A new wireless sensor platform with camera

Abstractthere are several platforms of wireless sensor networks such as micaz, mica2, etc. Each of them has specific characteristics. But the complexity of novel applications requires new characteristics, which more and more new designs of wireless sensor networks are needed. In this paper, the design of a sensor named Lacuna is proposed, which is a new sensor network platform implementing reliable detecting by taking real-time pictures. The paper presents a simplified model of wireless sensor networks (WSN) which is composed of the Lacuna sensors using IEEE 802.15.4 wireless technology. This model has been tested for many times and the model experimental results show that this system can run stably, reliably and efficiently. Stability, reliability, and efficiency are important because they make the operation robust to temporary disconnections or high packet loss. Due to the stability, reliability, and efficiency, the WSN transmits large amounts of continuous stable picture data messages to notebook when one of the nodes finishes taking a picture

Neural-Symbolic Recommendation with Graph-Enhanced Information

The recommendation system is not only a problem of inductive statistics from data but also a cognitive task that requires reasoning ability. The most advanced graph neural networks have been widely used in recommendation systems because they can capture implicit structured information from graph-structured data. However, like most neural network algorithms, they only learn matching patterns from a perception perspective. Some researchers use user behavior for logic reasoning to achieve recommendation prediction from the perspective of cognitive reasoning, but this kind of reasoning is a local one and ignores implicit information on a global scale. In this work, we combine the advantages of graph neural networks and propositional logic operations to construct a neuro-symbolic recommendation model with both global implicit reasoning ability and local explicit logic reasoning ability. We first build an item-item graph based on the principle of adjacent interaction and use graph neural networks to capture implicit information in global data. Then we transform user behavior into propositional logic expressions to achieve recommendations from the perspective of cognitive reasoning. Extensive experiments on five public datasets show that our proposed model outperforms several state-of-the-art methods, source code is avaliable at [https://github.com/hanzo2020/GNNLR].Comment: 12 pages, 2 figures, conferenc

Neuro-Symbolic Recommendation Model based on Logic Query

A recommendation system assists users in finding items that are relevant to them. Existing recommendation models are primarily based on predicting relationships between users and items and use complex matching models or incorporate extensive external information to capture association patterns in data. However, recommendation is not only a problem of inductive statistics using data; it is also a cognitive task of reasoning decisions based on knowledge extracted from information. Hence, a logic system could naturally be incorporated for the reasoning in a recommendation task. However, although hard-rule approaches based on logic systems can provide powerful reasoning ability, they struggle to cope with inconsistent and incomplete knowledge in real-world tasks, especially for complex tasks such as recommendation. Therefore, in this paper, we propose a neuro-symbolic recommendation model, which transforms the user history interactions into a logic expression and then transforms the recommendation prediction into a query task based on this logic expression. The logic expressions are then computed based on the modular logic operations of the neural network. We also construct an implicit logic encoder to reasonably reduce the complexity of the logic computation. Finally, a user's interest items can be queried in the vector space based on the computation results. Experiments on three well-known datasets verified that our method performs better compared to state of the art shallow, deep, session, and reasoning models.Comment: 17 pages, 6 figure

2-D DOA Estimation for Acoustic Vector Sensor Array Based on Greedy Block Coordinate Descent Algorithm

Keywords: two-dimensional direction of arrival (DOA) estimation, acoustic vector sensor (AVS), greedy block coordinate descent(GBCD). Abstract. This paper presents an approach for the estimation of two-directional (2D)direction-of-arrival (DOA) using Acoustic Vector Sensor array based on greedy block coordinate descent(GBCD) algorithm, which can achieve faster convergence rate and better estimation accuracy. Moreover, a weighted form of block selection rule is proposed with the MUSIC prior. The identifiability of the presented approach is studied using computer simulations. It is demonstrated that the 2D DOAs of AVS can be realized using the approach, which has a superior resolution

WindMill: A Parameterized and Pluggable CGRA Implemented by DIAG Design Flow

With the cross-fertilization of applications and the ever-increasing scale of models, the efficiency and productivity of hardware computing architectures have become inadequate. This inadequacy further exacerbates issues in design flexibility, design complexity, development cycle, and development costs (4-d problems) in divergent scenarios. To address these challenges, this paper proposed a flexible design flow called DIAG based on plugin techniques. The proposed flow guides hardware development through four layers: definition(D), implementation(I), application(A), and generation(G). Furthermore, a versatile CGRA generator called WindMill is implemented, allowing for agile generation of customized hardware accelerators based on specific application demands. Applications and algorithm tasks from three aspects is experimented. In the case of reinforcement learning algorithm, a significant performance improvement of $2.3\times$ compared to GPU is achieved.Comment: 7 pages, 10 figure

Genome-Wide Identification of the A20/AN1 Zinc Finger Proteon Family Genes in \u3cem\u3eIpomoea batatas\u3c/em\u3e and Its Two Relatives and Function Analysis of \u3cem\u3eIbSAP16\u3c/em\u3e in Salinity Tolerance

Stress-associated protein (SAP) genes—encoding A20/AN1 zinc-finger domain-containing proteins—play pivotal roles in regulating stress responses, growth, and development in plants. They are considered suitable candidates to improve abiotic stress tolerance in plants. However, the SAP gene family in sweet potato (Ipomoea batatas) and its relatives is yet to be investigated. In this study, 20 SAPs in sweet potato, and 23 and 26 SAPs in its wild diploid relatives Ipomoea triloba and Ipomoea trifida were identified. The chromosome locations, gene structures, protein physiological properties, conserved domains, and phylogenetic relationships of these SAPs were analyzed systematically. Binding motif analysis of IbSAPs indicated that hormone and stress responsive cis-acting elements were distributed in their promoters. RT-qPCR or RNA-seq data revealed that the expression patterns of IbSAP, ItbSAP, and ItfSAP genes varied in different organs and responded to salinity, drought, or ABA (abscisic acid) treatments differently. Moreover, we found that IbSAP16 driven by the 35 S promoter conferred salinity tolerance in transgenic Arabidopsis. These results provided a genome-wide characterization of SAP genes in sweet potato and its two relatives and suggested that IbSAP16 is involved in salinity stress responses. Our research laid the groundwork for studying SAP-mediated stress response mechanisms in sweet potato

Sensitizing Leukemia Stem Cells to NF-κB Inhibitor Treatment in Vivo by Inactivation of Both TNF and IL-1 Signaling

We previously reported that autocrine TNF-α (TNF) is responsible for JNK pathway activation in a subset of acute myeloid leukemia (AML) patient samples, providing a survival/proliferation signaling parallel to NF-κB in AML stem cells (LSCs). In this study, we report that most TNF-expressing AML cells (LCs) also express another pro-inflammatory cytokine, IL1β, which acts in a parallel manner. TNF was produced primarily by LSCs and leukemic progenitors (LPs), whereas IL1β was mainly produced by partially differentiated leukemic blasts (LBs). IL1β also stimulates an NF-κB-independent pro-survival and proliferation signal through activation of the JNK pathway. We determined that co-inhibition of signaling stimulated by both TNF and IL1β synergizes with NF-κB inhibition in eliminating LSCs both ex vivo and in vivo. Our studies show that such treatments are most effective in M4/5 subtypes of AML

The Technology of Mould Steel for Online Pre-hardening

AbstractThis article describes a production method of mould steel pre-hardening, and focus on the advantage of this method, The technical core of method is the variable frequency and variable amplitude pulse uniform high-precision temperature control, which achieved by using strong-medium-weak water cooling, gas-water cooling and gas mist cooling composite cooling control technology. Optimizing the cooling rate path is a good method of optimizing quenched organization and structure