A Model of Two-Way Selection System for Human Behavior

We propose a model of two-way selection system. It appears in the processes like choosing a mate between men and women, making contracts between job hunters and recruiters, and trading between buyers and sellers. In this paper, we propose a model of two-way selection system, and present its analytic solution for the expectation of successful matching total and the regular pattern that the matching rate trends toward an inverse proportion to either the ratio between the two sides or the ratio of the state total to the smaller people number. The proposed model is verified by empirical data of the matchmaking fairs. Results indicate that the model well predicts this typical real-world two- way selection behavior to the bounded error extent, thus it is helpful for understanding the dynamics mechanism of the real-world two-way selection system.Comment: 8 pages, 4 figure

Resolution of finite fuzzy relation equations based on strong pseudo-t-norms

AbstractThis work studies the problem of solving a sup-T composite finite fuzzy relation equation, where T is an infinitely distributive strong pseudo-t-norm. A criterion for the equation to have a solution is given. It is proved that if the equation is solvable then its solution set is determined by the greatest solution and a finite number of minimal solutions. A necessary and sufficient condition for the equation to have a unique solution is obtained. Also an algorithm for finding the solution set of the equation is presented

1,25-hydroxyvitamin D relieves colitis in rats via downregulation of toll-like receptor 9 expression

Aim To investigate the therapeutic and immunoregulatory effects of 1,25-dihydroxyvitamin D (1,25(OH)D3) on 2,4,6- trinitrobenzenesulfonic acid (TNBS) -induced colitis in rats. Methods Experimental colitis induced by enema administration of TNBS plus ethanol was treated with 5-aminosalicylic acid (5-ASA) and/or 1,25(OH)D3. Disease activity was measured using the disease activation index (DAI), colon macroscopic damage index (CMDI), histological colonic damage score, and myeloperoxidase (MPO) activity. The expression of toll-like receptor 9 (TLR9) in the colon was determined by reverse transcription-polymerase chain reaction and immunohistochemistry. Results Rats with TNBS-induced colitis had significantly elevated DAI, CMDI, histological colonic damage score, and MPO activity (all P < 0.001) compared to rats without colitis. Treatment with 5-ASA or 1,25(OH)D3 ameliorated colitis by lowering CMDI (P = 0.049, P = 0.040, respectively), histological colonic damage score (P = 0.010, P = 0.005, respectively), and MPO activity (P = 0.0003, P = 0.0013, respectively) compared with the TNBS group. Combined treatment with 5-ASA and 1,25(OH)D3 significantly decreased MPO activity (P = 0.003). 1,25(OH)D3 attenuated colitis without causing hypercalcemia or renal insufficiency. TNBS significantly increased the number of TLR9 positive cells compared to control (P < 0.010), while 5-ASA, 1,25(OH)D3, and combined treatment with 5-ASA and 1,25(OH)D3 significantly decreased it compared to TNBS group (all P < 0.010). In TNBS group a moderate correlation was observed between MPO activity and the number of TLR9-positive cells (r = 0.654, P < 0.001). Conclusion TLR9 expression correlates with the extent of inflammation in TNBS-induced colitis. 1,25(OH)D3 relieves this inflammation possibly by decreasing TLR9 expression

Adversarial Attack on Community Detection by Hiding Individuals

It has been demonstrated that adversarial graphs, i.e., graphs with imperceptible perturbations added, can cause deep graph models to fail on node/graph classification tasks. In this paper, we extend adversarial graphs to the problem of community detection which is much more difficult. We focus on black-box attack and aim to hide targeted individuals from the detection of deep graph community detection models, which has many applications in real-world scenarios, for example, protecting personal privacy in social networks and understanding camouflage patterns in transaction networks. We propose an iterative learning framework that takes turns to update two modules: one working as the constrained graph generator and the other as the surrogate community detection model. We also find that the adversarial graphs generated by our method can be transferred to other learning based community detection models.Comment: In Proceedings of The Web Conference 2020, April 20-24, 2020, Taipei, Taiwan. 11 page

Identifying and Compensating for Feature Deviation in Imbalanced Deep Learning

We investigate learning a ConvNet classifier with class-imbalanced data. We found that a ConvNet significantly over-fits the minor classes that do not have sufficient training instances, which is quite opposite to a traditional machine learning model like logistic regression that often under-fits minor classes. We conduct a series of analysis and argue that feature deviation between the training and test instances serves as the main cause. We propose to incorporate class-dependent temperatures (CDT) in learning a ConvNet: CDT forces the minor-class instances to have larger decision values in the training phase, so as to compensate for the effect of feature deviation in the test data. We validate our approach on several benchmark datasets and achieve promising performance. We hope that our insights can inspire new ways of thinking in resolving class-imbalanced deep learning

Contextualizing Multiple Tasks via Learning to Decompose

One single instance could possess multiple portraits and reveal diverse relationships with others according to different contexts. Those ambiguities increase the difficulty of learning a generalizable model when there exists one concept or mixed concepts in a task. We propose a general approach Learning to Decompose Network (LeadNet) for both two cases, which contextualizes a model through meta-learning multiple maps for concepts discovery -- the representations of instances are decomposed and adapted conditioned on the contexts. Through taking a holistic view over multiple latent components over instances in a sampled pseudo task, LeadNet learns to automatically select the right concept via incorporating those rich semantics inside and between objects. LeadNet demonstrates its superiority in various applications, including exploring multiple views of confusing tasks, out-of-distribution recognition, and few-shot image classification