pwc-archive/papers-with-abstracts huggingface dataset:
A Comparison of Neuroelectrophysiology Databases
Authors: Priyanka Subash, Alex Gray, Misque Boswell, Samantha L. Cohen, Rachael Garner, Sana Salehi, Calvary Fisher, Samuel Hobel, Satrajit Ghosh, Yaroslav Halchenko, Benjamin Dichter, Russell A. Poldrack, Chris Markiewicz, Dora Hermes, Arnaud Delorme, Scott Makeig, Brendan Behan, Alana Sparks, Stephen R Arnott, Zhengjia Wang, John Magnotti, Michael S. Beauchamp, Nader Pouratian, Arthur W. Toga, Dominique Duncan
Date: 2023-06-26 00:00:00
Tasks: Data Integration

As data sharing has become more prevalent, three pillars - archives, standards, and analysis tools - have emerged as critical components in facilitating effective data sharing and collaboration. This paper compares four freely available intracranial neuroelectrophysiology data repositories: Data Archive for the BRAIN Initiative (DABI), Distributed Archives for Neurophysiology Data Integration (DANDI), OpenNeuro, and Brain-CODE. The aim of this review is to describe archives that provide researchers with tools to store, share, and reanalyze both human and non-human neurophysiology data based on criteria that are of interest to the neuroscientific community. The Brain Imaging Data Structure (BIDS) and Neurodata Without Borders (NWB) are utilized by these archives to make data more accessible to researchers by implementing a common standard. As the necessity for integrating large-scale analysis into data repository platforms continues to grow within the neuroscientific community, this article will highlight the various analytical and customizable tools developed within the chosen archives that may advance the field of neuroinformatics.
Post-Processing Independent Evaluation of Sound Event Detection Systems
Authors: Janek Ebbers, Reinhold Haeb-Umbach, Romain Serizel
Date: 2023-06-27 00:00:00
Tasks: Event Detection, Sound Event Detection

Due to the high variation in the application requirements of sound event detection (SED) systems, it is not sufficient to evaluate systems only in a single operating mode. Therefore, the community recently adopted the polyphonic sound detection score (PSDS) as an evaluation metric, which is the normalized area under the PSD receiver operating characteristic (PSD-ROC). It summarizes the system performance over a range of operating modes resulting from varying the decision threshold that is used to translate the system output scores into a binary detection output. Hence, it provides a more complete picture of the overall system behavior and is less biased by specific threshold tuning. However, besides the decision threshold there is also the post-processing that can be changed to enter another operating mode. In this paper we propose the post-processing independent PSDS (piPSDS) as a generalization of the PSDS. Here, the post-processing independent PSD-ROC includes operating points from varying post-processings with varying decision thresholds. Thus, it summarizes even more operating modes of an SED system and allows for system comparison without the need of implementing a post-processing and without a bias due to different post-processings. While piPSDS can in principle combine different types of post-processing, we hear, as a first step, present median filter independent PSDS (miPSDS) results for this year's DCASE Challenge Task4a systems. Source code is publicly available in our sed_scores_eval package (https://github.com/fgnt/sed_scores_eval).
GenerTTS: Pronunciation Disentanglement for Timbre and Style Generalization in Cross-Lingual Text-to-Speech
Authors: Yahuan Cong, Haoyu Zhang, Haopeng Lin, Shichao Liu, Chunfeng Wang, Yi Ren, Xiang Yin, Zejun Ma
Date: 2023-06-27 00:00:00
Tasks: Disentanglement, Style Generalization, text-to-speech, Text to Speech

Cross-lingual timbre and style generalizable text-to-speech (TTS) aims to synthesize speech with a specific reference timbre or style that is never trained in the target language. It encounters the following challenges: 1) timbre and pronunciation are correlated since multilingual speech of a specific speaker is usually hard to obtain; 2) style and pronunciation are mixed because the speech style contains language-agnostic and language-specific parts. To address these challenges, we propose GenerTTS, which mainly includes the following works: 1) we elaborately design a HuBERT-based information bottleneck to disentangle timbre and pronunciation/style; 2) we minimize the mutual information between style and language to discard the language-specific information in the style embedding. The experiments indicate that GenerTTS outperforms baseline systems in terms of style similarity and pronunciation accuracy, and enables cross-lingual timbre and style generalization.
Wespeaker baselines for VoxSRC2023
Authors: Shuai Wang, Chengdong Liang, Xu Xiang, Bing Han, Zhengyang Chen, Hongji Wang, Wen Ding
Date: 2023-06-27 00:00:00

This report showcases the results achieved using the wespeaker toolkit for the VoxSRC2023 Challenge. Our aim is to provide participants, especially those with limited experience, with clear and straightforward guidelines to develop their initial systems. Via well-structured recipes and strong results, we hope to offer an accessible and good enough start point for all interested individuals. In this report, we describe the results achieved on the VoxSRC2023 dev set using the pretrained models, you can check the CodaLab evaluation server for the results on the evaluation set.
Detect Depression from Social Networks with Sentiment Knowledge Sharing
Authors: Yan Shi, Yao Tian, Chengwei Tong, Chunyan Zhu, Qianqian Li, Mengzhu Zhang, Wei Zhao, Yong Liao, Pengyuan Zhou
Date: 2023-06-13 00:00:00
Tasks: Depression Detection

Social network plays an important role in propagating people's viewpoints, emotions, thoughts, and fears. Notably, following lockdown periods during the COVID-19 pandemic, the issue of depression has garnered increasing attention, with a significant portion of individuals resorting to social networks as an outlet for expressing emotions. Using deep learning techniques to discern potential signs of depression from social network messages facilitates the early identification of mental health conditions. Current efforts in detecting depression through social networks typically rely solely on analyzing the textual content, overlooking other potential information. In this work, we conduct a thorough investigation that unveils a strong correlation between depression and negative emotional states. The integration of such associations as external knowledge can provide valuable insights for detecting depression. Accordingly, we propose a multi-task training framework, DeSK, which utilizes shared sentiment knowledge to enhance the efficacy of depression detection. Experiments conducted on both Chinese and English datasets demonstrate the cross-lingual effectiveness of DeSK.
A probabilistic algorithm for scheduling networked control systems under data losses
Authors: Anubhab Dasgupta, Darsana Udayakumar, Atreyee Kundu
Date: 2023-06-27 00:00:00
Tasks: Scheduling

This paper deals with the design of scheduling logics for networked control systems (NCSs) whose communication networks have limited capacity and are prone to data losses. Our contributions are twofold. First, we present a probabilistic algorithm to generate a scheduling logic that under certain conditions on the plant and the controller dynamics, the capacity of the network and the probability of data losses, ensures stochastic stability of each plant in the NCS. Second, given the plant dynamics, the capacity of the shared communication network and the probability of data losses, we discuss the design of state-feedback controllers such that our stability conditions are obeyed. Numerical examples are presented to demonstrate the results reported in this paper.
Fast and Automatic 3D Modeling of Antenna Structure Using CNN-LSTM Network for Efficient Data Generation
Authors: Zhaohui Wei, Zhao Zhou, Peng Wang, Jian Ren, Yingzeng Yin, Gert Frølund Pedersen, Ming Shen
Date: 2023-06-27 00:00:00

Deep learning-assisted antenna design methods such as surrogate models have gained significant popularity in recent years due to their potential to greatly increase design efficiencies by replacing the time-consuming full-wave electromagnetic (EM) simulations. However, a large number of training data with sufficiently diverse and representative samples (antenna structure parameters, scattering properties, etc.) is mandatory for these methods to ensure good performance. Traditional antenna modeling methods relying on manual model construction and modification are time-consuming and cannot meet the requirement of efficient training data acquisition. In this study, we proposed a deep learning-assisted and image-based intelligent modeling approach for accelerating the data acquisition of antenna samples with different physical structures. Specifically, our method only needs an image of the antenna structure, usually available in scientific publications, as the input while the corresponding modeling codes (VBA language) are generated automatically. The proposed model mainly consists of two parts: Convolutional Neural Network (CNN) and Long Short Term Memory (LSTM) networks. The former is used for capturing features of antenna structure images and the latter is employed to generate the modeling codes. Through training, the proposed model can achieve fast and automatic data acquisition of antenna physical structures based on antenna images. Experiment results show that the proposed method achieves a significant speed enhancement than the manual modeling approach. This approach lays the foundation for efficient data acquisition needed to build robust surrogate models in the future.
Output Regulation of Stochastic Sampled-Data Systems with Post-processing Internal Model
Authors: Himadri Basu, Francesco Ferrante, Mirko Fiacchini
Date: 2023-06-27 00:00:00

This paper deals with the output regulation problem (ORP) of a linear time-invariant (LTI) system in the presence of sporadically sampled measurement streams with the inter-sampling intervals following a stochastic process. Under such sporadically available measurement streams, a regulator consisting of a hybrid observer, continuous-time post-processing internal model, and stabilizer are proposed, which resets with the arrival of new measurements. The resulting system exhibits a deterministic behavior except for the jumps that occur at random sampling times and therefore the overall closed-loop system can be categorized as a piecewise deterministic Markov process (PDMP). In existing works on ORPs with aperiodic sampling, the requirement of boundedness on inter-sampling intervals precludes extending the solution to the random sampling intervals with possibly unbounded support. Using the Lyapunov-like theorem for the stability analysis of stochastic systems, we offer sufficient conditions to ensure that the overall closed-loop system is mean exponentially stable (MES) and the objectives of the ORP are achieved under stochastic sampling of measurement streams. The resulting LMI conditions lead to a numerically tractable design of the hybrid regulator. Finally, with the help of an illustrative example, the effectiveness of the theoretical results are verified.
On Gibbs Sampling Architecture for Labeled Random Finite Sets Multi-Object Tracking
Authors: Anthony Trezza, Donald J. Bucci Jr., Pramod K. Varshney
Date: 2023-06-27 00:00:00
Tasks: Multi-Object Tracking, Object Tracking

Gibbs sampling is one of the most popular Markov chain Monte Carlo algorithms because of its simplicity, scalability, and wide applicability within many fields of statistics, science, and engineering. In the labeled random finite sets literature, Gibbs sampling procedures have recently been applied to efficiently truncate the single-sensor and multi-sensor $\delta$-generalized labeled multi-Bernoulli posterior density as well as the multi-sensor adaptive labeled multi-Bernoulli birth distribution. However, only a limited discussion has been provided regarding key Gibbs sampler architecture details including the Markov chain Monte Carlo sample generation technique and early termination criteria. This paper begins with a brief background on Markov chain Monte Carlo methods and a review of the Gibbs sampler implementations proposed for labeled random finite sets filters. Next, we propose a short chain, multi-simulation sample generation technique that is well suited for these applications and enables a parallel processing implementation. Additionally, we present two heuristic early termination criteria that achieve similar sampling performance with substantially fewer Markov chain observations. Finally, the benefits of the proposed Gibbs samplers are demonstrated via two Monte Carlo simulations.
A Firewall Optimization for Threat-Resilient Micro-Segmentation in Power System Networks
Authors: Abhijeet Sahu, Patrick Wlazlo, Nastassja Gaudet, Ana Goulart, Edmond Rogers, Katherine Davis
Date: 2023-06-26 00:00:00

Electric power delivery relies on a communications backbone that must be secure. SCADA systems are essential to critical grid functions and include industrial control systems (ICS) protocols such as the Distributed Network Protocol-3 (DNP3). These protocols are vulnerable to cyber threats that power systems, as cyber-physical critical infrastructure, must be protected against. For this reason, the NERC Critical Infrastructure Protection standard CIP-005-5 specifies that an electronic system perimeter is needed, accomplished with firewalls. This paper presents how these electronic system perimeters can be optimally found and generated using a proposed meta-heuristic approach for optimal security zone formation for large-scale power systems. Then, to implement the optimal firewall rules in a large scale power system model, this work presents a prototype software tool that takes the optimization results and auto-configures the firewall nodes for different utilities in a cyber-physical testbed. Using this tool, firewall policies are configured for all the utilities and their substations within a synthetic 2000-bus model, assuming two different network topologies. Results generate the optimal electronic security perimeters to protect a power system's data flows and compare the number of firewalls, monetary cost, and risk alerts from path analysis.
A GPT-4 Reticular Chemist for Guiding MOF Discovery
Authors: Zhiling Zheng, Zichao Rong, Nakul Rampal, Christian Borgs, Jennifer T. Chayes, Omar M. Yaghi
Date: 2023-06-20 00:00:00
Tasks: In-Context Learning, Prompt Learning

We present a new framework integrating the AI model GPT-4 into the iterative process of reticular chemistry experimentation, leveraging a cooperative workflow of interaction between AI and a human researcher. This GPT-4 Reticular Chemist is an integrated system composed of three phases. Each of these utilizes GPT-4 in various capacities, wherein GPT-4 provides detailed instructions for chemical experimentation and the human provides feedback on the experimental outcomes, including both success and failures, for the in-context learning of AI in the next iteration. This iterative human-AI interaction enabled GPT-4 to learn from the outcomes, much like an experienced chemist, by a prompt-learning strategy. Importantly, the system is based on natural language for both development and operation, eliminating the need for coding skills, and thus, make it accessible to all chemists. Our collaboration with GPT-4 Reticular Chemist guided the discovery of an isoreticular series of MOFs, with each synthesis fine-tuned through iterative feedback and expert suggestions. This workflow presents a potential for broader applications in scientific research by harnessing the capability of large language models like GPT-4 to enhance the feasibility and efficiency of research activities.
A Theory of Complex Adaptive Learning and a Non-Localized Wave Equation in Quantum Mechanics
Authors: Leilei Shi, Xinshuai Guo, Jiuchang Wei, Wei zhang, Guocheng Wang, Bing-Hong Wang
Date: 2023-06-27 00:00:00

Complex adaptive learning is intelligent. It is adaptive, learns in feedback loops, and generates hidden patterns as many individuals, elements or particles interact in complex adaptive systems (CAS). CAS highlights adaptation in life and lifeless complex systems cutting across all traditional natural and social sciences disciplines. However, discovering a universal law in CAS and understanding the underlying mechanism of distribution formation, such as a non-Gauss distribution in complex quantum entanglement, remains highly challenging. Quantifying the uncertainty of CAS by probability wave functions, the authors explore the inherent logical relationship between Schr\"odinger's wave equation in quantum mechanics and Shi's trading volume-price wave equation in finance. Subsequently, the authors propose a non-localized wave equation in quantum mechanics if cumulative observable in a time interval represents momentum or momentum force in Skinner-Shi (reinforcement-frequency-interaction) coordinates. It reveals that the invariance of interaction as a universal law exists in quantum mechanics and finance. The theory shows that quantum entanglement is an interactively coherent state instead of a consequence of the superposition of coherent states. As a resource, quantum entanglement is non-separable, steerable, and energy-consumed. The entanglement state has opposite states subject to interaction conservation between the momentum and reversal forces. Keywords: complex adaptive systems, complex adaptive learning, complex quantum systems, non-localized wave equation, interaction conservation, interactively coherent entanglement PACS: 89.75.-k (Complex Systems); 89.65.Gh (Economics, Econophysics, Financial Markets, Business and Management); 03.65.Ud (Entanglement and Quantum Nonlocality)
Survival, extinction, and interface stability in a two--phase moving boundary model of biological invasion
Authors: Matthew J Simpson, Nizhum Rahman, Scott W McCue, Alexander KY Tam
Date: 2023-06-27 00:00:00

We consider a moving boundary mathematical model of biological invasion. The model describes the spatiotemporal evolution of two adjacent populations: each population undergoes linear diffusion and logistic growth, and the boundary between the two populations evolves according to a two--phase Stefan condition. This mathematical model describes situations where one population invades into regions occupied by the other population, such as the spreading of a malignant tumour into surrounding tissues. Full time--dependent numerical solutions are obtained using a level--set numerical method. We use these numerical solutions to explore several properties of the model including: (i) survival and extinction of one population initially surrounded by the other; and (ii) linear stability of the moving front boundary in the context of a travelling wave solution subjected to transverse perturbations. Overall, we show that many features of the well--studied one--phase single population analogue of this model can be very different in the more realistic two--phase setting. These results are important because realistic examples of biological invasion involve interactions between multiple populations and so great care should be taken when extrapolating predictions from a one--phase single population model to cases for which multiple populations are present. Open source Julia--based software is available on GitHub to replicate all results in this study.
Phonon dynamic behaviors induced by amorphous interlayer at heterointerfaces
Authors: Quanjie Wang, Jie Zhang, Vladimir Chernysh, Xiangjun Liu
Date: 2023-06-08 00:00:00

Interface impedes heat flow in heterostructures and the interfacial thermal resistance (ITR) has become a critical issue for thermal dissipation in electronic devices. To explore the mechanism leading to the ITR, in this work, the dynamic behaviors of phonons passing through the GaN/AlN interface with an amorphous interlayer is investigated by using phonon wave packet simulation. It is found the amorphous interlayer significantly impedes phonon transport across the interface, and leads to remarkable phonon mode conversions, such as LA$\rightarrow$TA, TA$\rightarrow$LA, and LA$\rightarrow$TO conversion. However, due to mode conversion and inelastic scattering, we found a portion of high-frequency TA phonons, which are higher than the cut-off frequency and cannot transmit across the ideal sharp interface, can partially transmit across the amorphous interlayer, which introduces additional thermal transport channels through the interface and has positive effect on interfacial thermal conductance. According to phonon transmission coefficient, it is found the ITR increases with increasing of amorphous interlayer thickness L. The phonon transmission coefficient exhibits an obvious oscillation behavior, which is attributed to the multiple phonon scattering in the amorphous interlayer, and the oscillation period is further revealed to be consistent with the theoretical prediction by the two-beam interference equation. In addition, obvious phonon frequency shifts and phonon energy localization phenomena were observed in the amorphous interlayer. Finally, to improve phonon transmission, the interface morphology was further optimized via the annealing reconstruction technique, which results in re-crystallization of the amorphous interlayer and the decrease of ITR by ~21% as L=2 nm.
Beyond Chemical Language: A Multimodal Approach to Enhance Molecular Property Prediction
Authors: Eduardo Soares, Emilio Vital Brazil, Karen Fiorela Aquino Gutierrez, Renato Cerqueira, Dan Sanders, Kristin Schmidt, Dmitry Zubarev
Date: 2023-06-22 00:00:00
Tasks: feature selection, Language Modeling, Language Modelling, Molecular Property Prediction, Property Prediction

We present a novel multimodal language model approach for predicting molecular properties by combining chemical language representation with physicochemical features. Our approach, MULTIMODAL-MOLFORMER, utilizes a causal multistage feature selection method that identifies physicochemical features based on their direct causal effect on a specific target property. These causal features are then integrated with the vector space generated by molecular embeddings from MOLFORMER. In particular, we employ Mordred descriptors as physicochemical features and identify the Markov blanket of the target property, which theoretically contains the most relevant features for accurate prediction. Our results demonstrate a superior performance of our proposed approach compared to existing state-of-the-art algorithms, including the chemical language-based MOLFORMER and graph neural networks, in predicting complex tasks such as biodegradability and PFAS toxicity estimation. Moreover, we demonstrate the effectiveness of our feature selection method in reducing the dimensionality of the Mordred feature space while maintaining or improving the model's performance. Our approach opens up promising avenues for future research in molecular property prediction by harnessing the synergistic potential of both chemical language and physicochemical features, leading to enhanced performance and advancements in the field.