Reviewerly/exHarmony huggingface dataset:
Feature-Robust Optimal Transport for High-Dimensional Data

Optimal transport is a machine learning problem with applications including distribution comparison, feature selection, and generative adversarial networks. In this paper, we propose feature-robust optimal transport (FROT) for high-dimensional data, which solves high-dimensional OT problems using feature selection to avoid the curse of dimensionality. Specifically, we find a transport plan with discriminative features. To this end, we formulate the FROT problem as a min–max optimization problem. We then propose a convex formulation of the FROT problem and solve it using a Frank–Wolfe-based optimization algorithm, whereby the subproblem can be efficiently solved using the Sinkhorn algorithm. Since FROT finds the transport plan from selected features, it is robust to noise features. To show the effectiveness of FROT, we propose using the FROT algorithm for the layer selection problem in deep neural networks for semantic correspondence. By conducting synthetic and benchmark experiments, we demonstrate that the proposed method can find a strong correspondence by determining important layers. We show that the FROT algorithm achieves state-of-the-art performance in real-world semantic correspondence datasets. Code can be found at https://github.com/Mathux/FROT .
TOMA: Topological Map Abstraction for Reinforcement Learning

Animals are able to discover the topological map (graph) of surrounding environment, which will be used for navigation. Inspired by this biological phenomenon, researchers have recently proposed to generate graph representation for Markov decision process (MDP) and use such graphs for planning in reinforcement learning (RL). However, existing graph generation methods suffer from many drawbacks. One drawback is that existing methods do not learn an abstraction for graphs, which results in high memory and computation cost. This drawback also makes generated graph non-robust, which degrades the planning performance. Another drawback is that existing methods cannot be used for facilitating exploration which is important in RL. In this paper, we propose a new method, called topological map abstraction (TOMA), for graph generation. TOMA can generate an abstract graph representation for MDP, which costs much less memory and computation cost than existing methods. Furthermore, TOMA can be used for facilitating exploration. In particular, we propose planning to explore, in which TOMA is used to accelerate exploration by guiding the agent towards unexplored states. A novel experience replay module called vertex memory is also proposed to improve exploration performance. Experimental results show that TOMA can outperform existing methods to achieve the state-of-the-art performance.
Schema-Guided Natural Language Generation

Yuheng Du, Shereen Oraby, Vittorio Perera, Minmin Shen, Anjali Narayan-Chen, Tagyoung Chung, Anushree Venkatesh, Dilek Hakkani-Tur. Proceedings of the 13th International Conference on Natural Language Generation. 2020.
Crisscrossed Captions: Extended Intramodal and Intermodal Semantic  Similarity Judgments for MS-COCO

By supporting multi-modal retrieval training and evaluation, image captioning datasets have spurred remarkable progress on representation learning. Unfortunately, datasets have limited cross-modal associations: images are not paired with other images, captions are only paired with other captions of the same image, there are no negative associations and there are missing positive cross-modal associations. This undermines research into how inter-modality learning impacts intra-modality tasks. We address this gap with Crisscrossed Captions (CxC), an extension of the MS-COCO dataset with human semantic similarity judgments for 267,095 intra- and inter-modality pairs. We report baseline results on CxC for strong existing unimodal and multimodal models. We also evaluate a multitask dual encoder trained on both image-caption and caption-caption pairs that crucially demonstrates CxC's value for measuring the influence of intra- and inter-modality learning.
Learning from Aggregate Observations

We study the problem of learning from aggregate observations where supervision signals are given to sets of instances instead of individual instances, while the goal is still to predict labels of unseen individuals. A well-known example is multiple instance learning (MIL). In this paper, we extend MIL beyond binary classification to other problems such as multiclass classification and regression. We present a general probabilistic framework that accommodates a variety of aggregate observations, e.g., pairwise similarity/triplet comparison for classification and mean/difference/rank observation for regression. Simple maximum likelihood solutions can be applied to various differentiable models such as deep neural networks and gradient boosting machines. Moreover, we develop the concept of consistency up to an equivalence relation to characterize our estimator and show that it has nice convergence properties under mild assumptions. Experiments on three problem settings -- classification via triplet comparison and regression via mean/rank observation indicate the effectiveness of the proposed method.
Categories of quantum walks

We propose categories of $1$-dimensional and multi-dimensional quantum walks. In the categories, an object is a quantum walk, and a morphism is an intertwining operator between two quantum walks. The new framework enables us to discuss quantum walks in a unified way. The purposes of this paper are the following: (1) We reinterpret known results in our new framework. (2) We show several new theorems. For example, it is proved that every space-homogeneous time-periodic analytic quantum walk on $\mathbb{Z}^d$ has a limit distribution of velocity for every initial unit vector. Analyticity is a very weak condition. (3) We ask whether there exists a continuous-time quantum walk $(V^{(t)})_{t \in \mathbb{R}}$ which realizes a given discrete-time quantum walk $U$. Existence of $(V^{(t)})_{t \in \mathbb{R}}$ is equivalent to that of a $1$-parameter group of automorphisms $(V^{(t)})_{t \in \mathbb{R}}$ from the object $U$ to $U$.
End-to-End Entity Classification on Multimodal Knowledge Graphs

End-to-end multimodal learning on knowledge graphs has been left largely unaddressed. Instead, most end-to-end models such as message passing networks learn solely from the relational information encoded in graphs' structure: raw values, or literals, are either omitted completely or are stripped from their values and treated as regular nodes. In either case we lose potentially relevant information which could have otherwise been exploited by our learning methods. To avoid this, we must treat literals and non-literals as separate cases. We must also address each modality separately and accordingly: numbers, texts, images, geometries, et cetera. We propose a multimodal message passing network which not only learns end-to-end from the structure of graphs, but also from their possibly divers set of multimodal node features. Our model uses dedicated (neural) encoders to naturally learn embeddings for node features belonging to five different types of modalities, including images and geometries, which are projected into a joint representation space together with their relational information. We demonstrate our model on a node classification task, and evaluate the effect that each modality has on the overall performance. Our result supports our hypothesis that including information from multiple modalities can help our models obtain a better overall performance.
Learning the Loss Functions in a Discriminative Space for Video  Restoration

With more advanced deep network architectures and learning schemes such as GANs, the performance of video restoration algorithms has greatly improved recently. Meanwhile, the loss functions for optimizing deep neural networks remain relatively unchanged. To this end, we propose a new framework for building effective loss functions by learning a discriminative space specific to a video restoration task. Our framework is similar to GANs in that we iteratively train two networks - a generator and a loss network. The generator learns to restore videos in a supervised fashion, by following ground truth features through the feature matching in the discriminative space learned by the loss network. In addition, we also introduce a new relation loss in order to maintain the temporal consistency in output videos. Experiments on video superresolution and deblurring show that our method generates visually more pleasing videos with better quantitative perceptual metric values than the other state-of-the-art methods.
Language-aware Indexing for Conjunctive Path Queries

Conjunctive path queries (CPQ) are one of the most frequently used queries for complex graph analysis. However, current graph indexes are not tailored to fully support the power of query languages to express CPQs. Consequently, current methods do not take advantage of significant pruning opportunities during <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$CPQ$</tex> evaluation, resulting in poor query processing performance. We propose the CPQ-aware path index CPQx, the first path index tailored to the expressivity of CPQ. CPQx is built on the partition of the set of source-target vertex pairs of paths in a graph based on the structural notion of path-bisimulation. Path-bisimulation is an equivalence relation on paths such that each partition block induced by the relation consists of paths in the graph indistinguishable with respect to CPQs. This language-aware partitioning of the graph can significantly reduce the cost of query evaluation. We present methods to support the full index life cycle: index construction, maintenance, and query processing with our index. We also develop interest-aware CPQx to reduce index size and index construction overhead while accelerating query evaluation for queries of interest. We demonstrate through extensive experiments on 14 real graphs that our methods accelerate query processing by up to multiple orders of magnitude over the state-of-the-art methods, with smaller index sizes. Our complete C++ codebase is available as open source for further research.
Data Augmentation using Pre-trained Transformer Models

Language model based pre-trained models such as BERT have provided significant gains across different NLP tasks. In this paper, we study different types of transformer based pre-trained models such as auto-regressive models (GPT-2), auto-encoder models (BERT), and seq2seq models (BART) for conditional data augmentation. We show that prepending the class labels to text sequences provides a simple yet effective way to condition the pre-trained models for data augmentation. Additionally, on three classification benchmarks, pre-trained Seq2Seq model outperforms other data augmentation methods in a low-resource setting. Further, we explore how different pre-trained model based data augmentation differs in-terms of data diversity, and how well such methods preserve the class-label information.
FİNANSAL GELİŞME İÇİN BİR KAMU POLİTİKASI OLARAK BİTCOİN: EL SALVADOR ÖRNEĞİ

In our rapidly globalizing and digitalizing world, data transfer can be done quickly in multimedia, communication, computer systems, etc. On the other hand, a blockchain database is a technology that allows us to transfer assets such as digital money that we attribute value to. This technology, which eliminates centralization and distributes reliability and transparency among all users, is also known as the technology under virtual currencies such as Bitcoin and Ethereum. El-Salvador is the first country to accept Bitcoin as a legal currency. It is one of the most fundamental issues to wonder how this will affect the economy of El-Salvador and the spread of financial services to all members of society. In this study, it is examined how the process of accepting Bitcoin as legal money has an effect on the economy of El-Salvador and the percentage of people's access to financial services. Although there is not a great background to make an empirical assessment on the subject, it seems that a large part of the society has started to use Bitcoin through the official mobile application of the state. It is seen that even citizens who did not have a bank account before can access financial services with this method. In addition, the rapid increase in the investments coming to the country due to the tax advantages provides a great added value for the country's economy.
Pseudo-Phoneme Label Loss for Text-Independent Speaker Verification

Compared with text-independent speaker verification (TI-SV) systems, text-dependent speaker verification (TD-SV) counterparts often have better performance for their efficient utilization of speech content information. On this account, some TI-SV methods tried to boost performance by incorporating an extra automatic speech recognition (ASR) component to explore content information, such as c-vector. However, the introduced ASR component requires a large amount of annotated data and consumes high computation resources. In this paper, we propose a pseudo-phoneme label (PPL) loss for the TI-SR task by integrating content cluster loss at the frame level and speaker recognition loss at the segment level in a unified network by multitask learning, without additional data requirement and exhausting computation. By referring to HuBERT, we generate pseudo-phoneme labels to adjust a frame level feature distribution by deep cluster to ensure each cluster corresponds to an implicit pronunciation unit in the feature space. We compare the proposed loss with the softmax loss, center loss, triplet loss, log-likelihood-ratio cost loss, additive margin softmax loss and additive angular margin loss on the VoxCeleb database. Experimental results demonstrate the effectiveness of our proposed method.
An Attention-Refined Light-Weight High-Resolution Network for Macaque Monkey Pose Estimation

Macaque monkey is a rare substitute which plays an important role for human beings in relation to psychological and spiritual science research. It is essential for these studies to accurately estimate the pose information of macaque monkeys. Many large-scale models have achieved state-of-the-art results in pose macaque estimation. However, it is difficult to deploy when computing resources are limited. Combining the structure of high-resolution network and the design principle of light-weight network, we propose the attention-refined light-weight high-resolution network for macaque monkey pose estimation (HR-MPE). The multi-branch parallel structure is adopted to maintain high-resolution representation throughout the process. Moreover, a novel basic block is designed by a powerful transformer structure and polarized self-attention, where there is a simple structure and fewer parameters. Two attention refined blocks are added at the end of the parallel structure, which are composed of light-weight asymmetric convolutions and a triplet attention with almost no parameter, obtaining richer representation information. An unbiased data processing method is also utilized to obtain an accurate flipping result. The experiment is conducted on a macaque dataset containing more than 13,000 pictures. Our network has reached a 77.0 AP score, surpassing HRFormer with fewer parameters by 1.8 AP.
Forming a therapeutic sphere to treat ailments using optimization techniques

Abstract Recently, National Geographic published a special bookazine titled Nature's Best Remedies . This bookazine reorganized nature's remedies as a therapeutic table for treating ailments. The table provides considerable knowledge but is inconvenient to use. This study converts the therapeutic table into a therapeutic sphere by using optimization techniques. In the therapeutic sphere, similar ailments are clustered together, and remedies related to specified ailments are allocated on the sphere. Two surveys demonstrated that, compared with the therapeutic table, the therapeutic sphere is more convenient to use, more efficient for making queries, and more useful for healthcare as well as more intuitive. The proposed therapeutic sphere system can be accessed at http://140.113.73.198/gbball/examples/medeng2.aspx .
Entanglement in Quantum Search Database: Periodicity Variations and Counting

Employing the single item search algorithm of N dimensional database it is shown that: First, the entanglement developed between two any-size parts of database space varies periodically during the course of searching. The periodic entanglement of the associated reduced density matrix quantified by several entanglement measures (linear entropy, von Neumann, Renyi), is found to vanish with period O(sqrt(N)). Second, functions of equal entanglement are shown to vary also with equal period. Both those phenomena, based on size-independent database bi-partition, manifest a general scale invariant property of entanglement in quantum search. Third, measuring the entanglement periodicity via the number of searching steps between successive canceling out, determines N, the database set cardinality, quadratically faster than ordinary counting. An operational setting that includes an Entropy observable and its quantum circuits realization is also provided for implementing fast counting. Rigging the marked item initial probability, either by initial advice or by guessing, improves hyper-quadratically the performance of those phenomena.
Trajectory Planning Technology for Lunar Launch Window Expansion Based on Improved PSO

A guidance method based on particle swarm optimization (PSO), suitable for expanding the launch window of lunar ascent phase, is developed. Firstly, the dynamic model of Lunar lander is constructed. Then, a PSO, whose inertia and acceleration coefficient have been improved, is established to solve the problem of accurate ascent guidance. Finally, the improved algorithm is compared with a traditional one. The results show that the improved PSO can effectively meet the requirement for accuracy: the error of semi major axis is in the order of 10 meters, the error of inclination and right ascension of the ascending node (RAAN) is in the order of 0.01°. For the problem of expanding launch window of lunar ascent phase, RAAN can be transformed from coplanar launch by 10° when using improved method, which is equal to 20 hours of launch delay or advance. Compared with the traditional PSO, improved PSO has significant advantages in solving the problem of accurate lunar liftoff, its convergence is stronger and the accuracy is significantly higher.
Fusion of Human Decision and Artificial Intelligence in Optical Character Recognition through Mobile Phone Interaction

Optical character recognition (OCR) is widely used for text recognition in various areas. It digitizes enormous texts efficiently with relatively high accuracy. However, the accuracy is severely undermined when texts are deformed, stained, or under dim light. The paper adds human decision to the recognition process of the deep learning algorithm and enhances the algorithm’s robustness and accuracy. We developed a mobile phone system that utilized the mainstream OCR technology to get the top N candidates sorted by confidence level and then properly displayed the candidates to the original text picture screened by the mobile phone camera. The final result is selected by a human finger tap on the right candidate. We conducted a first user study to compare the efficiency and accuracy between manual recognition and the developed system under several classical complex conditions, including stained, blurred, and incomplete text. The experiment results indicated that the system significantly improved text recognition efficiency and is dramatically faster than the manual recognition system. To explore more insights into our system, we conducted another user study to examine the influence of familiarity and encoding length. The results indicate that familiarity significantly influences recognition efficiency and correctness, but increasing length will actually increase the time spent on recognition.
A content-based recommender system with consideration of repeat purchase behavior

With the increasing popularity of online shopping and information explosion, personalized recommender systems for e-commerce become more and more necessary, which helps customers find the desired products efficiently among variety of categories based on their previous behavior such as buying pattern and rating history. However, most recommender systems for e-commerce adopt binary (purchase/non-purchase) or subjective weighting methods to represent the customer preferences, which is hard to predict their profiles precisely since rapid change in tastes. Therefore, this study focuses on the application of transactional data. A personalized recommender system for e-commerce (PROSE) is proposed in order to enhance the quality of recommendations by integrating the architecture of traditional content-based recommender system with a new component called feedback adjuster, which is designed to make customer implicit feedback reflects the reality of preferences as possible through taking into consideration their behavior of repeat purchase. The computational results indicate that the proposed algorithm is able to outperform other algorithms.
Percolation Features: An approach for evaluating fractal properties in colour images

<h2>Abstract</h2> Percolation is a physical phenomenon that describes the transport of fluids through porous media. We present a new approach for extracting local and global percolation features from RGB images. The proposed method is based on multiscale and multidimensional approaches for extracting other fractal measures from histology images. Our method is able to obtain three percolation properties: number of clusters, occurrence of percolation, and size of the largest cluster. This approach aimed to provide an alternative to fractal dimension and lacunarity as ways of evaluating fractal properties in an image.
Trajectory Optimization of Robot Manipulator in Static and Complex Environment

This paper presents an optimization-based approach for robotic motion planning in a static and complex environment. This method designs optimization functions and constraint in-equalities based on requirements, and then we use the functional gradient technique to improve the quality of the initial trajectory iteratively. It uses the numerical distance information between the environment’s point cloud and the robot’s boundary to describe the collision tendency. Constraint inequalities that are difficult to describe are handled by setting a threshold value γ, a maximum penetration depth point, and a bounding volume hierarchy. Finally, We do some experiments with this method on a six-degree-of-freedom manipulator UR3. Experiments demonstrate that the performance of the manipulators is improved, and the trajectory is effectively optimized while avoiding collisions with non-convex obstacles.