Miracle-dz/UniFAR-FTUs huggingface dataset:
Explaining a machine learning decision to physicians via counterfactuals

Machine learning models perform well on several healthcare tasks and can help reduce the burden on the healthcare system. However, the lack of explainability is a major roadblock to their adoption in hospitals. \textit{How can the decision of an ML model be explained to a physician? } The explanations considered in this paper are counterfactuals (CFs), hypothetical scenarios that would have resulted in the opposite outcome. Specifically, time-series CFs are investigated, inspired by the way physicians converse and reason out decisions `I would have given the patient a vasopressor if their blood pressure was lower and falling'. Key properties of CFs that are particularly meaningful in clinical settings are outlined: physiological plausibility, relevance to the task and sparse perturbations. Past work on CF generation does not satisfy these properties, specifically plausibility in that realistic time-series CFs are not generated. A variational autoencoder (VAE)-based approach is proposed that captures these desired properties. The method produces CFs that improve on prior approaches quantitatively (more plausible CFs as evaluated by their likelihood w.r.t original data distribution, and 100$\times$ faster at generating CFs) and qualitatively (2$\times$ more plausible and relevant) as evaluated by three physicians.
Forecasting Electric Vehicle Charging Station Occupancy: Smarter Mobility Data Challenge

The transport sector is a major contributor to greenhouse gas emissions in Europe. Shifting to electric vehicles (EVs) powered by a low-carbon energy mix would reduce carbon emissions. However, to support the development of electric mobility, a better understanding of EV charging behaviours and more accurate forecasting models are needed. To fill that gap, the Smarter Mobility Data Challenge has focused on the development of forecasting models to predict EV charging station occupancy. This challenge involved analysing a dataset of 91 charging stations across four geographical areas over seven months in 2020-2021. The forecasts were evaluated at three levels of aggregation (individual stations, areas and global) to capture the inherent hierarchical structure of the data. The results highlight the potential of hierarchical forecasting approaches to accurately predict EV charging station occupancy, providing valuable insights for energy providers and EV users alike. This open dataset addresses many real-world challenges associated with time series, such as missing values, non-stationarity and spatio-temporal correlations. Access to the dataset, code and benchmarks are available at https://gitlab.com/smarter-mobility-data-challenge/tutorials to foster future research.
Ising string beyond the Nambu-Goto action

A major result of the Effective String Theory (EST) description of confinement is the so called"low energy universality,"which states that the first few terms of the large distance expansion of any EST are universal and coincide with those of the Nambu-Goto action. Going beyond this approximation is one of the most interesting open problems in the EST. In the higher order terms beyond Nambu-Goto several important pieces of physical information are encoded, which could improve our understanding of the physical mechanisms behind confinement and of the physical degrees of freedoms which originate the EST. In this paper we evaluate numerically the first two of these corrections in the case of the three dimensional gauge Ising model. The first of them turns out to be negative: $\gamma_3=-0.00048(4)$, similar (but not equal) to the one recently measured in the $SU(2)$ Yang Mills theory in three dimensions and compatible with the bootstrap bound $\gamma_3 \geq -\frac{1}{768}$.
Impact of Pilot Contamination Between Operators With Interfering Reconfigurable Intelligent Surfaces

In this paper, we study the impact of pilot contamination in a system where two operators serve their respective users with the assistance of two wide-band reconfigurable intelligent surfaces (RIS), each belonging to a single operator. We consider one active user per operator and they use disjoint narrow frequency bands. Although each RIS is dedicated to a single operator, both users' transmissions are reflected by both RISs. We show that this creates a new kind of pilot contamination effect when pilots are transmitted simultaneously. Since combating inter-operator pilot contamination in RIS-assisted networks would require long pilot signal sequences to maintain orthogonality among the users of different operators, we propose the orthogonal configurations of the RISs. Numerical results show that this approach completely eliminates pilot contamination, and significantly improves the performance in terms of channel estimation and equalization by removing the channel estimation bias.
Zero-Shot Dialogue Relation Extraction by Relating Explainable Triggers and Relation Names

Developing dialogue relation extraction (DRE) systems often requires a large amount of labeled data, which can be costly and time-consuming to annotate. In order to improve scalability and support diverse, unseen relation extraction, this paper proposes a method for leveraging the ability to capture triggers and relate them to previously unseen relation names. Specifically, we introduce a model that enables zero-shot dialogue relation extraction by utilizing trigger-capturing capabilities. Our experiments on a benchmark DialogRE dataset demonstrate that the proposed model achieves significant improvements for both seen and unseen relations. Notably, this is the first attempt at zero-shot dialogue relation extraction using trigger-capturing capabilities, and our results suggest that this approach is effective for inferring previously unseen relation types. Overall, our findings highlight the potential for this method to enhance the scalability and practicality of DRE systems.
SE#PCFG: Semantically Enhanced PCFG for Password Analysis and Cracking

Much research has been done on user-generated textual passwords. Surprisingly, semantic information in such passwords remain underinvestigated, with passwords created by English- and/or Chinese-speaking users being more studied with limited semantics. This paper fills this gap by proposing a general framework based on semantically enhanced PCFG (probabilistic context-free grammars) named SE#PCFG. It allowed us to consider 43 types of semantic information, the richest set considered so far, for semantic password analysis. Applying SE#PCFG to 17 large leaked password databases of user speaking four languages (English, Chinese, German and French), we demonstrate its usefulness and report a wide range of new insights about password semantics at different levels such as cross-website password correlations. Furthermore, based on SE#PCFG and a new systematic smoothing method, we proposed the Semantically Enhanced Password Cracking Architecture (SEPCA). To compare the performance of SEPCA against three state-of-the-art (SOTA) benchmarks in terms of the password coverage rate: two other PCFG variants and FLA. Our experimental results showed that SEPCA outperformed all the three benchmarks consistently and significantly across 52 test cases, by up to 21.53%, 52.55% and 7.86%, respectively, at the user level (with duplicate passwords). At the level of unique passwords, SEPCA also beats the three benchmarks by up to 33.32%, 86.19% and 10.46%, respectively. The results demonstrated the power of SEPCA as a new password cracking framework.
Self-Enhancement Improves Text-Image Retrieval in Foundation Visual-Language Models

The emergence of cross-modal foundation models has introduced numerous approaches grounded in text-image retrieval. However, on some domain-specific retrieval tasks, these models fail to focus on the key attributes required. To address this issue, we propose a self-enhancement framework, A^{3}R, based on the CLIP-ViT/G-14, one of the largest cross-modal models. First, we perform an Attribute Augmentation strategy to enrich the textual description for fine-grained representation before model learning. Then, we propose an Adaption Re-ranking method to unify the representation space of textual query and candidate images and re-rank candidate images relying on the adapted query after model learning. The proposed framework is validated to achieve a salient improvement over the baseline and other teams' solutions in the cross-modal image retrieval track of the 1st foundation model challenge without introducing any additional samples. The code is available at \url{https://github.com/CapricornGuang/A3R}.
RECAP: Retrieval-Enhanced Context-Aware Prefix Encoder for Personalized Dialogue Response Generation

Endowing chatbots with a consistent persona is essential to an engaging conversation, yet it remains an unresolved challenge. In this work, we propose a new retrieval-enhanced approach for personalized response generation. Specifically, we design a hierarchical transformer retriever trained on dialogue domain data to perform personalized retrieval and a context-aware prefix encoder that fuses the retrieved information to the decoder more effectively. Extensive experiments on a real-world dataset demonstrate the effectiveness of our model at generating more fluent and personalized responses. We quantitatively evaluate our model’s performance under a suite of human and automatic metrics and find it to be superior compared to state-of-the-art baselines on English Reddit conversations.
Multi-agent Exploration with Sub-state Entropy Estimation

Researchers have integrated exploration techniques into multi-agent reinforcement learning (MARL) algorithms, drawing on their remarkable success in deep reinforcement learning. Nonetheless, exploration in MARL presents a more substantial challenge, as agents need to coordinate their efforts in order to achieve comprehensive state coverage. Reaching a unanimous agreement on which kinds of states warrant exploring can be a struggle for agents in this context. We introduce \textbf{M}ulti-agent \textbf{E}xploration based on \textbf{S}ub-state \textbf{E}ntropy (MESE) to address this limitation. This novel approach incentivizes agents to explore states cooperatively by directing them to achieve consensus via an extra team reward. Calculating the additional reward is based on the novelty of the current sub-state that merits cooperative exploration. MESE employs a conditioned entropy approach to select the sub-state, using particle-based entropy estimation to calculate the entropy. MESE is a plug-and-play module that can be seamlessly integrated into most existing MARL algorithms, which makes it a highly effective tool for reinforcement learning. Our experiments demonstrate that MESE can substantially improve the MAPPO's performance on various tasks in the StarCraft multi-agent challenge (SMAC).
Tailoring high precision polynomial architected material constitutive responses via inverse design

The design of specified nonlinear mechanical responses into a structure or material is a highly sought after capability, which would have a significant impact in areas such as wave tailoring in metamaterials, impact mitigation, soft robotics, and biomedicine. Here, we present a topology optimization approach to design structures for desired polynomial nonlinear behavior, wherein we formulate the problem in such a way as to decouple the nonlinear response from the stiffness. We show results across qualitatively different polynomial behaviors while achieving a high degree of precision, creating a path toward analytically tractable nonlinear dynamical systems. The approach enables access to previously difficult to design for, or hitherto unachieved, nonlinear behavior via optimized structures, which can furthermore be incorporated as unit cells of designer materials with tailored nonlinear properties.
Learning World Models with Identifiable Factorization

Extracting a stable and compact representation of the environment is crucial for efficient reinforcement learning in high-dimensional, noisy, and non-stationary environments. Different categories of information coexist in such environments -- how to effectively extract and disentangle these information remains a challenging problem. In this paper, we propose IFactor, a general framework to model four distinct categories of latent state variables that capture various aspects of information within the RL system, based on their interactions with actions and rewards. Our analysis establishes block-wise identifiability of these latent variables, which not only provides a stable and compact representation but also discloses that all reward-relevant factors are significant for policy learning. We further present a practical approach to learning the world model with identifiable blocks, ensuring the removal of redundants but retaining minimal and sufficient information for policy optimization. Experiments in synthetic worlds demonstrate that our method accurately identifies the ground-truth latent variables, substantiating our theoretical findings. Moreover, experiments in variants of the DeepMind Control Suite and RoboDesk showcase the superior performance of our approach over baselines.
An efficient implicit constraint resolution scheme for interactive FE simulations

This paper presents a novel implicit scheme for the constraint resolution in real-time finite element simulations in the presence of contact and friction. Instead of using the standard motion correction scheme, we propose an iterative method where the constraint forces are corrected in Newton iterations. In this scheme, we are able to update the constraint directions recursively, providing more accurate contact and friction response. However, updating the constraint matrices leads to massive computation costs. To address the issue, we propose separating the constraint direction and geometrical mapping in the contact Jacobian matrix and reformulating the schur-complement of the system matrix. When combined with GPU-based parallelization, the reformulation provides a very efficient updating process for the constraint matrices in the recursive corrective motion scheme. Our method enables the possibility to handle the inconsistency of constraint directions at the beginning and the end of time steps. At the same time, the resolution process is kept as efficient as possible. We evaluate the performance of our fast-updating scheme in a contact simulation and compare it with the standard updating scheme.
Randomness and early termination: what makes a game exciting?

In this paper we revisit an open problem posed by Aldous on the max-entropy win-probability martingale: given two players of equal strength, such that the win-probability is a martingale diffusion, which of these processes has maximum entropy and hence gives the most excitement for the spectators? We study a terminal-boundary value problem for the nonlinear parabolic PDE $2\partial_te(t,x)=\log(-\partial_{xx}e(t,x))$ derived by Aldous and prove its wellposedness and regularity of its solution by combining PDE analysis and probabilistic tools, in particular the reformulation as a stochastic control problem with restricted control set, which allows us to deduce strict ellipticity. We establish key qualitative properties of the solution including concavity, monotonicity, convergence to a steady state for long remaining time and the asymptotic behaviour shortly before the terminal time. Moreover, we construct convergent numerical approximations. The analytical and numerical results allow us to highlight the behaviour of the win-probability process in the present case where the match may end early, in contrast to recent work by Backhoff-Veraguas and Beiglb\"ock where the match always runs the full length.
A Survey of NOMA: State of the Art, Key Techniques, Open Challenges, Security Issues and Future Trends

Non-orthogonal multiple access (NOMA) systems can serve multiple users in contrast to orthogonal multiple-access (OMA), which makes use of the limited time or frequency domain resources. It can help to address the unprecedented technological advancements of the sixth generation (6G) network, which include high spectral efficiency, high flexibility, low transmission latency, massive connectivity, higher cell-edge throughput, and user fairness. NOMA has gained widespread recognition as a viable technology for future wireless networks. The main characteristic that sets NOMA apart from the conventional orthogonal multiple access (OMA) techniques is its ability to handle more users than orthogonal resource slots. NOMA techniques can serve multiple users in the same resource block by multiplexing users in power or code domain. The purpose of this paper is to provide a thorough overview of the promising NOMA systems. Initially, we discuss the state-of-the-art and existing literature on NOMA systems. This study also examines the practical deployment of NOMA implementation and key performance indicators. An overview of the most recent NOMA advancements and applications is also given in this survey. We also briefly discuss that multiple-input multiple-output (MIMO), visible light communications, cognitive and cooperative communications, intelligent reflecting surfaces (IRS), unmanned aerial vehicles (UAV), HetNets, backscatter communication, mobile edge computing (MEC), deep learning (DL), and other emerging and existing wireless technologies can all be flexibly combined with NOMA. This study surveys a thorough analysis of the interactions between NOMA and the aforementioned technologies. Lastly, we will highlight a number of difficult open problems and security issues that need to be resolved for NOMA, along with pertinent possibilities and potential future research directions.
Integrating Distributed Architectures in Highly Modular RL Libraries

Advancing reinforcement learning (RL) requires tools that are flexible enough to easily prototype new methods while avoiding impractically slow experimental turnaround times. To match the first requirement, the most popular RL libraries advocate for highly modular agent composability, which facilitates experimentation and development. To solve challenging environments within reasonable time frames, scaling RL to large sampling and computing resources has proved a successful strategy. However, this capability has been so far difficult to combine with modularity. In this work, we explore design choices to allow agent composability both at a local and distributed level of execution. We propose a versatile approach that allows the definition of RL agents at different scales through independent reusable components. We demonstrate experimentally that our design choices allow us to reproduce classical benchmarks, explore multiple distributed architectures, and solve novel and complex environments while giving full control to the user in the agent definition and training scheme definition. We believe this work can provide useful insights to the next generation of RL libraries.
Hidden Classification Layers: a study on Data Hidden Representations with a Higher Degree of Linear Separability between the Classes

In the context of classification problems, Deep Learning (DL) approaches represent state of art. Many DL approaches are based on variations of standard multi-layer feed-forward neural networks. These are also referred to as deep networks. The basic idea is that each hidden neural layer accomplishes a data transformation which is expected to make the data representation “somewhat more linearly separable” than the previous one to obtain a final data representation which is as linearly separable as possible. However, determining the appropriate neural network parameters that can perform these transformations is a critical problem. In this paper, we investigate the impact on deep network classifier performances of a training approach favouring solutions where data representations at the hidden layers have a higher degree of linear separability between the classes with respect to standard methods. To this aim, we propose a neural network architecture which induces an error function involving the outputs of all the network layers. Although similar approaches have already been partially discussed in the past literature, here we propose a new architecture with a novel error function and an extensive experimental analysis. This experimental analysis was made in the context of image classification tasks considering four widely used datasets. The results show that our approach improves the accuracy on the test set in all the considered cases.
Mechanism Design for Collaborative Normal Mean Estimation

We study collaborative normal mean estimation, where $m$ strategic agents collect i.i. d samples from a normal distribution $\mathcal{N}(\mu, \sigma^2)$ at a cost. They all wish to estimate the mean $\mu$. By sharing data with each other, agents can obtain better estimates while keeping the cost of data collection small. To facilitate this collaboration, we wish to design mechanisms that encourage agents to collect a sufficient amount of data and share it truthfully, so that they are all better off than working alone. In naive mechanisms, such as simply pooling and sharing all the data, an individual agent might find it beneficial to under-collect and/or fabricate data, which can lead to poor social outcomes. We design a novel mechanism that overcomes these challenges via two key techniques: first, when sharing the others' data with an agent, the mechanism corrupts this dataset proportional to how much the data reported by the agent differs from the others; second, we design minimax optimal estimators for the corrupted dataset. Our mechanism, which is Nash incentive compatible and individually rational, achieves a social penalty (sum of all agents' estimation errors and data collection costs) that is at most a factor 2 of the global minimum. When applied to high dimensional (non-Gaussian) distributions with bounded variance, this mechanism retains these three properties, but with slightly weaker results. Finally, in two special cases where we restrict the strategy space of the agents, we design mechanisms that essentially achieve the global minimum.
Budgeted Multi-Armed Bandits with Asymmetric Confidence Intervals

We study the stochastic Budgeted Multi-Armed Bandit (MAB) problem, where a player chooses from $K$ arms with unknown expected rewards and costs. The goal is to maximize the total reward under a budget constraint. A player thus seeks to choose the arm with the highest reward-cost ratio as often as possible. Current state-of-the-art policies for this problem have several issues, which we illustrate. To overcome them, we propose a new upper confidence bound (UCB) sampling policy, $\omega$-UCB, that uses asymmetric confidence intervals. These intervals scale with the distance between the sample mean and the bounds of a random variable, yielding a more accurate and tight estimation of the reward-cost ratio compared to our competitors. We show that our approach has logarithmic regret and consistently outperforms existing policies in synthetic and real settings.
Centrality in Production Networks and International Technology Diffusion

This study examines whether the structure of global value chains (GVCs) affects international spillovers of research and development (R&D). Although the presence of ``hub'' countries in GVCs has been confirmed by previous studies, the role of these hub countries in the diffusion of the technology has not been analyzed. Using a sample of 21 countries and 14 manufacturing industries during the period 1995-2007, I explore the role of hubs as the mediator of knowledge by classifying countries and industries based on a ``centrality'' measure. I find that R&D spillovers from exporters with High centrality are the largest, suggesting that hub countries play an important role in both gathering and diffusing knowledge. I also find that countries with Middle centrality are getting important in the diffusion of knowledge. Finally, positive spillover effects from own are observed only in the G5 countries.