МЕТОД ГЛУБОКОГО ОБУЧЕНИЯ С ПОДКРЕПЛЕНИЕМ ДЛЯ СИСТЕМ ИНТЕРНЕТА ВЕЩЕЙ НА БАЗЕ ТЕХНОЛОГИИ LORA С ОГРАНИЧЕННЫМИ РЕСУРСАМИ И ПОДДЕРЖКОЙ QoS

Achieving high Quality of Service (QoS) remains a challenge for LoRa technology. However, high QoS can be achieved via optimizing the transmission policy parameters such as bandwidth and code rate. Existing approaches do not provide an opportunity to optimize the LoRa networks' data transmission parameters. The article proposes transmission policy enforcementfor QoS-aware LoRanetworks.The QoSparameter ranking is implemented for IoT nodes where priority and nonpriority information is identified by the new field of LoRa frame structure(QRank).The optimaltransmissionpolicyenforcement uses fast deep reinforcement learning that utilizes the environmental parameters including QRank, signal quality, and signal-to-interference-plus-noise-ratio. The transmission policy is optimized for spreading factor, code rate, bandwidth, and carrier frequency. Performance evaluation is implemented using an NS3.26 LoRaWAN module. The performance is examined for various metrics such as delay and throughput. Достижение высокого качества обслуживания (QoS) по-прежнему остается достаточно сложной задачей для технологии LoRa. В принципе высокий уровень QoS может быть достигнут за счет оптимизации параметров передачи, например, пропускной способности и скорости передачи информации в сети. Известные на сегодняшний день решения не дают возможности оптимизировать параметры передачи данных для сетей LoRa. В статье предложен эффективный метод передачи данных, обеспечивающий требования по QoS при использовании технологии LoRa. Ранжирование параметров QoS для узлов интернета вещей определяется новым полем структуры фрейма LoRa (QRank) для приоритетной и неприоритетной информации. Для обеспечения эффективной передачи применяется быстрое глубокое обучение с подкреплением, для которого используются как параметры качества обслуживания, так и отношение сигнал/шум. Метод передачи оптимизирован с учетом коэффициента распространения, скорости передачи данных, полосы пропускания и несущей частоты. Оценка производительности при применении предложенного метода проведена с использованием модуля LoRaWAN в пакете имитационного моделирования NS3.26. Производительность оценивается на основе параметров задержки и пропускной способности.

The molecular logic of synaptic wiring at the single cell level

SUMMARYThe correct wiring of neuronal circuits is one of the most complex processes in development, since axons form highly specific connections out of a vast number of possibilities. Circuit structure is genetically determined in vertebrates and invertebrates, but the mechanism guiding each axon to precisely innervate a unique pre-specified target cell is poorly understood. Here, we used single cell genomics, imaging and genetics to show that single-cell specific connections between motoneurons and target muscles are specified through a combinatorial code of immunoglobulin domain proteins. These programs are orchestrated by a homeodomain transcription factor code that specifies cellular identities down to the level of biologically unique cells. Using spatial mapping, we show that this code follows spatial patterns already observed at early embryonic stages, while acting at a much later stage to specify motor circuits. Taken together, our data suggest that a relatively simple homeo-immunoglobulin-code determines neuronal circuit structure.

Temporal patterning of the central nervous system by a shared transcription factor code

The molecular mechanisms that ensure the reproducible generation of neuronal diversity in the vertebrate nervous system are incompletely understood. Here we provide evidence of a temporal patterning program consisting of cohorts of transcription factors expressed in neurons generated at successive developmental timepoints. This program acts in parallel to spatial patterning, diversifying neurons throughout the nervous system and in neurons differentiated in-vitro from stem cells. We demonstrate the TGFβ signalling pathway controls the pace of the temporal program. Furthermore, targeted perturbation of components of the temporal program, Nfia and Nfib, reveals their requirement for the generation of late-born neuronal subtypes. Together, our results provide evidence for the existence of a previously unappreciated global temporal program of neuronal subtype identity and suggest that the integration of spatial and temporal patterning programs diversifies and organises neuronal subtypes in the vertebrate nervous system.

Alih Kode dan Campur Kode oleh Pegawai Aerotravel di Denpasar

This research aims to identify types and causes of code switching and code mixing that are applied at Aerotravel Denpasar. This research is analyzed with “Agih and Padan” method in two different languages , Japan and Indonesian. On each of the language, it’s connected therefore found type and cause of factor code switching and code mixing. The theories used are code switching (Wardhaugh, 2006), code mixing (Hoffman, 1991) , and cause code mixing factor (Weinrich, 1979). From 20 data that is analyzed at Aerotravel, it resulted 6 type switching code: 1 methaporical code switching, 5 situational code switching. In addition there are 14 data of switching code consists of 10 lexical code mixing in lexical, 3 phonologic code mixing, and 1 sentence code mixing. The cause factors of switching codes are, topic, solidarity with communicator, social art distance, and motivation. The cause factor of code mixing are low frequency of word, need for synonym, social value, familiarity with another culture, and sufficiency differentiated. This research concludes that the situational code switching is the most used in code switching. Education background is the cause of code mixing existence.

Relative equilibrium states and class degree

Given a factor code $\unicode[STIX]{x1D70B}$ from a shift of finite type $X$ onto a sofic shift $Y$, an ergodic measure $\unicode[STIX]{x1D708}$ on $Y$, and a function $V$ on $X$ with sufficient regularity, we prove an invariant upper bound on the number of ergodic measures on $X$ which project to $\unicode[STIX]{x1D708}$ and maximize the measure pressure $h(\unicode[STIX]{x1D707})+\int V\,d\unicode[STIX]{x1D707}$ among all measures in the fiber $\unicode[STIX]{x1D70B}^{-1}(\unicode[STIX]{x1D708})$. If $\unicode[STIX]{x1D708}$ is fully supported, this bound is the class degree of $\unicode[STIX]{x1D70B}$. This generalizes a previous result for the special case of $V=0$ and thus settles a conjecture raised by Allahbakhshi and Quas.