This study focuses on evaluating the effects of perioperative psychological intervention on rehabilitation process of patients with total knee arthroplasty (TKA). Method: We selected 40 patients randomly which all need to receive total knee arthroplasty in Nanjing Drum Tower Hospital during the period from January 2022 to March 2022. The patients were randomly assigned to two Groups (20 in each group): an intervention group (Psychological intervention combined with routine nursing, drug and rehabilitation therapy) and a control group (routine nursing, drug rehabilitation therapy). During each patients’ perioperative TKA surgeries, three scales (including VAS, ROM and ADL) are used to assess two groups. Result: After one week of psychological intervention, the pain score of the intervention group was lower than that of control group, the knee motion was greater than that of control group, and the ADL score was higher than that of control group. There was a significant difference in the treatment recovery between the two groups (P<0.05) Conclusions: Perioperative psychological intervention can promote the rehabilitation process of TKA patients, It can significantly improve pain, joint activity limitation, disuse muscle atrophy and other problems in a short period of time after surgery. Besides, it will effectively help patients to overcome the fear of movement, anxiety and improve patients' confidence, rehabilitation cooperation and prevention of complications, make patients adapt to the later rehabilitation life

Decomposition-Based-Sorting and Angle-Based-Selection for Evolutionary Multiobjective and Many-Objective Optimization

Multiobjective evolutionary algorithm based on decomposition (MOEA/D) decomposes a multiobjective optimization problem (MOP) into a number of scalar optimization subproblems and then solves them in parallel. In many MOEA/D variants, each subproblem is associated with one and only one solution. An underlying assumption is that each subproblem has a different Pareto-optimal solution, which may not be held, for irregular Pareto fronts (PFs), e.g., disconnected and degenerate ones. In this paper, we propose a new variant of MOEA/D with sorting-and-selection (MOEA/D-SAS). Different from other selection schemes, the balance between convergence and diversity is achieved by two distinctive components, decomposition-based-sorting (DBS) and angle-based-selection (ABS). DBS only sorts ${L}$ closest solutions to each subproblem to control the convergence and reduce the computational cost. The parameter ${L}$ has been made adaptive based on the evolutionary process. ABS takes use of angle information between solutions in the objective space to maintain a more fine-grained diversity. In MOEA/D-SAS, different solutions can be associated with the same subproblems; and some subproblems are allowed to have no associated solution, more flexible to MOPs or many-objective optimization problems (MaOPs) with different shapes of PFs. Comprehensive experimental studies have shown that MOEA/D-SAS outperforms other approaches; and is especially effective on MOPs or MaOPs with irregular PFs. Moreover, the computational efficiency of DBS and the effects of ABS in MOEA/D-SAS are also investigated and discussed in detail

Self-attention based high order sequence feature reconstruction of dynamic functional connectivity networks with rs-fMRI for brain disease classification

Dynamic functional connectivity networks (dFCN) based on rs-fMRI have demonstrated tremendous potential for brain function analysis and brain disease classification. Recently, studies have applied deep learning techniques (i.e., convolutional neural network, CNN) to dFCN classification, and achieved better performance than the traditional machine learning methods. Nevertheless, previous deep learning methods usually perform successive convolutional operations on the input dFCNs to obtain high-order brain network aggregation features, extracting them from each sliding window using a series split, which may neglect non-linear correlations among different regions and the sequentiality of information. Thus, important high-order sequence information of dFCNs, which could further improve the classification performance, is ignored in these studies. Nowadays, inspired by the great success of Transformer in natural language processing and computer vision, some latest work has also emerged on the application of Transformer for brain disease diagnosis based on rs-fMRI data. Although Transformer is capable of capturing non-linear correlations, it lacks accounting for capturing local spatial feature patterns and modelling the temporal dimension due to parallel computing, even equipped with a positional encoding technique. To address these issues, we propose a self-attention (SA) based convolutional recurrent network (SA-CRN) learning framework for brain disease classification with rs-fMRI data. The experimental results on a public dataset (i.e., ADNI) demonstrate the effectiveness of our proposed SA-CRN method