pwc-archive/papers-with-abstracts huggingface dataset:
Cell atlas of the regenerating human liver after portal vein embolization
Authors: Agnieska Brazovskaja
Date: 2024-07-11 00:00:00
Proceeding: Nature Communications 2024 7

The liver has the remarkable capacity to regenerate. In the clinic, regeneration
is induced by portal vein embolization, which redirects portal blood flow,
resulting in liver hypertrophy in locations with increased blood supply, and
atrophy of embolized segments. Here, we apply single-cell and single-nucleus
transcriptomics on healthy, hypertrophied, and atrophied patient-derived
liver samples to explore cell states in the regenerating liver. Our data unveils
pervasive upregulation of genes associated with developmental processes,
cellular adhesion, and inflammation in post-portal ve i n e m b o l i z a t i o n l i v e r ,
disrupted portal-central hepatocyte zonation, and altered cell subtype com-
position of endothelial and immune cells. Interlineage crosstalk analysis
reveals mesenchymal cells as an interaction hub between immune and endo-
thelial cells, and highlights the importance of extracellular matrix proteins in
liver regeneration. Moreover, we establish tissue-scale iterative indirect
immunofluorescence imaging for high-dimensional spatial analysis of peri-
vascular microenvironments, uncovering changes to tissue architecture in
regenerating liver lobules. Altogether, our data is a rich resource revealing
cellular and histological changes in human liver regeneration.
Data-Driven Combined Central and Distributed Volt/Var Control in Active Distribution Networks
Authors: Xianzhuo Sun, Jing Qiu, Yuechuan Tao, Junhua Zhao
Date: 2022-10-11 00:00:00
Proceeding: IEEE Transactions on Smart Grid 2022 10
Tasks: Fairness

This paper proposes a two-stage combined central and distributed Volt/Var control (VVC) strategy to coordinate both utility-scale and customer-owned photovoltaics (PVs) for voltage regulation in active distribution networks (ADN). In the first stage, with day-ahead PV and load predictions, the central controller (CC) performs optimal power flow (OPF) to determine the hourly dispatch of on-load tap changers (OLTC) and capacitor banks (CBs). In the second stage, the ADN is partitioned into different zones only based on PV and load types. The zone controllers (ZC), which is formulated by deep neural networks (DNN), then communicate with their neighbors
and track the optimal total reactive power support of PVs in each zone. In the offline training process, an analytical derivation to minimize power losses is also provided that can help the input and output selection of DNN. To online dispatch reactive power for individual utility-scale and customer-owned PVs in corresponding zones, a multi-mode reactive power allocation strategy is proposed. Three different modes namely loss reduction mode (LRM), fair power sharing mode (FPM) and voltage security mode (VSM) are designed to meet varying operation requirements, which can mitigate voltage violations with fair participation for customers and reduced power losses for system operators. Numerical simulations are performed on the IEEE 33-bus distribution system to demonstrate the effectiveness of the proposed method in finding a trade-off between voltage security and loss minimization with fairness.
Cost-Effective Coordinated Voltage Control in Active Distribution Networks With Photovoltaics and Mobile Energy Storage Systems
Authors: Xianzhuo Sun, Jing Qiu, Yu Yi, and Yuechuan Tao
Date: 2022-01-01 00:00:00
Proceeding: IEEE TRANSACTIONS ON SUSTAINABLE ENERGY 2022 1

High penetration of intermittent renewable energy
resources (RES) will impose great challenges on the conventional
voltage control in distribution networks. This paper proposes a twostage cost-effective coordinated voltage control strategy to mitigate
fast voltage violations while minimizing the total voltage regulation
cost. In the first stage, the optimal power flow (OPF) is performed to
determine the day-ahead dispatch results of on-load tap changers
(OLTC) and capacitor banks (CBs). The hourly charging schedule
of mobile energy storage systems (MESS) is also obtained through
a spatial-temporal decision model embedded in the optimization
problem. In the second stage, the real-time voltage control strategy
is implemented to regulate the power output of photovoltaics (PVs)
and MESS with one minute resolution. A novel fine-tuning control
method is proposed to adjust the power output, which can further
reduce voltage regulation cost within a certain voltage dead band.
To reserve more fast-response power resources for PVs and MESS,
the voltage regulation cost curves are designed as a piecewise linear
function with different per unit cost in each range. The proposed
approach is tested on the IEEE 33-bus and 123-bus distribution
systems and comparative simulation results show the enhanced
control effect in mitigating voltage violations and saving costs.
TANGO: Training-free Embodied AI Agents for Open-world Tasks
Authors: Filippo Ziliotto, Tommaso Campari, Luciano Serafini, Lamberto Ballan
Date: 2024-12-05 00:00:00
Proceeding: CVPR 2025 1
Tasks: Embodied Question Answering, ObjectGoal Navigation, PointGoal Navigation, Question Answering

Large Language Models (LLMs) have demonstrated excellent capabilities in composing various modules together to create programs that can perform complex reasoning tasks on images. In this paper, we propose TANGO, an approach that extends the program composition via LLMs already observed for images, aiming to integrate those capabilities into embodied agents capable of observing and acting in the world. Specifically, by employing a simple PointGoal Navigation model combined with a memory-based exploration policy as a foundational primitive for guiding an agent through the world, we show how a single model can address diverse tasks without additional training. We task an LLM with composing the provided primitives to solve a specific task, using only a few in-context examples in the prompt. We evaluate our approach on three key Embodied AI tasks: Open-Set ObjectGoal Navigation, Multi-Modal Lifelong Navigation, and Open Embodied Question Answering, achieving state-of-the-art results without any specific fine-tuning in challenging zero-shot scenarios.
QSM-RimDS: A detection and segmentation tool for paramagnetic rim lesions in multiple sclerosis
Authors: Ha Luu, Mert Sisman, Ilhami Kovanlikaya, Tam Vu, Pascal Spincemaille, Yi Wang, Francesca Bagnato, Susan Gauthier, Thanh Nguyen
Date: 2024-12-13 00:00:00
Tasks: Lesion Detection, Segmentation

Paramagnetic rim lesions (PRLs) are an emerging biomarker in multiple sclerosis (MS). Manual identification and rim segmentation of PRLs on quantitative susceptibility mapping (QSM) images are time-consuming. Deep learning-based QSM-RimNet can provide automated PRL detection, but this method does not provide rim segmentation for microglial density quantification and requires precise QSM lesion masks. The purpose of this study is to develop a U-Net-based QSM-RimDS method for joint PRL detection and rim segmentation using readily available T2-weighted (T2W) fluid-attenuated inversion recovery (FLAIR) lesion masks. Two expert readers performed PRL classification and rim segmentation as the reference. Dice similarity coefficient (DSC) was used to assess the agreement between rim segmentation obtained by QSM-RimDS and the manual expert segmentation. The PRL detection performances of QSM-RimDS and QSM-RimNet were evaluated using receiver operating characteristic (ROC) and precision-recall (PR) plots in a five-fold cross validation. A total of 260 PRLs (3.3\%) and 7720 non-PRLs (96.7\%) were identified by the readers. Compared to the expert rim segmentation, QSM-RimDS provided a mean DSC of 0.57 \pm 0.02 with moderate to high agreement (DSC \leq 0.5) in 73.8pm 5.7\% of PRLs over five folds. QSM-RimDS produced better and more consistent detection performance with a mean area under curve (AUC) of 0.754 \pm 0.037 vs. 0.514 \pm 0.121 by QSM-RimNet (46.7\% improvement) on PR plots, and 0.956 \pm 0.034 vs. 0.908 \pm 0.073 (5.3\% improvement) on ROC plots. In conclusion, QSM-RimDS improves PRL detection accuracy compared to QSM-RimNet and unlike QSM-RimNet can provide reasonably accurate rim segmentation.
Predictive Simultaneous Interpretation: Harnessing Large Language Models for Democratizing Real-Time Multilingual Communication
Authors: Kurando IIDA, Kenjiro MIMURA, Nobuo Ito
Date: 2024-07-02 00:00:00

This study introduces a groundbreaking approach to simultaneous interpretation by directly leveraging the predictive capabilities of Large Language Models (LLMs). We present a novel algorithm that generates real-time translations by predicting speaker utterances and expanding multiple possibilities in a tree-like structure. This method demonstrates unprecedented flexibility and adaptability, potentially overcoming the structural differences between languages more effectively than existing systems. Our theoretical analysis, supported by illustrative examples, suggests that this approach could lead to more natural and fluent translations with minimal latency. The primary purpose of this paper is to share this innovative concept with the academic community, stimulating further research and development in this field. We discuss the theoretical foundations, potential advantages, and implementation challenges of this technique, positioning it as a significant step towards democratizing multilingual communication.
Practical IMT and EESS Spectrum Sharing in the 7 to 8 GHz Band
Authors: Elliot Eichen, Arvind Aradhya, Oren Collaco
Date: 2024-12-15 00:00:00
Tasks: Earth Observation

The 7.3 GHz (350 MHz bandwidth) Earth Observation Satellite (EOS) band, while not protected, is used for Passive Sea Surface Temperature (P-SST) measurements that provide important data for weather forecasts, coastal disaster prevention, climate modeling, and oceanographic research. The full 7 GHz band (7.125 to 8.4 GHz), which encompasses these EOS frequencies, is the largest contiguous block of potentially available mid-band spectrum and will play a significant role in meeting the anticipated demand for wireless services. A Real Time Geofenced Spectrum Sharing (RGSS) system is shown to be a practical and near term solution to spectrum sharing between P-SST measurements and 5G or 6G networks in the 7GHz band. RGSS enables IMT networks and EOS radiometers to share 350MHz of overlapping spectrum centered at 7.3 GHz. It prevents interference to P-SST measurements while simultaneously allowing IMT systems un-constrained access to the shared spectrum greater than 99.9% of the time. Subscriber impact during the less than 0.1 percent paused access time can be prevented by using 3GPP defined capabilities and O-RAN APIs to move subscribers to other frequencies. Paused access time data from a proof-of-concept RGSS system is available to academic, government, and industry researchers through a web or programmatic interface.
Cardiovascular Disease Detection By Leveraging Semi-Supervised Learning
Authors: Shaohan Chen, Zheyan Liu, Huili Zheng, Qimin Zhang, Yiru Gong
Date: 2024-12-13 00:00:00

Cardiovascular disease (CVD) persists as a primary cause of death on a global scale, which requires more effective and timely detection methods. Traditional supervised learning approaches for CVD detection rely heavily on large-labeled datasets, which are often difficult to obtain. This paper employs semi-supervised learning models to boost efficiency and accuracy of CVD detection when there are few labeled samples. By leveraging both labeled and vast amounts of unlabeled data, our approach demonstrates improvements in prediction performance, while reducing the dependency on labeled data. Experimental results in a publicly available dataset show that semi-supervised models outperform traditional supervised learning techniques, providing an intriguing approach for the initial identification of cardiovascular disease within clinical environments.
Elucidating microstructural influences on fatigue behavior for additively manufactured Hastelloy X using Bayesian-calibrated crystal plasticity model
Authors: Ajay Kushwaha, Eralp Demir, Amrita Basak
Date: 2024-12-06 00:00:00
Tasks: Bayesian Optimization

Crystal plasticity (CP) modeling is a vital tool for predicting the mechanical behavior of materials, but its calibration involves numerous (>8) constitutive parameters, often requiring time-consuming trial-and-error methods. This paper proposes a robust calibration approach using Bayesian optimization (BO) to identify optimal CP model parameters under fatigue loading conditions. Utilizing cyclic data from additively manufactured Hastelloy X specimens at 500 degree-F, the BO framework, integrated with a Gaussian process surrogate model, significantly reduces the number of required simulations. A novel objective function is developed to match experimental stress-strain data across different strain amplitudes. Results demonstrate that effective CP model calibration is achieved within 75 iterations, with as few as 50 initial simulations. Sensitivity analysis reveals the influence of CP parameters at various loading points on the stress-strain curve. The results show that the stress-strain response is predominantly controlled by parameters related to yield, with increased influence from backstress parameters during compressive loading. In addition, the effect of introducing twins into the synthetic microstructure on fatigue behavior is studied, and a relationship between microstructural features and the fatigue indicator parameter is established. Results show that larger diameter grains, which exhibit a higher Schmid factor and an average misorientation of approximately 42 degrees +/- 1.67 degree, are identified as probable sites for failure. The proposed optimization framework can be applied to any material system or CP model, streamlining the calibration process and improving the predictive accuracy of such models.
Geo-LLaVA: A Large Multi-Modal Model for Solving Geometry Math Problems with Meta In-Context Learning
Authors: Shihao Xu, Yiyang Luo, Wei Shi
Date: 2024-12-12 00:00:00
Tasks: Geometry Problem Solving, In-Context Learning, Math, Spatial Reasoning

Geometry mathematics problems pose significant challenges for large language models (LLMs) because they involve visual elements and spatial reasoning. Current methods primarily rely on symbolic character awareness to address these problems. Considering geometry problem solving is a relatively nascent field with limited suitable datasets and currently almost no work on solid geometry problem solving, we collect a geometry question-answer dataset by sourcing geometric data from Chinese high school education websites, referred to as GeoMath. It contains solid geometry questions and answers with accurate reasoning steps as compensation for existing plane geometry datasets. Additionally, we propose a Large Multi-modal Model (LMM) framework named Geo-LLaVA, which incorporates retrieval augmentation with supervised fine-tuning (SFT) in the training stage, called meta-training, and employs in-context learning (ICL) during inference to improve performance. Our fine-tuned model with ICL attains the state-of-the-art performance of 65.25% and 42.36% on selected questions of the GeoQA dataset and GeoMath dataset respectively with proper inference steps. Notably, our model initially endows the ability to solve solid geometry problems and supports the generation of reasonable solid geometry picture descriptions and problem-solving steps. Our research sets the stage for further exploration of LLMs in multi-modal math problem-solving, particularly in geometry math problems.
CEKER: A Generalizable LLM Framework for Literature Analysis with a Case Study in Unikernel Security
Authors: Alex Wollman, John Hastings
Date: 2024-12-14 00:00:00

Literature reviews are a critical component of formulating and justifying new research, but are a manual and often time-consuming process. This research introduces a novel, generalizable approach to literature analysis called CEKER which uses a three-step process to streamline the collection of literature, the extraction of key insights, and the summarized analysis of key trends and gaps. Leveraging Large Language Models (LLMs), this methodology represents a significant shift from traditional manual literature reviews, offering a scalable, flexible, and repeatable approach that can be applied across diverse research domains. A case study on unikernel security illustrates CEKER's ability to generate novel insights validated against previous manual methods. CEKER's analysis highlighted reduced attack surface as the most prominent theme. Key security gaps included the absence of Address Space Layout Randomization, missing debugging tools, and limited entropy generation, all of which represent important challenges to unikernel security. The study also revealed a reliance on hypervisors as a potential attack vector and emphasized the need for dynamic security adjustments to address real-time threats.
Client-Side Patching against Backdoor Attacks in Federated Learning
Authors: Borja Molina-Coronado
Date: 2024-12-13 00:00:00
Tasks: Federated Learning

Federated learning is a versatile framework for training models in decentralized environments. However, the trust placed in clients makes federated learning vulnerable to backdoor attacks launched by malicious participants. While many defenses have been proposed, they often fail short when facing heterogeneous data distributions among participating clients. In this paper, we propose a novel defense mechanism for federated learning systems designed to mitigate backdoor attacks on the clients-side. Our approach leverages adversarial learning techniques and model patching to neutralize the impact of backdoor attacks. Through extensive experiments on the MNIST and Fashion-MNIST datasets, we demonstrate that our defense effectively reduces backdoor accuracy, outperforming existing state-of-the-art defenses, such as LFighter, FLAME, and RoseAgg, in i.i.d. and non-i.i.d. scenarios, while maintaining competitive or superior accuracy on clean data.
Edge AI-based Radio Frequency Fingerprinting for IoT Networks
Authors: Ahmed Mohamed Hussain, Nada Abughanam, Panos Papadimitratos
Date: 2024-12-13 00:00:00
Tasks: Edge-computing

The deployment of the Internet of Things (IoT) in smart cities and critical infrastructure has enhanced connectivity and real-time data exchange but introduced significant security challenges. While effective, cryptography can often be resource-intensive for small-footprint resource-constrained (i.e., IoT) devices. Radio Frequency Fingerprinting (RFF) offers a promising authentication alternative by using unique RF signal characteristics for device identification at the Physical (PHY)-layer, without resorting to cryptographic solutions. The challenge is two-fold: how to deploy such RFF in a large scale and for resource-constrained environments. Edge computing, processing data closer to its source, i.e., the wireless device, enables faster decision-making, reducing reliance on centralized cloud servers. Considering a modest edge device, we introduce two truly lightweight Edge AI-based RFF schemes tailored for resource-constrained devices. We implement two Deep Learning models, namely a Convolution Neural Network and a Transformer-Encoder, to extract complex features from the IQ samples, forming device-specific RF fingerprints. We convert the models to TensorFlow Lite and evaluate them on a Raspberry Pi, demonstrating the practicality of Edge deployment. Evaluations demonstrate the Transformer-Encoder outperforms the CNN in identifying unique transmitter features, achieving high accuracy (> 0.95) and ROC-AUC scores (> 0.90) while maintaining a compact model size of 73KB, appropriate for resource-constrained devices.
ExclaveFL: Providing Transparency to Federated Learning using Exclaves
Authors: Jinnan Guo, Kapil Vaswani, Andrew Paverd, Peter Pietzuch
Date: 2024-12-13 00:00:00
Tasks: Federated Learning

In federated learning (FL), data providers jointly train a model without disclosing their training data. Despite its privacy benefits, a malicious data provider can simply deviate from the correct training protocol without being detected, thus attacking the trained model. While current solutions have explored the use of trusted execution environment (TEEs) to combat such attacks, there is a mismatch with the security needs of FL: TEEs offer confidentiality guarantees, which are unnecessary for FL and make them vulnerable to side-channel attacks, and focus on coarse-grained attestation, which does not capture the execution of FL training. We describe ExclaveFL, an FL platform that achieves end-to-end transparency and integrity for detecting attacks. ExclaveFL achieves this by employing a new hardware security abstraction, exclaves, which focus on integrity-only guarantees. ExclaveFL uses exclaves to protect the execution of FL tasks, while generating signed statements containing fine-grained, hardware-based attestation reports of task execution at runtime. ExclaveFL then enables auditing using these statements to construct an attested dataflow graph and then check that the FL training jobs satisfies claims, such as the absence of attacks. Our experiments show that ExclaveFL introduces a less than 9% overhead while detecting a wide-range of attacks.
Steganography in Game Actions
Authors: Ching-Chun Chang, Isao Echizen
Date: 2024-12-11 00:00:00
Tasks: Multi-agent Reinforcement Learning

The exchange of messages has always carried with it the timeless challenge of secrecy. From whispers in shadows to the enigmatic notes written in the margins of history, humanity has long sought ways to convey thoughts that remain imperceptible to all but the chosen few. The challenge of subliminal communication has been addressed in various forms of steganography. However, the field faces a fundamental paradox: as the art of concealment advances, so too does the science of revelation, leading to an ongoing evolutionary interplay. This study seeks to extend the boundaries of what is considered a viable steganographic medium. We explore a steganographic paradigm, in which hidden information is communicated through the episodes of multiple agents interacting with an environment. Each agent, acting as an encoder, learns a policy to disguise the very existence of hidden messages within actions seemingly directed toward innocent objectives. Meanwhile, an observer, serving as a decoder, learns to associate behavioural patterns with their respective agents despite their dynamic nature, thereby unveiling the hidden messages. The interactions of agents are governed by the framework of multi-agent reinforcement learning and shaped by feedback from the observer. This framework encapsulates a game-theoretic dilemma, wherein agents face decisions between cooperating to create distinguishable behavioural patterns or defecting to pursue individually optimal yet potentially overlapping episodic actions. As a proof of concept, we exemplify action steganography through the game of labyrinth, a navigation task where subliminal communication is concealed within the act of steering toward a destination, and systematically validate the stego-system in terms of distortion, capacity, secrecy and robustness when subjected to simulated passive and active adversaries.