Quantifying the efficiency of price-only contracts in push supply chains over demand distributions of known supports

In this paper, we quantify the efficiency of price-only contracts in supply chains with demand distributions by imposing prior knowledge only on the support, namely, those distributions with support [a, b] for 0 < a <_ b < +1. By characterizing the price of anarchy (PoA) under various push supply chain configurations, we enrich the application scope of the PoA concept in supply chain contracts along with complementary managerial insights. One of our major findings is that our quantitative analysis can identify scenarios where the price-only contract actually maintains its efficiency, namely, when the demand uncertainty, measured by the relative range b/a, is relatively low, entailing the price-only contract to be more attractive in this regard

Variational operator learning: A unified paradigm for training neural operators and solving partial differential equations

Based on the variational method, we propose a novel paradigm that provides a unified framework of training neural operators and solving partial differential equations (PDEs) with the variational form, which we refer to as the variational operator learning (VOL). We first derive the functional approximation of the system from the node solution prediction given by neural operators, and then conduct the variational operation by automatic differentiation, constructing a forward-backward propagation loop to derive the residual of the linear system. One or several update steps of the steepest decent method (SD) and the conjugate gradient method (CG) are provided in every iteration as a cheap yet effective update for training the neural operators. Experimental results show the proposed VOL can learn a variety of solution operators in PDEs of the steady heat transfer and the variable stiffness elasticity with satisfactory results and small error. The proposed VOL achieves nearly label-free training. Only five to ten labels are used for the output distribution-shift session in all experiments. Generalization benefits of the VOL are investigated and discussed.Comment: 35 pages, 22 figure

On the Population Monotonicity of Independent Set Games

An independent set game is a cooperative game defined on graphs and dealing with profit sharing in maximum independent set problems. A population monotonic allocation scheme is a rule specifying how to share the profit of each coalition among its participants such that every participant is better off when the coalition expands. In this paper, we provide a necessary and sufficient characterization for population monotonic allocation schemes in independent set games. Moreover, our characterization can be verified efficiently

A Trace-restricted Kronecker-Factored Approximation to Natural Gradient

Second-order optimization methods have the ability to accelerate convergence by modifying the gradient through the curvature matrix. There have been many attempts to use second-order optimization methods for training deep neural networks. Inspired by diagonal approximations and factored approximations such as Kronecker-Factored Approximate Curvature (KFAC), we propose a new approximation to the Fisher information matrix (FIM) called Trace-restricted Kronecker-factored Approximate Curvature (TKFAC) in this work, which can hold the certain trace relationship between the exact and the approximate FIM. In TKFAC, we decompose each block of the approximate FIM as a Kronecker product of two smaller matrices and scaled by a coefficient related to trace. We theoretically analyze TKFAC's approximation error and give an upper bound of it. We also propose a new damping technique for TKFAC on convolutional neural networks to maintain the superiority of second-order optimization methods during training. Experiments show that our method has better performance compared with several state-of-the-art algorithms on some deep network architectures

Scoring System for Tumor-Infiltrating Lymphocytes and Its Prognostic Value for Gastric Cancer

The tumor microenvironment (TME) is the internal environment of malignant tumor progression, and the host antitumor immune response and normal tissue destruction occur in the TME. Tumor-infiltrating lymphocytes (TIL) is a crucial component of the TME and reflect the host antitumor immune response. The purpose of this study was to discuss the methodology for TIL evaluation and assess the prognostic value of TIL in gastric cancer. In total, we reviewed 1,033 gastrectomy cases between 2002 and 2008 at the Third Affiliated Hospital of Soochow University. To understand the prognostic value of TIL in gastric cancer (GC), TIL were assessed by optical microscopy, and verified by immunohistochemistry. There is no current consensus on TIL scoring in GC. In this study, we discussed a TIL evaluation system that includes an analysis of the amount and percentage of TIL in a tumor. Ultimately, 439 (52.7%) cases showed high levels of TIL and 394 (47.3%) cases had low levels. There was a statistically significant relationship among TIL, tumor size, histological grade, LN metastasis, nerve invasion, tumor thrombus, pTN stage, and WHO subtypes (p &lt; 0.001, respectively). TILhi was a positive significant predictor of overall survival (OS) in Kaplan–Meier survival analysis (P &lt; 0.001) and multivariate Cox regression analysis (HR = 0.431, 95% CI: 0.347–0.534, P &lt; 0.001). After surgery, patients with malignant tumors underwent chemoradiotherapy according to standard therapeutic guidelines based on TNM stage. The TNM scoring system cannot reflect the full information of TME; therefore, TIL can be used as a diagnostic supplement. We constructed a nomogram model that showed more predictive accuracy for OS than pTN stage. In summary, this study proves that high levels of TIL are associated with a positive prognosis and that TIL reflect the protective host antitumor immune response

IL-1β-Mediated Up-Regulation of WT1D via miR-144-3p and Their Synergistic Effect with NF-κB/COX-2/HIF-1α Pathway on Cell Proliferation in LUAD

Background/Aims: IL-1β is an important mediator of “inflammation-cancer" transformation through IL-1β/NF-κB/COX-2/HIF-1α signaling pathway, whereas certain portion of patients with lung adenocarcinoma (LUAD) still suffer from rapid tumor progression in clinical practice, indicating the occurrence of potential bypass. Methods: Real-time polymerase chain reaction was applied to examine the expressions of mir-144-3p, WT1, NF-κB, COX2 and HIF-1α at the mRNA level in 127 LUAD samples and corresponding adjacent tissues. miR-144-3p mimic and antagormiR were used to trigger activation and suppression of miR-144-3p in A549 cells, respectively. MTT assay and Western blotting analysis were carried out to evaluate the cell proliferation. Stable clones with over-expression or knockdown of WT1 were generated with plasmid or shRNA by lentiviral vector technology in H1568 and H1650 NSCLC cell lines, respectively. Dual luciferase reporter assay was performed to validate the effect of miR-144-3p on WT1D. Xenograft model was established for in vivo experiment, and TCGA data were extracted for validation. Results: miR-144-3p could suppress the WT1D expression at the post-transcriptional level, hence regulating cell proliferation in LUAD. WT1 and COX-2 were independent prognostic factors of LUAD patients. In addition, inhibition of IL-1β/miR-144-3p/WT1D and IL-1β/NF-κB/COX-2/HIF-1α pathways using miR-144-3p mimic and Celecoxib, respectively, displayed synergistic suppressive effect on cell proliferation in LUAD. Conclusion: A de novo IL-1β/miR-144-3p/WT1D axis was involved in proliferative regulation of LUAD. Moreover, simultaneous blockade of both IL-1β/miR-144-3p/WT1D and IL-1β/NF-κB/COX-2/ HIF-1α pathways might have synergistic suppressive effect on cell proliferation in LUAD

Homology Inference of Protein-Protein Interactions via Conserved Binding Sites

The coverage and reliability of protein-protein interactions determined by high-throughput experiments still needs to be improved, especially for higher organisms, therefore the question persists, how interactions can be verified and predicted by computational approaches using available data on protein structural complexes. Recently we developed an approach called IBIS (Inferred Biomolecular Interaction Server) to predict and annotate protein-protein binding sites and interaction partners, which is based on the assumption that the structural location and sequence patterns of protein-protein binding sites are conserved between close homologs. In this study first we confirmed high accuracy of our method and found that its accuracy depends critically on the usage of all available data on structures of homologous complexes, compared to the approaches where only a non-redundant set of complexes is employed. Second we showed that there exists a trade-off between specificity and sensitivity if we employ in the prediction only evolutionarily conserved binding site clusters or clusters supported by only one observation (singletons). Finally we addressed the question of identifying the biologically relevant interactions using the homology inference approach and demonstrated that a large majority of crystal packing interactions can be correctly identified and filtered by our algorithm. At the same time, about half of biological interfaces that are not present in the protein crystallographic asymmetric unit can be reconstructed by IBIS from homologous complexes without the prior knowledge of crystal parameters of the query protein