pwc-archive/papers-with-abstracts huggingface dataset:
AAMDM: Accelerated Auto-regressive Motion Diffusion Model
Authors: Tianyu Li, Calvin Qiao, Guanqiao Ren, KangKang Yin, Sehoon Ha
Date: 2023-12-02 00:00:00
Proceeding: CVPR 2024 1
Tasks: Denoising, Diversity, model, Motion Synthesis

Interactive motion synthesis is essential in creating immersive experiences in entertainment applications, such as video games and virtual reality. However, generating animations that are both high-quality and contextually responsive remains a challenge. Traditional techniques in the game industry can produce high-fidelity animations but suffer from high computational costs and poor scalability. Trained neural network models alleviate the memory and speed issues, yet fall short on generating diverse motions. Diffusion models offer diverse motion synthesis with low memory usage, but require expensive reverse diffusion processes. This paper introduces the Accelerated Auto-regressive Motion Diffusion Model (AAMDM), a novel motion synthesis framework designed to achieve quality, diversity, and efficiency all together. AAMDM integrates Denoising Diffusion GANs as a fast Generation Module, and an Auto-regressive Diffusion Model as a Polishing Module. Furthermore, AAMDM operates in a lower-dimensional embedded space rather than the full-dimensional pose space, which reduces the training complexity as well as further improves the performance. We show that AAMDM outperforms existing methods in motion quality, diversity, and runtime efficiency, through comprehensive quantitative analyses and visual comparisons. We also demonstrate the effectiveness of each algorithmic component through ablation studies.
Reproducibility via neural fields of visual illusions induced by localized stimuli
Authors: Cyprien Tamekue, Dario Prandi, Yacine Chitour
Date: 2024-01-17 00:00:00

This paper focuses on the modeling of experiments conducted by Billock and Tsou [V. A. Billock and B. H. Tsou, Proc. Natl. Acad. Sci. USA, 104 (2007), pp. 8490--8495] using an Amari-type neural field that models the average membrane potential of neuronal activity in the primary visual cortex (V1). The study specifically focuses on a regular funnel pattern localized in the fovea or the peripheral visual field. It aims to comprehend and model the visual phenomena induced by this pattern, emphasizing their nonlinear nature. The research involves designing sensory inputs that mimic the visual stimuli from Billock and Tsou's experiments. The cortical outputs induced by these sensory inputs are then theoretically and numerically studied to assess their ability to model the experimentally observed visual effects at the V1 level. A crucial aspect of this study is the exploration of the effects induced by the nonlinear nature of neural responses. By highlighting the significance of excitatory and inhibitory neurons in the emergence of these visual phenomena, the research suggests that an interplay of both types of neuronal activities plays a crucial role in visual processes, challenging the assumption that the latter is primarily driven by excitatory activities alone.
A hybrid tau-leap for simulating chemical kinetics with applications to parameter estimation
Authors: Thomas Trigo Trindade, Konstantinos C. Zygalakis
Date: 2024-01-17 00:00:00
Tasks: Bayesian Inference, parameter estimation

We consider the problem of efficiently simulating stochastic models of chemical kinetics. The Gillespie Stochastic Simulation algorithm (SSA) is often used to simulate these models, however, in many scenarios of interest, the computational cost quickly becomes prohibitive. This is further exasperated in the Bayesian inference context when estimating parameters of chemical models, as the intractability of the likelihood requires multiple simulations of the underlying system. To deal with issues of computational complexity in this paper, we propose a novel hybrid $\tau$-leap algorithm for simulating well-mixed chemical systems. In particular, the algorithm uses $\tau$-leap when appropriate (high population densities), and SSA when necessary (low population densities, when discrete effects become non-negligible). In the intermediate regime, a combination of the two methods, which leverages the properties of the underlying Poisson formulation, is employed. As illustrated through a number of numerical experiments the hybrid $\tau$ offers significant computational savings when compared to SSA without however sacrificing the overall accuracy. This feature is particularly welcomed in the Bayesian inference context, as it allows for parameter estimation of stochastic chemical kinetics at reduced computational cost.
DeepSpeed-FastGen: High-throughput Text Generation for LLMs via MII and DeepSpeed-Inference
Authors: Connor Holmes, Masahiro Tanaka, Michael Wyatt, Ammar Ahmad Awan, Jeff Rasley, Samyam Rajbhandari, Reza Yazdani Aminabadi, Heyang Qin, Arash Bakhtiari, Lev Kurilenko, Yuxiong He
Date: 2024-01-09 00:00:00
Tasks: Benchmarking, Text Generation

The deployment and scaling of large language models (LLMs) have become critical as they permeate various applications, demanding high-throughput and low-latency serving systems. Existing frameworks struggle to balance these requirements, especially for workloads with long prompts. This paper introduces DeepSpeed-FastGen, a system that employs Dynamic SplitFuse, a novel prompt and generation composition strategy, to deliver up to 2.3x higher effective throughput, 2x lower latency on average, and up to 3.7x lower (token-level) tail latency, compared to state-of-the-art systems like vLLM. We leverage a synergistic combination of DeepSpeed-MII and DeepSpeed-Inference to provide an efficient and easy-to-use serving system for LLMs. DeepSpeed-FastGen's advanced implementation supports a range of models and offers both non-persistent and persistent deployment options, catering to diverse user scenarios from interactive sessions to long-running applications. We present a detailed benchmarking methodology, analyze the performance through latency-throughput curves, and investigate scalability via load balancing. Our evaluations demonstrate substantial improvements in throughput and latency across various models and hardware configurations. We discuss our roadmap for future enhancements, including broader model support and new hardware backends. The DeepSpeed-FastGen code is readily available for community engagement and contribution.
Can Synthetic Data Boost the Training of Deep Acoustic Vehicle Counting Networks?
Authors: Stefano Damiano, Luca Bondi, Shabnam Ghaffarzadegan, Andre Guntoro, Toon van Waterschoot
Date: 2024-01-17 00:00:00
Tasks: Deep Learning

In the design of traffic monitoring solutions for optimizing the urban mobility infrastructure, acoustic vehicle counting models have received attention due to their cost effectiveness and energy efficiency. Although deep learning has proven effective for visual traffic monitoring, its use has not been thoroughly investigated in the audio domain, likely due to real-world data scarcity. In this work, we propose a novel approach to acoustic vehicle counting by developing: i) a traffic noise simulation framework to synthesize realistic vehicle pass-by events; ii) a strategy to mix synthetic and real data to train a deep-learning model for traffic counting. The proposed system is capable of simultaneously counting cars and commercial vehicles driving on a two-lane road, and identifying their direction of travel under moderate traffic density conditions. With only 24 hours of labeled real-world traffic noise, we are able to improve counting accuracy on real-world data from $63\%$ to $88\%$ for cars and from $86\%$ to $94\%$ for commercial vehicles.
Two-pass Endpoint Detection for Speech Recognition
Authors: Anirudh Raju, Aparna Khare, Di He, Ilya Sklyar, Long Chen, Sam Alptekin, Viet Anh Trinh, Zhe Zhang, Colin Vaz, Venkatesh Ravichandran, Roland Maas, Ariya Rastrow
Date: 2024-01-17 00:00:00
Tasks: speech-recognition, Speech Recognition

Endpoint (EP) detection is a key component of far-field speech recognition systems that assist the user through voice commands. The endpoint detector has to trade-off between accuracy and latency, since waiting longer reduces the cases of users being cut-off early. We propose a novel two-pass solution for endpointing, where the utterance endpoint detected from a first pass endpointer is verified by a 2nd-pass model termed EP Arbitrator. Our method improves the trade-off between early cut-offs and latency over a baseline endpointer, as tested on datasets including voice-assistant transactional queries, conversational speech, and the public SLURP corpus. We demonstrate that our method shows improvements regardless of the first-pass EP model used.
Sub-band and Full-band Interactive U-Net with DPRNN for Demixing Cross-talk Stereo Music
Authors: Han Yin, Mou Wang, Jisheng Bai, Dongyuan Shi, Woon-Seng Gan, Jianfeng Chen
Date: 2024-01-11 00:00:00

This paper presents a detailed description of our proposed methods for the ICASSP 2024 Cadenza Challenge. Experimental results show that the proposed system can achieve better performance than official baselines.
An Energy-efficient Capacitive-RRAM Content Addressable Memory
Authors: Yihan Pan, Adrian Wheeldon, Mohammed Mughal, Shady Agwa, Themis Prodromakis, Alexantrou Serb
Date: 2024-01-17 00:00:00

Content addressable memory is popular in intelligent computing systems as it allows parallel content-searching in memory. Emerging CAMs show a promising increase in bitcell density and a decrease in power consumption than pure CMOS solutions. This article introduced an energy-efficient 3T1R1C TCAM cooperating with capacitor dividers and RRAM devices. The RRAM as a storage element also acts as a switch to the capacitor divider while searching for content. CAM cells benefit from working parallel in an array structure. We implemented a 64 x 64 array and digital controllers to perform with an internal built-in clock frequency of 875MHz. Both data searches and reads take three clock cycles. Its worst average energy for data match is reported to be 1.71fJ/bit-search and the worst average energy for data miss is found at 4.69fJ/bit-search. The prototype is simulated and fabricated in 0.18um technology with in-lab RRAM post-processing. Such memory explores the charge domain searching mechanism and can be applied to data centers that are power-hungry.
Experimentally implemented dynamic optogenetic optimization of ATPase expression using knowledge-based and Gaussian-process-supported models
Authors: Sebastián Espinel-Ríos, Gerrich Behrendt, Jasmin Bauer, Bruno Morabito, Johannes Pohlodek, Andrea Schütze, Rolf Findeisen, Katja Bettenbrock, Steffen Klamt
Date: 2024-01-16 00:00:00
Tasks: Gaussian Processes

Optogenetic modulation of adenosine triphosphatase (ATPase) expression represents a novel approach to maximize bioprocess efficiency by leveraging enforced adenosine triphosphate (ATP) turnover. In this study, we experimentally implement a model-based open-loop optimization scheme for optogenetic modulation of the expression of ATPase. Increasing the intracellular concentration of ATPase, and thus the level of ATP turnover, in bioprocesses with product synthesis coupled with ATP generation, can lead to increased product formation and substrate uptake. Previous simulation studies formulated optimal control problems using dynamic constraint-based models to find optimal light inputs in fermentations with optogenetically mediated ATPase expression. However, using these models poses challenges due to resulting bilevel optimizations and complex parameterization. Here, we outline a simplified unsegregated and quasi-unstructured kinetic modeling approach that reduces the number of dynamic states and leads to single-level optimizations. The models can be augmented with Gaussian processes to compensate for model uncertainties. We implement optimal control constrained by knowledge-based and hybrid models for optogenetic ATPase expression in $\textit{Escherichia coli}$ with lactate as the main product. To do so, we genetically engineer $\textit{E. coli}$ to obtain optogenetic expression of ATPase using the CcaS/CcaR system. This represents the first experimental implementation of model-based optimization of ATPase expression in bioprocesses.
Global and Local Error-Tolerant Decentralized State Estimation under Partially Ordered Observations
Authors: Dajiang Sun, Christoforos N. Hadjicostis, Zhiwu Li
Date: 2024-01-17 00:00:00
Tasks: State Estimation

We investigate decentralized state estimation for a discrete event system in a setting where the information received at a coordinator may be corrupted or tampered by a malicious attacker. Specifically, a system is observed by a set of (local) observation sites (OSs) which occasionally send their recorded sequences of observations to the coordinator that is in charge of estimating the system state. The malfunctions and attacks, referred to as errors in this paper, include symbol deletions, insertions and replacements, each of which bears a positive cost. Two types of errors, global errors and local errors, are proposed to describe the impact of errors on decentralized information processing. Global errors occur when all OSs record the same error, while local errors occur when different OSs record different errors. Distinguishing these types of errors is important for a proper design of decentralized information processing (so as to be more resilient and better equipped to handle attacks and failures). For each type of error, we propose two methods to efficiently perform state estimation: one based on appropriately modifying the original system and the other based on inferring the matching behavior of the original system. For each method, we adopt an estimation-by-release methodology to design an algorithm for constructing a corresponding synchronizer for state estimation.
Real-Time Control and Monitoring of Photovoltaic Arrays Using RTDS and BeagleBoard Technology
Authors: Md Fazley Rafy
Date: 2024-01-17 00:00:00
Tasks: Management

Increasing integration of alternate electricity generation due to declining fossil fuels is becoming an essential option for future generations. As photovoltaic technology brings forth enormous benefits to the alternate solution for future power grid systems, this paper presents a comprehensive real-time system designed to model, control, and monitor a PV array subjected to dynamic loads using the Real-Time Digital Simulator (RTDS) environment. Integration with the Generic Transducer Network (GTNET)- Socket(SKT) module allows for the simulation of various environmental conditions, such as changes in insolation and temperature, and their direct impact on the PV array's performance. Utilizing BeagleBoard technology, the system demonstrates the capability to modify these conditions through real-time data input, subsequently observing the effects on current and voltage output curves. The real-time simulation results are visualized as a SCADA system within the Real-time Simulation for Automated Controller Design (RSCAD) runtime environment, providing insights into the effective management of solar power systems.
Iterative Planning for Multi-agent Systems: An Application in Energy-Aware UAV-UGV Cooperative Task Site Assignments
Authors: Neelanga Thelasingha, Agung Julius, James Humann, Jean-Paul Reddinger, James Dotterweich, Marshal Childers
Date: 2024-01-16 00:00:00

This paper presents an iterative planning framework for multi-agent systems with hybrid state spaces. The framework uses transition systems to mathematically represent planning tasks and employs multiple solvers to iteratively improve the plan until computation resources are exhausted. When integrating different solvers for iterative planning, we establish theoretical guarantees on the mathematical framework to ensure recursive feasibility. The proposed framework enables continual improvement of solution optimality, efficiently using allocated computation resources. The proposed method is validated by applying it to an energy-aware UGV-UAV cooperative task site assignment. The results demonstrate the continual solution improvement while preserving real-time implementation ability compared to algorithms proposed in the literature.
Wind tunnel actuation movement system
Authors: Qiaoqiao Ren
Date: 2024-01-16 00:00:00

In this dissertation project, an actuation system was designed for the supersonic wind tunnel at the University of Manchester. The aim of this project is to build a remote control actuation system which could adjust the angle of attack for the aerodynamic shape to save researchers' time and improve the experimental efficiency. This project involves the model supporting system, a six component wind tunnel balance, a control system design, a virtual angle of attack adjustment interface and LabVIEW programming implementation, the angle of attack adjustment range is from -20 to 20 degree. The three-dimensional model of the mechanical part and its engineering drawing were finished in SolidWorks, and the control system including the sensors and rotary encoder control, the closed-loop control of the stepper motor and the wind tunnel balance feedback. The performance of the wind tunnel balance can be known in advance by finite element analysis. Finally, the virtual operating system was built based on the LabVIEW and Arduino interactive programs
Dual-Loop Robust Control of Biased Koopman Operator Model by Noisy Data of Nonlinear Systems
Authors: Tianyi He, Anuj Pal
Date: 2024-01-16 00:00:00

The Koopman operator approach for data-driven control design of a nonlinear system is on the rise because of its capability to capture the behaviours of global dynamics. However, the measurement noises of inputs and outputs will bias the Koopman model identification and cause model mismatch from the actual nonlinear dynamics. The current work evaluates the bounds of the noise-induced model bias of the Koopman operator model and proposes a data-driven robust dual-loop control framework (Koopman based robust control-KROC) for the biased model. First, the model mismatch is found bounded under radial basis functions (RBF) and the bounded noises, and the bound of model mismatch is assessed. Second, the pitfalls of linear quadratic Gaussian (LQG) control based on the biased Koopman model of Van Der Pol oscillator are shown. Motivated from the pitfalls, the dual-loop control is proposed, which consist of an observer-based state-feedback control based on the nominal Koopman model and an additional robust loop to compensate model mismatch. A linear matrix inequality (LMI) is derived, which can guarantee robust stability and performance under bounded noises for the finite-dimensional Koopman operator model. Finally, the proposed framework is implemented to a nonlinear Van Der Pol oscillator to demonstrate enhanced control performance by the dual-loop robust control.
Real-Time Dynamic Layout Optimization for Floating Offshore Wind Farm Control
Authors: Timothé Jard, Reda Snaiki
Date: 2024-01-16 00:00:00
Tasks: Model Predictive Control, Point Tracking

Downstream wind turbines operating behind upstream turbines face significant performance challenges due to reduced wind speeds and increased turbulence. This leads to decreased wind energy production and higher dynamic loads on downwind turbines. Consequently, real-time monitoring and control have become crucial for improving wind farm performance. One promising solution involves optimizing wind farm layouts in real-time, taking advantage of the added flexibility offered by floating offshore wind turbines (FOWTs). This study explores a dynamic layout optimization strategy to minimize wake effects in wind farms while meeting power requirements. Two scenarios are considered: power maximization and power set-point tracking. The methodology involves a centralized wind farm controller optimizing the layout, followed by wind turbine controllers to meet the prescribed targets. Each FOWT employs model predictive control to adjust aerodynamic thrust force. The control strategy integrates a dynamic wind farm model that considers floating platform motion and wake transport in changing wind conditions. In a case study with a 1x3 wind farm layout of 5 MW FOWTs, the results show a 25% increase in stable energy production compared to a static layout in one hour for the first scenario. In the second scenario, desired power production was swiftly and consistently achieved.
Co-existence of Terrestrial and Non-Terrestrial Networks in S-band
Authors: Niloofar Okati, Andre Noll Barreto, Luis Uzeda Garcia, Jeroen Wigard
Date: 2024-01-16 00:00:00

Co-existence of terrestrial and non-terrestrial networks (NTN) is foreseen as an important component to fulfill the global coverage promised for sixth-generation (6G) of cellular networks. Due to ever rising spectrum demand, using dedicated frequency bands for terrestrial network (TN) and NTN may not be feasible. As a result, certain S-band frequency bands allocated by radio regulations to NTN networks are overlapping with those already utilized by cellular TN, leading to significant performance degradation due to the potential co-channel interference. Early simulation-based studies on different co-existence scenarios failed to offer a comprehensive and insightful understanding of these networks' overall performance. Besides, the complexity of a brute force performance evaluation increases exponentially with the number of nodes and their possible combinations in the network. In this paper, we utilize stochastic geometry to analytically derive the performance of TN-NTN integrated networks in terms of the probability of coverage and average achievable data rate for two co-existence scenarios. From the numerical results, it can be observed that, depending on the network parameters, TN and NTN users' distributions, and traffic load, one co-existence case may outperform the other, resulting in optimal performance of the integrated network. The analytical results presented herein pave the way for designing state-of-the-art methods for spectrum sharing between TN and NTN and optimizing the integrated network performance.
A Physics-informed machine learning model for time-dependent wave runup prediction
Authors: Saeed Saviz Naeini, Reda Snaiki
Date: 2024-01-12 00:00:00
Tasks: Computational Efficiency, Generative Adversarial Network, Management, Physics-informed machine learning, Time Series

Wave runup is a critical factor affecting coastal flooding, shoreline changes, and damage to coastal structures. Climate change is also expected to amplify wave runup's impact on coastal areas. Therefore, fast and accurate wave runup estimation is essential for effective coastal engineering design and management. However, predicting the time-dependent wave runup is challenging due to the intrinsic nonlinearities and non-stationarity of the process, even with the use of the most advanced machine learning techniques. In this study, a physics-informed machine learning-based approach is proposed to efficiently and accurately simulate time-series wave runup. The methodology combines the computational efficiency of the Surfbeat (XBSB) mode with the accuracy of the nonhydrostatic (XBNH) mode of the XBeach model. Specifically, a conditional generative adversarial network (cGAN) is used to map the image representation of wave runup from XBSB to the corresponding image from XBNH. These images are generated by first converting wave runup signals into time-frequency scalograms and then transforming them into image representations. The cGAN model achieves improved performance in image-to-image mapping tasks by incorporating physics-based knowledge from XBSB. After training the model, the high-fidelity XBNH-based scalograms can be predicted, which are then employed to reconstruct the time-series wave runup using the inverse wavelet transform. The simulation results underscore the efficiency and robustness of the proposed model in predicting wave runup, suggesting its potential value for applications in risk assessment and management.