pwc-archive/papers-with-abstracts huggingface dataset:
Towards Suicide Prevention from Bipolar Disorder with Temporal Symptom-Aware Multitask Learning
Authors: Daeun Lee, Sejung Son, Hyolim Jeon, Seungbae Kim, Jinyoung Han
Date: 2023-07-03 00:00:00
Tasks: Multi-Task Learning

Bipolar disorder (BD) is closely associated with an increased risk of suicide. However, while the prior work has revealed valuable insight into understanding the behavior of BD patients on social media, little attention has been paid to developing a model that can predict the future suicidality of a BD patient. Therefore, this study proposes a multi-task learning model for predicting the future suicidality of BD patients by jointly learning current symptoms. We build a novel BD dataset clinically validated by psychiatrists, including 14 years of posts on bipolar-related subreddits written by 818 BD patients, along with the annotations of future suicidality and BD symptoms. We also suggest a temporal symptom-aware attention mechanism to determine which symptoms are the most influential for predicting future suicidality over time through a sequence of BD posts. Our experiments demonstrate that the proposed model outperforms the state-of-the-art models in both BD symptom identification and future suicidality prediction tasks. In addition, the proposed temporal symptom-aware attention provides interpretable attention weights, helping clinicians to apprehend BD patients more comprehensively and to provide timely intervention by tracking mental state progression.
IoT-Based Air Quality Monitoring System with Machine Learning for Accurate and Real-time Data Analysis
Authors: Hemanth Karnati
Date: 2023-07-02 00:00:00

Air pollution in urban areas has severe consequences for both human health and the environment, predominantly caused by exhaust emissions from vehicles. To address the issue of air pollution awareness, Air Pollution Monitoring systems are used to measure the concentration of gases like CO2, smoke, alcohol, benzene, and NH3 present in the air. However, current mobile applications are unable to provide users with real-time data specific to their location. In this paper, we propose the development of a portable air quality detection device that can be used anywhere. The data collected will be stored and visualized using the cloud-based web app ThinkSpeak. The device utilizes two sensors, MQ135 and MQ3, to detect harmful gases and measure air quality in parts per million (PPM). Additionally, machine learning analysis will be employed on the collected data.
WaveMixSR: A Resource-efficient Neural Network for Image Super-resolution
Authors: Pranav Jeevan, Akella Srinidhi, Pasunuri Prathiba, Amit Sethi
Date: 2023-07-01 00:00:00
Tasks: Efficient Neural Network, Image Super-Resolution, Inductive Bias, Super-Resolution

Image super-resolution research recently been dominated by transformer models which need higher computational resources than CNNs due to the quadratic complexity of self-attention. We propose a new neural network -- WaveMixSR -- for image super-resolution based on WaveMix architecture which uses a 2D-discrete wavelet transform for spatial token-mixing. Unlike transformer-based models, WaveMixSR does not unroll the image as a sequence of pixels/patches. It uses the inductive bias of convolutions along with the lossless token-mixing property of wavelet transform to achieve higher performance while requiring fewer resources and training data. We compare the performance of our network with other state-of-the-art methods for image super-resolution. Our experiments show that WaveMixSR achieves competitive performance in all datasets and reaches state-of-the-art performance in the BSD100 dataset on multiple super-resolution tasks. Our model is able to achieve this performance using less training data and computational resources while maintaining high parameter efficiency compared to current state-of-the-art models.
Texture Relative Superpixel Generation With Adaptive Parameters
Authors: Xiao Pan, Yuanfeng Zhou, Zhonggui Chen, Caiming Zhang
Date: 2019-01-25 00:00:00
Proceeding: IEEE 2019 1
Tasks: Superpixels

Abstract—Superpixel generation, which is an essential step
in many image processing applications, has attracted increasing
attention from researchers. In this paper, we present an efficient
flooding-based superpixel generation algorithm that generates
compact and highly boundary-adherent superpixels. In particular, by considering various superpixel properties , we measure
the similarities between image pixels by proposing a new distance
metric that combines various image features (e.g., colors, spatial
locations, neighbor information and texture features). To control
the relative significance of these image features, we acquire
the weights of image features (e.g., colors, texture features and
neighbor information of pixels) through a neural network. Then,
the final superpixels are obtained through a greedy optimization
that considers both the current superpixel and its neighboring
superpixels. We perform extensive experiments on two datasets
to verify the efficacy of our algorithm. The results show that
our algorithm has considerable advantages over existing stateof-the-art methods, particularly regarding the compactness of the
resulting superpixels.
Analysis of the influence of final resolution on ADC accuracy
Authors: Anzhelika Stakhova
Date: 2023-07-01 00:00:00
Tasks: Quantization

This work is devoted to the study of the influence of quantization noise on the spectral characteristics of a digital signal and the assessment of spectrum measurement errors that arise due to the quantization noise of an analog-to-digital converter. To achieve more accurate and reliable measurements of the spectrum, an error assessment was carried out, which allows taking into account the impact of quantization noise on the spectral data. This is important for obtaining more accurate results and ensuring high-quality measurements of the spectral components of the vibration signal. In addition, further research is aimed at developing methods for estimating spectrum measurement errors taking into account other possible sources of errors and contributing to the development of compensation algorithms to reduce the impact of quantization noise.
Efficient Harmonic Neural Networks with Compound Discrete Cosine Transform filters and Shared Reconstruction filters
Authors: Yao Lu, Le Zhang, Xiaofei Yang, and Yicong Zhou
Date: 2022-05-27 00:00:00
Proceeding: IEEE Transactions on Neural Networks and Learning Systems 2022 5

The harmonic neural network (HNN) learns a
combination of discrete cosine transform (DCT) filters to obtain
an integrated feature from all spectra in the frequency domain.
HNN, however, faces two challenges in learning and inference
processes. First, the spectrum feature learned by HNN is insufficient
and limited because the number of DCT filters is much
smaller than that of feature maps. In addition, the number of
parameters and the computation costs of HNN are significantly
high because the intermediate spectrum layers are expanded
multiple times. These two challenges will severely harm the
performance and efficiency of HNN. To solve these problems,
we first propose the compound DCT (C-DCT) filters integrating
the nearest DCT filters to retrieve rich spectrum features to
improve the performance. To significantly reduce the model size
and computation complexity for improving the efficiency, the
shared reconstruction filter is then proposed to share and dynamically
drop the meta-filters in every frequency branch. Integrating
the C-DCT filters with the shared reconstruction filters, the
efficient harmonic network (EH-Net) is introduced. Extensive
experiments on different datasets demonstrate that the proposed
EH-Nets can effectively reduce the model size and computation
complexity while maintaining the model performance. The code
has been released at https://github.com/zhangle408/EH-Nets.
Recursive Algorithmic Reasoning
Authors: Jonas Jürß, Dulhan Jayalath, Petar Veličković
Date: 2023-07-01 00:00:00

Learning models that execute algorithms can enable us to address a key problem in deep learning: generalizing to out-of-distribution data. However, neural networks are currently unable to execute recursive algorithms because they do not have arbitrarily large memory to store and recall state. To address this, we (1) propose a way to augment graph neural networks (GNNs) with a stack, and (2) develop an approach for capturing intermediate algorithm trajectories that improves algorithmic alignment with recursive algorithms over previous methods. The stack allows the network to learn to store and recall a portion of the state of the network at a particular time, analogous to the action of a call stack in a recursive algorithm. This augmentation permits the network to reason recursively. We empirically demonstrate that our proposals significantly improve generalization to larger input graphs over prior work on depth-first search (DFS).
Neural Chronos ODE: Unveiling Temporal Patterns and Forecasting Future and Past Trends in Time Series Data
Authors: C. Coelho, M. Fernanda P. Costa, L. L. Ferrás
Date: 2023-07-03 00:00:00
Tasks: Imputation, Time Series

This work introduces Neural Chronos Ordinary Differential Equations (Neural CODE), a deep neural network architecture that fits a continuous-time ODE dynamics for predicting the chronology of a system both forward and backward in time. To train the model, we solve the ODE as an initial value problem and a final value problem, similar to Neural ODEs. We also explore two approaches to combining Neural CODE with Recurrent Neural Networks by replacing Neural ODE with Neural CODE (CODE-RNN), and incorporating a bidirectional RNN for full information flow in both time directions (CODE-BiRNN), and variants with other update cells namely GRU and LSTM: CODE-GRU, CODE-BiGRU, CODE-LSTM, CODE-BiLSTM. Experimental results demonstrate that Neural CODE outperforms Neural ODE in learning the dynamics of a spiral forward and backward in time, even with sparser data. We also compare the performance of CODE-RNN/-GRU/-LSTM and CODE-BiRNN/-BiGRU/-BiLSTM against ODE-RNN/-GRU/-LSTM on three real-life time series data tasks: imputation of missing data for lower and higher dimensional data, and forward and backward extrapolation with shorter and longer time horizons. Our findings show that the proposed architectures converge faster, with CODE-BiRNN/-BiGRU/-BiLSTM consistently outperforming the other architectures on all tasks.
LMBot: Distilling Graph Knowledge into Language Model for Graph-less Deployment in Twitter Bot Detection
Authors: Zijian Cai, Zhaoxuan Tan, Zhenyu Lei, Zifeng Zhu, Hongrui Wang, Qinghua Zheng, Minnan Luo
Date: 2023-06-30 00:00:00
Tasks: Domain Adaptation, Language Modeling, Language Modelling, Misinformation, Twitter Bot Detection

As malicious actors employ increasingly advanced and widespread bots to disseminate misinformation and manipulate public opinion, the detection of Twitter bots has become a crucial task. Though graph-based Twitter bot detection methods achieve state-of-the-art performance, we find that their inference depends on the neighbor users multi-hop away from the targets, and fetching neighbors is time-consuming and may introduce bias. At the same time, we find that after finetuning on Twitter bot detection, pretrained language models achieve competitive performance and do not require a graph structure during deployment. Inspired by this finding, we propose a novel bot detection framework LMBot that distills the knowledge of graph neural networks (GNNs) into language models (LMs) for graph-less deployment in Twitter bot detection to combat the challenge of data dependency. Moreover, LMBot is compatible with graph-based and graph-less datasets. Specifically, we first represent each user as a textual sequence and feed them into the LM for domain adaptation. For graph-based datasets, the output of LMs provides input features for the GNN, enabling it to optimize for bot detection and distill knowledge back to the LM in an iterative, mutually enhancing process. Armed with the LM, we can perform graph-less inference, which resolves the graph data dependency and sampling bias issues. For datasets without graph structure, we simply replace the GNN with an MLP, which has also shown strong performance. Our experiments demonstrate that LMBot achieves state-of-the-art performance on four Twitter bot detection benchmarks. Extensive studies also show that LMBot is more robust, versatile, and efficient compared to graph-based Twitter bot detection methods.
A Spatiotemporal Multi-Channel Learning Framework for Automatic Modulation Recognition
Authors: Jialang Xu, Chunbo Luo, Gerard Parr, Yang Luo
Date: 2020-06-02 00:00:00
Proceeding: IEEE Wireless Communications Letters 2020 6
Tasks: Automatic Modulation Recognition

Automatic modulation recognition (AMR) plays a vital role in modern communication systems. This letter proposes a novel three-stream deep learning framework to extract the features from individual and combined in-phase/quadrature (I/Q) symbols of the modulated data. The proposed framework integrates one-dimensional (1D) convolutional, two-dimensional (2D) convolutional and long short-term memory (LSTM) layers to extract features more effectively from a time and space perspective. Experiments on the benchmark dataset show the proposed framework has efficient convergence speed and achieves improved recognition accuracy, especially for the signals modulated by higher dimensional schemes such as 16 quadrature amplitude modulation (16-QAM) and 64-QAM.
Remote Sensing Change Detection Based on Multidirectional Adaptive Feature Fusion and Perceptual Similarity
Authors: Jialang Xu, Chunbo Luo, Xinyue Chen, Shicai Wei, Yang Luo
Date: 2021-08-03 00:00:00
Proceeding: Remote Sensing 2021 8
Tasks: Change Detection, Change detection for remote sensing images, Earth Observation

Remote sensing change detection (RSCD) is an important yet challenging task in Earth observation. The booming development of convolutional neural networks (CNNs) in computer vision raises new possibilities for RSCD, and many recent RSCD methods have introduced CNNs to achieve promising improvements in performance. In this paper we propose a novel multidirectional fusion and perception network for change detection in bi-temporal very-high-resolution remote sensing images. First, we propose an elaborate feature fusion module consisting of a multidirectional fusion pathway (MFP) and an adaptive weighted fusion (AWF) strategy for RSCD to boost the way that information propagates in the network. The MFP enhances the flexibility and diversity of information paths by creating extra top-down and shortcut-connection paths. The AWF strategy conducts weight recalibration for every fusion node to highlight salient feature maps and overcome semantic gaps between different features. Second, a novel perceptual similarity module is designed to introduce perceptual loss into the RSCD task, which adds perceptual information, such as structure and semantic information, for high-quality change map generation. Extensive experiments on four challenging benchmark datasets demonstrate the superiority of the proposed network compared with eight state-of-the-art methods in terms of F1, Kappa, and visual qualities.
A discontinuous Galerkin Trefftz type method for solving the two dimensional Maxwell equations
Authors: Håkon Sem Fure1 • Se´bastien Pernet1 • Margot Sirdey1, 2 • Se´bastien Tordeux2
Date: 2020-08-14 00:00:00
Proceeding: SN Partial Differ. Equ. Appl. 2020 8

Trefftz methods are known to be very efficient to reduce the numerical pollution when
associated to plane wave basis. However, these local basis functions are not adapted to the
computation of evanescent modes or corner singularities. In this article, we consider a two
dimensional time-harmonic Maxwell system and we propose a formulation which allows to
design an electromagnetic Trefftz formulation associated to local Galerkin basis computed
thanks to an auxiliary Ne´de´lec finite element method. The results are illustrated with
numerous numerical examples. The considered test cases reveal that the short range and
long range propagation phenomena are both well taken into account.
Using joint training speaker encoder with consistency loss to achieve cross-lingual voice conversion and expressive voice conversion
Authors: Houjian Guo, Chaoran Liu, Carlos Toshinori Ishi, Hiroshi Ishiguro
Date: 2023-07-01 00:00:00
Tasks: speech-recognition, Speech Recognition, Voice Conversion

Voice conversion systems have made significant advancements in terms of naturalness and similarity in common voice conversion tasks. However, their performance in more complex tasks such as cross-lingual voice conversion and expressive voice conversion remains imperfect. In this study, we propose a novel approach that combines a jointly trained speaker encoder and content features extracted from the cross-lingual speech recognition model Whisper to achieve high-quality cross-lingual voice conversion. Additionally, we introduce a speaker consistency loss to the joint encoder, which improves the similarity between the converted speech and the reference speech. To further explore the capabilities of the joint speaker encoder, we use the phonetic posteriorgram as the content feature, which enables the model to effectively reproduce both the speaker characteristics and the emotional aspects of the reference speech.
SHARCS: Shared Concept Space for Explainable Multimodal Learning
Authors: Gabriele Dominici, Pietro Barbiero, Lucie Charlotte Magister, Pietro Liò, Nikola Simidjievski
Date: 2023-07-01 00:00:00
Tasks: Retrieval

Multimodal learning is an essential paradigm for addressing complex real-world problems, where individual data modalities are typically insufficient to accurately solve a given modelling task. While various deep learning approaches have successfully addressed these challenges, their reasoning process is often opaque; limiting the capabilities for a principled explainable cross-modal analysis and any domain-expert intervention. In this paper, we introduce SHARCS (SHARed Concept Space) -- a novel concept-based approach for explainable multimodal learning. SHARCS learns and maps interpretable concepts from different heterogeneous modalities into a single unified concept-manifold, which leads to an intuitive projection of semantically similar cross-modal concepts. We demonstrate that such an approach can lead to inherently explainable task predictions while also improving downstream predictive performance. Moreover, we show that SHARCS can operate and significantly outperform other approaches in practically significant scenarios, such as retrieval of missing modalities and cross-modal explanations. Our approach is model-agnostic and easily applicable to different types (and number) of modalities, thus advancing the development of effective, interpretable, and trustworthy multimodal approaches.
FFPDG: Fast, Fair and Private Data Generation
Authors: Weijie Xu, Jinjin Zhao, Francis Iannacci, Bo wang
Date: 2023-06-30 00:00:00
Tasks: Fairness, Synthetic Data Generation

Generative modeling has been used frequently in synthetic data generation. Fairness and privacy are two big concerns for synthetic data. Although Recent GAN [\cite{goodfellow2014generative}] based methods show good results in preserving privacy, the generated data may be more biased. At the same time, these methods require high computation resources. In this work, we design a fast, fair, flexible and private data generation method. We show the effectiveness of our method theoretically and empirically. We show that models trained on data generated by the proposed method can perform well (in inference stage) on real application scenarios.
DisCo: Disentangled Control for Realistic Human Dance Generation
Authors: Tan Wang, Linjie Li, Kevin Lin, Yuanhao Zhai, Chung-Ching Lin, Zhengyuan Yang, Hanwang Zhang, Zicheng Liu, Lijuan Wang
Date: 2023-06-30 00:00:00
Proceeding: CVPR 2024 1
Tasks: Attribute

Generative AI has made significant strides in computer vision, particularly in text-driven image/video synthesis (T2I/T2V). Despite the notable advancements, it remains challenging in human-centric content synthesis such as realistic dance generation. Current methodologies, primarily tailored for human motion transfer, encounter difficulties when confronted with real-world dance scenarios (e.g., social media dance), which require to generalize across a wide spectrum of poses and intricate human details. In this paper, we depart from the traditional paradigm of human motion transfer and emphasize two additional critical attributes for the synthesis of human dance content in social media contexts: (i) Generalizability: the model should be able to generalize beyond generic human viewpoints as well as unseen human subjects, backgrounds, and poses; (ii) Compositionality: it should allow for the seamless composition of seen/unseen subjects, backgrounds, and poses from different sources. To address these challenges, we introduce DISCO, which includes a novel model architecture with disentangled control to improve the compositionality of dance synthesis, and an effective human attribute pre-training for better generalizability to unseen humans. Extensive qualitative and quantitative results demonstrate that DisCc can generate high-quality human dance images and videos with diverse appearances and flexible motions. Code is available at https://disco-dance.github.io/.
Counterfactual Collaborative Reasoning
Authors: Jianchao Ji, Zelong Li, Shuyuan Xu, Max Xiong, Juntao Tan, Yingqiang Ge, Hao Wang, Yongfeng Zhang
Date: 2023-06-30 00:00:00
Tasks: counterfactual, Counterfactual Reasoning, Data Augmentation, Logical Reasoning, Recommendation Systems

Causal reasoning and logical reasoning are two important types of reasoning abilities for human intelligence. However, their relationship has not been extensively explored under machine intelligence context. In this paper, we explore how the two reasoning abilities can be jointly modeled to enhance both accuracy and explainability of machine learning models. More specifically, by integrating two important types of reasoning ability -- counterfactual reasoning and (neural) logical reasoning -- we propose Counterfactual Collaborative Reasoning (CCR), which conducts counterfactual logic reasoning to improve the performance. In particular, we use recommender system as an example to show how CCR alleviate data scarcity, improve accuracy and enhance transparency. Technically, we leverage counterfactual reasoning to generate "difficult" counterfactual training examples for data augmentation, which -- together with the original training examples -- can enhance the model performance. Since the augmented data is model irrelevant, they can be used to enhance any model, enabling the wide applicability of the technique. Besides, most of the existing data augmentation methods focus on "implicit data augmentation" over users' implicit feedback, while our framework conducts "explicit data augmentation" over users explicit feedback based on counterfactual logic reasoning. Experiments on three real-world datasets show that CCR achieves better performance than non-augmented models and implicitly augmented models, and also improves model transparency by generating counterfactual explanations.