Úton a rendszerváltoztatás felé: A gazdasági kamarák narratívái az államszocializmus utolsó éveiben.

<p>List of <i>JcSAG12H</i> genes identified in this study.</p

Efficient Federated Learning on Knowledge Graphs via Privacy-preserving Relation Embedding Aggregation

Federated Learning (FL) on knowledge graphs (KGs) has yet to be as well studied as other domains, such as computer vision and natural language processing. A recent study FedE first proposes an FL framework that shares entity embeddings of KGs across all clients. However, compared with model sharing in vanilla FL, entity embedding sharing from FedE would incur severe privacy leakage. Specifically, the known entity embedding can be used to infer whether a specific relation between two entities exists in a private client. In this paper, we first develop a novel attack that aims to recover the original data based on embedding information, which is further used to evaluate the vulnerabilities of FedE. Furthermore, we propose a Federated learning paradigm with privacy-preserving Relation embedding aggregation (FedR) to tackle the privacy issue in FedE. Compared to entity embedding sharing, relation embedding sharing policy can significantly reduce the communication cost due to its smaller size of queries. We conduct extensive experiments to evaluate FedR with five different embedding learning models and three benchmark KG datasets. Compared to FedE, FedR achieves similar utility and significant (nearly 2X) improvements in both privacy and efficiency on link prediction task.Comment: Accepted to ACL 2022 Workshop on Federated Learning for Natural Language Processin

Genome-wide identification of rubber tree (Hevea brasiliensis Muell. Arg.) aquaporin genes and their response to ethephon stimulation in the laticifer, a rubber-producing tissue

Expression profiles of the 51 HbAQP genes in the laticifer of rubber tree clone RRIM928. (PDF 36 kb

Identification of important charged residues for alkali cation exchange or pH regulation of NhaH, a Na+/H+ antiporter of Halobacillus dabanensis

AbstractNhaH is a novel Na+/H+ antiporter identified from the moderate halophile Halobacillus dabanensis. In this study, six conserved charged residues located in the putative transmembrane segments (TMS) including TMSV, TMSVI, TMSVIII and TMSXI of NhaH as well as two His residues in Loop III were replaced by site-directed mutagenesis for the identification of their potential roles in the antiport activity and pH regulation. Substitutions D137A, D166A and R325A caused a complete loss of Na+(Li+)/H+ antiport activity, revealing that D137, D166 and R325 are indispensable for the antiport activity. Substitution D137E led to a significant increase of the apparent Km values for Na+ and Li+ without affecting the changes of pH profile, confirming that D137 plays vital roles in alkali cation binding/translocation. Substitution D166E resulted in not only a significant increase of the apparent Km values for Na+ and Li+ but also an alkaline shift of pH profile, suggesting that D166 is involved in alkali cation binding/translocation as well as H+ binding or pH regulation. Substitutions E161N, D224A and D224E caused a significant increase of Km for Na+ and Li+, indicating that E161 and D224 partly contribute to alkali cation binding/translocation. Substitution E229K caused an over 50% elevation of the apparent Km for Li+, without affecting that for Na+, suggesting that E229 may be mainly responsible for Li+ binding/translocation. Substitutions H87A and H88A resulted in an acidic shift of pH profile without an effect on Km for Na+ and Li+, indicating that H87 and H88 are involved in H+ binding or pH regulation

Optimization of CNOT circuits on topological superconducting processors

We focus on optimization of the depth/size of CNOT circuits under topological connectivity constraints. We prove that any $n$-qubit CNOT circuit can be paralleled to $O(n)$ depth with $n^2$ ancillas for $2$-dimensional grid structure. For the high dimensional grid topological structure in which every quibit connects to $2\log n$ other qubits, we achieves the asymptotically optimal depth $O(\log n)$ with only $n^2$ ancillas. We also consider the synthesis without ancillas. We propose an algorithm uses at most $2n^2$ CNOT gates for arbitrary connected graph, considerably better than previous works. Experiments also confirmed the performance of our algorithm. We also designed an algorithm for dense graph, which is asymptotically optimal for regular graph. All these results can be applied to stabilizer circuits

Emulating Complex Synapses Using Interlinked Proton Conductors

In terms of energy efficiency and computational speed, neuromorphic electronics based on non-volatile memory devices is expected to be one of most promising hardware candidates for future artificial intelligence (AI). However, catastrophic forgetting, networks rapidly overwriting previously learned weights when learning new tasks, remains as a pivotal hurdle in either digital or analog AI chips for unleashing the true power of brain-like computing. To address catastrophic forgetting in the context of online memory storage, a complex synapse model (the Benna-Fusi model) has been proposed recently[1], whose synaptic weight and internal variables evolve following a diffusion dynamics. In this work, by designing a proton transistor with a series of charge-diffusion-controlled storage components, we have experimentally realized the Benna-Fusi artificial complex synapse. The memory consolidation from coupled storage components is revealed by both numerical simulations and experimental observations. Different memory timescales for the complex synapse are engineered by the diffusion length of charge carriers, the capacity and number of coupled storage components. The advantage of the demonstrated complex synapse in both memory capacity and memory consolidation is revealed by neural network simulations of face familiarity detection. Our experimental realization of the complex synapse suggests a promising approach to enhance memory capacity and to enable continual learning.Comment: 6 figure

Research on fault diagnosis of hydraulic pump using convolutional neural network

The failure mechanism of hydraulic pump is complex, and its faulty features are frequently submerged in the nonlinear interference caused by various components. The fault diagnosis of hydraulic pump is a challenge in the field of machinery. The conventional fault diagnosis approaches have several drawbacks. First, the operator should be cognizant of the mechanism of hydraulic pump. Second, the procedure is onerous, and has many parameters to set. Third, the shallow classification is weak for this complex problem, which leads to low accuracy rate. This paper developed a new scheme by using improved convolutional neural network. It can be directly used without human intervention, although the operator knows little knowledge about hydraulic pump. Therefore, it is simple to be employed and easy for widely promotion. Validated by fault diagnosis cases of hydraulic pump, the proposed scheme is not only simple for application, but also is superior to other machine learning algorithms, especially when the pump speed varies

Generating Supplementary Travel Guides from Social Media

In this paper we study how to summarize travel-related information in forum threads to gener-ate supplementary travel guides. Such summaries presumably can provide additional and more up-to-date information to tourists. Existing multi-document summarization methods have limita-tions for this task because (1) they do not generate structured summaries but travel guides usually follow a certain template, and (2) they do not put emphasis on named entities but travel guides often recommend points of interest to travelers. To overcome these limitations, we propose to use a latent variable model to align forum threads with the section structure of well-written travel guides. The model also assigns section labels to named entities in forum threads. We then propose to modify an ILP-based summarization method to generate section-specific summaries. Evaluation on threads from Yahoo! Answers shows that our proposed method is able to generate better summaries compared with a number of baselines based on ROUGE scores and coverage of named entities.