pwc-archive/papers-with-abstracts huggingface dataset:
EmMark: Robust Watermarks for IP Protection of Embedded Quantized Large Language Models
Authors: Ruisi Zhang, Farinaz Koushanfar
Date: 2024-02-27 00:00:00

This paper introduces EmMark,a novel watermarking framework for protecting the intellectual property (IP) of embedded large language models deployed on resource-constrained edge devices. To address the IP theft risks posed by malicious end-users, EmMark enables proprietors to authenticate ownership by querying the watermarked model weights and matching the inserted signatures. EmMark's novelty lies in its strategic watermark weight parameters selection, nsuring robustness and maintaining model quality. Extensive proof-of-concept evaluations of models from OPT and LLaMA-2 families demonstrate EmMark's fidelity, achieving 100% success in watermark extraction with model performance preservation. EmMark also showcased its resilience against watermark removal and forging attacks.
An Iterative Associative Memory Model for Empathetic Response Generation
Authors: Zhou Yang, Zhaochun Ren, Yufeng Wang, Chao Chen, Haizhou Sun, Xiaofei Zhu, Xiangwen Liao
Date: 2024-02-28 00:00:00
Tasks: Empathetic Response Generation, Response Generation

Empathetic response generation aims to comprehend the cognitive and emotional states in dialogue utterances and generate proper responses. Psychological theories posit that comprehending emotional and cognitive states necessitates iteratively capturing and understanding associated words across dialogue utterances. However, existing approaches regard dialogue utterances as either a long sequence or independent utterances for comprehension, which are prone to overlook the associated words between them. To address this issue, we propose an Iterative Associative Memory Model (IAMM) for empathetic response generation. Specifically, we employ a novel second-order interaction attention mechanism to iteratively capture vital associated words between dialogue utterances and situations, dialogue history, and a memory module (for storing associated words), thereby accurately and nuancedly comprehending the utterances. We conduct experiments on the Empathetic-Dialogue dataset. Both automatic and human evaluations validate the efficacy of the model. Variant experiments on LLMs also demonstrate that attending to associated words improves empathetic comprehension and expression.
Distributed Intelligent Integrated Sensing and Communications: The 6G-DISAC Approach
Authors: Emilio Calvanese Strinati, George C. Alexandropoulos, Madhusudan Giyyarpuram, Philippe Sehier, Sami Mekki, Vincenzo Sciancalepore, Maximilian Stark, Mohamed Sana, Benoit Denis, Maurizio Crozzoli, Navid Amani, Placido Mursia, Raffaele D Errico, Mauro Boldi, Francesca Costanzo, Francois Rivet, Henk Wymeerschx
Date: 2024-02-28 00:00:00
Tasks: ISAC, Management, Semantic Composition

This paper introduces the concept of Distributed Intelligent integrated Sensing and Communications (DISAC), which expands the capabilities of Integrated Sensing and Communications (ISAC) towards distributed architectures. Additionally, the DISAC framework integrates novel waveform design with new semantic and goal-oriented communication paradigms, enabling ISAC technologies to transition from traditional data fusion to the semantic composition of diverse sensed and shared information. This progress facilitates large-scale, energy-efficient support for high-precision spatial-temporal processing, optimizing ISAC resource utilization, and enabling effective multi-modal sensing performance. Addressing key challenges such as efficient data management and connect-compute resource utilization, 6G- DISAC stands to revolutionize applications in diverse sectors including transportation, healthcare, and industrial automation. Our study encapsulates the project vision, methodologies, and potential impact, marking a significant stride towards a more connected and intelligent world.
Joint Activity-Delay Detection and Channel Estimation for Asynchronous Massive Random Access: A Free Probability Theory Approach
Authors: Xinyu Bian, Yuyi Mao, Jun Zhang
Date: 2024-02-28 00:00:00
Tasks: Action Detection, Activity Detection

Grant-free random access (RA) has been recognized as a promising solution to support massive connectivity due to the removal of the uplink grant request procedures. While most endeavours assume perfect synchronization among users and the base station, this paper investigates asynchronous grant-free massive RA, and develop efficient algorithms for joint user activity detection, synchronization delay detection, and channel estimation. Considering the sparsity on user activity, we formulate a sparse signal recovery problem and propose to utilize the framework of orthogonal approximate message passing (OAMP) to deal with the non-independent and identically distributed (i.i.d.) Gaussian pilot matrices caused by the synchronization delays. In particular, an OAMP-based algorithm is developed to fully harness the common sparsity among received pilot signals from multiple base station antennas. To reduce the computational complexity, we further propose a free probability AMP (FPAMP)-based algorithm, which exploits the rectangular free cumulants to make the cost-effective AMP framework compatible to general pilot matrices. Simulation results demonstrate that the two proposed algorithms outperform various baselines, and the FPAMP-based algorithm reduces 40% of the computations while maintaining comparable detection/estimation accuracy with the OAMP-based algorithm.
AN An ica-ensemble learning approach for prediction of uwb nlos signals data classification
Authors: Jiya A. Enoch, Ilesanmi B. Oluwafemi, Francis A. Ibikunle, Olulope K. Paul
Date: 2024-02-27 00:00:00
Tasks: Ensemble Learning, Human Detection

Trapped human detection in search and rescue (SAR) scenarios poses a significant challenge in pervasive computing. This study addresses this issue by leveraging machine learning techniques, given their high accuracy. However, accurate identification of trapped individuals is hindered by the curse of dimensionality and noisy data. Particularly in non-line-of-sight (NLOS) situations during catastrophic events, the curse of dimensionality may lead to blind spots due to noise and uncorrelated values in detections. This research focuses on harmonizing information through wireless communication and identifying individuals in NLOS scenarios using ultra-wideband (UWB) radar signals. Employing independent component analysis (ICA) for feature extraction, the study evaluates classification performance using ensemble algorithms on both static and dynamic datasets. The experimental results demonstrate categorization accuracies of 88.37% for static data and 87.20% for dynamic data, highlighting the effectiveness of the proposed approach. Finally, this work can help scientists and engineers make instant decisions during SAR operations.
Improvement Of Audiovisual Quality Estimation Using A Nonlinear Autoregressive Exogenous Neural Network And Bitstream Parameters
Authors: Koffi Kossi, Stephane Coulombe, Christian Desrosiers, Ghyslain Gagnon
Date: 2024-02-28 00:00:00

With the increasing demand for audiovisual services, telecom service providers and application developers are compelled to ensure that their services provide the best possible user experience. Particularly, services such as videoconferencing are very sensitive to network conditions. Therefore, their performance should be monitored in real time in order to adjust parameters to any network perturbation. In this paper, we developed a parametric model for estimating the perceived audiovisual quality in videoconference services. Our model is developed with the nonlinear autoregressive exogenous (NARX) recurrent neural network and estimates the perceived quality in terms of mean opinion score (MOS). We validate our model using the publicly available INRS bitstream audiovisual quality dataset. This dataset contains bitstream parameters such as loss per frame, bit rate and video duration. We compare the proposed model against state-of-the-art methods based on machine learning and show our model to outperform these methods in terms of mean square error (MSE=0.150) and Pearson correlation coefficient (R=0.931)
NIIRF: Neural IIR Filter Field for HRTF Upsampling and Personalization
Authors: Yoshiki Masuyama, Gordon Wichern, François G. Germain, Zexu Pan, Sameer Khurana, Chiori Hori, Jonathan Le Roux
Date: 2024-02-27 00:00:00
Tasks: Spatial Interpolation

Head-related transfer functions (HRTFs) are important for immersive audio, and their spatial interpolation has been studied to upsample finite measurements. Recently, neural fields (NFs) which map from sound source direction to HRTF have gained attention. Existing NF-based methods focused on estimating the magnitude of the HRTF from a given sound source direction, and the magnitude is converted to a finite impulse response (FIR) filter. We propose the neural infinite impulse response filter field (NIIRF) method that instead estimates the coefficients of cascaded IIR filters. IIR filters mimic the modal nature of HRTFs, thus needing fewer coefficients to approximate them well compared to FIR filters. We find that our method can match the performance of existing NF-based methods on multiple datasets, even outperforming them when measurements are sparse. We also explore approaches to personalize the NF to a subject and experimentally find low-rank adaptation to be effective.
ByteComposer: a Human-like Melody Composition Method based on Language Model Agent
Authors: Xia Liang, Xingjian Du, Jiaju Lin, Pei Zou, Yuan Wan, Bilei Zhu
Date: 2024-02-24 00:00:00
Tasks: Language Modeling, Language Modelling, Music Generation

Large Language Models (LLM) have shown encouraging progress in multimodal understanding and generation tasks. However, how to design a human-aligned and interpretable melody composition system is still under-explored. To solve this problem, we propose ByteComposer, an agent framework emulating a human's creative pipeline in four separate steps : "Conception Analysis - Draft Composition - Self-Evaluation and Modification - Aesthetic Selection". This framework seamlessly blends the interactive and knowledge-understanding features of LLMs with existing symbolic music generation models, thereby achieving a melody composition agent comparable to human creators. We conduct extensive experiments on GPT4 and several open-source large language models, which substantiate our framework's effectiveness. Furthermore, professional music composers were engaged in multi-dimensional evaluations, the final results demonstrated that across various facets of music composition, ByteComposer agent attains the level of a novice melody composer.
WhaleNet: a Novel Deep Learning Architecture for Marine Mammals Vocalizations on Watkins Marine Mammal Sound Database
Authors: Alessandro Licciardi, Davide Carbone
Date: 2024-02-20 00:00:00
Tasks: Classification, Diversity

Marine mammal communication is a complex field, hindered by the diversity of vocalizations and environmental factors. The Watkins Marine Mammal Sound Database (WMMD) constitutes a comprehensive labeled dataset employed in machine learning applications. Nevertheless, the methodologies for data preparation, preprocessing, and classification documented in the literature exhibit considerable variability and are typically not applied to the dataset in its entirety. This study initially undertakes a concise review of the state-of-the-art benchmarks pertaining to the dataset, with a particular focus on clarifying data preparation and preprocessing techniques. Subsequently, we explore the utilization of the Wavelet Scattering Transform (WST) and Mel spectrogram as preprocessing mechanisms for feature extraction. In this paper, we introduce \textbf{WhaleNet} (Wavelet Highly Adaptive Learning Ensemble Network), a sophisticated deep ensemble architecture for the classification of marine mammal vocalizations, leveraging both WST and Mel spectrogram for enhanced feature discrimination. By integrating the insights derived from WST and Mel representations, we achieved an improvement in classification accuracy by $8-10\%$ over existing architectures, corresponding to a classification accuracy of $97.61\%$.
Extended Kalman filter -- Koopman operator for tractable stochastic optimal control
Authors: Mohammad S. Ramadan, Mihai Anitescu
Date: 2024-02-28 00:00:00

The theory of dual control was introduced more than seven decades ago. Although it has provided rich insights to the fields of control, estimation, and system identification, dual control is generally computationally prohibitive. In recent years, however, the use of Koopman operator theory for control applications has been emerging. This paper presents a new reformulation of the stochastic optimal control problem that, employing the Koopman operator, yields a standard LQR problem with the dual control as its solution. We provide a numerical example that demonstrates the effectiveness of the proposed approach compared with certainty equivalence control, when applied to systems with varying observability.
Reinforcement Learning and Graph Neural Networks for Probabilistic Risk Assessment
Authors: Joachim Grimstad, Andrey Morozov
Date: 2024-02-28 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning (RL)

This paper presents a new approach to the solution of Probabilistic Risk Assessment (PRA) models using the combination of Reinforcement Learning (RL) and Graph Neural Networks (GNNs). The paper introduces and demonstrates the concept using one of the most popular PRA models - Fault Trees. This paper's original idea is to apply RL algorithms to solve a PRA model represented with a graph model. Given enough training data, or through RL, such an approach helps train generic PRA solvers that can optimize and partially substitute classical PRA solvers that are based on existing formal methods. Such an approach helps to solve the problem of the fast-growing complexity of PRA models of modern technical systems.
Exergetic Port-Hamiltonian Systems for Multibody Dynamics
Authors: Markus Lohmayer, Giuseppe Capobianco, Sigrid Leyendecker
Date: 2024-02-28 00:00:00

Multibody dynamics simulation plays an important role in various fields, including mechanical engineering, robotics, and biomechanics. Setting up computational models however becomes increasingly challenging as systems grow in size and complexity. Especially the consistent combination of models across different physical domains usually demands a lot of attention. This motivates us to study formal languages for compositional modeling of multiphysical systems. This article shows how multibody systems, or more precisely assemblies of rigid bodies connected by lower kinematic pairs, fit into the framework of Exergetic Port-Hamiltonian Systems (EPHS). This approach is based on the hierarchical decomposition of systems into their ultimately primitive components, using a simple graphical syntax. Thereby, cognitive load can be reduced and communication is facilitated, even with non-experts. Moreover, the encapsulation and reuse of subsystems promotes efficient model development and management. In contrast to established modeling languages such as Modelica, the primitive components of EPHS are not defined by arbitrary equations. Instead, there are four kinds of components, each defined by a particular geometric structure with a clear physical interpretation. This higher-level approach could make the process of building and maintaining large-scale models simpler and also safer.
Online Ecological Gearshift Strategy via Neural Network with Soft-Argmax Operator
Authors: Xi Luo, Shiying Dong, Jinlong Hong, Bingzhao Gao, Hong Chen
Date: 2024-02-28 00:00:00
Tasks: Model Predictive Control

This paper presents a neural network optimizer with soft-argmax operator to achieve an ecological gearshift strategy in real-time. The strategy is reformulated as the mixed-integer model predictive control (MIMPC) problem to minimize energy consumption. Then the outer convexification is introduced to transform integer variables into relaxed binary controls. To approximate binary solutions properly within training, the soft-argmax operator is applied to the neural network with the fact that all the operations of this scheme are differentiable. Moreover, this operator can help push the relaxed binary variables close to 0 or 1. To evaluate the strategy effect, we deployed it to a 2-speed electric vehicle (EV). In contrast to the mature solver Bonmin, our proposed method not only achieves similar energy-saving effects but also significantly reduces the solution time to meet real-time requirements. This results in a notable energy savings of 6.02% compared to the rule-based method.
Dynamic Anchor Selection and Real-Time Pose Prediction for Ultra-wideband Tagless Gate
Authors: Junyoung Choi, Sagnik Bhattacharya, Joohyun Lee
Date: 2024-02-22 00:00:00
Tasks: Pose Prediction

Ultra-wideband (UWB) is emerging as a promising solution that can realize proximity services, such as UWB tagless gate (UTG), thanks to centimeter-level localization accuracy based on two different ranging methods such as downlink time-difference of arrival (DL-TDoA) and double-sided two-way ranging (DS-TWR). The UTG is a UWB-based proximity service that provides a seamless gate pass system without requiring real-time mobile device (MD) tapping. The location of MD is calculated using DL-TDoA, and the MD communicates with the nearest UTG using DS-TWR to open the gate. Therefore, the knowledge about the exact location of MD is the main challenge of UTG, and hence we provide the solutions for both DL-TDoA and DS-TWR. In this paper, we propose dynamic anchor selection for extremely accurate DL-TDoA localization and pose prediction for DS-TWR, called DynaPose. The pose is defined as the actual location of MD on the human body, which affects the localization accuracy. DynaPose is based on line-of-sight (LOS) and non-LOS (NLOS) classification using deep learning for anchor selection and pose prediction. Deep learning models use the UWB channel impulse response and the inertial measurement unit embedded in the smartphone. DynaPose is implemented on Samsung Galaxy Note20 Ultra and Qorvo UWB board to show the feasibility and applicability. DynaPose achieves a LOS/NLOS classification accuracy of 0.984, 62% higher DL-TDoA localization accuracy, and ultimately detects four different poses with an accuracy of 0.961 in real-time.
Dark energy reconstruction analysis with artificial neural networks: Application on simulated Supernova Ia data from Rubin Observatory
Authors: Ayan Mitra, Isidro Gómez-Vargas, Vasilios Zarikas
Date: 2024-02-28 00:00:00

In this paper, we present an analysis of Supernova Ia (SNIa) distance moduli $\mu(z)$ and dark energy using an Artificial Neural Network (ANN) reconstruction based on LSST simulated three-year SNIa data. The ANNs employed in this study utilize genetic algorithms for hyperparameter tuning and Monte Carlo Dropout for predictions. Our ANN reconstruction architecture is capable of modeling both the distance moduli and their associated statistical errors given redshift values. We compare the performance of the ANN-based reconstruction with two theoretical dark energy models: $\Lambda$CDM and Chevallier-Linder-Polarski (CPL). Bayesian analysis is conducted for these theoretical models using the LSST simulations and compared with observations from Pantheon and Pantheon+ SNIa real data. We demonstrate that our model-independent ANN reconstruction is consistent with both theoretical models. Performance metrics and statistical tests reveal that the ANN produces distance modulus estimates that align well with the LSST dataset and exhibit only minor discrepancies with $\Lambda$CDM and CPL.
Why Do Animals Need Shaping? A Theory of Task Composition and Curriculum Learning
Authors: Jin Hwa Lee, Stefano Sarao Mannelli, Andrew Saxe
Date: 2024-02-28 00:00:00
Tasks: Deep Reinforcement Learning, reinforcement-learning, Reinforcement Learning

Diverse studies in systems neuroscience begin with extended periods of curriculum training known as `shaping' procedures. These involve progressively studying component parts of more complex tasks, and can make the difference between learning a task quickly, slowly or not at all. Despite the importance of shaping to the acquisition of complex tasks, there is as yet no theory that can help guide the design of shaping procedures, or more fundamentally, provide insight into its key role in learning. Modern deep reinforcement learning systems might implicitly learn compositional primitives within their multilayer policy networks. Inspired by these models, we propose and analyse a model of deep policy gradient learning of simple compositional reinforcement learning tasks. Using the tools of statistical physics, we solve for exact learning dynamics and characterise different learning strategies including primitives pre-training, in which task primitives are studied individually before learning compositional tasks. We find a complex interplay between task complexity and the efficacy of shaping strategies. Overall, our theory provides an analytical understanding of the benefits of shaping in a class of compositional tasks and a quantitative account of how training protocols can disclose useful task primitives, ultimately yielding faster and more robust learning.
Neurological disorders leading to mechanical dysfunction of the esophagus: an emergent behavior of a neuromechanical dynamical system
Authors: Guy Elisha, Sourav Halder, Xinyi Liu, Dustin A. Carlson, Peter J. Kahrilas, John E. Pandolfino, Neelesh A. Patankar
Date: 2024-02-28 00:00:00

An understanding how neurological disorders lead to mechanical dysfunction of the esophagus requires knowledge of the neural circuit of the enteric nervous system. Historically, this has been elusive. Here, we present an empirically guided neural circuit for the esophagus. It has a chain of unidirectionally coupled relaxation oscillators, receiving excitatory signals from stretch receptors along the esophagus. The resulting neuromechanical model reveals complex patterns and behaviors that emerge from interacting components in the system. A wide variety of clinically observed normal and abnormal esophageal responses to distension are successfully predicted. Specifically, repetitive antegrade contractions (RACs) are conclusively shown to emerge from the coupled neuromechanical dynamics in response to sustained volumetric distension. Normal RACs are shown to have a robust balance between excitatory and inhibitory neuronal populations, and the mechanical input through stretch receptors. When this balance is affected, contraction patterns akin to motility disorders are observed. For example, clinically observed repetitive retrograde contractions emerge due to a hyper stretch sensitive wall. Such neuromechanical insights could be crucial to eventually develop targeted pharmacological interventions.