pwc-archive/papers-with-abstracts huggingface dataset:
Deep Learning-Based Traffic-Aware Base Station Sleep Mode and Cell Zooming Strategy in RIS-Aided Multi-Cell Networks
Authors: Shuo Sun, Chong Huang, Gaojie Chen, Pei Xiao, Rahim Tafazolli
Date: 2024-12-25 00:00:00

Advances in wireless technology have significantly increased the number of wireless connections, leading to higher energy consumption in networks. Among these, base stations (BSs) in radio access networks (RANs) account for over half of the total energy usage. To address this, we propose a multi-cell sleep strategy combined with adaptive cell zooming, user association, and reconfigurable intelligent surface (RIS) to minimize BS energy consumption. This approach allows BSs to enter sleep during low traffic, while adaptive cell zooming and user association dynamically adjust coverage to balance traffic load and enhance data rates through RIS, minimizing the number of active BSs. However, it is important to note that the proposed method may achieve energy-savings at the cost of increased delay, requiring a trade-off between these two factors. Moreover, minimizing BS energy consumption under the delay constraint is a complicated non-convex problem. To address this issue, we model the RIS-aided multi-cell network as a Markov decision process (MDP) and use the proximal policy optimization (PPO) algorithm to optimize sleep mode (SM), cell zooming, and user association. Besides, we utilize a double cascade correlation network (DCCN) algorithm to optimize the RIS reflection coefficients. Simulation results demonstrate that PPO balances energy-savings and delay, while DCCN-optimized RIS enhances BS energy-savings. Compared to systems optimised by the benchmark DQN algorithm, energy consumption is reduced by 49.61%
RIS-Assisted Aerial Non-Terrestrial Networks: An Intelligent Synergy with Deep Reinforcement Learning
Authors: Muhammad Umer, Muhammad Ahmed Mohsin, Aryan Kaushik, Qurrat-Ul-Ain Nadeem, Ali Arshad Nasir, Syed Ali Hassan
Date: 2024-12-25 00:00:00
Tasks: Deep Reinforcement Learning

Reconfigurable intelligent surface (RIS)-assisted aerial non-terrestrial networks (NTNs) offer a promising paradigm for enhancing wireless communications in the era of 6G and beyond. By integrating RIS with aerial platforms such as unmanned aerial vehicles (UAVs) and high-altitude platforms (HAPs), these networks can intelligently control signal propagation, extending coverage, improving capacity, and enhancing link reliability. This article explores the application of deep reinforcement learning (DRL) as a powerful tool for optimizing RIS-assisted aerial NTNs. We focus on hybrid proximal policy optimization (H-PPO), a robust DRL algorithm well-suited for handling the complex, hybrid action spaces inherent in these networks. Through a case study of an aerial RIS (ARIS)-aided coordinated multi-point non-orthogonal multiple access (CoMP-NOMA) network, we demonstrate how H-PPO can effectively optimize the system and maximize the sum rate while adhering to system constraints. Finally, we discuss key challenges and promising research directions for DRL-powered RIS-assisted aerial NTNs, highlighting their potential to transform next-generation wireless networks.
Towards Compatible Semantic Communication: A Perspective on Digital Coding and Modulation
Authors: Guangyi Zhang, Kequan Zhou, Yunlong Cai, Qiyu Hu, Guanding Yu
Date: 2024-12-25 00:00:00
Tasks: Informativeness, Semantic Communication

Semantic communication (SC) is emerging as a pivotal innovation within the 6G framework, aimed at enabling more intelligent transmission. This development has led to numerous studies focused on designing advanced systems through powerful deep learning techniques. Nevertheless, many of these approaches envision an analog transmission manner by formulating the transmitted signals as continuous-valued semantic representation vectors, limiting their compatibility with existing digital systems. To enhance compatibility, it is essential to explore digitized SC systems. This article systematically identifies two promising paradigms for designing digital SC: probabilistic and deterministic approaches, according to the modulation strategies. For both, we first provide a comprehensive analysis of the methodologies. Then, we put forward the principles of designing digital SC systems with a specific focus on informativeness and robustness of semantic representations to enhance performance, along with constellation design. Additionally, we present a case study to demonstrate the effectiveness of these methods. Moreover, this article also explores the intrinsic advantages and opportunities provided by digital SC systems, and then outlines several potential research directions for future investigation.
A System of BSDEs with Singular Terminal Values Arising in Optimal Liquidation with Regime Switching
Authors: Guanxing Fu, Xiaomin Shi, Zuo Quan Xu
Date: 2024-12-26 00:00:00

We study a stochastic control problem with regime switching arising in an optimal liquidation problem with dark pools and multiple regimes. The new feature of this model is that it introduces a system of BSDEs with jumps and with singular terminal values, which appears in literature for the first time. The existence result for this system is obtained. As a result, we solve the stochastic control problem with regime switching. More importantly, the uniqueness result of this system is also obtained, in contrast to merely minimal solutions established in most related literature.
Signatures of prediction during natural listening in MEG data?
Authors: Sahel Azizpour, Britta U. Westner, Jakub Szewczyk, Umut Güçlü, Linda Geerligs
Date: 2024-12-27 00:00:00
Tasks: Prediction, Word Embeddings

The brain uses contextual information and prior knowledge to anticipate upcoming content during language comprehension. Recent research has shown predictive signals can be revealed in pre-onset ECoG activity during naturalistic narrative listening, by building encoding models based on word embeddings from Large Language Models (LLMs). Similarly, evidence for long-range predictive encoding has been observed in fMRI data, where incorporating embeddings for multiple upcoming words in a narrative improves alignment with brain activity. This study examines whether similar predictive information can be detected in MEG, a technique with higher temporal resolution than fMRI but a lower signal-to-noise ratio than ECoG. Our findings indicate that MEG captures pre-onset representations up to 1 second before word onset, consistent with ECoG results. However, unlike fMRI findings, incorporating future word embeddings did not enhance MEG encoding, even for one word into the future, which suggests that the pre-onset encoding may not reflect predictive processing. This work demonstrates that MEG combined with LLMs is a valuable approach for studying language processing in naturalistic narratives and highlights the need to study further what constitutes evidence for prediction during natural listening.
The Illusion-Illusion: Vision Language Models See Illusions Where There are None
Authors: Tomer Ullman
Date: 2024-12-07 00:00:00
Tasks: Diagnostic, Philosophy

Illusions are entertaining, but they are also a useful diagnostic tool in cognitive science, philosophy, and neuroscience. A typical illusion shows a gap between how something "really is" and how something "appears to be", and this gap helps us understand the mental processing that lead to how something appears to be. Illusions are also useful for investigating artificial systems, and much research has examined whether computational models of perceptions fall prey to the same illusions as people. Here, I invert the standard use of perceptual illusions to examine basic processing errors in current vision language models. I present these models with illusory-illusions, neighbors of common illusions that should not elicit processing errors. These include such things as perfectly reasonable ducks, crooked lines that truly are crooked, circles that seem to have different sizes because they are, in fact, of different sizes, and so on. I show that many current vision language systems mistakenly see these illusion-illusions as illusions. I suggest that such failures are part of broader failures already discussed in the literature.
Bottom-up robust modeling for the foraging behavior of Physarum polycephalum
Authors: Damiano Reginato, Daniele Proverbio, Giulia Giordano
Date: 2024-12-27 00:00:00
Tasks: Computational Efficiency

The true slime mold \textit{Physarum polycephalum} has the remarkable capability to perform self-organized activities such as network formation among food sources. Despite well reproducing the emergence of slime networks, existing models are limited in the investigation of the minimal mechanisms, at the microscopic scale, that ensure robust problem-solving capabilities at the macroscopic scale. To this end, we develop three progressively more complex multi-agent models to provide a flexible framework to understand the self-organized foraging and network formation behaviors of \textit{Physarum}. The hierarchy of models allows for a stepwise investigation of the minimal set of rules that allow bio-inspired computing agents to achieve the desired behaviors on nutrient-poor substrates. By introducing a quantitative measure of connectedness among food sources, we assess the sensitivity of the model to user-defined and bio-inspired parameters, as well as the robustness of the model to parameter heterogeneity across agents. We ultimately observe the robust emergence of pattern formation, in line with experimental evidence. Overall, our study sheds light onto the basic mechanisms of self-organization and paves the way towards the development of decentralized strategies for network formation in engineered systems, focusing on trade-offs between biological fidelity and computational efficiency.
The impact of simultaneous infections on phage-host ecology
Authors: Jaye Sudweeks, Christoph Hauert
Date: 2024-12-27 00:00:00

Phages use bacterial host resources to replicate, intrinsically linking phage and host survival. To understand phage dynamics, it is essential to understand phage-host ecology. A key step in this ecology is infection of bacterial hosts. Previous work has explored single and multiple, sequential infections. Here we focus on the theory of simultaneous infections, where multiple phages simultaneously attach to and infect one bacterial host cell. Simultaneous infections are a relevant infection dynamic to consider, especially at high phage densities when many phages attach to a single host cell in a short time window. For high bacterial growth rates, simultaneous infection can result in bi-stability: depending on initial conditions phages go extinct or co-exist with hosts, either at stable densities or through periodic oscillations of a stable limit cycle. This bears important consequences for phage applications such as phage therapy: phages can persist even though they cannot invade. Consequently, through spikes in phage densities it is possible to infect a bacterial population even when the phage basic reproductive number is less than one. In the regime of stable limit cycles, if timed right, only small densities of phage may be necessary.
Metabolic scaling, life history, and the equal fitness paradigm
Authors: Joseph R. Burger
Date: 2024-12-25 00:00:00

Natural selection has produced an extraordinary diversity of life histories spanning many orders of magnitude in body size, vital rates, and biological times. In general, big and cold organisms grow and reproduce slowly and live long lives; small and warm organisms grow and reproduce quickly and live short lives. The Metabolic Theory of Ecology (MTE) predicts equal and opposite scaling exponents of mass-specific biological rates (e.g., respiration, growth, and reproduction) and times (e.g., development, lifespan, and generation) as a function of size. However, empirical support for these predictions varies depending on trait and taxon. Here I: 1) provide background and mixed support for the quarter-power scaling exponents for life history rates and times predicted by MTE, 2) discuss possible explanations, including effects of natural selection on taxonomic and functional groups, and inadequate data for life history traits, 3) briefly summarize the Equal Fitness Paradigm (EFP) as a unifying theory of bioenergetics, life history and demography that does not depend on any particular allometric scalings, and 4) discuss ramifications of the EFP for other biological phenomena, including physiological performance metrics and trophic energetics of ecosystems. I draw mostly from my knowledge of mammals, yet in many cases the mammalian examples can be generalized to other organisms. I end with prospects for further evaluating and extending the EFP.
Self-assembling T7 phage syringes with modular genomes for targeted delivery of penicillin against beta-lactam-resistant Escherichia coli
Authors: Hyunjin Shim
Date: 2024-12-24 00:00:00
Tasks: Specificity

Bacteriophages are promising alternative antimicrobial agents due to their high specificity for host bacteria and minimal immunogenicity in humans. However, their therapeutic application is limited by their nature as biological entities, which can lead to unintended evolutionary consequences such as horizontal gene transfer. In this study, we address these challenges by repurposing only the structural components of bacteriophages as vesicles to deliver antibiotics directly into the cytoplasm of bacterial hosts. This approach is based on two key hypotheses: first, antibiotics such as beta-lactams remain effective against resistant bacteria if injected directly into the cytoplasm, bypassing resistance mechanisms; second, phage structures can be synthesized and self-assembled in vitro using modular genomes and cell-free protein expression systems to carry small molecules such as antibiotics as cargo. To test these hypotheses, we utilized T7 phages and penicillin-resistant Escherichia coli as a model system. First, we designed the T7 phage genome into a modular format containing only the genes encoding structural components and synthesized the gene fragments via de novo gene synthesis. These phage structures were then rebooted in vitro using a cell-free protein expression system in the presence of penicillin G, allowing the antibiotics to be incorporated as cargo during the self-assembly process. Finally, we tested the antimicrobial activity of these antibiotic-loaded phage syringes against penicillin-resistant E. coli. The results demonstrate that phage syringes effectively reduce the population of penicillin-resistant E. coli compared to negative controls, including free penicillin and water. This study highlights the potential of using phage structures as antibiotic delivery vehicles, offering a novel strategy to overcome both antibiotic resistance and the limitations of phage therapy.
Robust and Sparse Portfolio Selection: Quantitative Insights and Efficient Algorithms
Authors: J. Chen, S. D. Ahipaşaoğlu, N. Zhang, Y. Yang
Date: 2024-12-27 00:00:00

We extend the classical mean-variance (MV) framework and propose a robust and sparse portfolio selection model incorporating an ellipsoidal uncertainty set to reduce the impact of estimation errors and fixed transaction costs to penalize over-diversification. In the literature, the MV model under fixed transaction costs is referred to as the sparse or cardinality-constrained MV optimization, which is a mixed integer problem and is challenging to solve when the number of assets is large. We develop an efficient semismooth Newton-based proximal difference-of-convex algorithm to solve the proposed model and prove its convergence to at least a local minimizer with a locally linear convergence rate. We explore properties of the robust and sparse portfolio both analytically and numerically. In particular, we show that the MV optimization is indeed a robust procedure as long as an investor makes the proper choice on the risk-aversion coefficient. We contribute to the literature by proving that there is a one-to-one correspondence between the risk-aversion coefficient and the level of robustness. Moreover, we characterize how the number of traded assets changes with respect to the interaction between the level of uncertainty on model parameters and the magnitude of transaction cost.
Quantiles under ambiguity and risk sharing
Authors: Peng Liu, Tiantian Mao, Ruodu Wang
Date: 2024-12-27 00:00:00
Tasks: Decision Making

Choquet capacities and integrals are central concepts in decision making under ambiguity or model uncertainty, pioneered by Schmeidler. Motivated by risk optimization problems for quantiles under ambiguity, we study the subclass of Choquet integrals, called Choquet quantiles, which generalizes the usual (probabilistic) quantiles, also known as Value-at-Risk in finance, from probabilities to capacities. Choquet quantiles share many features with probabilistic quantiles, in terms of axiomatic representation, optimization formulas, and risk sharing. We characterize Choquet quantiles via only one axiom, called ordinality. We prove that the inf-convolution of Choquet quantiles is again a Choquet quantile, leading to explicit optimal allocations in risk sharing problems for quantile agents under ambiguity. A new class of risk measures, Choquet Expected Shortfall, is introduced, which enjoys most properties of the coherent risk measure Expected Shortfall. Our theory is complemented by optimization algorithms, numerical examples, and a stylized illustration with financial data.
Large Language Models for Automated Data Science: Introducing CAAFE for Context-Aware Automated Feature Engineering
Authors: Noah Hollmann, Samuel Müller, Frank Hutter
Date: 2023-05-05 00:00:00
Proceeding: NeurIPS 2023 11
Tasks: Automated Feature Engineering, AutoML, Feature Engineering

As the field of automated machine learning (AutoML) advances, it becomes increasingly important to incorporate domain knowledge into these systems. We present an approach for doing so by harnessing the power of large language models (LLMs). Specifically, we introduce Context-Aware Automated Feature Engineering (CAAFE), a feature engineering method for tabular datasets that utilizes an LLM to iteratively generate additional semantically meaningful features for tabular datasets based on the description of the dataset. The method produces both Python code for creating new features and explanations for the utility of the generated features. Despite being methodologically simple, CAAFE improves performance on 11 out of 14 datasets -- boosting mean ROC AUC performance from 0.798 to 0.822 across all dataset - similar to the improvement achieved by using a random forest instead of logistic regression on our datasets. Furthermore, CAAFE is interpretable by providing a textual explanation for each generated feature. CAAFE paves the way for more extensive semi-automation in data science tasks and emphasizes the significance of context-aware solutions that can extend the scope of AutoML systems to semantic AutoML. We release our $\href{https://github.com/automl/CAAFE}{code}$, a simple $\href{https://colab.research.google.com/drive/1mCA8xOAJZ4MaB_alZvyARTMjhl6RZf0a}{demo}$ and a $\href{https://pypi.org/project/caafe/}{python\ package}$.
DualGNN: Dual Graph Neural Network for Multimedia Recommendation
Authors: Qifan Wang, Yinwei Wei, Jianhua Yin, Jianlong Wu, Xuemeng Song, Liqiang Nie
Date: 2021-12-24 00:00:00
Proceeding: IEEE Transactions on Multimedia (TMM) 2021 12
Tasks: Graph Neural Network, Multimedia recommendation, Multi-modal Recommendation, Recommendation Systems, Representation Learning

One of the important factors affecting micro-video recommender systems is to model the multi-modal user preference on the micro-video. Despite the remarkable performance of prior arts, they are still limited by fusing the user preference derived from different modalities in a unified manner, ignoring the users tend to place different emphasis on different modalities. Furthermore, modality-missing is ubiquity and unavoidable in the micro-video recommendation, some modalities information of micro-videos are lacked in many cases, which negatively affects the multi-modal fusion operations. To overcome these disadvantages, we propose a novel framework for the micro-video recommendation, dubbed Dual Graph Neural Network (DualGNN), upon the user-microvideo bipartite and user co-occurrence graphs, which leverages the correlation between users to collaboratively mine the particular fusion pattern for each user. Specifically, we first introduce a single-modal representation learning module, which performs graph operations on the user-microvideo graph in each modality to capture single-modal user preferences on different modalities. And then, we devise a multi-modal representation learning module to explicitly model the user’s attentions over different modalities and inductively learn the multi-modal user preference. Finally, we propose a prediction module to rank the potential micro-videos for users. Extensive experiments on two public datasets demonstrate the significant superiority of our DualGNN over state-of-the-arts methods.
Self-Supervised Learning for Multimedia Recommendation
Authors: Zhulin Tao, Xiaohao Liu, Yewei Xia, Xiang Wang, Lifang Yang, Xianglin Huang
Date: 2022-06-30 00:00:00
Proceeding: IEEE Transactions on Multimedia (TMM) 2022 6
Tasks: Contrastive Learning, Data Augmentation, Multimedia recommendation, Multi-modal Recommendation, Representation Learning, Self-Supervised Learning

Learning representations for multimedia content is critical for multimedia recommendation. Current representation learning methods roughly fall into two groups: (1) using the historical interactions to create ID embeddings of users and items, and (2) treating multi-modal data as the side information of items to enrich their ID embeddings. Each user-item interaction offers the supervisory signal to optimize the representation learning by the traditional supervised learning paradigm. Due to the overlook of the multi-modal patterns ( e.g. , co-occurrence of visual, acoustic, textual features in micro-videos a user saw before, and her behavioral features) hidden in the data, these methods are insufficient to create powerful representations and obtain satisfactory recommendation accuracy. To capture multi-modal patterns in the data itself, we go beyond the supervised learning paradigm, and incorporate the idea of self-supervised learning (SSL) into multimedia recommendation. Specifically, SSL consists of two components: (1) data augmentation upon multi-modal contents, where we design three operators — feature dropout (FD), feature masking (FM), feature fine and coarse spaces (FAC) — to generate multiple views of individual items; and (2) contrastive learning, which differentiates the views of an item from the others’ to distill additional supervisory signals. Clearly, SSL enables us to explore and exhibit the underlying relations among modalities, thereby resulting in powerful representations. We denote the generic framework by Self-supervised Learning-guided Multimedia Recommendation (SLMRec). Extensive experiments are performed on three real-world datasets, showing that SLMRec achieves significant improvements over several state-of-the-art baselines like LightGCN [1], MMGCN [2]. Further analysis shows how SSL affects recommendation performance.