Miracle-dz/UniFAR-FTUs huggingface dataset:
Towards Attributions of Input Variables in a Coalition

This paper aims to develop a new attribution method to explain the conflict between individual variables' attributions and their coalition's attribution from a fully new perspective. First, we find that the Shapley value can be reformulated as the allocation of Harsanyi interactions encoded by the AI model. Second, based the re-alloction of interactions, we extend the Shapley value to the attribution of coalitions. Third we ective. We derive the fundamental mechanism behind the conflict. This conflict come from the interaction containing partial variables in their coalition.
Heat transfer in drop-laden turbulence

Abstract Heat transfer by large deformable drops in a turbulent flow is a complex and rich-in-physics system, in which drop deformation, breakage and coalescence influence the transport of heat. We study this problem by coupling direct numerical simulation (DNS) of turbulence with a phase-field method for the interface description. Simulations are run at fixed-shear Reynolds and Weber numbers. To evaluate the influence of microscopic flow properties, like momentum/thermal diffusivity, on macroscopic flow properties, like mean temperature or heat transfer rates, we consider four different values of the Prandtl number, which is the momentum to thermal diffusivity ratio: $Pr=1$, $Pr=2$, $Pr=4$ and $Pr=8$. The drop volume fraction is $\varPhi \simeq 5.4\,\%$ for all cases. Drops are initially warmer than the turbulent carrier fluid and release heat at different rates depending on the value of $Pr$, but also on their size and on their own dynamics (topology, breakage, drop–drop interaction). Computing the time behaviour of the drops and carrier fluid average temperatures, we clearly show that an increase of $Pr$ slows down the heat transfer process. We explain our results by a simplified phenomenological model: we show that the time behaviour of the drop average temperature is self-similar, and a universal behaviour can be found upon rescaling by $t/Pr^{2/3}$. Accordingly, the heat transfer coefficient $\mathcal {H}$ (respectively its dimensionless counterpart, the Nusselt number $Nu$) scales as $\mathcal {H}\sim Pr^{-2/3}$ (respectively $Nu\sim Pr^{1/3}$) at the beginning of the simulation, and tends to $\mathcal {H}\sim Pr^{-1/2}$ (respectively $Nu\sim Pr^{1/2}$) at later times. These different scalings can be explained via the boundary layer theory and are consistent with previous theoretical/numerical predictions.
Walking-by-Logic: Signal Temporal Logic-Guided Model Predictive Control for Bipedal Locomotion Resilient to External Perturbations

This study proposes a novel planning framework based on a model predictive control formulation that incorporates signal temporal logic (STL) specifications for task completion guarantees and robustness quantification. This marks the first-ever study to apply STL-guided trajectory optimization for bipedal locomotion push recovery, where the robot experiences unexpected disturbances. Existing recovery strategies often struggle with complex task logic reasoning and locomotion robustness evaluation, making them susceptible to failures caused by inappropriate recovery strategies or insufficient robustness. To address this issue, the STL-guided framework generates optimal and safe recovery trajectories that simultaneously satisfy the task specification and maximize the locomotion robustness. Our framework outperforms a state-of-the-art locomotion controller in a high-fidelity dynamic simulation, especially in scenarios involving crossed-leg maneuvers. Furthermore, it demonstrates versatility in tasks such as locomotion on stepping stones, where the robot must select from a set of disjointed footholds to maneuver successfully.
Dynamic Smagorinsky Modeled Large-Eddy Simulations of Turbulence Using Tetrahedral Meshes

Eddy-resolving numerical computations of turbulent flows are emerging as viable alternatives to Reynolds Averaged Navier-Stokes (RANS) calculations for flows with an intrinsically steady mean state due to the advances in large-scale parallel computing. In these computations, medium to large turbulent eddies are resolved by the numerics while the smaller or subgrid scales are either modeled or taken care of by the inherent numerical dissipation. To advance the state of the art of unstructured-mesh turbulence simulation capabilities, large eddy simulations (LES) using the dynamic Smagorinsky model (DSM) on tetrahedral meshes are carried out with the space-time conservation element, solution element (CESE) method
Graph-Based Permutation Patterns for the Analysis of Task-Related FMRI Signals on DTI Networks in Mild Cognitive Impairment

Permutation Entropy ($PE$) is a powerful nonlinear analysis technique for univariate time series. Recently, Permutation Entropy for Graph signals ($PEG$) has been proposed to extend PE to data residing on irregular domains. However, $PEG$ is limited as it provides a single value to characterise a whole graph signal. Here, we introduce a novel approach to evaluate graph signals \emph{at the vertex level}: graph-based permutation patterns. Synthetic datasets show the efficacy of our method. We reveal that dynamics in graph signals, undetectable with $PEG$, can be discerned using our graph-based patterns. These are then validated in DTI and fMRI data acquired during a working memory task in mild cognitive impairment, where we explore functional brain signals on structural white matter networks. Our findings suggest that graph-based permutation patterns in individual brain regions change as the disease progresses, demonstrating potential as a method of analyzing graph-signals at a granular scale.
6-DOF All-Terrain Cyclocopter

This paper presents the design of a 6-DOF all-terrain micro aerial vehicle and two control strategies for multimodal flight, which are experimentally validated. The micro aerial vehicle is propelled by four motors and controlled by a single servo for the control of the cycloidal rotors(cyclorotors) speed and lift direction. Despite the addition of the servo, the system remains underactuated. To address the traditional underactuation problem of cycloidal rotor aircraft, we increase the number of control variables. We propose a PID and a nonlinear model predictive control (NMPC) framework to tackle the model's nonlinearities and achieve control of attitude, position, and their derivatives.Experimental results demonstrate the effectiveness of the proposed multimodal control strategy for 6-DOF all-terrain micro aerial vehicles. The vehicle can operate in aerial, terrestrial, and aquatic modes and can adapt to different terrains and environmental conditions. Our approach enhances the vehicle's performance in each mode of operation, and the results show the advantages of the proposed strategy compared to other control strategies.
Interactive Content Diversity and User Exploration in Online Movie Recommenders: A Field Experiment

Recommender systems often struggle to strike a balance between matching users' tastes and providing unexpected recommendations. When recommendations are too narrow and fail to cover the full range of users' preferences, the system is perceived as useless. Conversely, when the system suggests too many items that users don't like, it is considered impersonal or ineffective. To better understand user sentiment about the breadth of recommendations given by a movie recommender, we conducted interviews and surveys and found out that many users considered narrow recommendations to be useful, while a smaller number explicitly wanted greater breadth. Additionally, we designed and ran an online field experiment with a larger user group, evaluating two new interfaces designed to provide users with greater access to broader recommendations. We looked at user preferences and behavior for two groups of users: those with higher initial movie diversity and those with lower diversity. Among our findings, we discovered that different level of exploration control and users' subjective preferences on interfaces are more predictive of their satisfaction with the recommender.
Leukoaraiosis and stroke severity scores in post-rtPA intracerebral haemorrhage

Introduction Post thrombolytic symptomatic intracerebral haemorrhage (sICH) is a major concern in patients who had acute ischaemic stroke. Leukoaraiosis (LA) is reported to be related with sICH after intravenous thrombolytic treatment. However, the influence of LA and stroke neurological and imaging severity scores is still debated. Objective To evaluate if LA or severity scores are related with sICH in patients who had acute ischaemic stroke who received thrombolytic therapy. And, predictors for sICH were also studied with adjustment of baseline severity scores. Methods This was a retrospective, analytical study. The inclusion criteria were adult patients diagnosed as acute ischaemic stroke who received the recombinant tissue plasminogen activator (rtPA) treatment within 4.5 hours. The study period was between May 2007 and November 2016. Predictors for sICH were determined using logistic regression analysis. Results During the study period, there were 504 eligible patients. Of those, 45 patients (8.92%) had sICH. Among nine factors in the final model for predicting sICH, there were four independent factors including previous antiplatelet therapy, previous anticoagulant therapy, presence of LA and hyperdense artery sign. The highest adjusted OR was previous anticoagulant therapy (5.08 with 95% CI of 1.18 to 11.83), while the LA factor had adjusted OR (95% CI) of 2.52 (1.01 to 6.30). Conclusions LA, hyperdense artery sign, previous antiplatelet therapy and previous anticoagulant therapy were associated with post-rtPA sICH. Further studies are required to confirm the results of this study.
The Neurophysiology and Computational Mechanisms of Object Representation

A concise description of the representation of objects and faces provided by inferior temporal cortex neurons in the primate (macaque) brain is followed by new findings about how this representation operates in natural scenes and allows a number of objects and their relative spatial position in a scene to be encoded. Then a computational approach to how the object recognition processes described are performed in the primate brain is discussed as well as the types of strategy that the human brain uses to solve the enormous computational problem of invariant object recognition in complex natural scenes (Rolls 2008b; Rolls and Deco 2002; Rolls and Stringer 2006b). This contribution aims to provide a closely linked neurophysiological and computational approach to object recognition and categorization. Other approaches are represented in this volume and elsewhere (Biederman 1987; Fukushima 1989; Riesenhuber and Poggio 2000; Serre et al. 2007), and are compared with current approaches (Rolls 2008b; Rolls and Deco 2002; Rolls and Stringer 2006b).
SAMN: A Sample Attention Memory Network Combining SVM and NN in One Architecture

Support vector machine (SVM) and neural networks (NN) have strong complementarity. SVM focuses on the inner operation among samples while NN focuses on the operation among the features within samples. Thus, it is promising and attractive to combine SVM and NN, as it may provide a more powerful function than SVM or NN alone. However, current work on combining them lacks true integration. To address this, we propose a sample attention memory network (SAMN) that effectively combines SVM and NN by incorporating sample attention module, class prototypes, and memory block to NN. SVM can be viewed as a sample attention machine. It allows us to add a sample attention module to NN to implement the main function of SVM. Class prototypes are representatives of all classes, which can be viewed as alternatives to support vectors. The memory block is used for the storage and update of class prototypes. Class prototypes and memory block effectively reduce the computational cost of sample attention and make SAMN suitable for multi-classification tasks. Extensive experiments show that SAMN achieves better classification performance than single SVM or single NN with similar parameter sizes, as well as the previous best model for combining SVM and NN. The sample attention mechanism is a flexible module that can be easily deepened and incorporated into neural networks that require it.
A closer look at parameter identifiability, model selection and handling of censored data with Bayesian Inference in mathematical models of tumour growth

Mathematical models (MMs) are a powerful tool to help us understand and predict the dynamics of tumour growth under various conditions. In this work, we use 5 MMs with an increasing number of parameters to explore how certain (often overlooked) decisions in estimating parameters from data of experimental tumour growth affect the outcome of the analysis. In particular, we propose a framework for including tumour volume measurements that fall outside the upper and lower limits of detection, which are normally discarded. We demonstrate how excluding censored data results in an overestimation of the initial tumour volume and the MM-predicted tumour volumes prior to the first measurements, and an underestimation of the carrying capacity and the MM-predicted tumour volumes beyond the latest measurable time points. We show in which way the choice of prior for the MM parameters can impact the posterior distributions, and illustrate that reporting the highest-likelihood parameters and their 95% credible interval can lead to confusing or misleading interpretations. We hope this work will encourage others to carefully consider choices made in parameter estimation and to adopt the approaches we put forward herein.
Machine Learning-Aided First-Principles Calculations of Redox Potentials

Redox potentials of electron transfer reactions are of fundamental importance for the performance and description of electrochemical devices. Despite decades of research, accurate computational predictions for the redox potential of even simple metals remain very challenging. Here we use a combination of first principles calculations and machine learning to predict the redox potentials of three redox couples, $\mathrm{Fe}^{2+}$/$\mathrm{Fe}^{3+}$, $\mathrm{Cu}^{+}$/$\mathrm{Cu}^{2+}$ and $\mathrm{Ag}^{+}$/$\mathrm{Ag}^{2+}$. Using a hybrid functional with a fraction of 25\% exact exchange (PBE0) the predicted values are 0.92, 0.26 and 1.99 V in good agreement with the best experimental estimates (0.77, 0.15, 1.98 V). We explain in detail, how we combine machine learning, thermodynamic integration from machine learning to semi-local functionals, as well as a combination of thermodynamic perturbation theory and $\Delta$-machine learning to determine the redox potentials for computationally expensive hybrid functionals. The combination of these approaches allows one to obtain statistically accurate results.
Beam Enumeration: Probabilistic Explainability For Sample Efficient Self-conditioned Molecular Design

Generative molecular design has moved from proof-of-concept to real-world applicability, as marked by the surge in very recent papers reporting experimental validation. Key challenges in explainability and sample efficiency present opportunities to enhance generative design to directly optimize expensive high-fidelity oracles and provide actionable insights to domain experts. Here, we propose Beam Enumeration to exhaustively enumerate the most probable sub-sequences from language-based molecular generative models and show that molecular substructures can be extracted. When coupled with reinforcement learning, extracted substructures become meaningful, providing a source of explainability and improving sample efficiency through self-conditioned generation. Beam Enumeration is generally applicable to any language-based molecular generative model and notably further improves the performance of the recently reported Augmented Memory algorithm, which achieved the new state-of-the-art on the Practical Molecular Optimization benchmark for sample efficiency. The combined algorithm generates more high reward molecules and faster, given a fixed oracle budget. Beam Enumeration shows that improvements to explainability and sample efficiency for molecular design can be made synergistic.
Planning for health: generation, regeneration and food in Sandwell

Purpose – The purpose of this paper is to examine a case study for increasing supply of, and demand for, healthier food in the metropolitan Borough of Sandwell, West Midlands, UK. Sandwell has a declining and ageing population. Levels of cancer, coronary heart disease, diabetes and obesity are all high. These diseases should not just be regarded as medical problems with medical solutions. By considering both the built environment and the population's health the paper aims to demonstrate how local action is driving strategic food policy.Design/methodology/approach – Food mapping and children's food/obesity prevalence research provided the evidence base for a locally appropriate approach. From ongoing work generated by this evidence base, Sandwell's food policy has been developed to provide a focus and framework for action. This strategic approach has led to the development of neighbourhood renewal funded work “Eatwell in Sandwell”. Findings – By working in partnership with the private sector, i.e. retail bu.
Robust Principal Component Analysis using Density Power Divergence

Principal component analysis (PCA) is a widely employed statistical tool used primarily for dimensionality reduction. However, it is known to be adversely affected by the presence of outlying observations in the sample, which is quite common. Robust PCA methods using M-estimators have theoretical benefits, but their robustness drop substantially for high dimensional data. On the other end of the spectrum, robust PCA algorithms solving principal component pursuit or similar optimization problems have high breakdown, but lack theoretical richness and demand high computational power compared to the M-estimators. We introduce a novel robust PCA estimator based on the minimum density power divergence estimator. This combines the theoretical strength of the M-estimators and the minimum divergence estimators with a high breakdown guarantee regardless of data dimension. We present a computationally efficient algorithm for this estimate. Our theoretical findings are supported by extensive simulations and comparisons with existing robust PCA methods. We also showcase the proposed algorithm's applicability on two benchmark datasets and a credit card transactions dataset for fraud detection.
Machine Learning Technique Based Fake News Detection

False news has received attention from both the general public and the scholarly world. Such false information has the ability to affect public perception, giving nefarious groups the chance to influence the results of public events like elections. Anyone can share fake news or facts about anyone or anything for their personal gain or to cause someone trouble. Also, information varies depending on the part of the world it is shared on. Thus, in this paper, we have trained a model to classify fake and true news by utilizing the 1876 news data from our collected dataset. We have preprocessed the data to get clean and filtered texts by following the Natural Language Processing approaches. Our research conducts 3 popular Machine Learning (Stochastic gradient descent, Na\"ive Bayes, Logistic Regression,) and 2 Deep Learning (Long-Short Term Memory, ASGD Weight-Dropped LSTM, or AWD-LSTM) algorithms. After we have found our best Naive Bayes classifier with 56% accuracy and an F1-macro score of an average of 32%.
Virtual Probe Approach for Time-Dependent Arterial Travel Time Estimation

Travel time estimation on arterials is a challenging task for traffic engineers due to the interrupted nature of urban traffic flows. Many research efforts have been devoted on this topic, but their successes are limited and most of them can only be used for offline purposes due to the limited availability of traffic data from signalized intersections. With the advancement of intelligent transportation systems, high-resolution detector and signal status data are available but not fully explored. In this paper, we develop an innovative algorithm for time-dependent arterial travel time estimation by tracing a virtual probe vehicle. At each time step, the virtual probe has three possible maneuvers: acceleration, deceleration and no-speed-change. The maneuver decision is determined by its own status and its surrounding traffic conditions, which can be estimated based on the availability of traffic data at intersections. An interesting property of the proposed model is that travel time estimation errors can be self-corrected with the signal status data, because the differences between a virtual probe vehicle and a real probe can be reduced when both of them meet the red signal phase. A field study at an 11-intersections arterial corridor along France Avenue in Minneapolis, MN shows the proposed model can generate accurate time-dependent travel time under various traffic conditions.
Assessing the Impact of Personality on Affective States from Video Game Communication

Individual differences in personality determine our preferences, traits and values, which should similarly hold for the way we express ourselves. With current advancements and transformations of technology and society, text-based communication has become ordinary and often even surpasses natural voice conversations- with distinct challenges and opportunities. In this exploratory work, we investigate the impact of personality on the tendency how players of a team-based collaborative alternate reality game express themselves affectively. We collected chat logs from eleven players over two weeks, labeled them according to their affective state, and assessed the connection between them and the five-factor personality domains and facets. After applying multi-linear regression, we found a series of reasonable correlations between (combinations of) personality variables and expressed affect-as increased confusion could be predicted by lower self-competence (C1), personal annoyance by vulnerability to stress (N6) and expressing anger occured more often in players that are prone to anxiety (N1), less humble and modest (A5), think less carefully before they act (C6) and have higher neuroticism (N). Expanding the data set, sample size and input modalities in subsequent work, we aim to confirm these findings and reveal even more interesting connections that could inform affective computing and games user research equally.
A Chat About Boring Problems: Studying GPT-based text normalization

Text normalization - the conversion of text from written to spoken form - is traditionally assumed to be an ill-formed task for language models. In this work, we argue otherwise. We empirically show the capacity of Large-Language Models (LLM) for text normalization in few-shot scenarios. Combining self-consistency reasoning with linguistic-informed prompt engineering, we find LLM based text normalization to achieve error rates around 40\% lower than top normalization systems. Further, upon error analysis, we note key limitations in the conventional design of text normalization tasks. We create a new taxonomy of text normalization errors and apply it to results from GPT-3.5-Turbo and GPT-4.0. Through this new framework, we can identify strengths and weaknesses of GPT-based TN, opening opportunities for future work.