pwc-archive/papers-with-abstracts huggingface dataset:
Causal Reasoning Meets Visual Representation Learning: A Prospective Study
Authors: Yang Liu, Yushen Wei, Hong Yan, Guanbin Li, Liang Lin
Date: 2022-04-26 00:00:00
Tasks: Benchmarking, Out-of-Distribution Generalization, Representation Learning, Video Understanding

Visual representation learning is ubiquitous in various real-world applications, including visual comprehension, video understanding, multi-modal analysis, human-computer interaction, and urban computing. Due to the emergence of huge amounts of multi-modal heterogeneous spatial/temporal/spatial-temporal data in big data era, the lack of interpretability, robustness, and out-of-distribution generalization are becoming the challenges of the existing visual models. The majority of the existing methods tend to fit the original data/variable distributions and ignore the essential causal relations behind the multi-modal knowledge, which lacks unified guidance and analysis about why modern visual representation learning methods easily collapse into data bias and have limited generalization and cognitive abilities. Inspired by the strong inference ability of human-level agents, recent years have therefore witnessed great effort in developing causal reasoning paradigms to realize robust representation and model learning with good cognitive ability. In this paper, we conduct a comprehensive review of existing causal reasoning methods for visual representation learning, covering fundamental theories, models, and datasets. The limitations of current methods and datasets are also discussed. Moreover, we propose some prospective challenges, opportunities, and future research directions for benchmarking causal reasoning algorithms in visual representation learning. This paper aims to provide a comprehensive overview of this emerging field, attract attention, encourage discussions, bring to the forefront the urgency of developing novel causal reasoning methods, publicly available benchmarks, and consensus-building standards for reliable visual representation learning and related real-world applications more efficiently.
Adaptive Pseudo-Siamese Policy Network for Temporal Knowledge Prediction
Authors: Pengpeng Shao, Tong Liu, Feihu Che, Dawei Zhang, JianHua Tao
Date: 2022-04-26 00:00:00
Tasks: Knowledge Graphs, Link Prediction, Prediction, Relation

Temporal knowledge prediction is a crucial task for the event early warning that has gained increasing attention in recent years, which aims to predict the future facts by using relevant historical facts on the temporal knowledge graphs. There are two main difficulties in this prediction task. First, from the historical facts point of view, how to model the evolutionary patterns of the facts to predict the query accurately. Second, from the query perspective, how to handle the two cases where the query contains seen and unseen entities in a unified framework. Driven by the two problems, we propose a novel adaptive pseudo-siamese policy network for temporal knowledge prediction based on reinforcement learning. Specifically, we design the policy network in our model as a pseudo-siamese policy network that consists of two sub-policy networks. In sub-policy network I, the agent searches for the answer for the query along the entity-relation paths to capture the static evolutionary patterns. And in sub-policy network II, the agent searches for the answer for the query along the relation-time paths to deal with unseen entities. Moreover, we develop a temporal relation encoder to capture the temporal evolutionary patterns. Finally, we design a gating mechanism to adaptively integrate the results of the two sub-policy networks to help the agent focus on the destination answer. To assess our model performance, we conduct link prediction on four benchmark datasets, the experimental results demonstrate that our method obtains considerable performance compared with existing methods.
Estimating the Personality of White-Box Language Models
Authors: Saketh Reddy Karra, Son The Nguyen, Theja Tulabandhula
Date: 2022-04-25 00:00:00
Tasks: Text Generation

Technology for open-ended language generation, a key application of artificial intelligence, has advanced to a great extent in recent years. Large-scale language models, which are trained on large corpora of text, are being used in a wide range of applications everywhere, from virtual assistants to conversational bots. While these language models output fluent text, existing research shows that these models can and do capture human biases. Many of these biases, especially those that could potentially cause harm, are being well-investigated. On the other hand, studies that infer and change human personality traits inherited by these models have been scarce or non-existent. Our work seeks to address this gap by exploring the personality traits of several large-scale language models designed for open-ended text generation and the datasets used for training them. We build on the popular Big Five factors and develop robust methods that quantify the personality traits of these models and their underlying datasets. In particular, we trigger the models with a questionnaire designed for personality assessment and subsequently classify the text responses into quantifiable traits using a Zero-shot classifier. Our estimation scheme sheds light on an important anthropomorphic element found in such AI models and can help stakeholders decide how they should be applied as well as how society could perceive them. Additionally, we examined approaches to alter these personalities, adding to our understanding of how AI models can be adapted to specific contexts.
Offline Vehicle Routing Problem with Online Bookings: A Novel Problem Formulation with Applications to Paratransit
Authors: Amutheezan Sivagnanam, Salah Uddin Kadir, Ayan Mukhopadhyay, Philip Pugliese, Abhishek Dubey, Samitha Samaranayake, Aron Laszka
Date: 2022-04-25 00:00:00

Vehicle routing problems (VRPs) can be divided into two major categories: offline VRPs, which consider a given set of trip requests to be served, and online VRPs, which consider requests as they arrive in real-time. Based on discussions with public transit agencies, we identify a real-world problem that is not addressed by existing formulations: booking trips with flexible pickup windows (e.g., 3 hours) in advance (e.g., the day before) and confirming tight pickup windows (e.g., 30 minutes) at the time of booking. Such a service model is often required in paratransit service settings, where passengers typically book trips for the next day over the phone. To address this gap between offline and online problems, we introduce a novel formulation, the offline vehicle routing problem with online bookings. This problem is very challenging computationally since it faces the complexity of considering large sets of requests -- similar to offline VRPs -- but must abide by strict constraints on running time -- similar to online VRPs. To solve this problem, we propose a novel computational approach, which combines an anytime algorithm with a learning-based policy for real-time decisions. Based on a paratransit dataset obtained from the public transit agency of Chattanooga, TN, we demonstrate that our novel formulation and computational approach lead to significantly better outcomes in this setting than existing algorithms.
Spontaneous Emergence of Computation in Network Cascades
Authors: Galen Wilkerson, Sotiris Moschoyiannis, Henrik Jeldtoft Jensen
Date: 2022-04-25 00:00:00

Neuronal network computation and computation by avalanche supporting networks are of interest to the fields of physics, computer science (computation theory as well as statistical or machine learning) and neuroscience. Here we show that computation of complex Boolean functions arises spontaneously in threshold networks as a function of connectivity and antagonism (inhibition), computed by logic automata (motifs) in the form of computational cascades. We explain the emergent inverse relationship between the computational complexity of the motifs and their rank-ordering by function probabilities due to motifs, and its relationship to symmetry in function space. We also show that the optimal fraction of inhibition observed here supports results in computational neuroscience, relating to optimal information processing.
Discrete-Continuous Smoothing and Mapping
Authors: Kevin J. Doherty, Ziqi Lu, Kurran Singh, John J. Leonard
Date: 2022-04-25 00:00:00
Tasks: Point Cloud Registration

We describe a general approach for maximum a posteriori (MAP) inference in a class of discrete-continuous factor graphs commonly encountered in robotics applications. While there are openly available tools providing flexible and easy-to-use interfaces for specifying and solving inference problems formulated in terms of either discrete or continuous graphical models, at present, no similarly general tools exist enabling the same functionality for hybrid discrete-continuous problems. We aim to address this problem. In particular, we provide a library, DC-SAM, extending existing tools for inference problems defined in terms of factor graphs to the setting of discrete-continuous models. A key contribution of our work is a novel solver for efficiently recovering approximate solutions to discrete-continuous inference problems. The key insight to our approach is that while joint inference over continuous and discrete state spaces is often hard, many commonly encountered discrete-continuous problems can naturally be split into a "discrete part" and a "continuous part" that can individually be solved easily. Leveraging this structure, we optimize discrete and continuous variables in an alternating fashion. In consequence, our proposed work enables straightforward representation of and approximate inference in discrete-continuous graphical models. We also provide a method to approximate the uncertainty in estimates of both discrete and continuous variables. We demonstrate the versatility of our approach through its application to distinct robot perception applications, including robust pose graph optimization, and object-based mapping and localization.
Super-Prompting: Utilizing Model-Independent Contextual Data to Reduce Data Annotation Required in Visual Commonsense Tasks
Authors: Navid Rezaei, Marek Z. Reformat
Date: 2022-04-25 00:00:00
Tasks: Few-Shot Learning, In-Context Learning, Language Modelling, Visual Commonsense Reasoning

Pre-trained language models have shown excellent results in few-shot learning scenarios using in-context learning. Although it is impressive, the size of language models can be prohibitive to make them usable in on-device applications, such as sensors or smartphones. With smaller language models, task-specific data annotation is needed to fine-tune the language model for a specific purpose. However, data annotation can have a substantial financial and time burden for small research groups, startups, and even companies. In this paper, we analyze different prompt-based fine-tuning techniques to improve results on both language and multimodal causal transformer models. To evaluate our results, we use a dataset focusing on visual commonsense reasoning in time. Our results show that by simple model-agnostic prompt-based fine-tuning, comparable results can be reached by only using 35%-40% of the fine-tuning training dataset. The proposed approaches result in significant time and financial savings. As the proposed methods make minimal architectural assumptions, other researchers can use the results in their transformer models with minimal adaptations. We plan to release the source code freely to make it easier for the community to use and contribute to our work.
How can NLP Help Revitalize Endangered Languages? A Case Study and Roadmap for the Cherokee Language
Authors: Shiyue Zhang, Ben Frey, Mohit Bansal
Date: 2022-04-25 00:00:00
Proceeding: ACL 2022 5

More than 43% of the languages spoken in the world are endangered, and language loss currently occurs at an accelerated rate because of globalization and neocolonialism. Saving and revitalizing endangered languages has become very important for maintaining the cultural diversity on our planet. In this work, we focus on discussing how NLP can help revitalize endangered languages. We first suggest three principles that may help NLP practitioners to foster mutual understanding and collaboration with language communities, and we discuss three ways in which NLP can potentially assist in language education. We then take Cherokee, a severely-endangered Native American language, as a case study. After reviewing the language's history, linguistic features, and existing resources, we (in collaboration with Cherokee community members) arrive at a few meaningful ways NLP practitioners can collaborate with community partners. We suggest two approaches to enrich the Cherokee language's resources with machine-in-the-loop processing, and discuss several NLP tools that people from the Cherokee community have shown interest in. We hope that our work serves not only to inform the NLP community about Cherokee, but also to provide inspiration for future work on endangered languages in general. Our code and data will be open-sourced at https://github.com/ZhangShiyue/RevitalizeCherokee
Learning First-Order Symbolic Planning Representations That Are Grounded
Authors: Andrés Occhipinti Liberman, Blai Bonet, Hector Geffner
Date: 2022-04-25 00:00:00
Tasks: Sokoban

Two main approaches have been developed for learning first-order planning (action) models from unstructured data: combinatorial approaches that yield crisp action schemas from the structure of the state space, and deep learning approaches that produce action schemas from states represented by images. A benefit of the former approach is that the learned action schemas are similar to those that can be written by hand; a benefit of the latter is that the learned representations (predicates) are grounded on the images, and as a result, new instances can be given in terms of images. In this work, we develop a new formulation for learning crisp first-order planning models that are grounded on parsed images, a step to combine the benefits of the two approaches. Parsed images are assumed to be given in a simple O2D language (objects in 2D) that involves a small number of unary and binary predicates like "left", "above", "shape", etc. After learning, new planning instances can be given in terms of pairs of parsed images, one for the initial situation and the other for the goal. Learning and planning experiments are reported for several domains including Blocks, Sokoban, IPC Grid, and Hanoi.
ProCST: Boosting Semantic Segmentation Using Progressive Cyclic Style-Transfer
Authors: Shahaf Ettedgui, Shady Abu-Hussein, Raja Giryes
Date: 2022-04-25 00:00:00
Tasks: Domain Adaptation, Image-to-Image Translation, Semantic Segmentation, Style Transfer, Synthetic-to-Real Translation, Translation, Unsupervised Domain Adaptation

Using synthetic data for training neural networks that achieve good performance on real-world data is an important task as it can reduce the need for costly data annotation. Yet, synthetic and real world data have a domain gap. Reducing this gap, also known as domain adaptation, has been widely studied in recent years. Closing the domain gap between the source (synthetic) and target (real) data by directly performing the adaptation between the two is challenging. In this work, we propose a novel two-stage framework for improving domain adaptation techniques on image data. In the first stage, we progressively train a multi-scale neural network to perform image translation from the source domain to the target domain. We denote the new transformed data as "Source in Target" (SiT). Then, we insert the generated SiT data as the input to any standard UDA approach. This new data has a reduced domain gap from the desired target domain, which facilitates the applied UDA approach to close the gap further. We emphasize the effectiveness of our method via a comparison to other leading UDA and image-to-image translation techniques when used as SiT generators. Moreover, we demonstrate the improvement of our framework with three state-of-the-art UDA methods for semantic segmentation, HRDA, DAFormer and ProDA, on two UDA tasks, GTA5 to Cityscapes and Synthia to Cityscapes.
Evolutionary latent space search for driving human portrait generation
Authors: Benjamín Machín, Sergio Nesmachnow, Jamal Toutouh
Date: 2022-04-25 00:00:00
Tasks: Face Recognition, Generative Adversarial Network

This article presents an evolutionary approach for synthetic human portraits generation based on the latent space exploration of a generative adversarial network. The idea is to produce different human face images very similar to a given target portrait. The approach applies StyleGAN2 for portrait generation and FaceNet for face similarity evaluation. The evolutionary search is based on exploring the real-coded latent space of StyleGAN2. The main results over both synthetic and real images indicate that the proposed approach generates accurate and diverse solutions, which represent realistic human portraits. The proposed research can contribute to improving the security of face recognition systems.
Multi-objective Pointer Network for Combinatorial Optimization
Authors: Le-yang Gao, Rui Wang, Chuang Liu, Zhao-hong Jia
Date: 2022-04-25 00:00:00
Tasks: Combinatorial Optimization, Deep Reinforcement Learning, reinforcement-learning, Reinforcement Learning (RL), Transfer Learning

Multi-objective combinatorial optimization problems (MOCOPs), one type of complex optimization problems, widely exist in various real applications. Although meta-heuristics have been successfully applied to address MOCOPs, the calculation time is often much longer. Recently, a number of deep reinforcement learning (DRL) methods have been proposed to generate approximate optimal solutions to the combinatorial optimization problems. However, the existing studies on DRL have seldom focused on MOCOPs. This study proposes a single-model deep reinforcement learning framework, called multi-objective Pointer Network (MOPN), where the input structure of PN is effectively improved so that the single PN is capable of solving MOCOPs. In addition, two training strategies, based on representative model and transfer learning, respectively, are proposed to further enhance the performance of MOPN in different application scenarios. Moreover, compared to classical meta-heuristics, MOPN only consumes much less time on forward propagation to obtain the Pareto front. Meanwhile, MOPN is insensitive to problem scale, meaning that a trained MOPN is able to address MOCOPs with different scales. To verify the performance of MOPN, extensive experiments are conducted on three multi-objective traveling salesman problems, in comparison with one state-of-the-art model DRL-MOA and three classical multi-objective meta-heuristics. Experimental results demonstrate that the proposed model outperforms all the comparative methods with only 20\% to 40\% training time of DRL-MOA.
Data Uncertainty without Prediction Models
Authors: Bongjoon Park, Eunkyung Koh
Date: 2022-04-25 00:00:00
Tasks: Active Learning, Prediction

Data acquisition processes for machine learning are often costly. To construct a high-performance prediction model with fewer data, a degree of difficulty in prediction is often deployed as the acquisition function in adding a new data point. The degree of difficulty is referred to as uncertainty in prediction models. We propose an uncertainty estimation method named a Distance-weighted Class Impurity without explicit use of prediction models. We estimated uncertainty using distances and class impurities around the location, and compared it with several methods based on prediction models for uncertainty estimation by active learning tasks. We verified that the Distance-weighted Class Impurity works effectively regardless of prediction models.
GDGRU-DTA: Predicting Drug-Target Binding Affinity Based on GNN and Double GRU
Authors: Lyu Zhijian, Jiang Shaohua, Liang Yigao, Gao Min
Date: 2022-04-25 00:00:00
Tasks: Time Series, Time Series Analysis

The work for predicting drug and target affinity(DTA) is crucial for drug development and repurposing. In this work, we propose a novel method called GDGRU-DTA to predict the binding affinity between drugs and targets, which is based on GraphDTA, but we consider that protein sequences are long sequences, so simple CNN cannot capture the context dependencies in protein sequences well. Therefore, we improve it by interpreting the protein sequences as time series and extracting their features using Gate Recurrent Unit(GRU) and Bidirectional Gate Recurrent Unit(BiGRU). For the drug, our processing method is similar to that of GraphDTA, but uses two different graph convolution methods. Subsequently, the representation of drugs and proteins are concatenated for final prediction. We evaluate the proposed model on two benchmark datasets. Our model outperforms some state-of-the-art deep learning methods, and the results demonstrate the feasibility and excellent feature capture ability of our model.
Discovering Gateway Ports in Maritime Using Temporal Graph Neural Network Port Classification
Authors: Dogan Altan, Mohammad Etemad, Dusica Marijan, Tetyana Kholodna
Date: 2022-04-25 00:00:00
Tasks: Classification, Graph Neural Network

Vessel navigation is influenced by various factors, such as dynamic environmental factors that change over time or static features such as vessel type or depth of the ocean. These dynamic and static navigational factors impose limitations on vessels, such as long waiting times in regions outside the actual ports, and we call these waiting regions gateway ports. Identifying gateway ports and their associated features such as congestion and available utilities can enhance vessel navigation by planning on fuel optimization or saving time in cargo operation. In this paper, we propose a novel temporal graph neural network (TGNN) based port classification method to enable vessels to discover gateway ports efficiently, thus optimizing their operations. The proposed method processes vessel trajectory data to build dynamic graphs capturing spatio-temporal dependencies between a set of static and dynamic navigational features in the data, and it is evaluated in terms of port classification accuracy on a real-world data set collected from ten vessels operating in Halifax, NS, Canada. The experimental results indicate that our TGNN-based port classification method provides an f-score of 95% in classifying ports.