Counting hypergraph matchings up to uniqueness threshold

We study the problem of approximately counting matchings in hypergraphs of bounded maximum degree and maximum size of hyperedges. With an activity parameter $\lambda$, each matching $M$ is assigned a weight $\lambda^{|M|}$. The counting problem is formulated as computing a partition function that gives the sum of the weights of all matchings in a hypergraph. This problem unifies two extensively studied statistical physics models in approximate counting: the hardcore model (graph independent sets) and the monomer-dimer model (graph matchings). For this model, the critical activity $\lambda_c= \frac{d^d}{k (d-1)^{d+1}}$ is the threshold for the uniqueness of Gibbs measures on the infinite $(d+1)$-uniform $(k+1)$-regular hypertree. Consider hypergraphs of maximum degree at most $k+1$ and maximum size of hyperedges at most $d+1$. We show that when $\lambda < \lambda_c$, there is an FPTAS for computing the partition function; and when $\lambda = \lambda_c$, there is a PTAS for computing the log-partition function. These algorithms are based on the decay of correlation (strong spatial mixing) property of Gibbs distributions. When $\lambda > 2\lambda_c$, there is no PRAS for the partition function or the log-partition function unless NP$=$RP. Towards obtaining a sharp transition of computational complexity of approximate counting, we study the local convergence from a sequence of finite hypergraphs to the infinite lattice with specified symmetry. We show a surprising connection between the local convergence and the reversibility of a natural random walk. This leads us to a barrier for the hardness result: The non-uniqueness of infinite Gibbs measure is not realizable by any finite gadgets

Optimizing Biodiesel Production of a Cell-Free System by Feedback System Control Scheme

Due to environmental benefits, rising crude oil prices, and limited resources of fossil oil, there has been renewed focus on vegetable oils as a source of biodiesel fuels. Microalgae, which are characterized by rapid growth and high oil content, have excellent potential to provide algae-derived biodiesel to help alleviate the world's dependency on petroleum-based fuels. However, the cost of mass algal production remains high, and the potential to substitute algal biodiesel for traditional fuel is still unrealized. The initial goal of this thesis research was to optimize culture parameters for the alga, Botryococcus braunii, for increased production of fatty acids and generation of biodiesel. The results demonstrated that, with a supplied carbon source, lysed B. braunii could produce high levels of fatty acids at a rapid rate. Thus, a cell-free system was developed that can effectively produce biodiesel, saving significant effort by eliminating the need to maintain live cells. The new approach is not light-dependent, greatly reducing the requirement for land area. The newly designed system can maintain a rate of fatty acid production that is an order of magnitude greater than the production rate in traditional algal culture for at least four months. Furthermore, the new system uses an unorthodox top-down approach, called Feedback System Control (FSC), which employs experiment design for large dimensions and response surfaces method in searching optimum with only a small number of iterations. It enabled a replacement of commercial medium containing more than sixteen chemicals with a medium containing only four chemicals, reducing the cost of the medium tenfold. Overall, the new culture method significantly increases the cost efficiency of algal biodiesel production, and has the potential to provide a scalable and cost effective method for economically viable commercial use

5 Fluorouracil as firs t line treatment for low risk gestational trophoblastic neoplasia

Purpose: To investigate the efficacy and prognostic factors in response to 5-fluorouracil (5-FU) in lowrisk gestational trophoblastic neoplasia (GTN).Methods: This single-center retrospective study analyzed the hospital records of 204 LRGTN patients admitted to Department of Gynecology, Liaoning Cancer Hospital &amp; Institute of China from 2002 to 2016 for retrieval of their clinical data, chemotherapy regimens, related side-effects, and evaluation of treatment efficacy and prognostic factors.Results: The median progression-free survival (PFS) was 55 months (3 - 190 months). The overall cure rate was 100 %, with no tumor-related deaths. When a single-agent regimen i.e. 5-FU, was selected for initiation of treatment for 132 patients while only 49 of them were treated with chemotherapy, the effective cure rate was 62.88 % (83/132); while the overall drug resistance r was 27.27 % (36/132). For patients with FIGO scores ≥ 4 points, the incidence of drug resistance was 71.43 % (5/7), while the incidence of III/IV myelosuppression was 10.61 % (14/132). A total of 38 patients (18.63 %) received surgical treatment in addition to chemotherapy. A comparison was made between two groups of patients with non-drug resistance, i.e., patients with unexpected GTN diagnosed postoperatively and those who received chemotherapy preoperatively. It was found that the number of courses of GTN chemotherapy for those who were unexpectedly diagnosed postoperatively was more than that for those who received chemotherapy preoperatively (p = 0.004).Conclusion: The single drug (5-FU) was effective in the management of low-risk (LR)-GTN. Treatment failure was related to drug resistance, high tumor score, and severe toxicity. Multi-agent regiments in combination with surgery, were an effective treatment method for GTN. For patients without metastasis and fertility requirements, surgery after chemotherapy significantly shortened the treatment cycle without increasing complications

Refining the Optimization Target for Automatic Univariate Time Series Anomaly Detection in Monitoring Services

Time series anomaly detection is crucial for industrial monitoring services that handle a large volume of data, aiming to ensure reliability and optimize system performance. Existing methods often require extensive labeled resources and manual parameter selection, highlighting the need for automation. This paper proposes a comprehensive framework for automatic parameter optimization in time series anomaly detection models. The framework introduces three optimization targets: prediction score, shape score, and sensitivity score, which can be easily adapted to different model backbones without prior knowledge or manual labeling efforts. The proposed framework has been successfully applied online for over six months, serving more than 50,000 time series every minute. It simplifies the user's experience by requiring only an expected sensitive value, offering a user-friendly interface, and achieving desired detection results. Extensive evaluations conducted on public datasets and comparison with other methods further confirm the effectiveness of the proposed framework.Comment: Accepted by 2023 IJCAI Worksho

Towards Generalizable Reinforcement Learning for Trade Execution

Optimized trade execution is to sell (or buy) a given amount of assets in a given time with the lowest possible trading cost. Recently, reinforcement learning (RL) has been applied to optimized trade execution to learn smarter policies from market data. However, we find that many existing RL methods exhibit considerable overfitting which prevents them from real deployment. In this paper, we provide an extensive study on the overfitting problem in optimized trade execution. First, we model the optimized trade execution as offline RL with dynamic context (ORDC), where the context represents market variables that cannot be influenced by the trading policy and are collected in an offline manner. Under this framework, we derive the generalization bound and find that the overfitting issue is caused by large context space and limited context samples in the offline setting. Accordingly, we propose to learn compact representations for context to address the overfitting problem, either by leveraging prior knowledge or in an end-to-end manner. To evaluate our algorithms, we also implement a carefully designed simulator based on historical limit order book (LOB) data to provide a high-fidelity benchmark for different algorithms. Our experiments on the high-fidelity simulator demonstrate that our algorithms can effectively alleviate overfitting and achieve better performance.Comment: Accepted by IJCAI-2

Transmembrane Protein 100 Inhibits the Progression of Colorectal Cancer by Promoting the Ubiquitin/Proteasome Degradation of HIF-1α

Transmembrane protein 100 (TMEM100) is involved in embryonic cardiovascular system development. However, the biological role of TMEM100 in human cancers, particularly colorectal cancer (CRC), is unclear. In this study, tissue microarrays were stained using immunohistochemistry methods to evaluate the association between TMEM100 levels and clinic-pathological features for CRC. Kaplan–Meier and log-rank tests revealed that decreased levels of TMEM100 correlated with shorter overall survival. Cox regression revealed that reduced levels of TMEM100 was an independent prognostic factor for detrimental survival in CRC. A lentiviral vector was used to overexpress TMEM100 in HCT116 cells, and small interfering RNA was used to knockdown TMEM100 in SW480 cells. The CCK-8 assay, colony formation analysis, cell cycle analysis, cell migration assay, mouse xenograft model and mouse lung metastasis model showed that TMEM100 suppressed CRC cell proliferation and migration in vitro and in vivo. IHC scores of TMEM100 and HIF-1α were significantly negatively correlated. A half-time determination analysis in which cells were treated with cycloheximide revealed that TMEM100 shortened the HIF-1α half-life. Further immunoprecipitation experimental results showed that TMEM100 promoted the ubiquitination of HIF-1α, which caused HIF-1α degradation via the 26S proteasome pathway. Angiogenesis assay and migration assay results revealed that TMEM100 suppressed the migration and angiogenesis induction capacities of HCT116 cells, but this inhibitory effect was abolished when HIF-1α degradation was blocked by MG132 treatment. These results indicated that TMEM100 inhibited the migration and the angiogenesis induction capacities of CRC cells by enhancing HIF-1α degradation via ubiquitination/proteasome pathway

Polarized electron-beam acceleration driven by vortex laser pulses

We propose a new approach based on an all-optical set-up for generating relativistic polarized electron beams via vortex Laguerre-Gaussian (LG) laser-driven wakefield acceleration. Using a pre-polarized gas target, we find that the topology of the vortex wakefield resolves the depolarization issue of the injected electrons. In full three-dimensional particle-in-cell simulations, incorporating the spin dynamics via the Thomas-Bargmann Michel Telegdi equation, the LG laser preserves the electron spin polarization by more than 80% at high beam charge and flux. The method releases the limit on beam flux for polarized electron acceleration and promises more than an order of magnitude boost in peak flux, as compared to Gaussian beams. These results suggest a promising table-top method to produce energetic polarized electron beams.Comment: We replace some results and revise some description