A Lightweight Transmission Parameter Selection Scheme Using Reinforcement Learning for LoRaWAN

The number of IoT devices is predicted to reach 125 billion by 2023. The growth of IoT devices will intensify the collisions between devices, degrading communication performance. Selecting appropriate transmission parameters, such as channel and spreading factor (SF), can effectively reduce the collisions between long-range (LoRa) devices. However, most of the schemes proposed in the current literature are not easy to implement on an IoT device with limited computational complexity and memory. To solve this issue, we propose a lightweight transmission-parameter selection scheme, i.e., a joint channel and SF selection scheme using reinforcement learning for low-power wide area networking (LoRaWAN). In the proposed scheme, appropriate transmission parameters can be selected by simple four arithmetic operations using only Acknowledge (ACK) information. Additionally, we theoretically analyze the computational complexity and memory requirement of our proposed scheme, which verified that our proposed scheme could select transmission parameters with extremely low computational complexity and memory requirement. Moreover, a large number of experiments were implemented on the LoRa devices in the real world to evaluate the effectiveness of our proposed scheme. The experimental results demonstrate the following main phenomena. (1) Compared to other lightweight transmission-parameter selection schemes, collisions between LoRa devices can be efficiently avoided by our proposed scheme in LoRaWAN irrespective of changes in the available channels. (2) The frame success rate (FSR) can be improved by selecting access channels and using SFs as opposed to only selecting access channels. (3) Since interference exists between adjacent channels, FSR and fairness can be improved by increasing the interval of adjacent available channels.Comment: 14 pages, 12 figures, 8 tables. This work has been submitted to the IEEE for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessibl

Preliminary Report of the Waseda University Excavations at Dahshur North:Tenth Season,2004-2005

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エジプト古代末期社会の終焉とイスラーム文化の形成-赤色光沢土器を中心とする生活雑器と空間構成の移相-

早大学位記番号:新7383早稲田大

Multi-Rate VAE: Train Once, Get the Full Rate-Distortion Curve

Variational autoencoders (VAEs) are powerful tools for learning latent representations of data used in a wide range of applications. In practice, VAEs usually require multiple training rounds to choose the amount of information the latent variable should retain. This trade-off between the reconstruction error (distortion) and the KL divergence (rate) is typically parameterized by a hyperparameter $\beta$. In this paper, we introduce Multi-Rate VAE (MR-VAE), a computationally efficient framework for learning optimal parameters corresponding to various $\beta$ in a single training run. The key idea is to explicitly formulate a response function that maps $\beta$ to the optimal parameters using hypernetworks. MR-VAEs construct a compact response hypernetwork where the pre-activations are conditionally gated based on $\beta$. We justify the proposed architecture by analyzing linear VAEs and showing that it can represent response functions exactly for linear VAEs. With the learned hypernetwork, MR-VAEs can construct the rate-distortion curve without additional training and can be deployed with significantly less hyperparameter tuning. Empirically, our approach is competitive and often exceeds the performance of multiple $\beta$-VAEs training with minimal computation and memory overheads.Comment: 22 pages, 9 figure

Cure and Curse: E. coli Heat-Stable Enterotoxin and Its Receptor Guanylyl Cyclase C

Enterotoxigenic Escherichia coli (ETEC) associated diarrhea is responsible for roughly half a million deaths per year, the majority taking place in developing countries. The main agent responsible for these diseases is the bacterial heat-stable enterotoxin STa. STa is secreted by ETEC and after secretion binds to the intestinal receptor guanylyl cyclase C (GC-C), thus triggering a signaling cascade that eventually leads to the release of electrolytes and water in the intestine. Additionally, GC-C is a specific marker for colorectal carcinoma and STa is suggested to have an inhibitory effect on intestinal carcinogenesis. To understand the conformational events involved in ligand binding to GC-C and to devise therapeutic strategies to treat both diarrheal diseases and colorectal cancer, it is paramount to obtain structural information on the receptor ligand system. Here we summarize the currently available structural data and report on physiological consequences of STa binding to GC-C in intestinal epithelia and colorectal carcinoma cells

Indications of Neutrino Oscillation in a 250 km Long-baseline Experiment

The K2K experiment observes indications of neutrino oscillation: a reduction of $\nu_\mu$ flux together with a distortion of the energy spectrum. Fifty-six beam neutrino events are observed in Super-Kamiokande (SK), 250 km from the neutrino production point, with an expectation of $80.1^{+6.2}_{-5.4}$. Twenty-nine one ring $\mu$-like events are used to reconstruct the neutrino energy spectrum, which is better matched to the expected spectrum with neutrino oscillation than without. The probability that the observed flux at SK is explained by statistical fluctuation without neutrino oscillation is less than 1%.Comment: 5 pages, 3 figures embedded, LaTeX with RevTeX style, accepted for publication in PRL on December 13, 200

Risk-adjusted therapy for pediatric non-T cell ALL improves outcomes for standard risk patients: results of JACLS ALL-02

This study was a second multicenter trial on childhood ALL by the Japan Childhood Leukemia Study Group (JACLS) to improve outcomes in non-T ALL. Between April 2002 and March 2008, 1138 children with non-T ALL were enrolled in the JACLS ALL-02 trial. Patients were stratified into three groups using age, white blood cell count, unfavorable genetic abnormalities, and treatment response: standard risk (SR), high risk (HR), and extremely high risk (ER). Prophylactic cranial radiation therapy (PCRT) was abolished except for CNS leukemia. Four-year event-free survival (4yr-EFS) and 4-year overall survival (4yr-OS) rates for all patients were 85.4% ± 1.1% and 91.2% ± 0.9%, respectively. Risk-adjusted therapy resulted in 4yr-EFS rates of 90.4% ± 1.4% for SR, 84.9% ± 1.6% for HR, and 66.5% ± 4.0% for ER. Based on NCI risk classification, 4yr-EFS rates were 88.2% in NCI-SR and 76.4% in NCI-HR patients, respectively. Compared to previous trial ALL-97, 4yr-EFS of NCI-SR patients was significantly improved (88.2% vs 81.2%, log rank p = 0.0004). The 4-year cumulative incidence of isolated (0.9%) and total (1.5%) CNS relapse were significantly lower than those reported previously. In conclusion, improved EFS in NCI-SR patients and abolish of PCRT was achieved in ALL-02

Integer Quantum Hall Effect in Double-Layer Systems

We consider the localization of independent electron orbitals in double-layer two-dimensional electron systems in the strong magnetic field limit. Our study is based on numerical Thouless number calculations for realistic microscopic models and on transfer matrix calculations for phenomenological network models. The microscopic calculations indicate a crossover regime for weak interlayer tunneling in which the correlation length exponent appears to increase. Comparison of network model calculations with microscopic calculations casts doubt on their generic applicability.Comment: 14 pages, 12 figures included, RevTeX 3.0 and epsf. Additional reference