pwc-archive/papers-with-abstracts huggingface dataset:
Making Tennis Fairer: The Grand Tiebreaker
Authors: Steven J. Brams, Mehmet S. Ismail, D. Marc Kilgour
Date: 2025-01-13 00:00:00

Tennis, like other games and sports, is governed by rules, including the rules that determine the winner of points, games, sets, and matches. If the two players are equally skilled -- each has the same probability of winning a point when serving or when receiving -- we show that each has an equal chance of winning games, sets, and matches, whether or not sets go to a tiebreak. However, in a women's match that is decided by 2 out of 3 sets, and a men's match that is decided by 3 out of 5 sets, it is possible that the player who wins the most games may not be the player who wins the match. We calculate the probability that this happens and show that it has actually occurred -- most notably, in the 2019 men's Wimbledon final between Novak Djokovic and Roger Federer, which took almost five hours to complete and is considered one of the greatest tennis matches ever (Djokovic won). We argue that the discrepancy between the game winner and the match winner, when it occurs, should be resolved by a Grand Tiebreak (GT) -- played according to the rules of tiebreaks in sets -- because each player has a valid claim to being called the rightful winner. A GT would have the salutary effect of -- even every point -- lest he/she win in sets but lose more games. This would make competition keener throughout a match and probably decrease the need for a GT, because the game and set winner would more likely coincide when the players fight hard for every point.
A Federated Deep Learning Framework for Cell-Free RSMA Networks
Authors: S. Ali Mousavi, Mehdi Monemi, Reza Mohseni, Matti Latva-aho
Date: 2025-01-13 00:00:00
Tasks: CPU, Deep Learning, Deep Reinforcement Learning, Federated Learning

Next-generation wireless networks are poised to benefit significantly from the integration of three key technologies (KTs): Rate-Splitting Multiple Access (RSMA), cell-free architectures, and federated learning. Each of these technologies offers distinct advantages in terms of security, robustness, and distributed structure. In this paper, we propose a novel cell-free network architecture that incorporates RSMA and employs machine learning techniques within a federated framework. This combination leverages the strengths of each KT, creating a synergistic effect that maximizes the benefits of security, robustness, and distributed structure. We formally formulate the access point (AP) selection and precoder design for max-min rate optimization in a cell-free MIMO RSMA network. Our proposed solution scheme involves a three-block procedure. The first block trains deep reinforcement learning (DRL) neural networks to obtain RSMA precoders, assuming full connectivity between APs and user equipments (UEs). The second block uses these precoders and principal component analysis (PCA) to assign APs to UEs by removing a subset of AP-UE connections. The final block fine-tunes the RSMA precoders by incorporating the associated APs into a second DRL network. To leverage the distributed nature of the cell-free network, this process is implemented in a Federated Deep Reinforcement Learning (FDRL) structure operating through the cooperation of APs and a central processing unit (CPU). Simulation results demonstrate that the proposed FDRL approach performs comparably to a benchmark centralized DRL scheme. Our FDRL approach, provides a balanced trade-off, maintaining high performance with enhanced security and reduced processing demands.
QoE-oriented Communication Service Provision for Annotation Rendering in Mobile Augmented Reality
Authors: Lulu Sun, Conghao Zhou, Shisheng Hu, Yupeng Zhu, Nan Cheng, Xu Xia
Date: 2025-01-13 00:00:00

As mobile augmented reality (MAR) continues to evolve, future 6G networks will play a pivotal role in supporting immersive and personalized user experiences. In this paper, we address the communication service provision problem for annotation rendering in edge-assisted MAR, with the objective of optimizing spectrum resource utilization while ensuring the required quality of experience (QoE) for MAR users. To overcome the challenges of user-specific uplink data traffic patterns and the complex operational mechanisms of annotation rendering, we propose a digital twin (DT)-based approach. We first design a DT specifically tailored for MAR applications to learn key annotation rendering mechanisms, enabling the network controller to access MAR application-specific information. Then, we develop a DT based QoE modeling approach to capture the unique relationship between individual user QoE and spectrum resource demands. Finally, we propose a QoE-oriented resource allocation algorithm that decreases resource utilization compared to conventional net work slicing-based approaches. Simulation results demonstrate that our DT-based approach outperforms benchmark approaches in the accuracy and granularity of QoE modeling.
Improved joint modelling of breast cancer radiomics features and hazard by image registration aided longitudinal CT data
Authors: Subrata Mukherjee
Date: 2025-01-12 00:00:00
Tasks: Image Registration, Lesion Detection, Survival Analysis, Variable Selection

Patients with metastatic breast cancer (mBC) undergo continuous medical imaging during treatment, making accurate lesion detection and monitoring over time critical for clinical decisions. Predicting drug response from post-treatment data is essential for personalized care and pharmacological research. In collaboration with the U.S. Food and Drug Administration and Novartis Pharmaceuticals, we analyzed serial chest CT scans from two large-scale Phase III trials, MONALEESA 3 and MONALEESA 7. This paper has two objectives (a) Data Structuring developing a Registration Aided Automated Correspondence (RAMAC) algorithm for precise lesion tracking in longitudinal CT data, and (b) Survival Analysis creating imaging features and models from RAMAC structured data to predict patient outcomes. The RAMAC algorithm uses a two phase pipeline: three dimensional rigid registration aligns CT images, and a distance metric-based Hungarian algorithm tracks lesion correspondence. Using structured data, we developed interpretable models to assess progression-free survival (PFS) in mBC patients by combining baseline radiomics, post-treatment changes (Weeks 8, 16, 24), and demographic features. Radiomics effects were studied across time points separately and through a non-correlated additive framework. Radiomics features were reduced using (a) a regularized (L1-penalized) additive Cox proportional hazards model, and (b) variable selection via best subset selection. Performance, measured using the concordance index (C-index), improved with additional time points. Joint modeling, considering correlations among radiomics effects over time, provided insights into relationships between longitudinal radiomics and survival outcomes.
Collaborative Human Activity Recognition with Passive Inter-Body Electrostatic Field
Authors: Sizhen Bian, Vitor Fortes Rey, Siyu Yuan, Paul Lukowicz
Date: 2025-01-12 00:00:00
Tasks: Activity Recognition, Human Activity Recognition, Sensitivity

The passive body-area electrostatic field has recently been aspiringly explored for wearable motion sensing, harnessing its two thrilling characteristics: full-body motion sensitivity and environmental sensitivity, which potentially empowers human activity recognition both independently and jointly from a single sensing front-end and theoretically brings significant competition against traditional inertial sensor that is incapable in environmental variations sensing. While most works focus on exploring the electrostatic field of a single body as the target, this work, for the first time, quantitatively evaluates the mutual effect of inter-body electrostatic fields and its contribution to collaborative activity recognition. A wearable electrostatic field sensing front-end and wrist-worn prototypes are built, and a sixteen-hour, manually annotated dataset is collected, involving an experiment of manipulating objects both independently and collaboratively. A regression model is finally used to recognize the collaborative activities among users. Despite the theoretical advantages of the body electrostatic field, the recognition of both single and collaborative activities shows unanticipated less-competitive recognition performance compared with the accelerometer. However, It is worth mentioning that this novel sensing modality improves the recognition F-score of user collaboration by 16\% in the fusion result of the two wearable motion sensing modalities, demonstrating the potential of bringing body electrostatic field as a complementary power-efficient signal for collaborative activity tracking using wearables.
Anonymous Attention and Abuse
Authors: Florian Ederer, Paul Goldsmith-Pinkham, Kyle Jensen
Date: 2025-01-13 00:00:00
Tasks: Ethics

We analyze the content of the anonymous online discussion forum Economics Job Market Rumors (EJMR) and document its evolving interactions with external information sources. We focus on three key aspects: the prevalence and impact of links to external domains, the surge in discussions driven by Twitter posts since 2018, and the categorization of individuals whose tweets are most frequently discussed on EJMR. Using data on linked domains, we show how these trends reflect broader changes in the economics profession's digital footprint. Our analysis sheds light on EJMR's informational role but also raises questions about inclusivity and professional ethics in economics.
How low-cost AI universal approximators reshape market efficiency
Authors: Paolo Barucca, Flaviano Morone
Date: 2025-01-13 00:00:00

The efficient market hypothesis (EMH) famously stated that prices fully reflect the information available to traders. This critically depends on the transfer of information into prices through trading strategies. Traders optimise their strategy with models of increasing complexity that identify the relationship between information and profitable trades more and more accurately. Under specific conditions, the increased availability of low-cost universal approximators, such as AI systems, should be naturally pushing towards more advanced trading strategies, potentially making it harder and harder for inefficient traders to profit. In this paper, we leverage on a generalised notion of market efficiency, based on the definition of an equilibrium price process, that allows us to distinguish different levels of model complexity through investors' beliefs, and trading strategies optimisation, and discuss the relationship between AI-powered trading and the time-evolution of market efficiency. Finally, we outline the need for and the challenge of describing out-of-equilibrium market dynamics in an adaptive multi-agent environment.
A neuromorphic camera for tracking passive and active matter with lower data throughput
Authors: Gabriel Britto Monteiro, Megan Lim, Tiffany Cheow Yuen Tan, Avinash Upadhya, Zhuo Liang, Benjamin Agnew, Tomonori Hu, Benjamin J. Eggleton, Christopher Perrella, Kylie Dunning, Kishan Dholakia
Date: 2025-01-13 00:00:00
Tasks: Edge-computing

We demonstrate the merits of using a neuromorphic, or event-based camera (EBC), for tracking of both passive and active matter. For passive matter, we tracked the Brownian motion of different micro-particles and estimated their diffusion coefficient. For active matter, we explored the case of tracking murine spermatozoa and extracted motility parameters from the motion of cells. This has applications in enhancing outcomes for clinical fertility treatments. Using the EBC, we obtain results equivalent to those from an sCMOS camera, yet achieve a reduction in file size of up to two orders of magnitude. This is important in the modern computer era, as it reduces data throughput, and is well-aligned with edge-computing applications. We believe the EBC is an excellent choice, particularly for long-term studies of active matter.
A Reduced Order Iterative Linear Quadratic Regulator (ILQR) Technique for the Optimal Control of Nonlinear Partial Differential Equations
Authors: Aayushman Sharma, Suman Chakravorty
Date: 2025-01-11 00:00:00
Tasks: Model-based Reinforcement Learning

In this paper, we introduce a reduced order model-based reinforcement learning (MBRL) approach, utilizing the Iterative Linear Quadratic Regulator (ILQR) algorithm for the optimal control of nonlinear partial differential equations (PDEs). The approach proposes a novel modification of the ILQR technique: it uses the Method of Snapshots to identify a reduced order Linear Time Varying (LTV) approximation of the nonlinear PDE dynamics around a current estimate of the optimal trajectory, utilizes the identified LTV model to solve a time-varying reduced order LQR problem to obtain an improved estimate of the optimal trajectory along with a new reduced basis, and iterates till convergence. The convergence behavior of the reduced order approach is analyzed and the algorithm is shown to converge to a limit set that is dependent on the truncation error in the reduction. The proposed approach is tested on the viscous Burger's equation and two phase-field models for microstructure evolution in materials, and the results show that there is a significant reduction in the computational burden over the standard ILQR approach, without significantly sacrificing performance.
Estimating Sequential Search Models Based on a Partial Ranking Representation
Authors: Tinghan Zhang
Date: 2025-01-13 00:00:00

The rapid growth of online shopping has made consumer search data increasingly available, opening up new possibilities for empirical research. Sequential search models offer a structured approach for analyzing such data, but their estimation remains difficult. This is because consumers make optimal decisions based on private information revealed in search, which is not observed in typical data. As a result, the model's likelihood function involves high-dimensional integrals that require intensive simulation. This paper introduces a new representation that shows a consumer's optimal search decision-making can be recast as a partial ranking over all actions available throughout the consumer's search process. This reformulation yields the same choice probabilities as the original model but leads to a simpler likelihood function that relies less on simulation. Based on this insight, we provide identification arguments and propose a modified GHK-style simulator that improves both estimation performances and ease of implementation. The proposed approach also generalizes to a wide range of model variants, including those with incomplete search data and structural extensions such as search with product discovery. It enables a tractable and unified estimation strategy across different settings in sequential search models, offering both a new perspective on understanding sequential search and a practical tool for its application.
When Training-Free NAS Meets Vision Transformer: A Neural Tangent Kernel Perspective
Authors: Qiqi Zhou, Yichen Zhu
Date: 2024-03-15 00:00:00
Tasks: image-classification, Image Classification, Semantic Segmentation

This paper investigates the Neural Tangent Kernel (NTK) to search vision transformers without training. In contrast with the previous observation that NTK-based metrics can effectively predict CNNs performance at initialization, we empirically show their inefficacy in the ViT search space. We hypothesize that the fundamental feature learning preference within ViT contributes to the ineffectiveness of applying NTK to NAS for ViT. We both theoretically and empirically validate that NTK essentially estimates the ability of neural networks that learn low-frequency signals, completely ignoring the impact of high-frequency signals in feature learning. To address this limitation, we propose a new method called ViNTK that generalizes the standard NTK to the high-frequency domain by integrating the Fourier features from inputs. Experiments with multiple ViT search spaces on image classification and semantic segmentation tasks show that our method can significantly speed up search costs over prior state-of-the-art NAS for ViT while maintaining similar performance on searched architectures.
Non-Parametric Representation Learning with Kernels
Authors: Pascal Esser, Maximilian Fleissner, Debarghya Ghoshdastidar
Date: 2023-09-05 00:00:00
Tasks: Representation Learning, Self-Supervised Learning

Unsupervised and self-supervised representation learning has become popular in recent years for learning useful features from unlabelled data. Representation learning has been mostly developed in the neural network literature, and other models for representation learning are surprisingly unexplored. In this work, we introduce and analyze several kernel-based representation learning approaches: Firstly, we define two kernel Self-Supervised Learning (SSL) models using contrastive loss functions and secondly, a Kernel Autoencoder (AE) model based on the idea of embedding and reconstructing data. We argue that the classical representer theorems for supervised kernel machines are not always applicable for (self-supervised) representation learning, and present new representer theorems, which show that the representations learned by our kernel models can be expressed in terms of kernel matrices. We further derive generalisation error bounds for representation learning with kernel SSL and AE, and empirically evaluate the performance of these methods in both small data regimes as well as in comparison with neural network based models.
Evaluating SAT and SMT Solvers on Large-Scale Sudoku Puzzles
Authors: Liam Davis, Tairan Ji
Date: 2025-01-15 00:00:00
Tasks: Benchmarking

Modern SMT solvers have revolutionized the approach to constraint satisfaction problems by integrating advanced theory reasoning and encoding techniques. In this work, we evaluate the performance of modern SMT solvers in Z3, CVC5 and DPLL(T) against a standard SAT solver in DPLL. By benchmarking these solvers on novel, diverse 25x25 Sudoku puzzles of various difficulty levels created by our improved Sudoku generator, we examine the impact of advanced theory reasoning and encoding techniques. Our findings demonstrate that modern SMT solvers significantly outperform classical SAT solvers. This work highlights the evolution of logical solvers and exemplifies the utility of SMT solvers in addressing large-scale constraint satisfaction problems.
A new perspective on brain stimulation interventions: Optimal stochastic tracking control of brain network dynamics
Authors: Kangli Dong, Siya Chen, Ying Dan, Lu Zhang, Xinyi Li, Wei Liang, Yue Zhao, Yu Sun
Date: 2025-01-15 00:00:00

Network control theory (NCT) has recently been utilized in neuroscience to facilitate our understanding of brain stimulation effects. A particularly useful branch of NCT is optimal control, which focuses on applying theoretical and computational principles of control theory to design optimal strategies to achieve specific goals in neural processes. However, most existing research focuses on optimally controlling brain network dynamics from the original state to a target state at a specific time point. In this paper, we present the first investigation of introducing optimal stochastic tracking control strategy to synchronize the dynamics of the brain network to a target dynamics rather than to a target state at a specific time point. We utilized fMRI data from healthy groups, and cases of stroke and post-stroke aphasia. For all participants, we utilized a gradient descent optimization method to estimate the parameters for the brain network dynamic system. We then utilized optimal stochastic tracking control techniques to drive original unhealthy dynamics by controlling a certain number of nodes to synchronize with target healthy dynamics. Results show that the energy associated with optimal stochastic tracking control is negatively correlated with the intrinsic average controllability of the brain network system, while the energy of the optimal state approaching control is significantly related to the target state value. For a 100-dimensional brain network system, controlling the five nodes with the lowest tracking energy can achieve relatively acceptable dynamics control effects. Our results suggest that stochastic tracking control is more aligned with the objective of brain stimulation interventions, and is closely related to the intrinsic characteristics of the brain network system, potentially representing a new direction for future brain network optimal control research.
CityDreamer4D: Compositional Generative Model of Unbounded 4D Cities
Authors: Haozhe Xie, Zhaoxi Chen, Fangzhou Hong, Ziwei Liu
Date: 2025-01-15 00:00:00
Tasks: Scene Generation

3D scene generation has garnered growing attention in recent years and has made significant progress. Generating 4D cities is more challenging than 3D scenes due to the presence of structurally complex, visually diverse objects like buildings and vehicles, and heightened human sensitivity to distortions in urban environments. To tackle these issues, we propose CityDreamer4D, a compositional generative model specifically tailored for generating unbounded 4D cities. Our main insights are 1) 4D city generation should separate dynamic objects (e.g., vehicles) from static scenes (e.g., buildings and roads), and 2) all objects in the 4D scene should be composed of different types of neural fields for buildings, vehicles, and background stuff. Specifically, we propose Traffic Scenario Generator and Unbounded Layout Generator to produce dynamic traffic scenarios and static city layouts using a highly compact BEV representation. Objects in 4D cities are generated by combining stuff-oriented and instance-oriented neural fields for background stuff, buildings, and vehicles. To suit the distinct characteristics of background stuff and instances, the neural fields employ customized generative hash grids and periodic positional embeddings as scene parameterizations. Furthermore, we offer a comprehensive suite of datasets for city generation, including OSM, GoogleEarth, and CityTopia. The OSM dataset provides a variety of real-world city layouts, while the Google Earth and CityTopia datasets deliver large-scale, high-quality city imagery complete with 3D instance annotations. Leveraging its compositional design, CityDreamer4D supports a range of downstream applications, such as instance editing, city stylization, and urban simulation, while delivering state-of-the-art performance in generating realistic 4D cities.