pwc-archive/papers-with-abstracts huggingface dataset:
Dual-trigger release of berberine chloride from the Gelatin/Perfluorohexane core-shell structure
Authors: Mahshid Givarian, Fathollah Moztarzadeh, Maryam Ghaffari, AmirHossein Bahmanpour, Maryam Mollazadeh-Bajestani, Manijhe Mokhtari-Dizaji, Fatemeh Mehradnia
Date: 2024-01-25 00:00:00

Background: The development of smart nanocarriers that enable controlled drug release in response to internal and external triggers is an emerging approach for targeted therapy. This study focused on designing pH-sensitive, ultrasound-responsive gelatin/perfluorohexane (PFH) nanodroplets loaded with berberine chloride as a model drug. Results:The nanodroplets were prepared using an emulsion technique and optimized by varying process parameters like homogenization rate, polymer concentration, surfactant, drug, and perfluorocarbon content. The optimal formulation yielded nanodroplets with a particle size of 281.7 nm, a drug encapsulation efficiency of 66.8+- 1.7%, and a passive drug release of 15.4+- 0.2% within 24 hours. Characterization confirmed successful encapsulation and pH-responsive behavior. Ultrasound stimulation significantly enhanced drug release, with 150 kHz being more effective than 1 MHz in triggering acoustic droplet vaporization while minimizing heat generation. After 10 minutes of radiation, the optimal formulation showed 89.4% cumulative drug release. The nanodroplets displayed stability over one month at 4{\deg}C. Conclusions: Overall, the dual-triggered nanodroplets demonstrate excellent potential for controlled delivery and targeted release of berberine chloride.
Label-free detection of exosomes from different cellular sources based on surface-enhanced Raman spectroscopy combined with machine learning models
Authors: Yang Li, Xiaoming Lyu, Kuo Zhan, Haoyu Ji, Lei Qin, JianAn Huang
Date: 2024-01-25 00:00:00

Exosomes are significant facilitators of inter-cellular communication that can unveil cell-cell interactions, signaling pathways, regulatory mechanisms and disease diagnostics. Nonetheless, current analysis required large amount of data for exosome identification that it hampers efficient and timely mechanism study and diagnostics. Here, we used a machine-learning assisted Surface-enhanced Raman spectroscopy (SERS) method to detect exosomes derived from six distinct cell lines (HepG2, Hela, 143B, LO-2, BMSC, and H8) with small amount of data. By employing sodium borohydride-reduced silver nanoparticles and sodium borohydride solution as an aggregating agent, 100 SERS spectra of the each types of exosomes were collected and then subjected to multivariate and machine learning analysis. By integrating Principal Component Analysis with Support Vector Machine (PCA-SVM) models, our analysis achieved a high accuracy rate of 94.4% in predicting exosomes originating from various cellular sources. In comparison to other machine learning analysis, our method used small amount of SERS data to allow a simple and rapid exosome detection, which enables a timely subsequent study of cell-cell interactions, communication mechanisms, and disease mechanisms in life sciences.
InfiniteEn: A Multi-Source Energy Harvesting System with Load Monitoring Module for Batteryless Internet of Things
Authors: Priyesh Pappinisseri Puluckul, Maarten Weyn
Date: 2024-01-25 00:00:00

This paper presents InfiniteEn, a multi-source energy harvesting platform designed for the Internet of Batteryless Things (IoBT). InfiniteEn incorporates an efficient energy combiner to combine energy from different harvesting sources. The energy combiner uses capacitor-to-capacitor energy transfer to combine energy from multiple sources and achieves a nominal efficiency of 88\%. In addition to multiplexing different sources, the energy combiner facilitates the estimation of the harvesting rate and the calibration of the capacity of the energy buffer. The energy storage architecture of InfiniteEn employs an array of storage buffers that can be configured on demand to cope with varying energy harvesting rates and load's energy requirements. To address the challenge of tracking the energy state of batteryless devices with minimum energy overhead, this work introduces the concept of a Load Monitoring Module (LMM). InfiniteEn is a load-agnostic platform, meaning that it does not require any prior knowledge of the energy profile of the load to track its energy states. The LMM assists InfiniteEn in tracking the energy state of the load and dynamically modifying the storage buffers to meet the load's energy requirements. Furthermore, the module can detect and signal any abnormalities in the energy consumption pattern of the load caused by a hardware or software defect. Experiments demonstrate that LMM has a response time of less than 11 ms to energy state changes.
Process Mining for Unstructured Data: Challenges and Research Directions
Authors: Agnes Koschmider, Milda Aleknonytė-Resch, Frederik Fonger, Christian Imenkamp, Arvid Lepsien, Kaan Apaydin, Maximilian Harms, Dominik Janssen, Dominic Langhammer, Tobias Ziolkowski, Yorck Zisgen
Date: 2023-11-30 00:00:00

The application of process mining for unstructured data might significantly elevate novel insights into disciplines where unstructured data is a common data format. To efficiently analyze unstructured data by process mining and to convey confidence into the analysis result, requires bridging multiple challenges. The purpose of this paper is to discuss these challenges, present initial solutions and describe future research directions. We hope that this article lays the foundations for future collaboration on this topic.
Transforming Agriculture with Intelligent Data Management and Insights
Authors: Yu Pan, Jianxin Sun, Hongfeng Yu, Geng Bai, Yufeng Ge, Joe Luck, Tala Awada
Date: 2023-11-07 00:00:00
Tasks: Management

Modern agriculture faces grand challenges to meet increased demands for food, fuel, feed, and fiber with population growth under the constraints of climate change and dwindling natural resources. Data innovation is urgently required to secure and improve the productivity, sustainability, and resilience of our agroecosystems. As various sensors and Internet of Things (IoT) instrumentation become more available, affordable, reliable, and stable, it has become possible to conduct data collection, integration, and analysis at multiple temporal and spatial scales, in real-time, and with high resolutions. At the same time, the sheer amount of data poses a great challenge to data storage and analysis, and the \textit{de facto} data management and analysis practices adopted by scientists have become increasingly inefficient. Additionally, the data generated from different disciplines, such as genomics, phenomics, environment, agronomy, and socioeconomic, can be highly heterogeneous. That is, datasets across disciplines often do not share the same ontology, modality, or format. All of the above make it necessary to design a new data management infrastructure that implements the principles of Findable, Accessible, Interoperable, and Reusable (FAIR). In this paper, we propose Agriculture Data Management and Analytics (ADMA), which satisfies the FAIR principles. Our new data management infrastructure is intelligent by supporting semantic data management across disciplines, interactive by providing various data management/analysis portals such as web GUI, command line, and API, scalable by utilizing the power of high-performance computing (HPC), extensible by allowing users to load their own data analysis tools, trackable by keeping track of different operations on each file, and open by using a rich set of mature open source technologies.
A Characterization of Optimal Queueing Regimes
Authors: Marco Scarsini, Eran Shmaya
Date: 2024-01-24 00:00:00

We consider an M/M/1 queueing model where customers can strategically decide to enter or leave the queue. We characterize the class of queueing regimes such that, for any parameters of the model, the socially efficient behavior is an equilibrium outcome.
Challenge design roadmap
Authors: Hugo Jair Escalante Balderas, Isabelle Guyon, Addison Howard, Walter Reade, Sebastien Treguer
Date: 2024-01-15 00:00:00

Challenges can be seen as a type of game that motivates participants to solve serious tasks. As a result, competition organizers must develop effective game rules. However, these rules have multiple objectives beyond making the game enjoyable for participants. These objectives may include solving real-world problems, advancing scientific or technical areas, making scientific discoveries, and educating the public. In many ways, creating a challenge is similar to launching a product. It requires the same level of excitement and rigorous testing, and the goal is to attract ''customers'' in the form of participants. The process begins with a solid plan, such as a competition proposal that will eventually be submitted to an international conference and subjected to peer review. Although peer review does not guarantee quality, it does force organizers to consider the impact of their challenge, identify potential oversights, and generally improve its quality. This chapter provides guidelines for creating a strong plan for a challenge. The material draws on the preparation guidelines from organizations such as Kaggle 1 , ChaLearn 2 and Tailor 3 , as well as the NeurIPS proposal template, which some of the authors contributed to.
Statistical Characterization of RIS-assisted UAV Communications in Terrestrial and Non-Terrestrial Networks Under Channel Aging
Authors: Thanh Luan Nguyen, Georges Kaddoum, Tri Nhu Do, Zygmunt J. Haas
Date: 2024-01-25 00:00:00

This paper studies the statistical characterization of ground-to-air (G2A) and reconfigurable intelligent surface (RIS)-assisted air-to-ground (A2G) communications with unmanned aerial vehicles (UAVs) in terrestrial and non-terrestrial networks under the impact of channel aging. We first model the G2A and A2G signal-to-noise ratios (SNRs) as non-central complex Gaussian quadratic random variables (RVs) and derive their exact probability density functions, offering a unique characterization for the A2G SNR as the product of two scaled non-central chi-square RVs. Moreover, we also find that, for a large number of RIS elements, the RIS-assisted A2G channel can be characterized as a single Rician fading channel. Our results reveal the presence of channel hardening in A2G communication under low UAV speeds, where we derive the maximum target spectral efficiency (SE) for a system to maintain a consistent required outage level. Meanwhile, high UAV speeds, exceeding 50 m/s, lead to a significant performance degradation, which cannot be mitigated by increasing the number of RIS elements.
MOS-FAD: Improving Fake Audio Detection Via Automatic Mean Opinion Score Prediction
Authors: Wangjin Zhou, Zhengdong Yang, Chenhui Chu, Sheng Li, Raj Dabre, Yi Zhao, Tatsuya Kawahara
Date: 2024-01-24 00:00:00
Tasks: FAD

Automatic Mean Opinion Score (MOS) prediction is employed to evaluate the quality of synthetic speech. This study extends the application of predicted MOS to the task of Fake Audio Detection (FAD), as we expect that MOS can be used to assess how close synthesized speech is to the natural human voice. We propose MOS-FAD, where MOS can be leveraged at two key points in FAD: training data selection and model fusion. In training data selection, we demonstrate that MOS enables effective filtering of samples from unbalanced datasets. In the model fusion, our results demonstrate that incorporating MOS as a gating mechanism in FAD model fusion enhances overall performance.
Optimization-based motion primitive automata for autonomous driving
Authors: Matheus V. A. Pedrosa, Patrick Scheffe, Bassam Alrifaee, Kathrin Flaßkamp
Date: 2024-01-25 00:00:00
Tasks: Autonomous Driving, Multiobjective Optimization, Trajectory Planning

Trajectory planning for autonomous cars can be addressed by primitive-based methods, which encode nonlinear dynamical system behavior into automata. In this paper, we focus on optimal trajectory planning. Since, typically, multiple criteria have to be taken into account, multiobjective optimization problems have to be solved. For the resulting Pareto-optimal motion primitives, we introduce a universal automaton, which can be reduced or reconfigured according to prioritized criteria during planning. We evaluate a corresponding multi-vehicle planning scenario with both simulations and laboratory experiments.
Latency vs precision: Stability preserving perception scheduling
Authors: Rodrigo Aldana-López, Rosario Aragüés, Carlos Sagüés
Date: 2024-01-24 00:00:00
Tasks: Scheduling

In robotic systems, perception latency is a term that refers to the computing time measured from the data acquisition to the moment in which perception output is ready to be used to compute control commands. There is a compromise between perception latency, precision for the overall robotic system, and computational resource usage referred to here as the latency-precision trade-off. In this work, we analyze a robot model given by a linear system, a zero-order hold controller, and measurements taken by several perception mode possibilities with different noise levels. We show that the analysis of this system is reduced to studying an equivalent switching system. Our goal is to schedule perception modes such that stability is attained while optimizing a cost function that models the latency-precision trade-off. Our solution framework comprises three main tools: the construction of perception scheduling policy candidates, admissibility verification for policy candidates, and optimal strategies based on admissible policies.
VALL-T: Decoder-Only Generative Transducer for Robust and Decoding-Controllable Text-to-Speech
Authors: Chenpeng Du, Yiwei Guo, Hankun Wang, Yifan Yang, Zhikang Niu, Shuai Wang, HUI ZHANG, Xie Chen, Kai Yu
Date: 2024-01-25 00:00:00
Tasks: Decoder, Hallucination, Position, text-to-speech, Text to Speech

Recent TTS models with decoder-only Transformer architecture, such as SPEAR-TTS and VALL-E, achieve impressive naturalness and demonstrate the ability for zero-shot adaptation given a speech prompt. However, such decoder-only TTS models lack monotonic alignment constraints, sometimes leading to hallucination issues such as mispronunciation, word skipping and repeating. To address this limitation, we propose VALL-T, a generative Transducer model that introduces shifting relative position embeddings for input phoneme sequence, explicitly indicating the monotonic generation process while maintaining the architecture of decoder-only Transformer. Consequently, VALL-T retains the capability of prompt-based zero-shot adaptation and demonstrates better robustness against hallucinations with a relative reduction of 28.3% in the word error rate.
Improving Design of Input Condition Invariant Speech Enhancement
Authors: Wangyou Zhang, Jee-weon Jung, Shinji Watanabe, Yanmin Qian
Date: 2024-01-25 00:00:00
Tasks: Speech Enhancement

Building a single universal speech enhancement (SE) system that can handle arbitrary input is a demanded but underexplored research topic. Towards this ultimate goal, one direction is to build a single model that handles diverse audio duration, sampling frequencies, and microphone variations in noisy and reverberant scenarios, which we define here as "input condition invariant SE". Such a model was recently proposed showing promising performance; however, its multi-channel performance degraded severely in real conditions. In this paper we propose novel architectures to improve the input condition invariant SE model so that performance in simulated conditions remains competitive while real condition degradation is much mitigated. For this purpose, we redesign the key components that comprise such a system. First, we identify that the channel-modeling module's generalization to unseen scenarios can be sub-optimal and redesign this module. We further introduce a two-stage training strategy to enhance training efficiency. Second, we propose two novel dual-path time-frequency blocks, demonstrating superior performance with fewer parameters and computational costs compared to the existing method. All proposals combined, experiments on various public datasets validate the efficacy of the proposed model, with significantly improved performance on real conditions. Recipe with full model details is released at https://github.com/espnet/espnet.
Combined Generative and Predictive Modeling for Speech Super-resolution
Authors: Heming Wang, Eric W. Healy, DeLiang Wang
Date: 2024-01-25 00:00:00
Tasks: Super-Resolution

Speech super-resolution (SR) is the task that restores high-resolution speech from low-resolution input. Existing models employ simulated data and constrained experimental settings, which limit generalization to real-world SR. Predictive models are known to perform well in fixed experimental settings, but can introduce artifacts in adverse conditions. On the other hand, generative models learn the distribution of target data and have a better capacity to perform well on unseen conditions. In this study, we propose a novel two-stage approach that combines the strengths of predictive and generative models. Specifically, we employ a diffusion-based model that is conditioned on the output of a predictive model. Our experiments demonstrate that the model significantly outperforms single-stage counterparts and existing strong baselines on benchmark SR datasets. Furthermore, we introduce a repainting technique during the inference of the diffusion process, enabling the proposed model to regenerate high-frequency components even in mismatched conditions. An additional contribution is the collection of and evaluation on real SR recordings, using the same microphone at different native sampling rates. We make this dataset freely accessible, to accelerate progress towards real-world speech super-resolution.
Intelli-Z: Toward Intelligible Zero-Shot TTS
Authors: Sunghee Jung, Won Jang, Jaesam Yoon, BongWan Kim
Date: 2024-01-25 00:00:00

Although numerous recent studies have suggested new frameworks for zero-shot TTS using large-scale, real-world data, studies that focus on the intelligibility of zero-shot TTS are relatively scarce. Zero-shot TTS demands additional efforts to ensure clear pronunciation and speech quality due to its inherent requirement of replacing a core parameter (speaker embedding or acoustic prompt) with a new one at the inference stage. In this study, we propose a zero-shot TTS model focused on intelligibility, which we refer to as Intelli-Z. Intelli-Z learns speaker embeddings by using multi-speaker TTS as its teacher and is trained with a cycle-consistency loss to include mismatched text-speech pairs for training. Additionally, it selectively aggregates speaker embeddings along the temporal dimension to minimize the interference of the text content of reference speech at the inference stage. We substantiate the effectiveness of the proposed methods with an ablation study. The Mean Opinion Score (MOS) increases by 9% for unseen speakers when the first two methods are ap- plied, and it further improves by 16% when selective temporal aggregation is applied.
Bayesian adaptive learning to latent variables via Variational Bayes and Maximum a Posteriori
Authors: Hu Hu, Sabato Marco Siniscalchi, Chin-Hui Lee
Date: 2024-01-24 00:00:00
Tasks: Acoustic Scene Classification, Scene Classification, Transfer Learning

In this work, we aim to establish a Bayesian adaptive learning framework by focusing on estimating latent variables in deep neural network (DNN) models. Latent variables indeed encode both transferable distributional information and structural relationships. Thus the distributions of the source latent variables (prior) can be combined with the knowledge learned from the target data (likelihood) to yield the distributions of the target latent variables (posterior) with the goal of addressing acoustic mismatches between training and testing conditions. The prior knowledge transfer is accomplished through Variational Bayes (VB). In addition, we also investigate Maximum a Posteriori (MAP) based Bayesian adaptation. Experimental results on device adaptation in acoustic scene classification show that our proposed approaches can obtain good improvements on target devices, and consistently outperforms other cut-edging algorithms.
Constraint-Aware Mesh Refinement Method by Reachability Set Envelope of Curvature Bounded Paths
Authors: Juho Bae, Ji Hoon Bai, Byung-Yoon Lee, Jun-Yong Lee
Date: 2024-01-25 00:00:00

This paper presents an enhanced direct-method-based approach for the real-time solution of optimal control problems to handle path constraints, such as obstacles. The principal contributions of this work are twofold: first, the existing methods for constructing reachability sets in the literature are extended to derive the envelope of these sets, which determines the region swept by all feasible trajectories between adjacent sample points. Second, we propose a novel method to guarantee constraint violation-free between discrete states in two dimensions through mesh refinement approach. To illustrate the effectiveness of the proposed methodology, numerical simulations are conducted on real-time path planning for fixed-wing unmanned aerial vehicles.
PWM strategy with harmonics injection and modulated frequency triangular carrier. A review
Authors: Antonio Ruiz-Gonzalez, Mario Meco-Gutierrez, Francisco Perez- Hidalgo, Francisco Vargas-Merino, JuanR Heredia-Larrubia
Date: 2024-01-25 00:00:00

A new, programmed pulse width modulation (PWM) technique to control power inverters, which uses a harmonic injection modulator and a frequency modulated triangular carrier, synchronized with the modulating signal is presented in this paper. The instantaneous carrier frequency is adjusted according to a periodic function synchronized with the fundamental term of the modulating signal, in order to maintain the average value of the instantaneous frequency as an odd positive integer multiple of 3, for each period of the modulating signal which is known as the average modulation order. The advantages of using the proposed technique over the conventional PWM techniques are the reduction in the total harmonic distortion and shift the frequency up of the temporal harmonics for any average modulation order. The experimental results show the viability of optimizing the time harmonics generated to minimize the vibrations in an induction motor or avoid the resonant frequencies.The mathematical formulation for the output modulated voltage is defined and the results are also checked experimentally and compared to a sinusoidal PWM technique