pwc-archive/papers-with-abstracts huggingface dataset:
Mean Field Portfolio Games with Consumption
Authors: Guanxing Fu
Date: 2022-06-11 00:00:00

We study mean field portfolio games with consumption. For general market parameters, we establish a one-to-one correspondence between Nash equilibria of the game and solutions to some FBSDE, which is proved to be equivalent to some BSDE. Our approach, which is general enough to cover power, exponential and log utilities, relies on martingale optimality principle in [3,9] and dynamic programming principle in [6,7]. When the market parameters do not depend on the Brownian paths, we get the unique Nash equilibrium in closed form. As a byproduct, when all market parameters are time-independent, we answer the question proposed in [12]: the strong equilibrium obtained in [12] is unique in the essentially bounded space.
Cryptocurrency Bubble Detection: A New Stock Market Dataset, Financial Task & Hyperbolic Models
Authors: Ramit Sawhney, Shivam Agarwal, Vivek Mittal, Paolo Rosso, Vikram Nanda, Sudheer Chava
Date: 2022-05-11 00:00:00
Proceeding: NAACL 2022 7

The rapid spread of information over social media influences quantitative trading and investments. The growing popularity of speculative trading of highly volatile assets such as cryptocurrencies and meme stocks presents a fresh challenge in the financial realm. Investigating such "bubbles" - periods of sudden anomalous behavior of markets are critical in better understanding investor behavior and market dynamics. However, high volatility coupled with massive volumes of chaotic social media texts, especially for underexplored assets like cryptocoins pose a challenge to existing methods. Taking the first step towards NLP for cryptocoins, we present and publicly release CryptoBubbles, a novel multi-span identification task for bubble detection, and a dataset of more than 400 cryptocoins from 9 exchanges over five years spanning over two million tweets. Further, we develop a set of sequence-to-sequence hyperbolic models suited to this multi-span identification task based on the power-law dynamics of cryptocurrencies and user behavior on social media. We further test the effectiveness of our models under zero-shot settings on a test set of Reddit posts pertaining to 29 "meme stocks", which see an increase in trade volume due to social media hype. Through quantitative, qualitative, and zero-shot analyses on Reddit and Twitter spanning cryptocoins and meme-stocks, we show the practical applicability of CryptoBubbles and hyperbolic models.
Am I No Good? Towards Detecting Perceived Burdensomeness and Thwarted Belongingness from Suicide Notes
Authors: Soumitra Ghosh, Asif Ekbal, Pushpak Bhattacharyya
Date: 2022-05-20 00:00:00

The World Health Organization (WHO) has emphasized the importance of significantly accelerating suicide prevention efforts to fulfill the United Nations' Sustainable Development Goal (SDG) objective of 2030. In this paper, we present an end-to-end multitask system to address a novel task of detection of two interpersonal risk factors of suicide, Perceived Burdensomeness (PB) and Thwarted Belongingness (TB) from suicide notes. We also introduce a manually translated code-mixed suicide notes corpus, CoMCEASE-v2.0, based on the benchmark CEASE-v2.0 dataset, annotated with temporal orientation, PB and TB labels. We exploit the temporal orientation and emotion information in the suicide notes to boost overall performance. For comprehensive evaluation of our proposed method, we compare it to several state-of-the-art approaches on the existing CEASE-v2.0 dataset and the newly announced CoMCEASE-v2.0 dataset. Empirical evaluation suggests that temporal and emotional information can substantially improve the detection of PB and TB.
OHM: GPU Based Occupancy Map Generation
Authors: Kazys Stepanas, Jason Williams, Emili Hernández, Fabio Ruetz, Thomas Hines
Date: 2022-04-26 00:00:00
Tasks: CPU, GPU

Occupancy grid maps (OGMs) are fundamental to most systems for autonomous robotic navigation. However, CPU-based implementations struggle to keep up with data rates from modern 3D lidar sensors, and provide little capacity for modern extensions which maintain richer voxel representations. This paper presents OHM, our open source, GPU-based OGM framework. We show how the algorithms can be mapped to GPU resources, resolving difficulties with contention to obtain a successful implementation. The implementation supports many modern OGM algorithms including NDT-OM, NDT-TM, decay-rate and TSDF. A thorough performance evaluation is presented based on tracked and quadruped UGV platforms and UAVs, and data sets from both outdoor and subterranean environments. The results demonstrate excellent performance improvements both offline, and for online processing in embedded platforms. Finally, we describe how OHM was a key enabler for the UGV navigation solution for our entry in the DARPA Subterranean Challenge, which placed second at the Final Event.
Visual Transformer for Object Detection
Authors: Michael Yang
Date: 2022-06-01 00:00:00
Tasks: Image Captioning, Machine Translation, Object, object-detection, Object Detection

Convolutional Neural networks (CNN) have been the first choice of paradigm in many computer vision applications. The convolution operation however has a significant weakness which is it only operates on a local neighborhood of pixels, thus it misses global information of the surrounding neighbors. Transformers, or Self-attention networks to be more specific, on the other hand, have emerged as a recent advance to capture long range interactions of the input, but they have mostly been applied to sequence modeling tasks such as Neural Machine Translation, Image captioning and other Natural Language Processing tasks. Transformers has been applied to natural language related tasks and achieved promising results. However, its applications in visual related tasks are far from being satisfying. Taking into consideration of both the weaknesses of Convolutional Neural Networks and those of the Transformers, in this paper, we consider the use of self-attention for discriminative visual tasks, object detection, as an alternative to convolutions. In this paper, we propose our model: DetTransNet. Extensive experiments show that our model leads to consistent improvements in object detection on COCO across many different models and scales, including ResNets, while keeping the number of parameters similar. In particular, our method achieves a 1.2% Average Precision improvement on COCO object detection task over other baseline models.
LiVeR: Lightweight Vehicle Detection and Classification in Real-Time
Authors: Chandra Shekhar, Jagnyashini Debadarshini, Sudipta Saha
Date: 2022-05-27 00:00:00
Tasks: Classification, vehicle detection

Detection and classification of vehicles are very significant components in an Intelligent-Transportation System. Existing solutions not only use heavy-weight and costly equipment, but also largely depend on constant cloud (Internet) connectivity, as well as adequate uninterrupted power-supply. Such dependencies make these solutions fundamentally impractical considering the possible adversities of outdoor environment as well as requirement of correlated wide-area operation. For practical use, apart from being technically sound and accurate, a solution has to be lightweight, cost-effective, easy-to-install, flexible as well as supporting efficient time-correlated coverage over large area. In this work we propose an IoT-assisted strategy to fulfil all these goals together. We adopt a top-down approach where we first introduce a lightweight framework for time-correlated low-cost wide-area measurement and then reuse the concept for developing the individual measurement units. Our extensive outdoor measurement studies and trace-based simulation on the empirical data show about 98% accuracy in vehicle detection and upto 93% of accuracy in classification of the vehicles over moderately busy urban roads.
Concept Identification for Complex Engineering Datasets
Authors: Felix Lanfermann, Sebastian Schmitt
Date: 2022-06-09 00:00:00
Tasks: Decision Making

Finding meaningful concepts in engineering application datasets which allow for a sensible grouping of designs is very helpful in many contexts. It allows for determining different groups of designs with similar properties and provides useful knowledge in the engineering decision making process. Also, it opens the route for further refinements of specific design candidates which exhibit certain characteristic features. In this work, an approach to define meaningful and consistent concepts in an existing engineering dataset is presented. The designs in the dataset are characterized by a multitude of features such as design parameters, geometrical properties or performance values of the design for various boundary conditions. In the proposed approach the complete feature set is partitioned into several subsets called description spaces. The definition of the concepts respects this partitioning which leads to several desired properties of the identified concepts. This cannot be achieved with state-of-the-art clustering or concept identification approaches. A novel concept quality measure is proposed, which provides an objective value for a given definition of concepts in a dataset. The usefulness of the measure is demonstrated by considering a realistic engineering dataset consisting of about 2500 airfoil profiles, for which the performance values (lift and drag) for three different operating conditions were obtained by a computational fluid dynamics simulation. A numerical optimization procedure is employed, which maximizes the concept quality measure and finds meaningful concepts for different setups of the description spaces, while also incorporating user preference. It is demonstrated how these concepts can be used to select archetypal representatives of the dataset which exhibit characteristic features of each concept.
Tackling Multiple Tasks with One Single Learning Framework
Authors: Michael X. Yang
Date: 2022-05-29 00:00:00
Tasks: Multi-Task Learning

Deep Multi-Task Learning (DMTL) has been widely studied in the machine learning community and applied to a broad range of real-world applications. Searching for the optimal knowledge sharing in DMTL is more challenging for sequential learning problems, as the task relationship will change in the temporal dimension. In this paper, we propose a flexible and efficient framework called HierarchicalTemporal Activation Network (HTAN) to simultaneously explore the optimal sharing of the neural network hierarchy (hierarchical axis) and the time-variant task relationship (temporal axis). HTAN learns a set of time-variant activation functions to encode the task relation. A functional regularization implemented by a modulated SPDNet and adversarial learning is further proposed to enhance the DMTL performance. Comprehensive experiments on several challenging applications demonstrate that our HTAN-SPD framework outperforms SOTA methods significantly in sequential DMTL.
Graph Neural Networks Intersect Probabilistic Graphical Models: A Survey
Authors: Chenqing Hua, Sitao Luan, Qian Zhang, Jie Fu
Date: 2022-05-24 00:00:00
Tasks: Survey

Graphs are a powerful data structure to represent relational data and are widely used to describe complex real-world data structures. Probabilistic Graphical Models (PGMs) have been well-developed in the past years to mathematically model real-world scenarios in compact graphical representations of distributions of variables. Graph Neural Networks (GNNs) are new inference methods developed in recent years and are attracting growing attention due to their effectiveness and flexibility in solving inference and learning problems over graph-structured data. These two powerful approaches have different advantages in capturing relations from observations and how they conduct message passing, and they can benefit each other in various tasks. In this survey, we broadly study the intersection of GNNs and PGMs. Specifically, we first discuss how GNNs can benefit from learning structured representations in PGMs, generate explainable predictions by PGMs, and how PGMs can infer object relationships. Then we discuss how GNNs are implemented in PGMs for more efficient inference and structure learning. In the end, we summarize the benchmark datasets used in recent studies and discuss promising future directions.
A General Framework for the Representation of Function and Affordance: A Cognitive, Causal, and Grounded Approach, and a Step Toward AGI
Authors: Seng-Beng Ho
Date: 2022-06-02 00:00:00
Tasks: Descriptive

In AI research, so far, the attention paid to the characterization and representation of function and affordance has been sporadic and sparse, even though this aspect features prominently in an intelligent system's functioning. In the sporadic and sparse, though commendable efforts so far devoted to the characterization and understanding of function and affordance, there has also been no general framework that could unify all the different use domains and situations related to the representation and application of functional concepts. This paper develops just such a general framework, with an approach that emphasizes the fact that the representations involved must be explicitly cognitive and conceptual, and they must also contain causal characterizations of the events and processes involved, as well as employ conceptual constructs that are grounded in the referents to which they refer, in order to achieve maximal generality. The basic general framework is described, along with a set of basic guiding principles with regards to the representation of functionality. To properly and adequately characterize and represent functionality, a descriptive representation language is needed. This language is defined and developed, and many examples of its use are described. The general framework is developed based on an extension of the general language meaning representational framework called conceptual dependency. To support the general characterization and representation of functionality, the basic conceptual dependency framework is enhanced with representational devices called structure anchor and conceptual dependency elaboration, together with the definition of a set of ground level concepts. These novel representational constructs are defined, developed, and described. A general framework dealing with functionality would represent a major step toward achieving Artificial General Intelligence.
RIS-ADMM: A RIS and ADMM-Based Passive and Sparse Sensing Method With Interference Removal
Authors: Peng Chen, Zhimin Chen, Pu Miao, Yun Chen
Date: 2022-05-25 00:00:00

Reconfigurable Intelligent Surfaces (RIS) emerge as promising technologies in future radar and wireless communication domains. This letter addresses the passive sensing issue utilizing wireless communication signals and RIS amidst interference from wireless access points (APs). We introduce an atomic norm minimization (ANM) approach to leverage spatial domain target sparsity and estimate the direction of arrival (DOA). However, the conventional semidefinite programming (SDP)-based solutions for the ANM problem are complex and lack efficient realization. Consequently, we propose a RIS-ADMM method, an innovative alternating direction method of multipliers (ADMM)-based iterative approach. This method yields closed-form expressions and effectively suppresses interference signals. Simulation outcomes affirm that our RIS-ADMM method surpasses existing techniques in DOA estimation accuracy while maintaining low computational complexity. The code for the proposed method is available online \url{https://github.com/chenpengseu/RIS-ADMM.git}.
Video-Based Frame-Level Facial Analysis of Affective Behavior on Mobile Devices Using EfficientNets
Authors: Savchenko A.V.
Date: 2022-06-10 00:00:00
Proceeding: CVPR Workshop 2022 6
Tasks: Action Unit Detection, Arousal Estimation, Emotion Recognition, Facial Expression Recognition, Facial Expression Recognition (FER), Multi-Task Learning

In this paper, we consider the problem of real-time video-based facial emotion analytics, namely, facial expression recognition, prediction of valence and arousal and detection of action unit points. We propose the novel frame-level emotion recognition algorithm by extracting facial features with the single EfficientNet model pre-trained on AffectNet. The predictions for sequential frames are smoothed using mean or median filters. It is demonstrated that our approach may be implemented even for video analytics on mobile devices. Experimental results for the large scale Aff-Wild2 database from the third Affective Behavior Analysis in-the-wild Competition demonstrate that our simple model is significantly better when compared to the VggFace baseline. In particular, our method is characterized by 0.1-0.5 higher performance measures for test sets in the uni-task Expression Classification, Valence-Arousal Estimation, Action Unit Detection and Multi-Task Learning. Our team took the 3rd place in the multi-task learning challenge and 4th places in Valence-Arousal and Expression challenges. Due to simplicity, the proposed approach may be considered as a new baseline for all four sub-challenges.
Robust facial expression recognition with global‑local joint representation learning
Authors: Chunxiao Fan, zhenxing Wang, Jia Li, Shanshan Wang, Xiao Sun
Date: 2022-03-07 00:00:00
Proceeding: Multimedia Systems 2022 3
Tasks: Facial Expression Recognition, Facial Expression Recognition (FER), Representation Learning

As an important part in computer vision, facial expression recognition (FER) has received extensive attention, but it still has lots of challenges in this area. One of the important difficulties is to remain the topological information in the feature extraction operation. In this paper, we propose a novel facial expression recognition method with lite dual channel neural network based on graph convolutional networks (DCNN-GCN). In the proposed method, (1) the topological structure information and texture feature of regions of interest (ROIs) are modeled as graphs and processed with graph convolutional network (GCN) to remain the topological features. (2) The local features of ROIs and global features are extracted with dual channel neural networks, which can improve the performance of features extraction and reduce the complexity of networks. The proposed method is evaluated on CK+, Oulu-CASIA and MMI data sets. Experiment results show that the proposed method can significantly improve the accuracy of facial expression recognition. In addition, the network is much lite and suitable for application.
Evaluating histopathology transfer learning with ChampKit
Authors: Jakub R. Kaczmarzyk, Tahsin M. Kurc, Shahira Abousamra, Rajarsi Gupta, Joel H. Saltz, Peter K. Koo
Date: 2022-06-14 00:00:00
Tasks: Benchmarking, Cell Detection, General Classification, image-classification, Image Classification, Medical Image Classification, Transfer Learning

Histopathology remains the gold standard for diagnosis of various cancers. Recent advances in computer vision, specifically deep learning, have facilitated the analysis of histopathology images for various tasks, including immune cell detection and microsatellite instability classification. The state-of-the-art for each task often employs base architectures that have been pretrained for image classification on ImageNet. The standard approach to develop classifiers in histopathology tends to focus narrowly on optimizing models for a single task, not considering the aspects of modeling innovations that improve generalization across tasks. Here we present ChampKit (Comprehensive Histopathology Assessment of Model Predictions toolKit): an extensible, fully reproducible benchmarking toolkit that consists of a broad collection of patch-level image classification tasks across different cancers. ChampKit enables a way to systematically document the performance impact of proposed improvements in models and methodology. ChampKit source code and data are freely accessible at https://github.com/kaczmarj/champkit .
FibeRed: Fiberwise Dimensionality Reduction of Topologically Complex Data with Vector Bundles
Authors: Luis Scoccola, Jose A. Perea
Date: 2022-06-13 00:00:00
Tasks: Dimensionality Reduction

Datasets with non-trivial large scale topology can be hard to embed in low-dimensional Euclidean space with existing dimensionality reduction algorithms. We propose to model topologically complex datasets using vector bundles, in such a way that the base space accounts for the large scale topology, while the fibers account for the local geometry. This allows one to reduce the dimensionality of the fibers, while preserving the large scale topology. We formalize this point of view, and, as an application, we describe an algorithm which takes as input a dataset together with an initial representation of it in Euclidean space, assumed to recover part of its large scale topology, and outputs a new representation that integrates local representations, obtained through local linear dimensionality reduction, along the initial global representation. We demonstrate this algorithm on examples coming from dynamical systems and chemistry. In these examples, our algorithm is able to learn topologically faithful embeddings of the data in lower target dimension than various well known metric-based dimensionality reduction algorithms.
MACQ: A Holistic View of Model Acquisition Techniques
Authors: Ethan Callanan, Rebecca De Venezia, Victoria Armstrong, Alison Paredes, Tathagata Chakraborti, Christian Muise
Date: 2022-06-14 00:00:00
Tasks: model

For over three decades, the planning community has explored countless methods for data-driven model acquisition. These range in sophistication (e.g., simple set operations to full-blown reformulations), methodology (e.g., logic-based vs. planing-based), and assumptions (e.g., fully vs. partially observable). With no fewer than 43 publications in the space, it can be overwhelming to understand what approach could or should be applied in a new setting. We present a holistic characterization of the action model acquisition space and further introduce a unifying framework for automated action model acquisition. We have re-implemented some of the landmark approaches in the area, and our characterization of all the techniques offers deep insight into the research opportunities that remain; i.e., those settings where no technique is capable of solving.