This paper presents a triaxial accelerometer readout application-specific integrated circuit (ASIC) based on time-division multiplexing (TDM) and a digital on-chip compensation scheme based on a modified Prandtl–Ishlinskii (MPI) model. By adopting the TDM scheme, three sensing elements share the forward path of the analog signal chain, which significantly reduces the chip area. Furthermore, the MPI model is introduced for accelerometer temperature compensation for the first time. The proposed model can accurately compensate both the major and minor hysteresis loops under different temperature variation rates. This compensation approach overcomes the limitation of conventional polynomial methods, which are typically effective only under either static or dynamic temperature conditions. The readout ASIC is fabricated in a 1P6M 180 nm BCD process and powered by a 5 V single supply. Experimental results show that the designed accelerometer achieves a measurement range of ±30 g with a power consumption of 47 mW and a bandwidth of 57 Hz per axis. The sensitivities of the three axes are 0.1273 V/g, 0.1204 V/g, 0.1255 V/g, with corresponding nonlinearities of 0.129%, 0.072%, and 0.081%, respectively. After MPI compensation, a 36-dB bias temperature drift rejection ratio is achieved from$- 5~^{\circ }$C to$55~^{\circ }$C.

Biomedical relation extraction is crucial for many applications such as biomedical knowledge graph construction and question answering. It is difficult for a typical neural network model to clearly understand the meaning of the complex biomedical text without any knowledge. The knowledge includes external knowledge and inherent prior knowledge within the dataset. The existing methods always integrate the embedded external knowledge obtained through translation-based models like TransE, which is insufficient for a clear understanding of the biomedical entities and relations. In addition, the corpus itself contains abundant prior knowledge that can aid in learning discriminative features. However, this valuable knowledge is not fully utilized in current methods for biomedical relation extraction. In this work, we propose a multi-channel knowledge-enhanced model to extract biomedical relation. It incorporates the external knowledge and the inherent prior knowledge within the dataset into a neural network using multiple channels. On the one hand, the external entity knowledge is deeply exploited by the sequential and structural knowledge channels, respectively. On the other hand, we also explore the prior knowledge in the dataset using an external attention in the prior knowledge channel. The experimental results demonstrate that the proposed model is effective for the biomedical relation extraction.

Zero-shot event-relational reasoning is an important task in natural language processing, and existing methods jointly learn a variety of event-relational prefixes and inference-form prefixes to achieve such tasks. However, training prefixes consumes large computational resources and lacks interpretability. Additionally, learning various relational and inferential knowledge inefficiently exploits the connections between tasks. Therefore, we first propose a method for Reasoning-Oriented Locating and Editing (ROLE), which locates and edits the key modules of the language model for reasoning about event relations, enhancing interpretability and also resource-efficiently optimizing the reasoning ability. Subsequently, we propose a method for Analogy-Based Locating and Editing (ABLE), which efficiently exploits the similarities and differences between tasks to optimize the zero-shot reasoning capability. Experimental results show that ROLE improves interpretability and reasoning performance with reduced computational cost. ABLE achieves SOTA results in zero-shot reasoning.

Biomedical document-level relation extraction (RE) aims to extract relation facts from unstructured biomedical documents and plays an important role in downstream tasks. Graph-based methods solve the problem that sequence-based methods cannot extract long-distance entity relationships, but ignore the fact that graph node connections should be dynamic rather than static. Besides, the existing methods usually introduce external knowledge to address the method's performance bottleneck caused by the limited information contained in the dataset itself. But they fail to consider utilizing external knowledge through explicitly enriching graph connectivity. For the above problems, we propose a novel document-level relation extraction model based on a knowledge-enhanced graph and dynamic generative adversarial network (KG-DGAN). Specifically, a knowledge-enhanced graph is constructed based on the documents and external knowledge information together, where the external knowledge is used to explicitly enhance the connectivity of the graph. Then, the dynamic generative adversarial network (DGAN) can dynamically soften the edge weights and node representations, which reduces the redundant information and enhances useful information during aggregation. We evaluate our method on the widely used CDR and CHR dataset. The final experimental results confirm that the proposed method achieves novel state-of-the-art performances.

Few-shot Event Detection (FSED) is a meaningful task due to the limited labeled data and expensive manual labeling. Some prompt-based methods are used in FSED. However, these methods require large GPU memory due to the increased length of input tokens caused by concatenating prompts, as well as additional human effort for designing verbalizers. Moreover, they ignore instance and prompt biases arising from the confounding effects between prompts and texts. In this paper, we propose a prototype-based prompt-instance Interaction with causal Intervention (2xInter) model to conveniently utilize both prompts and verbalizers and effectively eliminate all biases. Specifically, 2xInter first presents a Prototype-based Prompt-Instance Interaction (PPII) module that applies an interactive approach for texts and prompts to reduce memory and regards class prototypes as verbalizers to avoid design costs. Next, 2xInter constructs a Structural Causal Model (SCM) to explain instance and prompt biases and designs a Double-View Causal Intervention (DVCI) module to eliminate these biases. Due to limited supervised information, DVCI devises a generation-based prompt adjustment for instance intervention and a Siamese network-based instance contrasting for prompt intervention. Finally, the experimental results show that 2xInter achieves state-of-the-art performance on RAMS and ACE datasets.

When the acoustic waves interact with the ground, seismic waves can be generated. These seismic waves are influenced by the incident angles of acoustic waves and the near-surface seismic-wave velocities. To understand the acoustic-seismic coupling signals, we designed a hard-stratum model (where the transverse wave of the subsurface velocity is higher than acoustic wave velocity) and a soft-stratum model (where the transverse wave of the subsurface velocity is lower than acoustic wave velocity) for acoustic-seismic signals simulation. We employed the hybrid Galerkin method to simulate the acoustic-seismic signals generated by moving acoustic sources. We then validated the existence of Rayleigh and evanescent waves induced by the acoustic-seismic coupling process. Even in high-attenuation subsurfaces, relatively high-amplitude seismic waves can still be received underground at a depth of 200 m. When the transverse wave of the subsurface velocity is greater than the acoustic wave velocity, the surface geophone detects four kinds of seismic waves: longitudinal, transverse, Rayleigh, and evanescent waves. We could successfully capture the Doppler frequency shift phenomenon in both the generated seismic waves and acoustic waves. Moreover, the velocities of both these waves can influence the shape of the Doppler curves.

The Doppler shift of sound signals has been widely studied. However, monitoring and analyzing the Doppler shift characteristics of aircraft-generated seismic signals is still a relatively new field that requires further exploration. We studied the air-to-ground coupled seismic waves generated by moving aircraft, which were measured by 12 short-period seismometers installed near the Beijing Capital International Airport. The coupled seismic signals generated by 127 aircrafts flying over the observation system were effectively recorded, which confirms the feasibility of using seismic methods to monitor air traffic. We clearly observed the Doppler shift of the coupled signals, which is most noticeable in the frequency range above 500 Hz and serves as important input for analyzing aerial trajectories. Based on the theoretical formula of the Doppler shift, we analyzed the influence of various parameters on the curve shape. Then we proposed a new algorithm for tracking aircraft trajectories using Simulated Annealing inversion method. Finally, using the data collected during the experiment and the proposed trajectory inversion method, we successfully calculated the aircraft trajectory. The implications of our research are significant in integrating seismic technology and data analysis for detecting and monitoring aircraft signals in the field of air traffic.

The structural knowledge graph is crucial external resource for Drug-Drug Interaction (DDI) extraction. However, it is challenging to combine the structural information of knowledge graph and the semantic representation derived from the neural network. Therefore, we propose to extract DDI by constructing a drug knowledge graph, and pre-train a model to learn drug knowledge embeddings adaptive for DDI. Our model performs well on DDIExtraction 2013 dataset, which demonstrates the effectiveness.

Document-level biomedical relation extraction (Bio-DocuRE) is an important branch of biomedical text mining that aims to automatically extract all relation facts from the biomedical text. Since there are a considerable number of relations in biomedical documents that need to be judged by other existing relations, logical reasoning has become a research hotspot in the past two years. However, current models with reasoning are single-granularity only based on one element information, ignoring the complementary fact of different granularity reasoning information. In addition, obtaining rich document information is a prerequisite for logical reasoning, but most of the previous models cannot sufficiently utilize document information, which limits the reasoning ability of the model. In this paper, we propose a novel Bio-DocuRE model called FILR, based on Multi-Dimensional Fusion Information and Multi-Granularity Logical Reasoning. Specifically, FILR presents a multi-dimensional information fusion module MDIF to extract sufficient global document information. Then FILR proposes a multi-granularity reasoning module MGLR to obtain rich inference information through the reasoning of both entity-pairs and mention-pairs. We evaluate our FILR model on two widely used biomedical corpora CDR and GDA. Experimental results show that FILR achieves state-of-the-art performance.

BACKGROUND: Biomedical event extraction is a fundamental task in biomedical text mining, which provides inspiration for medicine research and disease prevention. Biomedical events include simple events and complex events. Existing biomedical event extraction methods usually deal with simple events and complex events uniformly, and the performance of complex event extraction is relatively low. RESULTS: In this paper, we propose a fine-grained Bidirectional Long Short Term Memory method for biomedical event extraction, which designs different argument detection models for simple and complex events respectively. In addition, multi-level attention is designed to improve the performance of complex event extraction, and sentence embeddings are integrated to obtain sentence level information which can resolve the ambiguities for some types of events. Our method achieves state-of-the-art performance on the commonly used dataset Multi-Level Event Extraction. CONCLUSIONS: The sentence embeddings enrich the global sentence-level information. The fine-grained argument detection model improves the performance of complex biomedical event extraction. Furthermore, the multi-level attention mechanism enhances the interactions among relevant arguments. The experimental results demonstrate the effectiveness of the proposed method for biomedical event extraction.

Document-level relation extraction (RE) aims to extract relations among entities within a document, which is more complex than its sentence-level counterpart, especially in biomedical text mining. Chemical-disease relation (CDR) extraction aims to extract complex semantic relationships between chemicals and diseases entities in documents. In order to identify the relations within and across multiple sentences at the same time, existing methods try to build different document-level heterogeneous graph. However, the entity relation representations captured by these models do not make full use of the document information and disregard the noise introduced in the process of integrating various information. In this paper, we propose a novel model DAM-GAN to document-level biomedical RE, which can extract entity-level and mention-level representations of relation instances with R-GCN and Dual-Attention Multi-Instance Learning (DAM) respectively, and eliminate the noise with Generative Adversarial Network (GAN). Entity-level representations of relation instances model the semantic information of all entity pairs from the perspective of the whole document, while the mention-level representations from the perspective of mention pairs related to these entity pairs in different sentences. Therefore, entity- and mention-level representations can be better integrated to represent relation instances. Experimental results demonstrate that our model achieves superior performance on public document-level biomedical RE dataset BioCreative V Chemical Disease Relation(CDR).

We propose ViNO (Virtual Network Overlay), an orchestration service that can be used to create arbitrary network topologies with OVS (Open vSwitch) switches and VMs. ViNO connects switches and VMs through an overlay network using VXLAN encapsulation. ViNO provisions VMs by making API calls to the underlying platform. Users specify the desired topology using an expressive Domain Specific Language that allows users to easily express commonly used network topologies while hiding the underlying complexity. An important use case for ViNO is enabling the seamless migration of Linux services across VMs in different regions with very little downtime.

This paper presents a flexible deployment service for cloud computing. The service facilitates the specification and the execution of cloud deployment plans for applications. An application is described through a pattern, an abstract view that captures the logical view of the application and its mapping into cloud resources. The services instantiate the pattern in the cloud and allows for runtime updates of the deployment. The service is accessible through a RESTful interface. We identify the requirements for the service, describe its interfaces and show several case studies that capture the main features of the service.

Pattern Matching (PM) is a key building block for many emerging network applications. Modern multicore platforms are becoming performance competitive with traditional hardware solutions, which are expensive and hard to adapt to the rapid diversification of Internet applications. However, due to uneven network flow sizes and the need to retain packet order within each flow, traditional parallel processing models using packet flows as the basic unit to partition the workload cannot fully take advantage of multicore platforms' power, exhibiting low CPU utilization and poor scalability with increasing numbers of CPUs or cores. In this paper, we propose a novel parallel inspection model called Dynamic Differentiated Distributed Detection (D4). D4deploys balanced parallel detection by adding one more dimension on PM workload partition. The pattern set is prepartitioned into several subsets so as to distribute the workload of the hot flows across multiple cores while still maintaining packet order within each flow. We also show theoretically that higher number of subsets leads to higher algorithmic overhead. To achieve optimal throughput for all flow size distributions, D4prepartitions the pattern set in several ways for use in different detection modes beforehand, and then, dynamically switches among these modes on-the-fly according to the flow and runtime information it senses. D4also allows multiple PM algorithms to work simultaneously on different pattern subsets. According to several heuristics and the algorithms' characteristics, the detection mode selection and subset partitioning algorithms are designed to maximize the CPU/core utilization while avoiding unnecessary overheads. Experiments show that D4features high core utilization and low overhead, thus achieving distinct performance gains against traditional load balancing schemes, as shown by experimental results using real-world pattern sets and traffic traces.

Pattern Matching (PM) over network packet flows for Network Intrusion Detection/Prevention System is becoming more and more performance sensitive due to the rapid progress of Internet applications in terms of data volumes. Meanwhile, modern multicore platforms are becoming performance competitive with traditional hardware solutions for PM. But due to the unbalance of network flow sizes, traditional flow- based data parallel processing/programming model can not fully exert multicore platforms' computing power and results in poor performance scalability. In this paper, a novel parallel inspection model, Dynamic Differentiated Distributed Detection (D4) is proposed. D4deploys distributed parallel operations by adding one more dimension on workload partition/allocation. It proposes an effective and efficient scheme to pre-partition the pattern set in several candidate ways, called "Detection Modes", and let multiple candidate PM methods to handle the subsets, respectively; the most suitable Detection Mode would be selected specifically for each incoming flows at the run-time, and the workload would be dynamically allocated among multiple CPU cores. Experimental results on real-world pattern set and traffic traces show that D4scales much better than traditional schemes by better balancing the load among the processors while avoiding unnecessary overheads.

The ability to inspect both packet headers and payloads to identify attack signatures makes network intrusion detection system (NIDS) a promising approach to protect Internet systems. Since most of the known attacks can be represented with strings or combinations of multiple substrings, string matching is a key component, as well as the bottleneck in NIDS to address the requirement of constantly increasing capacity. We propose a memory-efficient multiple-character-approaching architecture consisting of multiple parallel deterministic finite automata (DFAs), called TDP-DFA. By employing efficient representations for the transition rules in each DFA, TDP-DFA significantly reduces the complexity. We also present a novel scheme to share the storage of transition rules among multiple DFAs, substantially decreasing the total storage cost, and avoiding the cost increase being proportional to the number of DFAs. We evaluate this design through theoretical analysis and comprehensive experiments. Results show that TDP-DFA is able to meet the critical requirement of OC-768 wirespeed processing, as well as constituting a promising way for scaling up to cope with throughput over 100 Gb/s in the future.

Ternary content-addressable memory (TCAM) is widely used in high-speed route lookup engines. However, restricted by the memory access speed, the route lookup engines for next-generation terabit routers demand exploiting parallelism among multiple TCAMs. Traditional parallel methods always incur excessive redundancy and high power consumption. We propose An original TCAM-based IP lookup scheme that achieves an ultra high lookup throughput and a high utilization of the memory while being power efficient. In our multichip scheme, we devise a load-balanced TCAM table construction algorithm together with an adaptive load balancing mechanism. The power efficiency is well controlled by decreasing the number of TCAM entries triggered in each lookup operation. Using 133 MHz TCAM chips and given 25% more TCAM entries than the original route table, the proposed scheme achieves a lookup throughput of up to 533 Mpps and is simple for ASIC implementation.