Miracle-dz/UniFAR-FTUs huggingface dataset:
Second- and High-Order Graph Matching for Correspondence Problems

Correspondence problems are challenging due to the complexity of real-world scenes. One way to solve this problem is to improve the graph matching (GM) process, which is flexible for matching non-rigid objects. GM can be classified into three categories that correspond with the variety of object functions: first-order, second-order, and high-order matching. Graph and hypergraph matching have been proposed separately in previous works. The former is equivalent to the second-order GM, and the latter is equivalent to high-order GM, but we use the terms second- and high-order GM to unify the terminology in this paper. Second- and high-order GM fit well with different types of problems; the key goal for these processes is to find better-optimized algorithms. Because the optimal problems for second- and high-order GM are different, we propose two novel optimized algorithms for them in this paper. (1) For the second-order GM, we first introduce a $K$ -nearest-neighbor-pooling matching method that integrates feature pooling into GM and reduces the complexity. Meanwhile, we evaluate each matching candidate using discriminative weights on its $k$ -nearest neighbors by taking locality as well as sparsity into consideration. (2) High-order GM introduces numerous outliers, because precision is rarely considered in related methods. Therefore, we propose a sub-pattern structure to construct a robust high-order GM method that better integrates geometric information. To narrow the search space and solve the optimization problem, a new prior strategy and a cell-algorithm-based Markov Chain Monte Carlo framework are proposed. In addition, experiments demonstrate the robustness and improvements of these algorithms with respect to matching accuracy compared with other state-of-the-art algorithms.
Deep Localized Metric Learning

Metric learning has been widely used in many visual analysis applications, which learns new distance metrics to measure the similarities of samples effectively. Conventional metric learning methods learn a single linear Mahalanobis metric, yet such linear projections are not powerful enough to capture the nonlinear relationships. Recently, deep metric learning approaches, such as discriminative deep metric learning and deep transfer metric learning, have been introduced to fully exploit the nonlinearity of samples by learning hierarchical nonlinear transformations. However, these methods only learn holistic metrics over the input space and are limited for the heterogeneous data sets, where data varies locally. In this paper, we propose a deep localized metric learning approach for visual recognition by learning multiple fine-grained deep localized metrics. We first learn $K$ local subspaces and one holistic subspace with the K-auto-encoders-based clustering. Then, given an input pair, we compute its localized distance on each learned subspace and obtain the final distance representation. Finally, we train the entire neural networks to ensure the distances of positive pairs smaller than negative pairs by a large margin. Experimental results on three visual recognition applications, including face recognition, person re-identification, and scene recognition, show that our DLML outperforms most existing metric learning approaches.
A Probabilistic Annotation Model for Crowdsourcing Coreference

The availability of large scale annotated corpora for coreference is essential to the development of the field. However, creating resources at the required scale via expert annotation would be too expensive. Crowdsourcing has been proposed as an alternative; but this approach has not been widely used for coreference. This paper addresses one crucial hurdle on the way to make this possible, by introducing a new model of annotation for aggregating crowdsourced anaphoric annotations. The model is evaluated along three dimensions: the accuracy of the inferred mention pairs, the quality of the post-hoc constructed silver chains, and the viability of using the silver chains as an alternative to the expert-annotated chains in training a state of the art coreference system. The results suggest that our model can extract from crowdsourced annotations coreference chains of comparable quality to those obtained with expert annotation.
Multilayer Value Metrics Using Lexical Link Analysis and Game Theory for Discovering Innovation from Big Data and Crowd-Sourcing

We demonstrated a machine learning and artificial intelligence method, i.e., lexical link analysis (LLA) to discover different layers of semantic network that contribute to innovative ideas from big data. The LLA is an unsupervised machine learning paradigm that does not require manually labeled training data. Multilayer value metrics are defined based on game theory for LLA. We showed the following results: 1) the value metrics generated from LLA in a use case of an internet game and crowd-sourcing; 2) the results from LLA are validated and correlated with the ground truth; 3) the game-theoretic LLA can help an information provider to present the information in the most valuable way. The information presentation can solve a problem (e.g., a search request of innovation) that no other information providers can solve (i.e., expertise). In addition, it ties also to a broader context that the unique value can propagate through the consensus. Based on the game-theoretic LLA, an information provider should not always present expertise content or authoritative content but rather with a mixed strategy where each type of content is presented with certain probabilities for the best value overall.
Learning Latent Semantic Annotations for Grounding Natural Language to Structured Data

Previous work on grounded language learning did not fully capture the semantics underlying the correspondences between structured world state representations and texts, especially those between numerical values and lexical terms. In this paper, we attempt at learning explicit latent semantic annotations from paired structured tables and texts, establishing correspondences between various types of values and texts. We model the joint probability of data fields, texts, phrasal spans, and latent annotations with an adapted semi-hidden Markov model, and impose a soft statistical constraint to further improve the performance. As a by-product, we leverage the induced annotations to extract templates for language generation. Experimental results suggest the feasibility of the setting in this study, as well as the effectiveness of our proposed framework.
A Unified Metric Learning-Based Framework for Co-Saliency Detection

Co-saliency detection, which focuses on extracting commonly salient objects in a group of relevant images, has been attracting research interest because of its broad applications. In practice, the relevant images in a group may have a wide range of variations, and the salient objects may also have large appearance changes. Such wide variations usually bring about large intra-co-salient objects (intra-COs) diversity and high similarity between COs and background, which makes the co-saliency detection task more difficult. To address these problems, we make the earliest effort to introduce metric learning to co-saliency detection. Specifically, we propose a unified metric learning-based framework to jointly learn discriminative feature representation and co-salient object detector. This is achieved by optimizing a new objective function that explicitly embeds a metric learning regularization term into support vector machine (SVM) training. Here, the metric learning regularization term is used to learn a powerful feature representation that has small intra-COs scatter, but big separation between background and COs and the SVM classifier is used for subsequent co-saliency detection. In the experiments, we comprehensively evaluate the proposed method on two commonly used benchmark data sets. The state-of-the-art results are achieved in comparison with the existing co-saliency detection methods.
Finding Top-k Optimal Sequenced Routes

Motivated by many practical applications in logistics and mobility-as-a-service, we study the top-k optimal sequenced routes (KOSR) querying on large, general graphs where the edge weights may not satisfy the triangle inequality, e.g., road network graphs with travel times as edge weights. The KOSR querying strives to find the top-k optimal routes (i.e., with the top-k minimal total costs) from a given source to a given destination, which must visit a number of vertices with specific vertex categories (e.g., gas stations, restaurants, and shopping malls) in a particular order (e.g., visiting gas stations before restaurants and then shopping malls). To efficiently find the top-k optimal sequenced routes, we propose two algorithms PruningKOSR and StarKOSR. In PruningKOSR, we define a dominance relationship between two partially-explored routes. The partially-explored routes that can be dominated by other partially-explored routes are postponed being extended, which leads to a smaller searching space and thus improves efficiency. In StarKOSR, we further improve the efficiency by extending routes in an A manner. With the help of a judiciously designed heuristic estimation that works for general graphs, the cost of partially explored routes to the destination can be estimated such that the qualified complete routes can be found early. In addition, we demonstrate the high extensibility of the proposed algorithms by incorporating Hop Labeling, an effective label indexing technique for shortest path queries, to further improve efficiency. Extensive experiments on multiple real-world graphs demonstrate that the proposed methods significantly outperform the baseline method. Furthermore, when k = 1, StarKOSR also outperforms the state-of-the-art method for the optimal sequenced route queries.
Design of a High-Frequency Dual-Active Bridge Converter with GaN Devices for an Output Power of 3.7 kW

In the automotive industry weight and volume are important issues to design power electronics besides costs. With the upcoming of wide band-gap devices like Gallium Nitride (GaN) devices high switching frequencies become a potential for converters. Operating at high switching frequencies reduces the volume of passive components e.g., transformer significantly. Auxiliary supplies or on-board charging systems for electric vehicles are typical areas of application for a reduction of volume and weight. For this kind of application dc-dc converters like a Dual Active Bridge converter can be used. This paper describes a detailed electrical analysis of a compact single phase Dual Active Bridge converter operated at a high switching frequency of 500 kHz at a dc voltage of 400 V on both full bridges to achieve a power transfer of 3.7 kW. In this paper it is shown how a fast switching and compact dual active bridge converter is designed that operates with a high efficiency of about 96 % at nominal power.
Influence of work function on the multipactor threshold

The multipactor effect is characterized by a very fast growth of the electronic population in vacuum Radio-Frequency (RF) devices. As it limits the transmitted RF power and can severely damage RF systems, multipactor has been subject to an extensive research for the past decades. Simulation tools are relatively accurate for the simplest cases, but less reliable for advanced problems: presence of an external magnetic field, complex geometry, time or temperature-dependent materials properties, etc. A code simulating the multipactor within an infinite parallelplate waveguide with a dielectric coating on the bottom plate has been developed. The inclusion of a realistic energy distribution for the secondary electrons and a new emission model shows a strong dependence of the multipactor threshold on the metal work function and on the dielectric electric charge.
Switched Reluctance Motor Design for a Forklift Traction Application

This paper presents the design of a switched reluctance motor (SRM) for the traction motor of a forklift application. Design constraints and input parameters are defined according to the specifications of a forklift traction motor. Three-phase induction machines are commonly used for forklift propulsion systems. Several SRM configurations with higher number of rotor than stator poles are designed in order to compare their characteristics and capabilities. These configurations are 12/16, 12/20 and 18/24, and they are compared in terms of average torque, weight, RMS torque ripple, efficiency and temperature rise. Initial designs are obtained by basic analytical sizing equations. Optimization of initial designs is performed using combined analytical-FEA approach and genetic algorithm control optimization. Optimization goal is to achieve the design with the lowest volume and mass to increase torque density (torque/volume) and specific torque (torque/mass). The results show that 12/16 SRM is a suitable configuration for the forklift propulsion application due to its higher torque density.
Time-Aware Boolean Spatial Keyword Queries (Extended Abstract)

This paper explores the Time-Aware Boolean Spatial Keyword Query (TABSKQ) that finds the geo-tagged objects satisfying user's spatial, textual, and temporal constraints. Towards this, we propose an efficient index structure so-called the TA-tree and its corresponding algorithms, which can efficiently prune the search space using both spatio-temporal and textual information. Extensive experiments with real datasets offer insight into the performance of our proposed index and algorithms.
Knowledge Base Enhancement via Data Facts and Crowdsourcing

Recently, knowledge base systems such as Freebase, YAGO, etc. have been designed and widely applied while most of the knowledge bases are far from being of a high quality. According to the recent researches, the low quality is mainly caused by the loss and low accuracy of the RDF triples, which are the main components of knowledge base systems. In this paper, we propose approaches to enhance the RDF triples in knowledge bases, which is significant for providing good information retrieval service. Specifically, we utilize data facts stored in database systems to obtain possible updates for knowledge bases. Furthermore, inspired by the popular and successful applications of crowdsourcing platforms, we explore the use of crowdsourcing to verify the updates. We propose KD graph to model the possible updates and design a comprehensive framework for knowledge base enhancement problem. Since crowdsourcing employs human power and requires expenditure, we propose an optimal and dynamic method to select candidates for crowdsourcing within a limited budget so that the benefit of enhancing the knowledge base can be maximized. To reduce the time cost, we adopt split techniques and design Simple Split(SS) and Dynamic Split(DS) algorithms. We verify the effectiveness of our solutions by conducting crowdsourcing simulation experiments and experiments on a crowdsourcing platform namely gMission.
Control of a 6.6-kV Transformerless STATCOM Based on the MMCC-SDBC using SiC MOSFETs

This paper describes a transformerless STATCOM (static synchronous compensator) intended for 6.6-kV power distribution system in Japan. The STATCOM is characterized by the use of MMCC-SDBC (modular multilevel cascade converter – single-delta bridge-cells) topology and SiC MOSFETs. The paper discusses a control method developed for the 6.6-kV system for reactive-power and negative-sequence compensation with focus on dc-capacitor voltage control. The dc-capacitor voltage control is comprised of (a) inter-phase balancing control and (b) intra-phase balancing control. The latter uses a new technique that is based on the control of dead times of bride cells. A three-phase 200-V 6-kVA experimental system is designed, constructed and tested to verify the effectiveness of the control method.
Suppression of Ripple Current in High Step-Up DC-DC Converter utilizing Cockcroft-Walton Circuit with Inductor

It has become necessary to reduce the volume of high step-up DC-DC converters for high-voltage DC power supplies. In this report, an isolated high step-up DC-DC converter is proposed for them. The proposed converter consists of the LLC converter and the Cockcroft-Walton (CW) circuit with an inductor. The proposed converter boosts the output voltage by double LC resonances of primary circuit and secondary circuit respectively. It is experimentally clarified that the inductor on the secondary side in the proposed converter plays the role of not only boosting the output voltage, but also suppressing ripples of transformer primary and secondary currents.
A Probabilistic Model for Joint Learning of Word Embeddings from Texts and Images

Several recent studies have shown the benefits of combining language and perception to infer word embeddings. These multimodal approaches either simply combine pre-trained textual and visual representations (e.g. features extracted from convolutional neural networks), or use the latter to bias the learning of textual word embeddings. In this work, we propose a novel probabilistic model to formalize how linguistic and perceptual inputs can work in concert to explain the observed word-context pairs in a text corpus. Our approach learns textual and visual representations jointly: latent visual factors couple together a skip-gram model for co-occurrence in linguistic data and a generative latent variable model for visual data. Extensive experimental studies validate the proposed model. Concretely, on the tasks of assessing pairwise word similarity and image/caption retrieval, our approach attains equally competitive or stronger results when compared to other state-of-the-art multimodal models.
Control and Performance of New Asymmetrical Operation for Switched-Capacitor-based Resonant Converters

This paper proposes a new asymmetrical control method for a switched-capacitor-based resonant converter consisting of two half-bridge circuits using four switching devices. While the existing control method only adjusts the switching frequency and phase-shift angle, the proposed method additionally controls the duty ratio of each half-bridge circuit independently. The proposed method makes it possible to adjust the average voltage across the switched capacitor although the resonant current has an asymmetric waveform. The proposed control method also enables to operate the SCRC at a frequency closer to the resonant frequency between the resonant inductor and the switched capacitor than the existing control method. Moreover, the switching devices in three of the four arms are turned off at their minimum current requirement for ZVS operation. Thus, it is possible to reduce the switching power loss significantly in a wide power range. A 350-kHz, 3-kW prototype is constructed and tested in this paper. The experimental result shows an improved power conversion efficiency as high as 98% even under various output voltage conditions.
Practical Design Technique for High Power Density LLC Resonant Converter

The LLC resonant converter is widely used for consumer and industrial applications due to its high efficiency, small size and low noise. In a practical design, a trade-off is needed between the input voltage operation range and the transformer size for miniaturization. The transformer size is determined at the minimum input voltage with full load. It is very important to have an accurate design technique so that the transformer does not have any unnecessary size margin. In this paper, inductance (L)-matrix transformer model-based design is introduced and compared with the conventional transformer model such as T-type and L-type. A 200 kHz, 240W LLC resonant converter is implemented and verified that the inductance (L)-matrix method is accurate, and it can contribute to miniaturization.
Improved Lower Bounds on Mutual Information Accounting for Nonlinear Signal–Noise Interaction

In fiber-optic communications, evaluation of mutual information (MI) is still an open issue due to the unavailability of an exact and mathematically tractable channel model. Traditionally, lower bounds on MI are computed by approximating the (original) channel with an auxiliary forward channel. In this paper, lower bounds are computed using an auxiliary backward channel, which has not been previously considered in the context of fiber-optic communications. Distributions obtained through two variations of the stochastic digital backpropagation (SDBP) algorithm are used as auxiliary backward channels and these bounds are compared with bounds obtained through the conventional digital backpropagation. Through simulations, higher information rates were achieved with the SDBP, which can be explained by the ability of SDBP to account for nonlinear signal–noise interactions.
Learning Association Relationship and Accurate Geometric Structures for Multi-Type Relational Data

Non-negative Matrix Factorization (NMF) methods have been effectively used for clustering high dimensional data. Manifold learning is combined with the NMF framework to ensure the projected lower dimensional representations preserve the local geometric structure of data. In this paper, considering the context of multi-type relational data clustering, we develop a new formulation of manifold learning to be embedded in the factorization process such that the new low-dimensional space can maintain both local and global structures of original data. We also propose to include the interactions between clusters of different data types by enforcing a Normalize Cut-type constraint that leads to a comprehensive NMF-based framework. A theoretical analysis and extensive experiments are provided to validate the effectiveness of the proposed work.
Improving Neural Abstractive Document Summarization with Explicit Information Selection Modeling

Information selection is the most important component in document summarization task. In this paper, we propose to extend the basic neural encoding-decoding framework with an information selection layer to explicitly model and optimize the information selection process in abstractive document summarization. Specifically, our information selection layer consists of two parts: gated global information filtering and local sentence selection. Unnecessary information in the original document is first globally filtered, then salient sentences are selected locally while generating each summary sentence sequentially. To optimize the information selection process directly, distantly-supervised training guided by the golden summary is also imported. Experimental results demonstrate that the explicit modeling and optimizing of the information selection process improves document summarization performance significantly, which enables our model to generate more informative and concise summaries, and thus significantly outperform state-of-the-art neural abstractive methods.
Limit Operating Frequency of Peak Current-Mode Control DC-DC Converter Considering Turn-Off Delay Time

The aim of this paper is to present the mechanism of the frequency dividing operation and the limit operating frequency in the peak current-mode control DC-DC converter considering the turn-off delay time. The peak current mode control has the higher stability and the responsiveness than the conventional voltage mode control. However, if the switching frequency is increased ignoring the turn-off delay time, it would become the frequency dividing operation that the switching frequency operates lower than the clock frequency at the certain frequency. In this operation, there is a problem that the switching loss increases and the high frequency switching drive is hindered. In this paper, we clarified the mechanism of the dividing frequency operation and derived the relationship between the turn-off delay time and the limit operating frequency. Also, we derived the relationship when the slope compensation is applied as well. As experimental results, when there is the turn-off delay time, it was confirmed that the limit operating frequency of the DC-DC converter was limited to lower as the duty ratio was lower and the slope ratio of the slope compensation is close to 1.0.