pwc-archive/papers-with-abstracts huggingface dataset:
European baskets in discrete-time continuous-binomial market models
Authors: Jarek Kędra, Assaf Libman, Victoria Steblovskaya
Date: 2023-01-12 00:00:00

We consider a discrete-time incomplete multi-asset market model with continuous price jumps. For a wide class of contingent claims, including European basket call options, we compute the bounds of the interval containing the no-arbitrage prices. We prove that the lower bound coincides, in fact, with Jensen's bound. The upper bound can be computed by restricting to a binomial model for which an explicit expression for the bound is known by an earlier work of the authors. We describe explicitly a maximal hedging strategy which is the best possible in the sense that its value is equal to the upper bound of the price interval of the claim. Our results show that for any $c$ in the interval of the non-arbitrage contingent claim price at time $0$, one can change the boundaries of the price jumps to obtain a model in which $c$ is the upper bound at time $0$ of this interval. The lower bound of this interval remains unaffected.
Non-linear correlation analysis in financial markets using hierarchical clustering
Authors: J. E. Salgado-Hernández, Manan Vyas
Date: 2023-01-12 00:00:00
Tasks: Clustering

Distance correlation coefficient (DCC) can be used to identify new associations and correlations between multiple variables. The distance correlation coefficient applies to variables of any dimension, can be used to determine smaller sets of variables that provide equivalent information, is zero only when variables are independent, and is capable of detecting nonlinear associations that are undetectable by the classical Pearson correlation coefficient (PCC). Hence, DCC provides more information than the PCC. We analyze numerous pairs of stocks in S\&P500 database with the distance correlation coefficient and provide an overview of stochastic evolution of financial market states based on these correlation measures obtained using agglomerative clustering.
Unsupervised Question Duplicate and Related Questions Detection in e-learning platforms
Authors: Maksimjeet Chowdhary, Sanyam Goyal, Venktesh V, Mukesh Mohania, Vikram Goyal
Date: 2022-12-20 00:00:00

Online learning platforms provide diverse questions to gauge the learners' understanding of different concepts. The repository of questions has to be constantly updated to ensure a diverse pool of questions to conduct assessments for learners. However, it is impossible for the academician to manually skim through the large repository of questions to check for duplicates when onboarding new questions from external sources. Hence, we propose a tool QDup in this paper that can surface near-duplicate and semantically related questions without any supervised data. The proposed tool follows an unsupervised hybrid pipeline of statistical and neural approaches for incorporating different nuances in similarity for the task of question duplicate detection. We demonstrate that QDup can detect near-duplicate questions and also suggest related questions for practice with remarkable accuracy and speed from a large repository of questions. The demo video of the tool can be found at https://www.youtube.com/watch?v=loh0_-7XLW4.
iiTransformer: A Unified Approach to Exploiting Local and Non-Local Information for Image Restoration
Authors: Soo Min Kang, Youngchan Song, Hanul Shin, Tammy Lee
Date: 2022-11-21 00:00:00
Proceeding: BMVC 2022 11
Tasks: Color Image Denoising, Image Restoration, Jpeg Compression Artifact Reduction

The goal of image restoration is to recover a high-quality image from its degraded input. While impressive results on various image restoration tasks have been achieved using CNNs, the convolution operation has limited its ability to utilize information outside of its receptive field. Transformers, which use the self-attention mechanism to model long-range dependencies of its input, have demonstrated promising results in various high-level vision tasks. In this paper, we propose intra-inter Transformer (iiTransformer) by explicitly modelling long-range dependencies at the pixel- and patch-levels since there are benefits to considering both local and non-local feature correlations. In addition, we provide a boundary artifact-free solution to support images with arbitrary sizes. We demonstrate the potential of iiTransformer as a general purpose backbone architecture through extensive experiments on various image restoration tasks.
Super-resolution photoacoustic microscopy using blind structured illumination
Authors: Todd W. Murray
Date: 2016-12-21 00:00:00
Proceeding: optica 2016 12
Tasks: Object, Super-Resolution

We present a method for enhancing the spatial resolution of optical-absorption-based photoacoustic imaging through
or within highly scattering media. The optical speckle pattern that emerges as light propagates through diffuse media
provides structured illumination to an object placed behind a scattering wall. The photoacoustic signal produced by
such illumination is detected using a focused ultrasound transducer. We demonstrate through both simulation and
experiment that, by acquiring multiple photoacoustic images, each produced by a different random and unknown
speckle pattern, an image of an absorbing object can be reconstructed with a spatial resolution far exceeding that of the
ultrasound transducer. The imaging approach and reconstruction method exploiting joint sparsity will be valuable
for biomedical applications and other fields where high-frequency structural information is scrambled by diffusive
processes.
Forward propagation closed loop learning
Authors: Bernd Porr, Paul Miller
Date: 2020-06-01 00:00:00
Proceeding: Adaptive Behaviour 2020 6

For an autonomous agent, the inputs are the sensory data that inform the agent of the state of the world, and the outputs are their actions, which act on the world and consequently produce new sensory inputs. The agent only knows of its own actions via their effect on future inputs; therefore desired states, and error signals, are most naturally defined in terms of the inputs. Most machine learning algorithms, however, operate in terms of desired outputs. For example, backpropagation takes target output values and propagates the corresponding error backwards through the network in order to change the weights. In closed loop settings, it is far more obvious how to define desired sensory inputs than desired actions, however. To train a deep network using errors defined in the input space would call for an algorithm that can propagate those errors forwards through the network, from input layer to output layer, in much the same way that activations are propagated. In this article, we present a novel learning algorithm which performs such ‘forward-propagation’ of errors. We demonstrate its performance, first in a simple line follower and then in a 1st person shooter game.
GridMix: Strong regularization through local context mapping
Authors: Kyungjune Baek, Duhyeon Bang, Hyunjung Shim
Date: 2021-01-01 00:00:00
Proceeding: Pattern Recognition 2021 1
Tasks: Adversarial Robustness, Data Augmentation, Object Localization, Weakly-Supervised Object Localization

Recently developed regularization techniques improve the networks generalization by only considering the global context. Therefore, the network tends to focus on a few most discriminative subregions of an image for prediction accuracy, leading the network being sensitive to unseen or noisy data. To address this disadvantage, we introduce the concept of local context mapping by predicting patch-level labels and combine it with a method of local data augmentation by grid-based mixing, called GridMix. Through our analysis of intermediate representations, we show that our GridMix can effectively regularize the network model. Finally, our evaluation results indicate that GridMix outperforms state-of-the-art techniques in classification and adversarial robustness, and it achieves a comparable performance in weakly supervised object localization.
SmoothMix: A Simple Yet Effective Data Augmentation to Train Robust Classifiers
Authors: Jin-ha Lee, Muhammad Zaigham Zaheer, Marcella Astrid, Seung-Ik Lee
Date: 2020-06-01 00:00:00
Proceeding: Conference on Computer Vision and Pattern Recognition (CVPR) Workshops 2020 6
Tasks: Data Augmentation, image-classification, Image Classification

Data augmentation has been proven effective which, by preventing overfitting, can not only enhances the performance of a deep neural network but also leads to a better generalization even with limited dataset. Recently introduced regional dropout based data augmentation strategies remove (or replace) some parts of an input image with a desideratum to make the network focus on less discriminative portions of an image, which results in an improved performance. However, such approaches usually possess 'strong-edge' problem caused by an obvious change in the pixels at the positions where the image is manipulated. It may not only impact on the local convolution operation but can also provide clues for the network to latch on to, which do not align well with the basic purpose of augmentation. In order to minimize such peculiarities, we introduce SmoothMix in which blending of images is done based on soft edges and the training labels are computed accordingly. Extensive analysis performed on CIFAR-10, CIFAR-100 and ImageNet for image classification demonstrate state-of-the-art results. Furthermore, SmoothMix significantly increases robustness of a network against image corruption. Results on CIFAR-100-C & ImageNet-C corruption datasets also shows superiority of our proposed approach.
Contrast with Major Classifier Vectors for Federated Medical Relation Extraction with Heterogeneous Label Distribution
Authors: Chunhui Du, Hao He, Yaohui Jin
Date: 2023-01-13 00:00:00
Tasks: Medical Relation Extraction, Relation, Relation Extraction

Federated medical relation extraction enables multiple clients to train a deep network collaboratively without sharing their raw medical data. In order to handle the heterogeneous label distribution across clients, most of the existing works only involve enforcing regularization between local and global models during optimization. In this paper, we fully utilize the models of all clients and propose a novel concept of \textit{major classifier vectors}, where a group of class vectors is obtained in an ensemble rather than the weighted average method on the server. The major classifier vectors are then distributed to all clients and the local training of each client is Contrasted with Major Classifier vectors (FedCMC), so the local model is not prone to overfitting to the local label distribution. FedCMC requires only a small amount of additional transfer of classifier parameters without any leakage of raw data, extracted representations, and label distributions. Our extensive experiments show that FedCMC outperforms the other state-of-the-art FL algorithms on three medical relation extraction datasets.
Decentralized model-free reinforcement learning in stochastic games with average-reward objective
Authors: Romain Cravic, Nicolas Gast, Bruno Gaujal
Date: 2023-01-13 00:00:00
Tasks: Q-Learning, reinforcement-learning, Reinforcement Learning (RL)

We propose the first model-free algorithm that achieves low regret performance for decentralized learning in two-player zero-sum tabular stochastic games with infinite-horizon average-reward objective. In decentralized learning, the learning agent controls only one player and tries to achieve low regret performances against an arbitrary opponent. This contrasts with centralized learning where the agent tries to approximate the Nash equilibrium by controlling both players. In our infinite-horizon undiscounted setting, additional structure assumptions is needed to provide good behaviors of learning processes : here we assume for every strategy of the opponent, the agent has a way to go from any state to any other. This assumption is the analogous to the "communicating" assumption in the MDP setting. We show that our Decentralized Optimistic Nash Q-Learning (DONQ-learning) algorithm achieves both sublinear high probability regret of order $T^{3/4}$ and sublinear expected regret of order $T^{2/3}$. Moreover, our algorithm enjoys a low computational complexity and low memory space requirement compared to the previous works of (Wei et al. 2017) and (Jafarnia-Jahromi et al. 2021) in the same setting.
Investigating the Combination of Planning-Based and Data-Driven Methods for Goal Recognition
Authors: Nils Wilken, Lea Cohausz, Johannes Schaum, Stefan Lüdtke, Heiner Stuckenschmidt
Date: 2023-01-13 00:00:00

An important feature of pervasive, intelligent assistance systems is the ability to dynamically adapt to the current needs of their users. Hence, it is critical for such systems to be able to recognize those goals and needs based on observations of the user's actions and state of the environment. In this work, we investigate the application of two state-of-the-art, planning-based plan recognition approaches in a real-world setting. So far, these approaches were only evaluated in artificial settings in combination with agents that act perfectly rational. We show that such approaches have difficulties when used to recognize the goals of human subjects, because human behaviour is typically not perfectly rational. To overcome this issue, we propose an extension to the existing approaches through a classification-based method trained on observed behaviour data. We empirically show that the proposed extension not only outperforms the purely planning-based- and purely data-driven goal recognition methods but is also able to recognize the correct goal more reliably, especially when only a small number of observations were seen. This substantially improves the usefulness of hybrid goal recognition approaches for intelligent assistance systems, as recognizing a goal early opens much more possibilities for supportive reactions of the system.
Sem@$K$: Is my knowledge graph embedding model semantic-aware?
Authors: Nicolas Hubert, Pierre Monnin, Armelle Brun, Davy Monticolo
Date: 2023-01-13 00:00:00
Tasks: Graph Embedding, Knowledge Graph Embedding, Knowledge Graphs, Link Prediction

Using knowledge graph embedding models (KGEMs) is a popular approach for predicting links in knowledge graphs (KGs). Traditionally, the performance of KGEMs for link prediction is assessed using rank-based metrics, which evaluate their ability to give high scores to ground-truth entities. However, the literature claims that the KGEM evaluation procedure would benefit from adding supplementary dimensions to assess. That is why, in this paper, we extend our previously introduced metric Sem@K that measures the capability of models to predict valid entities w.r.t. domain and range constraints. In particular, we consider a broad range of KGs and take their respective characteristics into account to propose different versions of Sem@K. We also perform an extensive study to qualify the abilities of KGEMs as measured by our metric. Our experiments show that Sem@K provides a new perspective on KGEM quality. Its joint analysis with rank-based metrics offers different conclusions on the predictive power of models. Regarding Sem@K, some KGEMs are inherently better than others, but this semantic superiority is not indicative of their performance w.r.t. rank-based metrics. In this work, we generalize conclusions about the relative performance of KGEMs w.r.t. rank-based and semantic-oriented metrics at the level of families of models. The joint analysis of the aforementioned metrics gives more insight into the peculiarities of each model. This work paves the way for a more comprehensive evaluation of KGEM adequacy for specific downstream tasks.
Short-length SSVEP data extension by a novel generative adversarial networks based framework
Authors: Yudong Pan, Ning li, Yangsong Zhang, Peng Xu, Dezhong Yao
Date: 2023-01-13 00:00:00
Tasks: Brain Computer Interface, EEG, Electroencephalogram (EEG), SSVEP

Steady-state visual evoked potentials (SSVEPs) based brain-computer interface (BCI) has received considerable attention due to its high information transfer rate (ITR) and available quantity of targets. However, the performance of frequency identification methods heavily hinges on the amount of user calibration data and data length, which hinders the deployment in real-world applications. Recently, generative adversarial networks (GANs)-based data generation methods have been widely adopted to create synthetic electroencephalography (EEG) data, holds promise to address these issues. In this paper, we proposed a GAN-based end-to-end signal transformation network for Time-window length Extension, termed as TEGAN. TEGAN transforms short-length SSVEP signals into long-length artificial SSVEP signals. By incorporating a novel U-Net generator architecture and an auxiliary classifier into the network architecture, the TEGAN could produce conditioned features in the synthetic data. Additionally, we introduced a two-stage training strategy and the LeCam-divergence regularization term to regularize the training process of GAN during the network implementation. The proposed TEGAN was evaluated on two public SSVEP datasets (a 4-class dataset and a 12-class dataset). With the assistance of TEGAN, the performance of traditional frequency recognition methods and deep learning-based methods have been significantly improved under limited calibration data. And the classification performance gap of various frequency recognition methods has been narrowed. This study substantiates the feasibility of the proposed method to extend the data length for short-time SSVEP signals for developing a high-performance BCI system. The proposed GAN-based methods have the great potential of shortening the calibration time and cutting down the budget for various real-world BCI-based applications.
Toward General Design Principles for Generative AI Applications
Authors: Justin D. Weisz, Michael Muller, Jessica He, Stephanie Houde
Date: 2023-01-13 00:00:00

Generative AI technologies are growing in power, utility, and use. As generative technologies are being incorporated into mainstream applications, there is a need for guidance on how to design those applications to foster productive and safe use. Based on recent research on human-AI co-creation within the HCI and AI communities, we present a set of seven principles for the design of generative AI applications. These principles are grounded in an environment of generative variability. Six principles are focused on designing for characteristics of generative AI: multiple outcomes & imperfection; exploration & control; and mental models & explanations. In addition, we urge designers to design against potential harms that may be caused by a generative model's hazardous output, misuse, or potential for human displacement. We anticipate these principles to usefully inform design decisions made in the creation of novel human-AI applications, and we invite the community to apply, revise, and extend these principles to their own work.
Deep learning-based approaches for human motion decoding in smart walkers for rehabilitation
Authors: Carolina Gonçalves, João M. Lopes, Sara Moccia, Daniele Berardini, Lucia Migliorelli, Cristina P. Santos
Date: 2023-01-13 00:00:00
Tasks: Action Detection, Action Recognition

Gait disabilities are among the most frequent worldwide. Their treatment relies on rehabilitation therapies, in which smart walkers are being introduced to empower the user's recovery and autonomy, while reducing the clinicians effort. For that, these should be able to decode human motion and needs, as early as possible. Current walkers decode motion intention using information of wearable or embedded sensors, namely inertial units, force and hall sensors, and lasers, whose main limitations imply an expensive solution or hinder the perception of human movement. Smart walkers commonly lack a seamless human-robot interaction, which intuitively understands human motions. A contactless approach is proposed in this work, addressing human motion decoding as an early action recognition/detection problematic, using RGB-D cameras. We studied different deep learning-based algorithms, organised in three different approaches, to process lower body RGB-D video sequences, recorded from an embedded camera of a smart walker, and classify them into 4 classes (stop, walk, turn right/left). A custom dataset involving 15 healthy participants walking with the device was acquired and prepared, resulting in 28800 balanced RGB-D frames, to train and evaluate the deep networks. The best results were attained by a convolutional neural network with a channel attention mechanism, reaching accuracy values of 99.61% and above 93%, for offline early detection/recognition and trial simulations, respectively. Following the hypothesis that human lower body features encode prominent information, fostering a more robust prediction towards real-time applications, the algorithm focus was also evaluated using Dice metric, leading to values slightly higher than 30%. Promising results were attained for early action detection as a human motion decoding strategy, with enhancements in the focus of the proposed architectures.
Multilingual Alzheimer's Dementia Recognition through Spontaneous Speech: a Signal Processing Grand Challenge
Authors: Saturnino Luz, Fasih Haider, Davida Fromm, Ioulietta Lazarou, Ioannis Kompatsiaris, Brian MacWhinney
Date: 2023-01-13 00:00:00

This Signal Processing Grand Challenge (SPGC) targets a difficult automatic prediction problem of societal and medical relevance, namely, the detection of Alzheimer's Dementia (AD). Participants were invited to employ signal processing and machine learning methods to create predictive models based on spontaneous speech data. The Challenge has been designed to assess the extent to which predictive models built based on speech in one language (English) generalise to another language (Greek). To the best of our knowledge no work has investigated acoustic features of the speech signal in multilingual AD detection. Our baseline system used conventional machine learning algorithms with Active Data Representation of acoustic features, achieving accuracy of 73.91% on AD detection, and 4.95 root mean squared error on cognitive score prediction.
Classification of Cross-cultural News Events
Authors: Abdul Sittar, Dunja Mladenic
Date: 2023-01-13 00:00:00
Tasks: Classification, Cultural Vocal Bursts Intensity Prediction

We present a methodology to support the analysis of culture from text such as news events and demonstrate its usefulness on categorizing news events from different categories (society, business, health, recreation, science, shopping, sports, arts, computers, games and home) across different geographical locations (different places in 117 countries). We group countries based on the culture that they follow and then filter the news events based on their content category. The news events are automatically labelled with the help of Hofstedes cultural dimensions. We present combinations of events across different categories and check the performances of different classification methods. We also presents experimental comparison of different number of features in order to find a suitable set to represent the culture.