pwc-archive/papers-with-abstracts huggingface dataset:
Advancing the Biological Plausibility and Efficacy of Hebbian Convolutional Neural Networks
Authors: Julian Jimenez Nimmo, Esther Mondragon
Date: 2025-01-06 00:00:00

The research presented in this paper advances the integration of Hebbian learning into Convolutional Neural Networks (CNNs) for image processing, systematically exploring different architectures to build an optimal configuration, adhering to biological tenability. Hebbian learning operates on local unsupervised neural information to form feature representations, providing an alternative to the popular but arguably biologically implausible and computationally intensive backpropagation learning algorithm. The suggested optimal architecture significantly enhances recent research aimed at integrating Hebbian learning with competition mechanisms and CNNs, expanding their representational capabilities by incorporating hard Winner-Takes-All (WTA) competition, Gaussian lateral inhibition mechanisms, and Bienenstock-Cooper-Munro (BCM) learning rule in a single model. Mean accuracy classification measures during the last half of test epochs on CIFAR-10 revealed that the resulting optimal model matched its end-to-end backpropagation variant with 75.2% each, critically surpassing the state-of-the-art hard-WTA performance in CNNs of the same network depth (64.6%) by 10.6%. It also achieved competitive performance on MNIST (98%) and STL-10 (69.5%). Moreover, results showed clear indications of sparse hierarchical learning through increasingly complex and abstract receptive fields. In summary, our implementation enhances both the performance and the generalisability of the learnt representations and constitutes a crucial step towards more biologically realistic artificial neural networks.
Leveraging Multimodal LLM for Inspirational User Interface Search
Authors: SeokHyeon Park, Yumin Song, Soohyun Lee, Jaeyoung Kim, Jinwook Seo
Date: 2025-01-29 00:00:00
Tasks: Language Modeling, Language Modelling, Large Language Model, Multimodal Large Language Model

Inspirational search, the process of exploring designs to inform and inspire new creative work, is pivotal in mobile user interface (UI) design. However, exploring the vast space of UI references remains a challenge. Existing AI-based UI search methods often miss crucial semantics like target users or the mood of apps. Additionally, these models typically require metadata like view hierarchies, limiting their practical use. We used a multimodal large language model (MLLM) to extract and interpret semantics from mobile UI images. We identified key UI semantics through a formative study and developed a semantic-based UI search system. Through computational and human evaluations, we demonstrate that our approach significantly outperforms existing UI retrieval methods, offering UI designers a more enriched and contextually relevant search experience. We enhance the understanding of mobile UI design semantics and highlight MLLMs' potential in inspirational search, providing a rich dataset of UI semantics for future studies.
WARP: An Efficient Engine for Multi-Vector Retrieval
Authors: Jan Luca Scheerer, Matei Zaharia, Christopher Potts, Gustavo Alonso, Omar Khattab
Date: 2025-01-29 00:00:00
Tasks: Imputation, Retrieval

Multi-vector retrieval methods such as ColBERT and its recent variant, the ConteXtualized Token Retriever (XTR), offer high accuracy but face efficiency challenges at scale. To address this, we present WARP, a retrieval engine that substantially improves the efficiency of retrievers trained with the XTR objective through three key innovations: (1) WARP$_\text{SELECT}$ for dynamic similarity imputation; (2) implicit decompression, avoiding costly vector reconstruction during retrieval; and (3) a two-stage reduction process for efficient score aggregation. Combined with highly-optimized C++ kernels, our system reduces end-to-end latency compared to XTR's reference implementation by 41x, and achieves a 3x speedup over the ColBERTv2/PLAID engine, while preserving retrieval quality.
Distinguished Quantized Guidance for Diffusion-based Sequence Recommendation
Authors: Wenyu Mao, Shuchang Liu, Haoyang Liu, Haozhe Liu, Xiang Li, Lanatao Hu
Date: 2025-01-29 00:00:00
Tasks: Denoising, Quantization, Sequential Recommendation

Diffusion models (DMs) have emerged as promising approaches for sequential recommendation due to their strong ability to model data distributions and generate high-quality items. Existing work typically adds noise to the next item and progressively denoises it guided by the user's interaction sequence, generating items that closely align with user interests. However, we identify two key issues in this paradigm. First, the sequences are often heterogeneous in length and content, exhibiting noise due to stochastic user behaviors. Using such sequences as guidance may hinder DMs from accurately understanding user interests. Second, DMs are prone to data bias and tend to generate only the popular items that dominate the training dataset, thus failing to meet the personalized needs of different users. To address these issues, we propose Distinguished Quantized Guidance for Diffusion-based Sequence Recommendation (DiQDiff), which aims to extract robust guidance to understand user interests and generate distinguished items for personalized user interests within DMs. To extract robust guidance, DiQDiff introduces Semantic Vector Quantization (SVQ) to quantize sequences into semantic vectors (e.g., collaborative signals and category interests) using a codebook, which can enrich the guidance to better understand user interests. To generate distinguished items, DiQDiff personalizes the generation through Contrastive Discrepancy Maximization (CDM), which maximizes the distance between denoising trajectories using contrastive loss to prevent biased generation for different users. Extensive experiments are conducted to compare DiQDiff with multiple baseline models across four widely-used datasets. The superior recommendation performance of DiQDiff against leading approaches demonstrates its effectiveness in sequential recommendation tasks.
Value Function Decomposition in Markov Recommendation Process
Authors: Xiaobei Wang, Shuchang Liu, Qingpeng Cai, Xiang Li, Lantao Hu, Han Li, Guangming Xie
Date: 2025-01-29 00:00:00
Tasks: Recommendation Systems

Recent advances in recommender systems have shown that user-system interaction essentially formulates long-term optimization problems, and online reinforcement learning can be adopted to improve recommendation performance. The general solution framework incorporates a value function that estimates the user's expected cumulative rewards in the future and guides the training of the recommendation policy. To avoid local maxima, the policy may explore potential high-quality actions during inference to increase the chance of finding better future rewards. To accommodate the stepwise recommendation process, one widely adopted approach to learning the value function is learning from the difference between the values of two consecutive states of a user. However, we argue that this paradigm involves a challenge of Mixing Random Factors: there exist two random factors from the stochastic policy and the uncertain user environment, but they are not separately modeled in the standard temporal difference (TD) learning, which may result in a suboptimal estimation of the long-term rewards and less effective action exploration. As a solution, we show that these two factors can be separately approximated by decomposing the original temporal difference loss. The disentangled learning framework can achieve a more accurate estimation with faster learning and improved robustness against action exploration. As an empirical verification of our proposed method, we conduct offline experiments with simulated online environments built on the basis of public datasets.
Unveiling Discrete Clues: Superior Healthcare Predictions for Rare Diseases
Authors: Chuang Zhao, Hui Tang, Jiheng Zhang, Xiaomeng Li
Date: 2025-01-23 00:00:00

Accurate healthcare prediction is essential for improving patient outcomes. Existing work primarily leverages advanced frameworks like attention or graph networks to capture the intricate collaborative (CO) signals in electronic health records. However, prediction for rare diseases remains challenging due to limited co-occurrence and inadequately tailored approaches. To address this issue, this paper proposes UDC, a novel method that unveils discrete clues to bridge consistent textual knowledge and CO signals within a unified semantic space, thereby enriching the representation semantics of rare diseases. Specifically, we focus on addressing two key sub-problems: (1) acquiring distinguishable discrete encodings for precise disease representation and (2) achieving semantic alignment between textual knowledge and the CO signals at the code level. For the first sub-problem, we refine the standard vector quantized process to include condition awareness. Additionally, we develop an advanced contrastive approach in the decoding stage, leveraging synthetic and mixed-domain targets as hard negatives to enrich the perceptibility of the reconstructed representation for downstream tasks. For the second sub-problem, we introduce a novel codebook update strategy using co-teacher distillation. This approach facilitates bidirectional supervision between textual knowledge and CO signals, thereby aligning semantically equivalent information in a shared discrete latent space. Extensive experiments on three datasets demonstrate our superiority.
Nested Removal of Strictly Dominated Strategies in Infinite Games
Authors: Michele Crescenzi
Date: 2025-01-29 00:00:00

We compare two procedures for the iterated removal of strictly dominated strategies. In the nested procedure, a strategy of a player is removed only if it is dominated by an unremoved strategy. The universal procedure is more comprehensive for it allows the removal of strategies that are dominated by previously removed ones. Outside the class of finite games, the two procedures may lead to different outcomes in that the universal one is always order independent while the other is not. Here we provide necessary and sufficient conditions for the equivalence of the two procedures. The conditions we give are variations of the bounded mechanisms from the literature on full implementation. The two elimination procedures are shown to be equivalent in quasisupermodular games as well as in games with compact strategy spaces and upper semicontinuous payoff functions. We show by example that order independence of the nested procedure is not sufficient for its being equivalent to the universal one.
Coalitional control: a bottom-up approach
Authors: Filiberto Fele, José M. Maestre, Eduardo F. Camacho
Date: 2025-01-29 00:00:00
Tasks: Management

The recent major developments in information technologies have opened interesting possibilities for the effective management of multi-agent systems. In many cases, the important role of central control nodes can now be undertaken by several controllers in a distributed topology that suits better the structure of the system. This opens as well the possibility to promote cooperation between control agents in competitive environments, establishing links between controllers in order to adapt the exchange of critical information to the degree of subsystems' interactions. In this paper a bottom-up approach to coalitional control is presented, where the structure of each agent's model predictive controller is adapted to the time-variant coupling conditions, promoting the formation of coalitions - clusters of control agents where communication is essential to ensure the cooperation - whenever it can bring benefit to the overall system performance.
Coalitional Control: Cooperative game theory and control
Authors: Filiberto Fele, José M. Maestre, Eduardo F. Camacho
Date: 2025-01-29 00:00:00
Tasks: Model Predictive Control

The evolution of information and communication technologies has yielded the means of sharing measurements and other information in an efficient and flexible way, which has enabled the size and complexity of control applications to increase. At the same time, the improvements in the computational and communicational capabilities of control devices have fostered the development of noncentralized control architectures, already motivated by the inherent structural constraints of large-scale systems. Computer-based control approaches such as model predictive control (MPC) are visible beneficiaries of these advances and have registered a significant growth regarding both theoretical and applied fields. Coalitional control focuses on the local interests that motivate the controllers to assemble, an aspect so far rarely contemplated in the distributed control literature. This article presents the main concepts and challenges in coalitional control, and the links with cooperative network game theory.
Artificial Intelligence Clones
Authors: Annie Liang
Date: 2025-01-28 00:00:00

Large language models, trained on personal data, may soon be able to mimic individual personalities. These ``AI clones'' or ``AI agents'' have the potential to transform how people search over one another in contexts ranging from marriage to employment -- indeed, several dating platforms have already begun using AI clones to evaluate potential pairings between users. This paper presents a theoretical framework to study the tradeoff between the substantially expanded search capacity of AI clones, and their imperfect representation of humans. Individual personalities are modeled as points in $k$-dimensional Euclidean space, and their AI clones are modeled as noisy approximations of these personalities. I compare two search regimes: an ``in-person regime'' -- where each person randomly meets some number of individuals and matches to the most compatible among them -- against an ``AI representation regime'' -- in which individuals match to the person whose AI clone is most compatible with their AI clone. I show that a finite number of in-person encounters exceeds the expected payoff from search over infinite AI clones. Moreover, when the dimensionality of personality is large, simply meeting two people in person produces a better expected match than entrusting the process to an AI platform, regardless of the size of its candidate pool.
SCDiar: a streaming diarization system based on speaker change detection and speech recognition
Authors: Naijun Zheng, Xucheng Wan, Kai Liu, Zhou Huan
Date: 2025-01-28 00:00:00
Tasks: Change Detection, speaker-diarization, Speaker Diarization, Speaker Identification, speech-recognition, Speech Recognition

In hours-long meeting scenarios, real-time speech stream often struggles with achieving accurate speaker diarization, commonly leading to speaker identification and speaker count errors. To address this challenge, we propose SCDiar, a system that operates on speech segments, split at the token level by a speaker change detection (SCD) module. Building on these segments, we introduce several enhancements to efficiently select the best available segment for each speaker. These improvements lead to significant gains across various benchmarks. Notably, on real-world meeting data involving more than ten participants, SCDiar outperforms previous systems by up to 53.6\% in accuracy, substantially narrowing the performance gap between online and offline systems.
Predictive Beamforming with Distributed MIMO
Authors: Hasret Taha Akçalı, Özlem Tuğfe Demir, Tolga Girici, Emil Björnson
Date: 2025-01-29 00:00:00

In vehicle-to-everything (V2X) applications, roadside units (RSUs) can be tasked with both sensing and communication functions to enable sensing-assisted communications. Recent studies have demonstrated that distance, angle, and velocity information obtained through sensing can be leveraged to reduce the overhead associated with communication beam tracking. In this work, we extend this concept to scenarios involving multiple distributed RSUs and distributed MIMO (multiple-input multiple-output) systems. We derive the state evolution model, formulate the extended Kalman-filter equations, and implement predictive beamforming for distributed MIMO. Simulation results indicate that, when compared with a co-located massive MIMO antenna array, distributed antennas lead to more uniform and robust sensing performance, coverage, and data rates, while the vehicular user is in motion.
Measurement-Based Modeling and Analysis of UAV Air-Ground Channels at 1 and 4 GHz
Authors: Zhuangzhuang Cui, Cesar Briso-Rodriguez, Ke Guan, Cesar Calvo-Ramirez, Bo Ai, Zhangdui Zhong
Date: 2025-01-28 00:00:00

In the design of unmanned aerial vehicle (UAV) wireless communications, a better understanding of propagation characteristics and an accurate channel model are required. Measurements and comprehensive analysis for the UAV-based air-ground (AG) propagation channel in the vertical dimension are presented in this letter. Based on the measurement data at 1 and 4 GHz, the large-scale and small-scale channel parameters are extracted in the line-of-sight (LOS) and nonLOS case, respectively. The altitude-dependent path loss model is proposed herein. Furthermore, shadow fading and fast fading are statistically analyzed for comprehensively describing the fading behavior. Our results will be useful in the modeling of AG channels and the performance analysis for UAV-enabled wireless communication systems.
Deep Learning in Wireless Communication Receiver: A Survey
Authors: Shadman Rahman Doha, Ahmed Abdelhadi
Date: 2025-01-25 00:00:00
Tasks: Deep Learning, Semantic Communication, Survey

The design of wireless communication receivers to enhance signal processing in complex and dynamic environments is going through a transformation by leveraging deep neural networks (DNNs). Traditional wireless receivers depend on mathematical models and algorithms, which do not have the ability to adapt or learn from data. In contrast, deep learning-based receivers are more suitable for modern wireless communication systems because they can learn from data and adapt accordingly. This survey explores various deep learning architectures such as multilayer perceptrons (MLPs), convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and autoencoders, focusing on their application in the design of wireless receivers. Key modules of a receiver such as synchronization, channel estimation, equalization, space-time decoding, demodulation, decoding, interference cancellation, and modulation classification are discussed in the context of advanced wireless technologies like orthogonal frequency division multiplexing (OFDM), multiple input multiple output (MIMO), semantic communication, task-oriented communication, and next-generation (Next-G) networks. The survey not only emphasizes the potential of deep learning-based receivers in future wireless communication but also investigates different challenges of deep learning-based receivers, such as data availability, security and privacy concerns, model interpretability, computational complexity, and integration with legacy systems.
Coalitional model predictive control of an irrigation canal
Authors: Filiberto Fele, José M. Maestre, Mehdi Hashemy Shahdany, David Muñoz de la Peña, Eduardo F. Camacho
Date: 2025-01-29 00:00:00
Tasks: Model Predictive Control

We present a hierarchical control scheme for large-scale systems whose components can exchange information through a data network. The main goal of the supervisory layer is to find the best compromise between control performance and communicational costs by actively modifying the network topology. The actions taken at the supervisory layer alter the control agents' knowledge of the complete system, and the set of agents with which they can communicate. Each group of linked subsystems, or coalition, is independently controlled based on a decentralized model predictive control (MPC) scheme, managed at the bottom layer. Hard constraints on the inputs are imposed, while soft constraints on the states are considered to avoid feasibility issues. The performance of the proposed control scheme is validated on a model of the Dez irrigation canal, implemented on the accurate simulator for water systems SOBEK. Finally, the results are compared with those obtained using a centralized MPC controller.
Beyond Human Intervention: Algorithmic Collusion through Multi-Agent Learning Strategies
Authors: Suzie Grondin, Arthur Charpentier, Philipp Ratz
Date: 2025-01-28 00:00:00

Collusion in market pricing is a concept associated with human actions to raise market prices through artificially limited supply. Recently, the idea of algorithmic collusion was put forward, where the human action in the pricing process is replaced by automated agents. Although experiments have shown that collusive market equilibria can be reached through such techniques, without the need for human intervention, many of the techniques developed remain susceptible to exploitation by other players, making them difficult to implement in practice. In this article, we explore a situation where an agent has a multi-objective strategy, and not only learns to unilaterally exploit market dynamics originating from other algorithmic agents, but also learns to model the behaviour of other agents directly. Our results show how common critiques about the viability of algorithmic collusion in real-life settings can be overcome through the usage of slightly more complex algorithms.
Propeller Motion of a Devil-Stick using Normal Forcing
Authors: Aakash Khandelwal, Ranjan Mukherjee
Date: 2025-01-29 00:00:00

The problem of realizing rotary propeller motion of a devil-stick in the vertical plane using forces purely normal to the stick is considered. This problem represents a nonprehensile manipulation task of an underactuated system. In contrast with previous approaches, the devil-stick is manipulated by controlling the normal force and its point of application. Virtual holonomic constraints are used to design the trajectory of the center-of-mass of the devil-stick in terms of its orientation angle, and conditions for stable propeller motion are derived. Intermittent large-amplitude forces are used to asymptotically stabilize a desired propeller motion. Simulations demonstrate the efficacy of the approach in realizing stable propeller motion without loss of contact between the actuator and devil-stick.
Event-Based Adaptive Koopman Framework for Optic Flow-Guided Landing on Moving Platforms
Authors: Bazeela Banday, Chandan Kumar Sah, Jishnu Keshavan
Date: 2025-01-28 00:00:00
Tasks: Model Predictive Control

This paper presents an optic flow-guided approach for achieving soft landings by resource-constrained unmanned aerial vehicles (UAVs) on dynamic platforms. An offline data-driven linear model based on Koopman operator theory is developed to describe the underlying (nonlinear) dynamics of optic flow output obtained from a single monocular camera that maps to vehicle acceleration as the control input. Moreover, a novel adaptation scheme within the Koopman framework is introduced online to handle uncertainties such as unknown platform motion and ground effect, which exert a significant influence during the terminal stage of the descent process. Further, to minimize computational overhead, an event-based adaptation trigger is incorporated into an event-driven Model Predictive Control (MPC) strategy to regulate optic flow and track a desired reference. A detailed convergence analysis ensures global convergence of the tracking error to a uniform ultimate bound while ensuring Zeno-free behavior. Simulation results demonstrate the algorithm's robustness and effectiveness in landing on dynamic platforms under ground effect and sensor noise, which compares favorably to non-adaptive event-triggered and time-triggered adaptive schemes.