pwc-archive/papers-with-abstracts huggingface dataset:
A 3D Pancreatic Cancer Model with Integrated Optical Sensors for Noninvasive Metabolism Monitoring and Drug Screening
Authors: Anna Chiara Siciliano, Stefania Forciniti, Valentina Onesto, Helena Iuele, Donatella Delle Cave, Federica Carnevali, Giuseppe Gigli, Enza Lonardo, Loretta L. del Mercato
Date: 2024-07-09 00:00:00

A distinct feature of pancreatic ductal adenocarcinoma (PDAC) is a prominent tumor microenvironment (TME) with remarkable cellular and spatial heterogeneity that meaningfully impacts disease biology and treatment resistance. The dynamic crosstalk between cancer cells and the dense stromal compartment leads to spatially and temporally heterogeneous metabolic alterations, such as acidic pH that contributes to drug resistance in PDAC. Thus, monitoring the extracellular pH metabolic fluctuations within the TME is crucial to predict and to quantify anticancer drug efficacy. Here, a simple and reliable alginate-based 3D PDAC model embedding ratiometric optical pH sensors and cocultures of tumor (AsPC-1) and stromal cells for simultaneously monitoring metabolic pH variations and quantify drug response is presented. By means of time-lapse confocal laser scanning microscopy (CLSM) coupled with a fully automated computational analysis, the extracellular pH metabolic variations are monitored and quantified over time during drug testing with gemcitabine, folfirinox, and paclitaxel, commonly used in PDAC therapy. In particular, the extracellular acidification is more pronounced after drugs treatment, resulting in increased antitumor effect correlated with apoptotic cell death. These findings highlight the importance of studying the influence of cellular metabolic mechanisms on tumor response to therapy in 3D tumor models, this being crucial for the development of personalized medicine approaches.
Asymptotic methods for transaction costs
Authors: Eberhard Mayerhofer
Date: 2024-06-20 00:00:00

We propose a general approximation method for determining optimal trading strategies in markets with proportional transaction costs, with a polynomial approximation of the residual value function. The method is exemplified by several problems from optimally tracking benchmarks, hedging the Log contract, to maximizing utility from terminal wealth. Strategies are also approximated by practically executable, discrete trades. We identify the necessary trade-off between trading frequency and trade sizes to have satisfactory agreement with the theoretically optimal, continuous strategies of infinite activity.
Multiple split approach -- multidimensional probabilistic forecasting of electricity markets
Authors: Katarzyna Maciejowska, Weronika Nitka
Date: 2024-07-10 00:00:00

In this article, a multiple split method is proposed that enables construction of multidimensional probabilistic forecasts of a selected set of variables. The method uses repeated resampling to estimate uncertainty of simultaneous multivariate predictions. This nonparametric approach links the gap between point and probabilistic predictions and can be combined with different point forecasting methods. The performance of the method is evaluated with data describing the German short-term electricity market. The results show that the proposed approach provides highly accurate predictions. The gains from multidimensional forecasting are the largest when functions of variables, such as price spread or residual load, are considered. Finally, the method is used to support a decision process of a moderate generation utility that produces electricity from wind energy and sells it on either a day-ahead or an intraday market. The company makes decisions under high uncertainty because it knows neither the future production level nor the prices. We show that joint forecasting of both market prices and fundamentals can be used to predict the distribution of a profit, and hence helps to design a strategy that balances a level of income and a trading risk.
Detecting new obfuscated malware variants: A lightweight and interpretable machine learning approach
Authors: Oladipo A. Madamidola, Felix Ngobigha, Adnane Ez-zizi
Date: 2024-07-07 00:00:00
Tasks: Interpretable Machine Learning, Malware Detection

Machine learning has been successfully applied in developing malware detection systems, with a primary focus on accuracy, and increasing attention to reducing computational overhead and improving model interpretability. However, an important question remains underexplored: How well can machine learning-based models detect entirely new forms of malware not present in the training data? In this study, we present a machine learning-based system for detecting obfuscated malware that is not only highly accurate, lightweight and interpretable, but also capable of successfully adapting to new types of malware attacks. Our system is capable of detecting 15 malware subtypes despite being exclusively trained on one malware subtype, namely the Transponder from the Spyware family. This system was built after training 15 distinct random forest-based models, each on a different malware subtype from the CIC-MalMem-2022 dataset. These models were evaluated against the entire range of malware subtypes, including all unseen malware subtypes. To maintain the system's streamlined nature, training was confined to the top five most important features, which also enhanced interpretability. The Transponder-focused model exhibited high accuracy, exceeding 99.8%, with an average processing speed of 5.7 microseconds per file. We also illustrate how the Shapley additive explanations technique can facilitate the interpretation of the model predictions. Our research contributes to advancing malware detection methodologies, pioneering the feasibility of detecting obfuscated malware by exclusively training a model on a single or a few carefully selected malware subtypes and applying it to detect unseen subtypes.
A Trustworthy AIoT-enabled Localization System via Federated Learning and Blockchain
Authors: Junfei Wang, He Huang, Jingze Feng, Steven Wong, Lihua Xie, Jianfei Yang
Date: 2024-07-08 00:00:00
Tasks: Federated Learning, Indoor Localization

There is a significant demand for indoor localization technology in smart buildings, and the most promising solution in this field is using RF sensors and fingerprinting-based methods that employ machine learning models trained on crowd-sourced user data gathered from IoT devices. However, this raises security and privacy issues in practice. Some researchers propose to use federated learning to partially overcome privacy problems, but there still remain security concerns, e.g., single-point failure and malicious attacks. In this paper, we propose a framework named DFLoc to achieve precise 3D localization tasks while considering the following two security concerns. Particularly, we design a specialized blockchain to decentralize the framework by distributing the tasks such as model distribution and aggregation which are handled by a central server to all clients in most previous works, to address the issue of the single-point failure for a reliable and accurate indoor localization system. Moreover, we introduce an updated model verification mechanism within the blockchain to alleviate the concern of malicious node attacks. Experimental results substantiate the framework's capacity to deliver accurate 3D location predictions and its superior resistance to the impacts of single-point failure and malicious attacks when compared to conventional centralized federated learning systems.
Benchmarking GNNs Using Lightning Network Data
Authors: Rainer Feichtinger, Florian Grötschla, Lioba Heimbach, Roger Wattenhofer
Date: 2024-07-05 00:00:00
Tasks: Benchmarking

The Bitcoin Lightning Network is a layer 2 protocol designed to facilitate fast and inexpensive Bitcoin transactions. It operates by establishing channels between users, where Bitcoin is locked and transactions are conducted off-chain until the channels are closed, with only the initial and final transactions recorded on the blockchain. Routing transactions through intermediary nodes is crucial for users without direct channels, allowing these routing nodes to collect fees for their services. Nodes announce their channels to the network, forming a graph with channels as edges. In this paper, we analyze the graph structure of the Lightning Network and investigate the statistical relationships between node properties using machine learning, particularly Graph Neural Networks (GNNs). We formulate a series of tasks to explore these relationships and provide benchmarks for GNN architectures, demonstrating how topological and neighbor information enhances performance. Our evaluation of several models reveals the effectiveness of GNNs in these tasks and highlights the insights gained from their application.
Vulnerability Detection in Smart Contracts: A Comprehensive Survey
Authors: Christopher De Baets, Basem Suleiman, Armin Chitizadeh, Imran Razzak
Date: 2024-07-08 00:00:00
Tasks: Articles, Survey, Vulnerability Detection

In the growing field of blockchain technology, smart contracts exist as transformative digital agreements that execute transactions autonomously in decentralised networks. However, these contracts face challenges in the form of security vulnerabilities, posing significant financial and operational risks. While traditional methods to detect and mitigate vulnerabilities in smart contracts are limited due to a lack of comprehensiveness and effectiveness, integrating advanced machine learning technologies presents an attractive approach to increasing effective vulnerability countermeasures. We endeavour to fill an important gap in the existing literature by conducting a rigorous systematic review, exploring the intersection between machine learning and smart contracts. Specifically, the study examines the potential of machine learning techniques to improve the detection and mitigation of vulnerabilities in smart contracts. We analysed 88 articles published between 2018 and 2023 from the following databases: IEEE, ACM, ScienceDirect, Scopus, and Google Scholar. The findings reveal that classical machine learning techniques, including KNN, RF, DT, XG-Boost, and SVM, outperform static tools in vulnerability detection. Moreover, multi-model approaches integrating deep learning and classical machine learning show significant improvements in precision and recall, while hybrid models employing various techniques achieve near-perfect performance in vulnerability detection accuracy. By integrating state-of-the-art solutions, this work synthesises current methods, thoroughly investigates research gaps, and suggests directions for future studies. The insights gathered from this study are intended to serve as a seminal reference for academics, industry experts, and bodies interested in leveraging machine learning to enhance smart contract security.
Temporal Grounding of Activities using Multimodal Large Language Models
Authors: Young Chol Song
Date: 2024-05-30 00:00:00
Tasks: Video Understanding

Temporal grounding of activities, the identification of specific time intervals of actions within a larger event context, is a critical task in video understanding. Recent advancements in multimodal large language models (LLMs) offer new opportunities for enhancing temporal reasoning capabilities. In this paper, we evaluate the effectiveness of combining image-based and text-based large language models (LLMs) in a two-stage approach for temporal activity localization. We demonstrate that our method outperforms existing video-based LLMs. Furthermore, we explore the impact of instruction-tuning on a smaller multimodal LLM, showing that refining its ability to process action queries leads to more expressive and informative outputs, thereby enhancing its performance in identifying specific time intervals of activities. Our experimental results on the Charades-STA dataset highlight the potential of this approach in advancing the field of temporal activity localization and video understanding.
An interpretable deep-learning architecture of capsule networks for identifying cell-type gene expression programs from single-cell RNA-sequencing data
Authors: Suijuan Zhong, Shu Zhang, Xiaoying Fan, Qian Wu, Liying Yan, Ji Dong, Haofeng Zhang, Long Li, Le Sun, Na Pan, Xiaohui Xu, Fuchou Tang, Jun Zhang, Jie Qiao, Xiaoqun Wang1
Date: 2018-05-22 00:00:00
Proceeding: Nature 2018 5

The mammalian prefrontal cortex comprises a set of highly
specialized brain areas containing billions of cells and serves as the
centre of the highest-order cognitive functions, such as memory,
cognitive ability, decision-making and social behaviour1,2. Although
neural circuits are formed in the late stages of human embryonic
development and even after birth, diverse classes of functional cells
are generated and migrate to the appropriate locations earlier in
development. Dysfunction of the prefrontal cortex contributes
to cognitive deficits and the majority of neurodevelopmental
disorders; there is therefore a need for detailed knowledge of the
development of the prefrontal cortex. However, it is still difficult to
identify cell types in the developing human prefrontal cortex and
to distinguish their developmental features. Here we analyse more
than 2,300 single cells in the developing human prefrontal cortex
from gestational weeks 8 to 26 using RNA sequencing. We identify
35 subtypes of cells in six main classes and trace the developmental
trajectories of these cells. Detailed analysis of neural progenitor
cells highlights new marker genes and unique developmental
features of intermediate progenitor cells. We also map the timeline
of neurogenesis of excitatory neurons in the prefrontal cortex and
detect the presence of interneuron progenitors in early developing
prefrontal cortex. Moreover, we reveal the intrinsic development-
dependent signals that regulate neuron generation and circuit
formation using single-cell transcriptomic data analysis. Our
screening and characterization approach provides a blueprint for
understanding the development of the human prefrontal cortex
in the early and mid-gestational stages in order to systematically
dissect the cellular basis and molecular regulation of prefrontal
cortex function in humans.
A single-cell RNA-seq survey of the developmental landscape of the human prefrontal cortex
Authors: Suijuan Zhong, Shu Zhang, Xiaoying Fan, Qian Wu, Liying Yan, Ji Dong, Haofeng Zhang, Long Li, Le Sun, Na Pan, Xiaohui Xu, Fuchou Tang, Jun Zhang, Jie Qiao, Xiaoqun Wang1
Date: 2018-05-22 00:00:00
Proceeding: Nature 2018 5

The mammalian prefrontal cortex comprises a set of highly
specialized brain areas containing billions of cells and serves as the
centre of the highest-order cognitive functions, such as memory,
cognitive ability, decision-making and social behaviour1,2. Although
neural circuits are formed in the late stages of human embryonic
development and even after birth, diverse classes of functional cells
are generated and migrate to the appropriate locations earlier in
development. Dysfunction of the prefrontal cortex contributes
to cognitive deficits and the majority of neurodevelopmental
disorders; there is therefore a need for detailed knowledge of the
development of the prefrontal cortex. However, it is still difficult to
identify cell types in the developing human prefrontal cortex and
to distinguish their developmental features. Here we analyse more
than 2,300 single cells in the developing human prefrontal cortex
from gestational weeks 8 to 26 using RNA sequencing. We identify
35 subtypes of cells in six main classes and trace the developmental
trajectories of these cells. Detailed analysis of neural progenitor
cells highlights new marker genes and unique developmental
features of intermediate progenitor cells. We also map the timeline
of neurogenesis of excitatory neurons in the prefrontal cortex and
detect the presence of interneuron progenitors in early developing
prefrontal cortex. Moreover, we reveal the intrinsic development-
dependent signals that regulate neuron generation and circuit
formation using single-cell transcriptomic data analysis. Our
screening and characterization approach provides a blueprint for
understanding the development of the human prefrontal cortex
in the early and mid-gestational stages in order to systematically
dissect the cellular basis and molecular regulation of prefrontal
cortex function in humans.
Diffusion Models: Tutorial and Survey
Authors: Benyamin Ghojogh, Ali Ghodsi
Date: 2024-07-08 00:00:00
Proceeding: OSF Preprints 2024 7
Tasks: Denoising, Survey

Diffusion models are a family of generative models which work based on a Markovian process. In their forward process, they gradually add noise to data until it becomes a complete noise. In the backward process, the data are gradually generated out of noise. In this tutorial paper, the Denoising Diffusion Probabilistic Model (DDPM) is fully explained. Detailed simplification of the variational lower bound of its likelihood, parameters of the distributions, and the loss function of the diffusion model are discussed. Some modifications to the original DDPM, including non-fixed covariance matrix, reducing the gradient noise, improving the noise schedule, and non-standard Gaussian noise distribution, and conditional diffusion model are introduced. Finally, continuous noise schedule by Stochastic Differential Equation (SDE), where the noise schedule is in a continuous domain, is explained.
Graph Neural Network, ChebNet, Graph Convolutional Network, and Graph Autoencoder: Tutorial and Survey
Authors: Benyamin Ghojogh, Ali Ghodsi
Date: 2024-07-08 00:00:00
Proceeding: OSF Preprints 2024 7
Tasks: Graph Attention, Graph Neural Network, Graph Reconstruction

This is a tutorial paper on graph neural networks including ChebNet, graph convolutional network, graph attention network, and graph autoencoder. It starts with Laplacian of graph, graph Fourier transform, and graph convolution. Then, it is explained how Chebyshev polynomials are used in graph networks to have ChebNet. Afterwards, graph convolutional network and its general framework are introduced. Then, graph attention network is explained as a combination of attention mechanism and graph neural networks. Finally, graph reconstruction autoencoder and graph variational autoencoder are introduced.
Refusing Safe Prompts for Multi-modal Large Language Models
Authors: Zedian Shao, Hongbin Liu, Yuepeng Hu, Neil Zhenqiang Gong
Date: 2024-07-12 00:00:00

Multimodal large language models (MLLMs) have become the cornerstone of today's generative AI ecosystem, sparking intense competition among tech giants and startups. In particular, an MLLM generates a text response given a prompt consisting of an image and a question. While state-of-the-art MLLMs use safety filters and alignment techniques to refuse unsafe prompts, in this work, we introduce MLLM-Refusal, the first method that induces refusals for safe prompts. In particular, our MLLM-Refusal optimizes a nearly-imperceptible refusal perturbation and adds it to an image, causing target MLLMs to likely refuse a safe prompt containing the perturbed image and a safe question. Specifically, we formulate MLLM-Refusal as a constrained optimization problem and propose an algorithm to solve it. Our method offers competitive advantages for MLLM model providers by potentially disrupting user experiences of competing MLLMs, since competing MLLM's users will receive unexpected refusals when they unwittingly use these perturbed images in their prompts. We evaluate MLLM-Refusal on four MLLMs across four datasets, demonstrating its effectiveness in causing competing MLLMs to refuse safe prompts while not affecting non-competing MLLMs. Furthermore, we explore three potential countermeasures-adding Gaussian noise, DiffPure, and adversarial training. Our results show that though they can mitigate MLLM-Refusal's effectiveness, they also sacrifice the accuracy and/or efficiency of the competing MLLM. The code is available at https://github.com/Sadcardation/MLLM-Refusal.
Automatic Pruning of Fine-tuning Datasets for Transformer-based Language Models
Authors: Mohammadreza Tayaranian, Seyyed Hasan Mozafari, Brett H. Meyer, James J. Clark, Warren J. Gross
Date: 2024-07-11 00:00:00
Tasks: Natural Language Understanding, Navigate

Transformer-based language models have shown state-of-the-art performance on a variety of natural language understanding tasks. To achieve this performance, these models are first pre-trained on general corpus and then fine-tuned on downstream tasks. Previous work studied the effect of pruning the training set of the downstream tasks on the performance of the model on its evaluation set. In this work, we propose an automatic dataset pruning method for the training set of fine-tuning tasks. Our method is based on the model's success rate in correctly classifying each training data point. Unlike previous work which relies on user feedback to determine subset size, our method automatically extracts training subsets that are adapted for each pair of model and fine-tuning task. Our method provides multiple subsets for use in dataset pruning that navigate the trade-off between subset size and evaluation accuracy. Our largest subset, which we also refer to as the winning ticket subset, is on average $3 \times$ smaller than the original training set of the fine-tuning task. Our experiments on 5 downstream tasks and 2 language models show that, on average, fine-tuning on the winning ticket subsets results in a $0.1 \%$ increase in the evaluation performance of the model.
CCML: Curriculum and Contrastive Learning Enhanced Meta-Learner for Personalized Spatial Trajectory Prediction
Authors: Jing Zhao, Jiajie Xu, Yuan Xu, Junhua Fang, Pingfu Chao and Xiaofang Zhou
Date: 2024-03-01 00:00:00
Proceeding: journal 2024 3
Tasks: Contrastive Learning, Meta-Learning, Trajectory Prediction

Spatial trajectory prediction is a fundamental problem for diverse location-based applications. However, existing methods fall short in learning and generalization, and cannot sufficiently capture users’ spatiotemporal preferences, especially for cold-start users. Moreover, these methods do not explicitly consider the diversity of moving patterns among users and trajectories, i.e., the learning difficulty of different user and trajectory samples, thus hindering the improvement of prediction accuracy. To solve these problems, we propose a novel Curriculum and Contrastive Learning Enhanced Meta-Learner (CCML) that transfers knowledge from users with rich data to cold-start users. Specifically, a Contrastive-based Trajectory Predictor (CTP) is designed as the base model, which utilizes contrastive learning technique on both user-level and trajectory-level, aiming to facilitate a more profound understanding and differentiation of the varied travel behaviors and preferences exhibited by individuals. Meanwhile, CCML also incorporates the curriculum learning and the hard sample mining strategies. It simultaneously considers the learning difficulty of both user and trajectory samples, and presents the learning tasks by an easy-to-hard curriculum. By learning more challenging combinations of user and trajectory samples in each meta-learning iteration, the meta-learner can converge to a better status. Extensive experiments on two real-world datasets demonstrate the superiority of our models.