pwc-archive/papers-with-abstracts huggingface dataset:
Multi-Label ECG Classification using Temporal Convolutional Neural Network
Authors: Eedara Prabhakararao, Samarendra Dandapt
Date: 2023-06-06 00:00:00
Tasks: Classification, ECG Classification, Multi-Label Classification, MUlTI-LABEL-ClASSIFICATION

Automated analysis of 12-lead electrocardiogram (ECG) plays a crucial role in the early screening and management of cardiovascular diseases (CVDs). In practice, it is common to see multiple co-occurring cardiac disorders, i.e., multi-label or multimorbidity in patients with CVDs, which increases the risk for mortality. Most current research focuses on the single-label ECG classification, i.e., each ECG record corresponds to one cardiac disorder, ignoring ECG records with multi-label phenomenon. In this paper, we propose an ensemble of attention-based temporal convolutional neural network (ATCNN) models for the multi-label classification of 12-lead ECG records. Specifically, a set of ATCNN-based single-lead binary classifiers are trained one for each cardiac disorder, and the predictions from these classifiers with simple thresholding generate the final multi-label decisions. The ATCNN contains a stack of TCNN layers followed by temporal and spatial attention layers. The TCNN layers operate at different dilation rates to represent the multi-scaled pathological ECG features dynamics, and attention layers emphasize/reduce the diagnostically relevant/redundant 12-lead ECG information. The proposed framework is evaluated on the PTBXL-2020 dataset and achieved an average F1-score of 76.51%. Moreover, the spatial and temporal attention weights visualization provides the optimal ECG-lead subset selection for each disease and model interpretability results, respectively. The improved performance and interpretability of the proposed approach demonstrate its ability to screen multimorbidity patients and help clinicians initiate timely treatment.
Experimental Performance of Blind Position Estimation Using Deep Learning
Authors: Ivo Bizon, Zhongju Li, Ahmad Nimr, Marwa Chafii, Gerhard P. Fettweis
Date: 2023-06-06 00:00:00
Tasks: Deep Learning, Position

Accurate indoor positioning for wireless communication systems represents an important step towards enhanced reliability and security, which are crucial aspects for realizing Industry 4.0. In this context, this paper presents an investigation on the real-world indoor positioning performance that can be obtained using a deep learning (DL)-based technique. For obtaining experimental data, we collect power measurements associated with reference positions using a wireless sensor network in an indoor scenario. The DL-based positioning scheme is modeled as a supervised learning problem, where the function that describes the relation between measured signal power values and their corresponding transmitter coordinates is approximated. We compare the DL approach to two different schemes with varying degrees of online computational complexity. Namely, maximum likelihood estimation and proximity. Furthermore, we provide a performance comparison of DL positioning trained with data generated exclusively based on a statistical path loss model and tested with experimental data.
Blind Transmitter Localization Using Deep Learning: A Scalability Study
Authors: Ivo Bizon, Ahmad Nimr, Philipp Schulz, Marwa Chafii, Gerhard P. Fettweis
Date: 2023-06-06 00:00:00
Tasks: Deep Learning

This work presents an investigation on the scalability of a deep leaning (DL)-based blind transmitter positioning system for addressing the multi transmitter localization (MLT) problem. The proposed approach is able to estimate relative coordinates of non-cooperative active transmitters based solely on received signal strength measurements collected by a wireless sensor network. A performance comparison with two other solutions of the MLT problem are presented for demonstrating the benefits with respect to scalability of the DL approach. Our investigation aims at highlighting the potential of DL to be a key technique that is able to provide a low complexity, accurate and reliable transmitter positioning service for improving future wireless communications systems.
5G Campus Network Factory Floor Measurements with Varying Channel and QoS Flow Priorities
Authors: Damir Hamidovic, Armin Hadziaganovic, Raheeb Muzaffar, Hans-Peter Bernhard
Date: 2023-06-06 00:00:00

5G is considered a promising wireless communication technology to fulfill the high-demanding communication requirements of many Industry 4.0 applications. This work evaluates a 5G campus network for indoor factory floor scenarios using the latest commercially available 3GPP release-16 developments. The measurement campaign is conducted to obtain detailed coverage maps with reference signal received power, channel quality indicators, and downlink and uplink throughput (TP). Moreover, end-to-end delay measurements with varying channel conditions, 5G quality of service (QoS) priorities, and traffic loads were evaluated. It was concluded that even without ultra-reliable low-latency features, the TP and latency performance could be controlled by configuring QoS parameters. The evaluations suggest scenarios where QoS allocation and retention priority levels can be used in order to ensure the required performance of a specific QoS flow within the 5G system.
Event Encryption: Rethinking Privacy Exposure for Neuromorphic Imaging
Authors: Pei Zhang, Shuo Zhu, Edmund Y. Lam
Date: 2023-06-06 00:00:00
Tasks: Privacy Preserving

Bio-inspired neuromorphic cameras sense illumination changes on a per-pixel basis and generate spatiotemporal streaming events within microseconds in response, offering visual information with high temporal resolution over a high dynamic range. Such devices often serve in surveillance systems due to their applicability and robustness in environments with high dynamics and harsh lighting, where they can still supply clearer recordings than traditional imaging. In other words, when it comes to privacy-relevant cases, neuromorphic cameras also expose more sensitive data and pose serious security threats. Therefore, asynchronous event streams necessitate careful encryption before transmission and usage. This work discusses several potential attack scenarios and approaches event encryption from the perspective of neuromorphic noise removal, in which we inversely introduce well-crafted noise into raw events until they are obfuscated. Our evaluations show that the encrypted events can effectively protect information from attacks of low-level visual reconstruction and high-level neuromorphic reasoning, and thus feature dependable privacy-preserving competence. The proposed solution gives impetus to the security of event data and paves the way to a highly encrypted technique for privacy-protective neuromorphic imaging.
Form Follows Function: A Method of Discovering How Neurons Are Connected To Process Information
Authors: Lane Yoder
Date: 2023-06-06 00:00:00
Tasks: Anatomy, Form

A method of discovering how neurons are connected to process information is presented here: Design a simple logic circuit that can perform a single, biologically advantageous function. Engineering concepts can be helpful in choosing the function and in designing the logic circuit. Several implementations of the method are reviewed to demonstrate how a biologically advantageous function can be chosen, how one simple network can generate major phenomena that are widely considered unrelated, and how one network design can lead to others that explain entirely different aspects of the brain. These results show that the method can benefit neuromorphic engineering as well as neuroscience, and that some brain functions can be carried out remarkably simply, at least in principle if not in the details.
Str2Str: A Score-based Framework for Zero-shot Protein Conformation Sampling
Authors: Jiarui Lu, Bozitao Zhong, Zuobai Zhang, Jian Tang
Date: 2023-06-05 00:00:00
Tasks: Benchmarking, Denoising, Translation

The dynamic nature of proteins is crucial for determining their biological functions and properties, for which Monte Carlo (MC) and molecular dynamics (MD) simulations stand as predominant tools to study such phenomena. By utilizing empirically derived force fields, MC or MD simulations explore the conformational space through numerically evolving the system via Markov chain or Newtonian mechanics. However, the high-energy barrier of the force fields can hamper the exploration of both methods by the rare event, resulting in inadequately sampled ensemble without exhaustive running. Existing learning-based approaches perform direct sampling yet heavily rely on target-specific simulation data for training, which suffers from high data acquisition cost and poor generalizability. Inspired by simulated annealing, we propose Str2Str, a novel structure-to-structure translation framework capable of zero-shot conformation sampling with roto-translation equivariant property. Our method leverages an amortized denoising score matching objective trained on general crystal structures and has no reliance on simulation data during both training and inference. Experimental results across several benchmarking protein systems demonstrate that Str2Str outperforms previous state-of-the-art generative structure prediction models and can be orders of magnitude faster compared to long MD simulations. Our open-source implementation is available at https://github.com/lujiarui/Str2Str
Scalable telomere-to-telomere assembly for diploid and polyploid genomes with double graph
Authors: Haoyu Cheng, Mobin Asri, Julian Lucas, Sergey Koren, Heng Li
Date: 2023-06-06 00:00:00

Despite recent advances in the length and the accuracy of long-read data, building haplotype-resolved genome assemblies from telomere to telomere still requires considerable computational resources. In this study, we present an efficient de novo assembly algorithm that combines multiple sequencing technologies to scale up population-wide telomere-to-telomere assemblies. By utilizing twenty-two human and two plant genomes, we demonstrate that our algorithm is around an order of magnitude cheaper than existing methods, while producing better diploid and haploid assemblies. Notably, our algorithm is the only feasible solution to the haplotype-resolved assembly of polyploid genomes.
Trial matching: capturing variability with data-constrained spiking neural networks
Authors: Christos Sourmpis, Carl Petersen, Wulfram Gerstner, Guillaume Bellec
Date: 2023-06-06 00:00:00
Proceeding: NeurIPS 2023 11

Simultaneous behavioral and electrophysiological recordings call for new methods to reveal the interactions between neural activity and behavior. A milestone would be an interpretable model of the co-variability of spiking activity and behavior across trials. Here, we model a mouse cortical sensory-motor pathway in a tactile detection task reported by licking with a large recurrent spiking neural network (RSNN), fitted to the recordings via gradient-based optimization. We focus specifically on the difficulty to match the trial-to-trial variability in the data. Our solution relies on optimal transport to define a distance between the distributions of generated and recorded trials. The technique is applied to artificial data and neural recordings covering six cortical areas. We find that the resulting RSNN can generate realistic cortical activity and predict jaw movements across the main modes of trial-to-trial variability. Our analysis also identifies an unexpected mode of variability in the data corresponding to task-irrelevant movements of the mouse.
Navigation through the complex world -- the neurophysiology of decision-making processes
Authors: Ugurcan Mugan, Seiichiro Amemiya, Paul S. Regier, A. David Redish
Date: 2023-06-05 00:00:00
Tasks: Decision Making, Hippocampus

Current theories suggest that adaptive decision-making necessitates the interaction between multiple decision-making systems. The computational definitions of different models of decision-making suggest interactions with task demands and complexity. We review these computational theories and derive experimental predictions that will shed light on the underlying neurobiological mechanisms. We use a well-established multi-strategy task and novel neurophysiological analyses from hippocampus and striatum as a case study in the interaction between task structure and navigational complexity. This approach reveals how task structure and navigational complexity interact with each other to identify differences between habitual and planned action choices.
The feasibility of combining communication BCIs with FES for individuals with locked-in syndrome
Authors: Evan Canny, Mariska J. Vansteensel, Sandra M. A. van der Salm, Gernot R. Müller-Putz, Julia Berezutskaya
Date: 2023-06-05 00:00:00
Tasks: Brain Computer Interface

Individuals with a locked-in state live with severe whole-body paralysis that limits their ability to communicate with family and loved ones. Recent advances in the brain-computer interface (BCI) technology have presented a potential alternative for these people to communicate by detecting neural activity associated with attempted hand or speech movements and translating the decoded intended movements to a control signal for a computer. A technique that could potentially enrich the communication capacity of BCIs is functional electrical stimulation (FES) of paralyzed limbs and face to restore body and facial movements of paralyzed individuals, allowing to add body language and facial expression to communication BCI utterances. Here, we review the current state of the art of existing BCI and FES work in people with paralysis of body and face and propose that a combined BCI-FES approach, which has already proved successful in several applications in stroke and spinal cord injury, can provide a novel promising mode of communication for locked-in individuals.
Swing contract pricing: with and without Neural Networks
Authors: Vincent Lemaire, Gilles Pagès, Christian Yeo
Date: 2023-06-06 00:00:00

We propose two parametric approaches to evaluate swing contracts with firm constraints. Our objective is to define approximations for the optimal control, which represents the amounts of energy purchased throughout the contract. The first approach involves approximating the optimal control by means of an explicit parametric function, where the parameters are determined using stochastic gradient descent based algorithms. The second approach builds on the first one, where we replace parameters in the first approach by the output of a neural network. Our numerical experiments demonstrate that by using Langevin based algorithms, both parameterizations provide, in a short computation time, better prices compared to state-of-the-art methods.
ChatGPT Informed Graph Neural Network for Stock Movement Prediction
Authors: Zihan Chen, Lei Nico Zheng, Cheng Lu, Jialu Yuan, Di Zhu
Date: 2023-05-28 00:00:00
Tasks: Graph Neural Network, Prediction

ChatGPT has demonstrated remarkable capabilities across various natural language processing (NLP) tasks. However, its potential for inferring dynamic network structures from temporal textual data, specifically financial news, remains an unexplored frontier. In this research, we introduce a novel framework that leverages ChatGPT's graph inference capabilities to enhance Graph Neural Networks (GNN). Our framework adeptly extracts evolving network structures from textual data, and incorporates these networks into graph neural networks for subsequent predictive tasks. The experimental results from stock movement forecasting indicate our model has consistently outperformed the state-of-the-art Deep Learning-based benchmarks. Furthermore, the portfolios constructed based on our model's outputs demonstrate higher annualized cumulative returns, alongside reduced volatility and maximum drawdown. This superior performance highlights the potential of ChatGPT for text-based network inferences and underscores its promising implications for the financial sector.
'Ergodicity Economics' is Pseudoscience
Authors: Alexis Akira Toda
Date: 2023-06-05 00:00:00

In a series of papers, Ole Peters and his collaborators claim that the 'conceptual basis of mainstream economic theory' is 'flawed' and that the approach they call 'ergodicity economics' gives 'reason to hope for a future economic science that is more parsimonious, conceptually clearer and less subjective' (Peters, 2019). This paper argues that 'ergodicity economics' is pseudoscience because it has not produced falsifiable implications and should be taken with skepticism.
Gauge symmetries and the Higgs mechanism in Quantum Finance
Authors: Ivan Arraut
Date: 2023-05-10 00:00:00

By using the Hamiltonian formulation, we demonstrate that the Merton-Garman equation emerges naturally from the Black-Scholes equation after imposing invariance (symmetry) under local (gauge) transformations over changes in the stock price. This is the case because imposing gauge symmetry implies the appearance of an additional field, which corresponds to the stochastic volatility. The gauge symmetry then imposes some constraints over the free-parameters of the Merton-Garman Hamiltonian. Finally, we analyze how the stochastic volatility gets massive dynamically via Higgs mechanism.
Non-parametric Probabilistic Time Series Forecasting via Innovations Representation
Authors: Xinyi Wang, Meijen Lee, Qing Zhao, Lang Tong
Date: 2023-06-05 00:00:00
Tasks: Decision Making, Decoder, Probabilistic Time Series Forecasting, Time Series, Time Series Forecasting

Probabilistic time series forecasting predicts the conditional probability distributions of the time series at a future time given past realizations. Such techniques are critical in risk-based decision-making and planning under uncertainties. Existing approaches are primarily based on parametric or semi-parametric time-series models that are restrictive, difficult to validate, and challenging to adapt to varying conditions. This paper proposes a nonparametric method based on the classic notion of {\em innovations} pioneered by Norbert Wiener and Gopinath Kallianpur that causally transforms a nonparametric random process to an independent and identical uniformly distributed {\em innovations process}. We present a machine-learning architecture and a learning algorithm that circumvent two limitations of the original Wiener-Kallianpur innovations representation: (i) the need for known probability distributions of the time series and (ii) the existence of a causal decoder that reproduces the original time series from the innovations representation. We develop a deep-learning approach and a Monte Carlo sampling technique to obtain a generative model for the predicted conditional probability distribution of the time series based on a weak notion of Wiener-Kallianpur innovations representation. The efficacy of the proposed probabilistic forecasting technique is demonstrated on a variety of electricity price datasets, showing marked improvement over leading benchmarks of probabilistic forecasting techniques.
Three-way Imbalanced Learning based on Fuzzy Twin SVM
Authors: Wanting Cai, Mingjie Cai, Qingguo Li, Qiong Liu
Date: 2023-05-19 00:00:00
Tasks: Binary Classification, Decision Making, imbalanced classification

Three-way decision (3WD) is a powerful tool for granular computing to deal with uncertain data, commonly used in information systems, decision-making, and medical care. Three-way decision gets much research in traditional rough set models. However, three-way decision is rarely combined with the currently popular field of machine learning to expand its research. In this paper, three-way decision is connected with SVM, a standard binary classification model in machine learning, for solving imbalanced classification problems that SVM needs to improve. A new three-way fuzzy membership function and a new fuzzy twin support vector machine with three-way membership (TWFTSVM) are proposed. The new three-way fuzzy membership function is defined to increase the certainty of uncertain data in both input space and feature space, which assigns higher fuzzy membership to minority samples compared with majority samples. To evaluate the effectiveness of the proposed model, comparative experiments are designed for forty-seven different datasets with varying imbalance ratios. In addition, datasets with different imbalance ratios are derived from the same dataset to further assess the proposed model's performance. The results show that the proposed model significantly outperforms other traditional SVM-based methods.
Pre-training Tensor-Train Networks Facilitates Machine Learning with Variational Quantum Circuits
Authors: Jun Qi, Chao-Han Huck Yang, Pin-Yu Chen, Min-Hsiu Hsieh
Date: 2023-05-18 00:00:00
Tasks: Binary Classification, Quantum Machine Learning, Transfer Learning

Variational quantum circuits (VQCs) hold promise for quantum machine learning on noisy intermediate-scale quantum (NISQ) devices. While tensor-train networks (TTNs) can enhance VQC representation and generalization, the resulting hybrid model, TTN-VQC, faces optimization challenges due to the Polyak-Lojasiewicz (PL) condition. To mitigate this challenge, we introduce Pre+TTN-VQC, a pre-trained TTN model combined with a VQC. Our theoretical analysis, grounded in two-stage empirical risk minimization, provides an upper bound on the transfer learning risk. It demonstrates the approach's advantages in overcoming the optimization challenge while maintaining TTN-VQC's generalization capability. We validate our findings through experiments on quantum dot and handwritten digit classification using simulated and actual NISQ environments.
Revisiting Out-of-distribution Robustness in NLP: Benchmark, Analysis, and LLMs Evaluations
Authors: Lifan Yuan, Yangyi Chen, Ganqu Cui, Hongcheng Gao, Fangyuan Zou, Xingyi Cheng, Heng Ji, Zhiyuan Liu, Maosong Sun
Date: 2023-06-07 00:00:00
Tasks: In-Context Learning

This paper reexamines the research on out-of-distribution (OOD) robustness in the field of NLP. We find that the distribution shift settings in previous studies commonly lack adequate challenges, hindering the accurate evaluation of OOD robustness. To address these issues, we propose a benchmark construction protocol that ensures clear differentiation and challenging distribution shifts. Then we introduce BOSS, a Benchmark suite for Out-of-distribution robustneSS evaluation covering 5 tasks and 20 datasets. Based on BOSS, we conduct a series of experiments on pre-trained language models for analysis and evaluation of OOD robustness. First, for vanilla fine-tuning, we examine the relationship between in-distribution (ID) and OOD performance. We identify three typical types that unveil the inner learning mechanism, which could potentially facilitate the forecasting of OOD robustness, correlating with the advancements on ID datasets. Then, we evaluate 5 classic methods on BOSS and find that, despite exhibiting some effectiveness in specific cases, they do not offer significant improvement compared to vanilla fine-tuning. Further, we evaluate 5 LLMs with various adaptation paradigms and find that when sufficient ID data is available, fine-tuning domain-specific models outperform LLMs on ID examples significantly. However, in the case of OOD instances, prioritizing LLMs with in-context learning yields better results. We identify that both fine-tuned small models and LLMs face challenges in effectively addressing downstream tasks. The code is public at \url{https://github.com/lifan-yuan/OOD_NLP}.