Why It Takes So Long to Connect to a WiFi Access Point

Today's WiFi networks deliver a large fraction of traffic. However, the performance and quality of WiFi networks are still far from satisfactory. Among many popular quality metrics (throughput, latency), the probability of successfully connecting to WiFi APs and the time cost of the WiFi connection set-up process are the two of the most critical metrics that affect WiFi users' experience. To understand the WiFi connection set-up process in real-world settings, we carry out measurement studies on $5$ million mobile users from $4$ representative cities associating with $7$ million APs in $0.4$ billion WiFi sessions, collected from a mobile "WiFi Manager" App that tops the Android/iOS App market. To the best of our knowledge, we are the first to do such large scale study on: how large the WiFi connection set-up time cost is, what factors affect the WiFi connection set-up process, and what can be done to reduce the WiFi connection set-up time cost. Based on the measurement analysis, we develop a machine learning based AP selection strategy that can significantly improve WiFi connection set-up performance, against the conventional strategy purely based on signal strength, by reducing the connection set-up failures from $33\%$ to $3.6\%$ and reducing $80\%$ time costs of the connection set-up processes by more than $10$ times.Comment: 11pages, conferenc

Periodic Variable Star Classification with Deep Learning: Handling Data Imbalance in an Ensemble Augmentation Way

Time-domain astronomy is progressing rapidly with the ongoing and upcoming large-scale photometric sky surveys led by the Vera C. Rubin Observatory project (LSST). Billions of variable sources call for better automatic classification algorithms for light curves. Among them, periodic variable stars are frequently studied. Different categories of periodic variable stars have a high degree of class imbalance and pose a challenge to algorithms including deep learning methods. We design two kinds of architectures of neural networks for the classification of periodic variable stars in the Catalina Survey's Data Release 2: a multi-input recurrent neural network (RNN) and a compound network combing the RNN and the convolutional neural network (CNN). To deal with class imbalance, we apply Gaussian Process to generate synthetic light curves with artificial uncertainties for data augmentation. For better performance, we organize the augmentation and training process in a "bagging-like" ensemble learning scheme. The experimental results show that the better approach is the compound network combing RNN and CNN, which reaches the best result of 86.2% on the overall balanced accuracy and 0.75 on the macro F1 score. We develop the ensemble augmentation method to solve the data imbalance when classifying variable stars and prove the effectiveness of combining different representations of light curves in a single model. The proposed methods would help build better classification algorithms of periodic time series data for future sky surveys (e.g., LSST).Comment: 10 pages, 8 figures, accepte

閉鎖鉱山再生に関する評価結果とその指標システム

The systematic theoretic study on quantitative evaluation of mine closure is still blank, although some experts and scholars have taken unceasing efforts to the research of assessing theories and methods, and achieved some successes. Effect assessing index for closed mine reclamation are direct reflection of implementation of mine closure planning and relevant circumstances, being used to describe feature values of overall quantity and quality of the planning contents. The principles to establish the index system are made. The classification and contents of the index system are discussed. According to the values range of various index, and contrasting to background value related to quality index, the result grades of elements assessing can be divided. The whole result grade of assessing can be taken. This is the basic of mine closure planning, being applied to the course of whole reclamation scheme formulation and its implementation, taking great significances to closed mine reclamation.特集 : 「資源、新エネルギー、環境、防災研究国際セミナー

Exact Boundary Controller Design for a Kind of Enhanced Oil Recovery Models

The exact boundary controllability of a class of enhanced oil recovery systems is discussed in this paper. With a simple transformation, the enhanced oil recovery model is first affirmed to be neither genuinely nonlinear nor linearly degenerate. It is then shown that the enhanced oil recovery system with nonlinear boundary conditions is exactly boundary controllable by applying a constructed method. Moreover, an interval of the control time is presented to not only give the optimal control time but also show the time for avoiding the blowup of the controllable solution. Finally, an example is given to illustrate the effectiveness of the proposed criterion

Effects of Pressure and Doping on Ruddlesden-Popper phases Lan+1NinO3n+1

Recently the discovery of superconductivity with a critical temperature Tc up to 80 K in Ruddlesden-Popper phases Lan+1NinO3n+1 (n = 2) under pressure has garnered considerable attention. Up to now, the superconductivity was only observed in La3Ni2O7 single crystal grown with the optical-image floating zone furnace under oxygen pressure. It remains to be understood the effect of chemical doping on superconducting La3Ni2O7 as well as other Ruddlesden-Popper phases. Here, we systematically investigate the effect of external pressure and chemical doping on polycrystalline Ruddlesden-Popper phases. Our results demonstrate the application of pressure and doping effectively tunes the transport properties of Ruddlesden-Popper phases. We find pressure-induced superconductivity up to 86 K in La3Ni2O7 polycrystalline sample, while no signatures of superconductivity are observed in La2NiO4 and La4Ni3O10 systems under high pressure up to 50 GPa. Our study sheds light on the exploration of high-Tc superconductivity in nickelates.Comment: 21 papes, 8 figures and 1 tabl

Whole exome sequencing of well-differentiated liposarcoma and dedifferentiated liposarcoma in older woman: a case report

BackgroundCommon kinds of soft tissue sarcomas (STS) include well-differentiated liposarcoma (WDLPS) and dedifferentiated liposarcoma (DDLPS). In this case, we present a comprehensive clinical profile of a patient who underwent multiple recurrences during the progression from WDLPS to DDLPS.Case presentationA 62-year-old Asian female underwent retroperitoneal resection of a large tumor 11 years ago, the initial pathology revealed a fibrolipoma-like lesion. Over the next six years, the patient underwent three resections for recurrence of abdominal tumors. Postoperative histology shows mature adipose tissue with scattered “adipoblast”-like cells with moderate-to-severe heterogeneous spindle cells, pleomorphic cells, or tumor giant cells. Immunohistochemistry (IHC) demonstrated positive staining for MDM2 and CDK4, confirming that the abdominal tumor was WDLPS and gradually progressing to DDLPS. Post-operative targeted sequencing and IHC confirmed the POC1B::ROS1 fusion gene in DDLPS. Whole-exome sequencing (WES) revealed that WDLPS and DDLPS shared similar somatic mutations and copy number variations (CNVs), whereas DDLPS had more mutated genes and a higher and more concentrated amplification of the chromosome 12q region. Furthermore, somatic mutations in DDLPS were significantly reduced after treatment with CDK4 inhibitors, while CNVs remained elevated.ConclusionDue to the high likelihood of recurrence of liposarcoma, various effective treatments should be taken into consideration even if surgery is the primary treatment for recurrent liposarcoma. To effectively control the course of the disease following surgery, combination targeted therapy may be a viable alternative to chemotherapy and radiotherapy in the treatment of liposarcoma

Targeting of Embryonic Stem Cells by Peptide-Conjugated Quantum Dots

Targeting stem cells holds great potential for studying the embryonic stem cell and development of stem cell-based regenerative medicine. Previous studies demonstrated that nanoparticles can serve as a robust platform for gene delivery, non-invasive cell imaging, and manipulation of stem cell differentiation. However specific targeting of embryonic stem cells by peptide-linked nanoparticles has not been reported.Here, we developed a method for screening peptides that specifically recognize rhesus macaque embryonic stem cells by phage display and used the peptides to facilitate quantum dot targeting of embryonic stem cells. Through a phage display screen, we found phages that displayed an APWHLSSQYSRT peptide showed high affinity and specificity to undifferentiated primate embryonic stem cells in an enzyme-linked immunoabsorbent assay. These results were subsequently confirmed by immunofluorescence microscopy. Additionally, this binding could be completed by the chemically synthesized APWHLSSQYSRT peptide, indicating that the binding capability was specific and conferred by the peptide sequence. Through the ligation of the peptide to CdSe-ZnS core-shell nanocrystals, we were able to, for the first time, target embryonic stem cells through peptide-conjugated quantum dots.These data demonstrate that our established method of screening for embryonic stem cell specific binding peptides by phage display is feasible. Moreover, the peptide-conjugated quantum dots may be applicable for embryonic stem cell study and utilization

Beyond Sharing: Conflict-Aware Multivariate Time Series Anomaly Detection

Massive key performance indicators (KPIs) are monitored as multivariate time series data (MTS) to ensure the reliability of the software applications and service system. Accurately detecting the abnormality of MTS is very critical for subsequent fault elimination. The scarcity of anomalies and manual labeling has led to the development of various self-supervised MTS anomaly detection (AD) methods, which optimize an overall objective/loss encompassing all metrics' regression objectives/losses. However, our empirical study uncovers the prevalence of conflicts among metrics' regression objectives, causing MTS models to grapple with different losses. This critical aspect significantly impacts detection performance but has been overlooked in existing approaches. To address this problem, by mimicking the design of multi-gate mixture-of-experts (MMoE), we introduce CAD, a Conflict-aware multivariate KPI Anomaly Detection algorithm. CAD offers an exclusive structure for each metric to mitigate potential conflicts while fostering inter-metric promotions. Upon thorough investigation, we find that the poor performance of vanilla MMoE mainly comes from the input-output misalignment settings of MTS formulation and convergence issues arising from expansive tasks. To address these challenges, we propose a straightforward yet effective task-oriented metric selection and p&s (personalized and shared) gating mechanism, which establishes CAD as the first practicable multi-task learning (MTL) based MTS AD model. Evaluations on multiple public datasets reveal that CAD obtains an average F1-score of 0.943 across three public datasets, notably outperforming state-of-the-art methods. Our code is accessible at https://github.com/dawnvince/MTS_CAD.Comment: 11 pages, ESEC/FSE industry track 202