Structured entity querying over unstructured text

The web is a collection of unstructured webpages. This characteristic makes it very hard for users to search complex queries -- in most of the times, queries are just plain text and it is very difficult for users to describe the internal relationships that they contain. The EntityRank system allows users to specify the target entities for which they are interested within the query, which brings us one step closer to being able to describe the entity relationships. But this expressiveness is still limited because the queries are still in a flat format. On the other hand, SQL queries for relational databases are very expressive. Users can easily specify multiple entities and the relationships between them. But in order to use SQL queries, we need to extract all the entities and relationships existing in the domain beforehand. The cost of maintaining the tables is very large. Also it is very hard if we want to add or modify the schema after the initial domain design. Thus we want to build a system that can combine the advantages of the flexibility and informativeness of unstructured webpages and the expressiveness of SQL queries. In this work we design a system which allows users to use structured SQL queries on unstructured webpages using the help of the EntityRank system. We design the conceptual framework to map the concepts between two systems and also propose a ranking algorithm for the final results

A Network Celebrity Identification and Evaluation Model Based on Hybrid Trust Relation

Trust-based celebrity user identification is the key to the industry\u27s reputation for electronic word of mouth. However, trust and mistrust are independent and coexistent concepts. In this context, we need to consider the existence of the two kinds of user relations brought about by the impact. This paper analyzes the characteristics of trust and distrust in social networks, and gives formal descriptions of trust networks, untrusted networks, and mixed trust networks. Based on the indicators such as degree distribution, correlation coefficient, and matching coefficient, the structural properties of mixed trust networks are studied. Based on the PageRank algorithm, the HTMM metrics affecting users under the mixed trust network environment are proposed. Finally, the validity of HTMM is verified through a real data set containing trust and distrust. Experimental results show that the results of HTMM\u27s celebrity user identification method still have a low level of trust

Robust Transductive Few-shot Learning via Joint Message Passing and Prototype-based Soft-label Propagation

Few-shot learning (FSL) aims to develop a learning model with the ability to generalize to new classes using a few support samples. For transductive FSL tasks, prototype learning and label propagation methods are commonly employed. Prototype methods generally first learn the representative prototypes from the support set and then determine the labels of queries based on the metric between query samples and prototypes. Label propagation methods try to propagate the labels of support samples on the constructed graph encoding the relationships between both support and query samples. This paper aims to integrate these two principles together and develop an efficient and robust transductive FSL approach, termed Prototype-based Soft-label Propagation (PSLP). Specifically, we first estimate the soft-label presentation for each query sample by leveraging prototypes. Then, we conduct soft-label propagation on our learned query-support graph. Both steps are conducted progressively to boost their respective performance. Moreover, to learn effective prototypes for soft-label estimation as well as the desirable query-support graph for soft-label propagation, we design a new joint message passing scheme to learn sample presentation and relational graph jointly. Our PSLP method is parameter-free and can be implemented very efficiently. On four popular datasets, our method achieves competitive results on both balanced and imbalanced settings compared to the state-of-the-art methods. The code will be released upon acceptance

The Analysis and Possible Solutions on the Problem of Female Undergraduate Students’ Employment Difficulties

The global financial crisis, which burst out in 2009, had caused great decline on global economy. The employment problems in every country are extreme severe. Chinese economy has grew in a relatively lower speed in recent years and the amount of undergraduates has been increasing all along. The employment issue for undergraduates in our country has been into a dilemma. Under such circumstance, the problem of female college students employment has been much more serious. This article will fully illustrate the status quo of the difficulties in employment for female college students and analyze on the causes and reasons of the employment difficulties for female college students as well as further come up with some possible solutions to release the situation of employment difficulties

YOLO-Drone:Airborne real-time detection of dense small objects from high-altitude perspective

Unmanned Aerial Vehicles (UAVs), specifically drones equipped with remote sensing object detection technology, have rapidly gained a broad spectrum of applications and emerged as one of the primary research focuses in the field of computer vision. Although UAV remote sensing systems have the ability to detect various objects, small-scale objects can be challenging to detect reliably due to factors such as object size, image degradation, and real-time limitations. To tackle these issues, a real-time object detection algorithm (YOLO-Drone) is proposed and applied to two new UAV platforms as well as a specific light source (silicon-based golden LED). YOLO-Drone presents several novelties: 1) including a new backbone Darknet59; 2) a new complex feature aggregation module MSPP-FPN that incorporated one spatial pyramid pooling and three atrous spatial pyramid pooling modules; 3) and the use of Generalized Intersection over Union (GIoU) as the loss function. To evaluate performance, two benchmark datasets, UAVDT and VisDrone, along with one homemade dataset acquired at night under silicon-based golden LEDs, are utilized. The experimental results show that, in both UAVDT and VisDrone, the proposed YOLO-Drone outperforms state-of-the-art (SOTA) object detection methods by improving the mAP of 10.13% and 8.59%, respectively. With regards to UAVDT, the YOLO-Drone exhibits both high real-time inference speed of 53 FPS and a maximum mAP of 34.04%. Notably, YOLO-Drone achieves high performance under the silicon-based golden LEDs, with a mAP of up to 87.71%, surpassing the performance of YOLO series under ordinary light sources. To conclude, the proposed YOLO-Drone is a highly effective solution for object detection in UAV applications, particularly for night detection tasks where silicon-based golden light LED technology exhibits significant superiority

Study on Mechanism and Improvement of Triple Frequency Noise of Rotary Compressor

With the continuous improvement of social life, people have more stringent noise requirements for home air conditioners. As the kernel of an air conditioner, compressor provides power for the whole system, inevitably generating vibration and noise. Therefore, Reducing the vibration and noise of the compressor is great significance for the noise reduction of the air conditioner. Generally, vibration is mainly transferred through the suction and exhaust pipes to the air conditioning pipe system. However, due to the complicated configuration, there are intensive modals for the pipe system, especially those in low frequency range, which may lead to resonance and large acoustic radiation. This paper studies the generation and transmission mechanism of triple frequency vibration of compressor, the compressor exhaust pressure fluctuation stimulates the exhaust pipe to vibrate, and then results in vibration of the air conditioning pipe systems, and vibration generated by the rotor is transferred to intake pipe via the accumulator, and cause the pipe systems to vibrate. Based on this research, we find some main factors which influence the triple frequency vibration and noise of the compressor, which are the exhaust pressure pulsation, the natural frequency of the rotor-crankshaft system swing, the natural frequency of the accumulator swing. Then, above factors which affect the compressor vibration and noise are analyzed and improved separately, and conducted noise tests on the improved compressor at 90Hz. The results show that the compressor noise are reduced by 29.8% around 250Hz

Global Three-Dimensional Radiation Magnetohydrodynamic Simulations of Accretion onto a Stellar Mass Black Hole at Sub- and Near-critical Accretion Rates

We present global 3D radiation magnetohydrodynamical simulations of accretion onto a 6.62 solar mass black hole with quasi-steady state accretion rates reaching 0.016 to 0.9 times the critical accretion rate, which is defined as the accretion rate to power the Eddington luminosity assuming a 10% radiative efficiency, in different runs. The simulations show no sign of thermal instability over hundreds of thermal timescales at 10 $r_{\rm g}$. The energy dissipation happens close to the mid-plane in the near-critical runs and near the disk surface in the low accretion rate run. The total radiative luminosity inside $\sim$20 $r_{\rm g}$ is about 1% to 30% the Eddington limit, with a radiative efficiency of about 6% and 3%, respectively, in the sub- and near-critical accretion regimes. In both cases, self-consistent turbulence generated by the magnetorotational instability (MRI) leads to angular momentum transfer, and the disk is supported by magnetic pressure. Outflows from the central low-density funnel with a terminal velocity of $\sim$0.1$c$ are seen only in the near-critical runs. We conclude that these magnetic pressure dominated disks are thermally stable and thicker than the $\alpha$ disk, and the effective temperature profiles are much flatter than that in the $\alpha$ disks. The magnetic pressure of these disks are comparable within an order of magnitude with the previous analytical magnetic pressure dominated disk model.Comment: 17 pages, 13 figures, 3 tables, accepted for publication in Ap

Research on low frequency ripple suppression technology of inverter based on model prediction

The low frequency ripple of the input side current of the single-phase inverter will reduce the efficiency of the power generation system and affect the overall performance of the system. Aiming at this problem, this paper proposes a two-modal modulation method and its MPC multi-loop composite control strategy on the circuit topology of a single-stage boost inverter with a buffer unit. The control strategy achieves the balance of active power on both sides of AC and DC by controlling the stable average value of the buffer capacitor voltage, and provides a current reference for inductance current of the DC input side. At the same time, the MPC controller uses the minimum inductor current error as the cost function to control inductor current to track its reference to achieve low frequency ripple suppression of the input current. In principle, it is expounded that the inverter using the proposed control strategy has better low frequency ripple suppression effect than the multi-loop PI control strategy, and the conclusion is proved by the simulation data. Finally, an experimental device of a single-stage boost inverter using MPC multi-loop composite control strategy is designed and fabricated, and the experimental results show that the proposed research scheme has good low frequency ripple suppression effect and strong adaptability to different types of loads