pwc-archive/papers-with-abstracts huggingface dataset:
Unifying Distributionally Robust Optimization via Optimal Transport Theory
Authors: Jose Blanchet, Daniel Kuhn, Jiajin Li, Bahar Taskesen
Date: 2023-08-10 00:00:00

In the past few years, there has been considerable interest in two prominent approaches for Distributionally Robust Optimization (DRO): Divergence-based and Wasserstein-based methods. The divergence approach models misspecification in terms of likelihood ratios, while the latter models it through a measure of distance or cost in actual outcomes. Building upon these advances, this paper introduces a novel approach that unifies these methods into a single framework based on optimal transport (OT) with conditional moment constraints. Our proposed approach, for example, makes it possible for optimal adversarial distributions to simultaneously perturb likelihood and outcomes, while producing an optimal (in an optimal transport sense) coupling between the baseline model and the adversarial model.Additionally, the paper investigates several duality results and presents tractable reformulations that enhance the practical applicability of this unified framework.
An abstractive text summarization technique using transformer model with self-attention mechanism
Authors: Sandeep Kumar, Arun Solanki
Date: 2023-06-01 00:00:00
Proceeding: Neural Computing and Applications 2023 6
Tasks: Abstractive Text Summarization, Text Summarization

Creating a summarized version of a text document that still conveys precise meaning is an incredibly complex endeavor in natural language processing (NLP). Abstract text summarization (ATS) is the process of using facts from source sentences and merging them into concise representations while maintaining the content and intent of the text. Manually summarizing large amounts of text are challenging and time-consuming for humans. Therefore, text summarization has become an exciting research focus in NLP. This research paper proposed an ATS model using a Transformer Technique with Self-Attention Mechanism (T2SAM). The self-attention mechanism is added to the transformer to solve the problem of coreference in text. This makes the system to understand the text better. The proposed T2SAM model improves the performance of text summarization. It is trained on the Inshorts News dataset combined with the DUC-2004 shared tasks dataset. The performance of the proposed model has been evaluated using the ROUGE metrics, and it has been shown to outperform the existing state-of-the-art baseline models. The proposed model gives the training loss minimum to 1.8220 from 10.3058 (at the starting point) up to 30 epochs, and it achieved model accuracy 48.50% F1-Score on both the Inshorts and DUC-2004 news datasets.
A coupled-mechanisms modelling framework for neurodegeneration
Authors: Tiantian He, Elinor Thompson, Anna Schroder, Neil P. Oxtoby, Ahmed Abdulaal, Frederik Barkhof, Daniel C. Alexander
Date: 2023-08-10 00:00:00
Tasks: Feature Importance, Model Selection

Computational models of neurodegeneration aim to emulate the evolving pattern of pathology in the brain during neurodegenerative disease, such as Alzheimer's disease. Previous studies have made specific choices on the mechanisms of pathology production and diffusion, or assume that all the subjects lie on the same disease progression trajectory. However, the complexity and heterogeneity of neurodegenerative pathology suggests that multiple mechanisms may contribute synergistically with complex interactions, meanwhile the degree of contribution of each mechanism may vary among individuals. We thus put forward a coupled-mechanisms modelling framework which non-linearly combines the network-topology-informed pathology appearance with the process of pathology spreading within a dynamic modelling system. We account for the heterogeneity of disease by fitting the model at the individual level, allowing the epicenters and rate of progression to vary among subjects. We construct a Bayesian model selection framework to account for feature importance and parameter uncertainty. This provides a combination of mechanisms that best explains the observations for each individual from the ADNI dataset. With the obtained distribution of mechanism importance for each subject, we are able to identify subgroups of patients sharing similar combinations of apparent mechanisms.
Blockchain-based Optimized Client Selection and Privacy Preserved Framework for Federated Learning
Authors: Attia Qammar, Abdenacer Naouri, Jianguo Ding, Huansheng Ning
Date: 2023-07-25 00:00:00
Tasks: Federated Learning

Federated learning is a distributed mechanism that trained large-scale neural network models with the participation of multiple clients and data remains on their devices, only sharing the local model updates. With this feature, federated learning is considered a secure solution for data privacy issues. However, the typical FL structure relies on the client-server, which leads to the single-point-of-failure (SPoF) attack, and the random selection of clients for model training compromised the model accuracy. Furthermore, adversaries try for inference attacks i.e., attack on privacy leads to gradient leakage attacks. We proposed the blockchain-based optimized client selection and privacy-preserved framework in this context. We designed the three kinds of smart contracts such as 1) registration of clients 2) forward bidding to select optimized clients for FL model training 3) payment settlement and reward smart contracts. Moreover, fully homomorphic encryption with Cheon, Kim, Kim, and Song (CKKS) method is implemented before transmitting the local model updates to the server. Finally, we evaluated our proposed method on the benchmark dataset and compared it with state-of-the-art studies. Consequently, we achieved a higher accuracy rate and privacy-preserved FL framework with decentralized nature.
NLLG Quarterly arXiv Report 06/23: What are the most influential current AI Papers?
Authors: Steffen Eger, Christoph Leiter, Jonas Belouadi, Ran Zhang, Aida Kostikova, Daniil Larionov, Yanran Chen, Vivian Fresen
Date: 2023-07-31 00:00:00

The rapid growth of information in the field of Generative Artificial Intelligence (AI), particularly in the subfields of Natural Language Processing (NLP) and Machine Learning (ML), presents a significant challenge for researchers and practitioners to keep pace with the latest developments. To address the problem of information overload, this report by the Natural Language Learning Group at Bielefeld University focuses on identifying the most popular papers on arXiv, with a specific emphasis on NLP and ML. The objective is to offer a quick guide to the most relevant and widely discussed research, aiding both newcomers and established researchers in staying abreast of current trends. In particular, we compile a list of the 40 most popular papers based on normalized citation counts from the first half of 2023. We observe the dominance of papers related to Large Language Models (LLMs) and specifically ChatGPT during the first half of 2023, with the latter showing signs of declining popularity more recently, however. Further, NLP related papers are the most influential (around 60\% of top papers) even though there are twice as many ML related papers in our data. Core issues investigated in the most heavily cited papers are: LLM efficiency, evaluation techniques, ethical considerations, embodied agents, and problem-solving with LLMs. Additionally, we examine the characteristics of top papers in comparison to others outside the top-40 list (noticing the top paper's focus on LLM related issues and higher number of co-authors) and analyze the citation distributions in our dataset, among others.
Big Bang, Low Bar -- Risk Assessment in the Public Arena
Authors: Huw Price
Date: 2023-07-23 00:00:00
Tasks: Management

One of the basic principles of risk management is that we should always keep an eye on ways that things could go badly wrong, even if they seem unlikely. The more disastrous a potential failure, the more improbable it needs to be, before we can safely ignore it. This principle may seem obvious, but it is easily overlooked in public discourse about risk, even by well-qualified commentators who should certainly know better. The present piece is prompted by neglect of the principle in recent discussions about the potential existential risks of artificial intelligence. The failing is not peculiar to this case, but recent debates in this area provide some particularly stark examples of how easily the principle can be overlooked.
The Two Faces of AI in Green Mobile Computing: A Literature Review
Authors: Wander Siemers, June Sallou, Luís Cruz
Date: 2023-07-21 00:00:00
Tasks: Recommendation Systems

Artificial intelligence is bringing ever new functionalities to the realm of mobile devices that are now considered essential (e.g., camera and voice assistants, recommender systems). Yet, operating artificial intelligence takes up a substantial amount of energy. However, artificial intelligence is also being used to enable more energy-efficient solutions for mobile systems. Hence, artificial intelligence has two faces in that regard, it is both a key enabler of desired (efficient) mobile functionalities and a major power draw on these devices, playing a part in both the solution and the problem. In this paper, we present a review of the literature of the past decade on the usage of artificial intelligence within the realm of green mobile computing. From the analysis of 34 papers, we highlight the emerging patterns and map the field into 13 main topics that are summarized in details. Our results showcase that the field is slowly increasing in the past years, more specifically, since 2019. Regarding the double impact AI has on the mobile energy consumption, the energy consumption of AI-based mobile systems is under-studied in comparison to the usage of AI for energy-efficient mobile computing, and we argue for more exploratory studies in that direction. We observe that although most studies are framed as solution papers (94%), the large majority do not make those solutions publicly available to the community. Moreover, we also show that most contributions are purely academic (28 out of 34 papers) and that we need to promote the involvement of the mobile software industry in this field.
Explicifying Neural Implicit Fields for Efficient Dynamic Human Avatar Modeling via a Neural Explicit Surface
Authors: Ruiqi Zhang, Jie Chen, Qiang Wang
Date: 2023-08-07 00:00:00
Tasks: Computational Efficiency

This paper proposes a technique for efficiently modeling dynamic humans by explicifying the implicit neural fields via a Neural Explicit Surface (NES). Implicit neural fields have advantages over traditional explicit representations in modeling dynamic 3D content from sparse observations and effectively representing complex geometries and appearances. Implicit neural fields defined in 3D space, however, are expensive to render due to the need for dense sampling during volumetric rendering. Moreover, their memory efficiency can be further optimized when modeling sparse 3D space. To overcome these issues, the paper proposes utilizing Neural Explicit Surface (NES) to explicitly represent implicit neural fields, facilitating memory and computational efficiency. To achieve this, the paper creates a fully differentiable conversion between the implicit neural fields and the explicit rendering interface of NES, leveraging the strengths of both implicit and explicit approaches. This conversion enables effective training of the hybrid representation using implicit methods and efficient rendering by integrating the explicit rendering interface with a newly proposed rasterization-based neural renderer that only incurs a texture color query once for the initial ray interaction with the explicit surface, resulting in improved inference efficiency. NES describes dynamic human geometries with pose-dependent neural implicit surface deformation fields and their dynamic neural textures both in 2D space, which is a more memory-efficient alternative to traditional 3D methods, reducing redundancy and computational load. The comprehensive experiments show that NES performs similarly to previous 3D approaches, with greatly improved rendering speed and reduced memory cost.
Information decomposition reveals hidden high-order contributions to temporal irreversibility
Authors: Andrea I Luppi, Fernando E. Rosas, Gustavo Deco, Morten L. Kringelbach, Pedro A. M. Mediano
Date: 2023-08-10 00:00:00
Tasks: Time Series

Temporal irreversibility, often referred to as the arrow of time, is a fundamental concept in statistical mechanics. Markers of irreversibility also provide a powerful characterisation of information processing in biological systems. However, current approaches tend to describe temporal irreversibility in terms of a single scalar quantity, without disentangling the underlying dynamics that contribute to irreversibility. Here we propose a broadly applicable information-theoretic framework to characterise the arrow of time in multivariate time series, which yields qualitatively different types of irreversible information dynamics. This multidimensional characterisation reveals previously unreported high-order modes of irreversibility, and establishes a formal connection between recent heuristic markers of temporal irreversibility and metrics of information processing. We demonstrate the prevalence of high-order irreversibility in the hyperactive regime of a biophysical model of brain dynamics, showing that our framework is both theoretically principled and empirically useful. This work challenges the view of the arrow of time as a monolithic entity, enhancing both our theoretical understanding of irreversibility and our ability to detect it in practical applications.
Fully-Passive versus Semi-Passive IRS-Enabled Sensing: SNR Analysis
Authors: Xianxin Song, Xinmin Li, Xiaoqi Qin, Jie Xu
Date: 2023-08-10 00:00:00

This paper compares the signal-to-noise ratio (SNR) performance between the fully-passive intelligent reflecting surface (IRS)-enabled non-line-of-sight (NLoS) sensing versus its semi-passive counterpart. In particular, we consider a basic setup with one base station (BS), one uniform linear array (ULA) IRS, and one point target at the BS's NLoS region, in which the BS and the IRS jointly design the transmit and reflective beamforming for performance optimization. By considering two special cases with the BS-IRS channels being line-of-sight (LoS) and Rayleigh fading, respectively, we derive the corresponding asymptotic sensing SNR when the number of reflecting elements $N$ at the IRS becomes sufficiently large. It is revealed that in the two special cases, the sensing SNR increases proportional to $N^2$ for the semi-passive IRS sensing system, but proportional to $N^4$ for the fully-passive IRS sensing system. As such, the fully-passive IRS sensing system is shown to outperform the semi-passive counterpart when $N$ becomes large, which is due to the fact that the fully-passive IRS sensing enjoys additional reflective beamforming gain from the IRS to the BS that outweighs the resultant path loss in this case. Finally, numerical results are presented to validate our analysis under different transmit and reflective beamforming design schemes.
Wireless Edge Content Broadcast via Integrated Terrestrial and Non-terrestrial Networks
Authors: Feng Wang, Giovanni Geraci, Lingxiang Li, Peng Wang, Tony Q. S. Quek
Date: 2023-08-10 00:00:00

Non-terrestrial networks (NTN) have emerged as a transformative solution to bridge the digital divide and deliver essential services to remote and underserved areas. In this context, low Earth orbit (LEO) satellite constellations offer remarkable potential for efficient cache content broadcast in remote regions, thereby extending the reach of digital services. In this paper, we introduce a novel approach to optimize wireless edge content placement using NTN. Despite wide coverage, the varying NTN transmission capabilities must be carefully aligned with each content placement to maximize broadcast efficiency. In this paper, we introduce a novel approach to optimize wireless edge content placement using NTN, positioning NTN as a complement to TN for achieving optimal content broadcasting. Specifically, we dynamically select content for placement via NTN links. This selection is based on popularity and suitability for delivery through NTN, while considering the orbital motion of LEO satellites. Our system-level case studies, based on a practical LEO constellation, demonstrate the significant improvement in placement speed compared to existing methods, which neglect network mobility. We also demonstrate that NTN links significantly outperform standalone wireless TN solutions, particularly in the early stages of content delivery. This advantage is amplified when there is a higher correlation of content popularity across geographical regions.
Missile guidance law design based on free-time convergent error dynamics
Authors: Yuanhe Liu, Nianhao Xie, Kebo Li, Yangang Liang
Date: 2023-08-09 00:00:00

The design of guidance law can be considered a kind of finite-time error-tracking problem. A unified free-time convergent guidance law design approach based on the error dynamics and the free-time convergence method is proposed in this paper. Firstly, the desired free-time convergent error dynamics approach is proposed, and its convergent time can be set freely, which is independent of the initial states and the guidance parameters. Then, the illustrative guidance laws considering the leading angle constraint, impact angle constraint, and impact time constraint are derived based on the proposed free-time convergent error dynamics respectively. The connection and distinction between the proposed and the existing guidance laws are analyzed theoretically. Finally, the performance of the proposed guidance laws is verified by simulation comparison.
Stabilizing Training with Soft Dynamic Time Warping: A Case Study for Pitch Class Estimation with Weakly Aligned Targets
Authors: Johannes Zeitler, Simon Deniffel, Michael Krause, Meinard Müller
Date: 2023-08-10 00:00:00
Tasks: Dynamic Time Warping, Scheduling

Soft dynamic time warping (SDTW) is a differentiable loss function that allows for training neural networks from weakly aligned data. Typically, SDTW is used to iteratively compute and refine soft alignments that compensate for temporal deviations between the training data and its weakly annotated targets. One major problem is that a mismatch between the estimated soft alignments and the reference alignments in the early training stage leads to incorrect parameter updates, making the overall training procedure unstable. In this paper, we investigate such stability issues by considering the task of pitch class estimation from music recordings as an illustrative case study. In particular, we introduce and discuss three conceptually different strategies (a hyperparameter scheduling, a diagonal prior, and a sequence unfolding strategy) with the objective of stabilizing intermediate soft alignment results. Finally, we report on experiments that demonstrate the effectiveness of the strategies and discuss efficiency and implementation issues.
Capturing Spectral and Long-term Contextual Information for Speech Emotion Recognition Using Deep Learning Techniques
Authors: Samiul Islam, Md. Maksudul Haque, Abu Jobayer Md. Sadat
Date: 2023-08-04 00:00:00
Tasks: Emotion Recognition, Speech Emotion Recognition

Traditional approaches in speech emotion recognition, such as LSTM, CNN, RNN, SVM, and MLP, have limitations such as difficulty capturing long-term dependencies in sequential data, capturing the temporal dynamics, and struggling to capture complex patterns and relationships in multimodal data. This research addresses these shortcomings by proposing an ensemble model that combines Graph Convolutional Networks (GCN) for processing textual data and the HuBERT transformer for analyzing audio signals. We found that GCNs excel at capturing Long-term contextual dependencies and relationships within textual data by leveraging graph-based representations of text and thus detecting the contextual meaning and semantic relationships between words. On the other hand, HuBERT utilizes self-attention mechanisms to capture long-range dependencies, enabling the modeling of temporal dynamics present in speech and capturing subtle nuances and variations that contribute to emotion recognition. By combining GCN and HuBERT, our ensemble model can leverage the strengths of both approaches. This allows for the simultaneous analysis of multimodal data, and the fusion of these modalities enables the extraction of complementary information, enhancing the discriminative power of the emotion recognition system. The results indicate that the combined model can overcome the limitations of traditional methods, leading to enhanced accuracy in recognizing emotions from speech.
Analysis of Insect-Plant Interactions Affected by Mining Operations, A Graph Mining Approach
Authors: Ali Bayat, Mohammad Heydari, Amir Albadvi
Date: 2023-08-08 00:00:00
Tasks: Graph Mining

The decline in ecological connections signifies the potential extinction of species, which can be attributed to disruptions and alterations. The decrease in interconnections among species reflects their susceptibility to changes. For example, certain insects and plants that rely on exclusive interactions with a limited number of species, or even a specific species, face the risk of extinction if they lose these crucial connections. Currently, mining activities pose significant harm to natural ecosystems, resulting in various adverse environmental impacts. In this study, we utilized network science techniques to analyze the ecosystem in a graph-based structure, aiming to conserve the ecosystem affected by mining operations in the northern region of Scotland. The research encompasses identifying the most vital members of the network, establishing criteria for identifying communities within the network, comparing, and evaluating them, using models to predict secondary extinctions that occur when a species is removed from the network, and assessing the extent of network damage. Our study's novelty is utilizing network science approaches to investigate the biological data related to interactions between insects and plants.
Effects of Dynamic and Stochastic Travel Times on the Operation of Mobility-on-Demand Services
Authors: Fynn Wolf, Roman Engelhardt, Yunfei Zhang, Florian Dandl, Klaus Bogenberger
Date: 2023-08-10 00:00:00
Tasks: Travel Time Estimation

Mobility-on-Demand (MoD) services have been an active research topic in recent years. Many studies focused on developing control algorithms to supply efficient services. To cope with a large search space to solve the underlying vehicle routing problem, studies usually apply hard time-constraints on pick-up and drop-off while considering static network travel times to reduce computational time. As travel times in real street networks are dynamic and stochastic, assigned routes considered feasible by the control algorithm in one time step might become infeasible in the next. Since once assigned and confirmed, customers should still be part of the solution, damage control is necessary to counteract this effect. In this study, a detailed simulation framework for MoD services is coupled with a microscopic traffic simulation to create dynamic and stochastic travel times, and tested in a case study for Munich, Germany. Results showed that the combination of inaccurate travel time estimation and damage control strategies for infeasible routes deteriorates the performance of MoD services -- hailing and pooling -- significantly. Moreover, customers suffer from unreliable pick-up time and travel time estimations. Allowing re-assignments of initial vehicle schedules according to updated system states helps to restore system efficiency and reliability, but only to a minor extent.