Miracle-dz/UniFAR-FTUs huggingface dataset:
Ensemble Learning to Assess Dynamics of Affective Experience Ratings and Physiological Change

The congruence between affective experiences and physiological changes has been a debated topic for centuries. Recent technological advances in measurement and data analysis provide hope to solve this epic challenge. Open science and open data practices, together with data analysis challenges open to the academic community, are also promising tools for solving this problem. In this entry to the Emotion Physiology and Experience Collaboration (EPiC) challenge, we propose a data analysis solution that combines theoretical assumptions with data-driven methodologies. We used feature engineering and ensemble selection. Each predictor was trained on subsets of the training data that would maximize the information available for training. Late fusion was used with an averaging step. We chose to average considering a “wisdom of crowds” strategy. This strategy yielded an overall RMSE of 1.19 in the test set. Future work should carefully explore if our assumptions are correct and the potential of weighted fusion.
Optimal Decentralized Composite Optimization for Strongly Convex Functions

This paper concentrates on decentralized composite optimization for strongly convex functions. Decentralized optimization, renowned for its robustness to network variations and the absence of a central server communication bottleneck, has garnered considerable interest in signal processing, control, and optimization communities. Although numerous algorithms have been proposed for smooth and strongly convex objective functions in recent years, the development of an optimal algorithm that includes a non-smooth regularization term remains an unresolved challenge in decentralized optimization. This paper bridges the divide between smooth decentralized optimization and decentralized composite optimization, introducing the first algorithm capable of achieving optimal computational and communication complexities. Moreover, our experimental results substantiate the efficacy and efficiency of our algorithm in both computation and communication aspects.
Evaluating Code Summarization Techniques: A New Metric and an Empirical Characterization

Several code summarization techniques have been proposed in the literature to automatically document a code snippet or a function. Ideally, software developers should be involved in assessing the quality of the generated summaries. However, in most cases, researchers rely on automatic evaluation metrics such as BLEU, ROUGE, and METEOR. These metrics are all based on the same assumption: The higher the textual similarity between the generated summary and a reference summary written by developers, the higher its quality. However, there are two reasons for which this assumption falls short: (i) reference summaries, e.g., code comments collected by mining software repositories, may be of low quality or even outdated; (ii) generated summaries, while using a different wording than a reference one, could be semantically equivalent to it, thus still being suitable to document the code snippet. In this paper, we perform a thorough empirical investigation on the complementarity of different types of metrics in capturing the quality of a generated summary. Also, we propose to address the limitations of existing metrics by considering a new dimension, capturing the extent to which the generated summary aligns with the semantics of the documented code snippet, independently from the reference summary. To this end, we present a new metric based on contrastive learning to capture said aspect. We empirically show that the inclusion of this novel dimension enables a more effective representation of developers' evaluations regarding the quality of automatically generated summaries.
Mesh-LOAM: Real-time Mesh-Based LiDAR Odometry and Mapping

Despite having achieved real-time performance in mesh construction, most of the current LiDAR odometry and meshing methods may struggle to deal with complex scenes due to relying on explicit meshing schemes. They are usually sensitive to noise. To overcome these limitations, we propose a real-time mesh-based LiDAR odometry and mapping approach for large-scale scenes via implicit reconstruction and a parallel spatial-hashing scheme. To efficiently reconstruct triangular meshes, we suggest an incremental voxel meshing method that updates every scan by traversing each point once and compresses space via a scalable partition module. By taking advantage of rapid accessing triangular meshes at any time, we design point-to-mesh odometry with location and feature-based data association to estimate the poses between the incoming point clouds and the recovered triangular meshes. The experimental results on four datasets demonstrate the effectiveness of our proposed approach in generating accurate motion trajectories and environmental mesh maps.
Finite almost simple groups whose holomorph contains a solvable regular subgroup

In our previous paper, we gave a complete list of the finite non-abelian simple groups whose holomorph contains a solvable regular subgroup. In this paper, we refine our previous work by considering all finite almost simple groups. In particular, our result yields a complete characterization of the finite almost simple groups which occur as the type of a Hopf-Galois structure on a solvable extension, or equivalently, the additive group of a skew brace having a solvable multiplicative group.
DeLTA-BIT: an open-source probabilistic tractography-based deep learning framework for thalamic targeting in functional neurological disorders

In the last years there has been a growing interest in the applications of diffusion tractography for target identification in neurofunctional disorders for an increasingly tailored approach. The growing diffusion of well-established neurosurgical procedures as deep brain stimulation (DBS) and trans-cranial Magnetic Resonance-guided Focused Ultrasound (tcMRgFUS) favored this trend. Tractography can indeed provide more accurate, patient-specific, information about the targeted region if compared to stereotactic atlases. On the other hand, this tractography-based approachs is not very physician-friendly, and its heavily time consuming since needs several hours for Magnetic Resonance Imaging (MRI) data processing. In this study we propose a novel open-source deep learning framework called DeLTA-BIT (acronym of Deep-learning Local TrActography for BraIn Targeting) for reconstructing thalamic probabilistic tractography maps enabling a personalized (patient-specific) target identification for functional neurological disorders. The proposed framework exploits two convolutional neural networks (CNNs) to segment the thalamus and reconstruct the connectivity map between each voxel inside the thalamus and any user-specified target cortical or subcortical masks. These CNNs were trained, validated, and tested on the datasets from the Human Connectome Project (HCP) database. Our results are comparable with the state-of-the-art data-driven segmentation and probabilistic tractography and demonstrate the high performance of proposed networks for personalized thalamic target identification with a Dice coefficient of 0.93 +/- 0.01 for thalamus segmentation and a Dice Similarity Coefficient for the projection to the precentral gyrus from the thalamus of 0.80 +/- 0.06. The code of these models is freely available on GitHub.
Mutual Information as Intrinsic Reward of Reinforcement Learning Agents for On-demand Ride Pooling

The emergence of on-demand ride pooling services allows each vehicle to serve multiple passengers at a time, thus increasing drivers' income and enabling passengers to travel at lower prices than taxi/car on-demand services (only one passenger can be assigned to a car at a time like UberX and Lyft). Although on-demand ride pooling services can bring so many benefits, ride pooling services need a well-defined matching strategy to maximize the benefits for all parties (passengers, drivers, aggregation companies and environment), in which the regional dispatching of vehicles has a significant impact on the matching and revenue. Existing algorithms often only consider revenue maximization, which makes it difficult for requests with unusual distribution to get a ride. How to increase revenue while ensuring a reasonable assignment of requests brings a challenge to ride pooling service companies (aggregation companies). In this paper, we propose a framework for vehicle dispatching for ride pooling tasks, which splits the city into discrete dispatching regions and uses the reinforcement learning (RL) algorithm to dispatch vehicles in these regions. We also consider the mutual information (MI) between vehicle and order distribution as the intrinsic reward of the RL algorithm to improve the correlation between their distributions, thus ensuring the possibility of getting a ride for unusually distributed requests. In experimental results on a real-world taxi dataset, we demonstrate that our framework can significantly increase revenue up to an average of 3\% over the existing best on-demand ride pooling method.
Hyper-K\"ahler instantons, symmetries, and flat spaces

We find all hyper-K\"ahler 4-manifolds admitting conformal K\"ahler structures with respect to either orientation, and we show that these structures can be expressed as a combination of twistor elementary states (and possibly a self-dual dyon) in locally flat spaces. The complex structures of different flat pieces are not compatible however, reflecting that the global geometry is not a linear superposition. For either orientation the space must be Gibbons-Hawking (thus excluding the Atiyah-Hitchin metric), and, if the orientations are opposite, it must also be toric and have an irreducible Killing tensor. We also show that the only hyper-K\"ahler 4-metric with a non-constant Killing-Yano tensor is the half-flat Taub-NUT instanton.
Lp-Norm Constrained One-Class Classifier Combination

Classifier fusion is established as an effective methodology for boosting performance in different settings and one-class classification is no exception. In this study, we consider the one-class classifier fusion problem by modelling the sparsity/uniformity of the ensemble. To this end, we formulate a convex objective function to learn the weights in a linear ensemble model and impose a variable Lp-norm constraint on the weight vector. The vector-norm constraint enables the model to adapt to the intrinsic uniformity/sparsity of the ensemble in the space of base learners and acts as a (soft) classifier selection mechanism by shaping the relative magnitudes of fusion weights. Drawing on the Frank-Wolfe algorithm, we then present an effective approach to solve the formulated convex constrained optimisation problem efficiently. We evaluate the proposed one-class classifier combination approach on multiple data sets from diverse application domains and illustrate its merits in comparison to the existing approaches.
ShallowBlocker: Improving Set Similarity Joins for Blocking

Blocking is a crucial step in large-scale entity matching but often requires significant manual engineering from an expert for each new dataset. Recent work has show that deep learning is state-of-the-art and has great potential for achieving hands-off and accurate blocking compared to classical methods. However, in practice, such deep learning methods are often unstable, offers little interpretability, and require hyperparameter tuning and significant computational resources. In this paper, we propose a hands-off blocking method based on classical string similarity measures: ShallowBlocker. It uses a novel hybrid set similarity join combining absolute similarity, relative similarity, and local cardinality conditions with a new effective pre-candidate filter replacing size filter. We show that the method achieves state-of-the-art pair effectiveness on both unsupervised and supervised blocking in a scalable way.
Robust Hamiltonicity

We study conditions under which a given hypergraph is randomly robust Hamiltonian, which means that a random sparsification of the host graph contains a Hamilton cycle with high probability. Our main contribution provides nearly optimal results whenever the host graph is Hamilton connected in a locally robust sense, which translates to a typical induced subgraph of constant order containing Hamilton paths between any pair of suitable ends. The proofs are based on the recent breakthrough on Talagrand's conjecture, which reduces the problem to specifying a distribution on the desired guest structure in the (deterministic) host structure. We find such a distribution via a new argument that reduces the problem to the case of perfect matchings in a higher uniformity. As an application, we obtain asymptotically optimal results for perfect tilings in graphs and hypergraphs both in the minimum degree and uniformly dense setting. We also prove random robustness for powers of cycles under asymptotically optimal minimum degrees and degree sequences. We solve the problem for loose and tight Hamilton cycles in hypergraphs under a range of asymptotic minimum degree conditions. This includes in particular the case of $3$-uniform tight Hamilton cycles under vertex degree conditions. In all cases, our bounds on the sparseness are essentially best-possible.
Genus 2 Seiberg-Witten curves for rank 2 N=4 superYang-Mills theories

We determine new genus 2 Seiberg-Witten curves for four dimensional rank 2 absolute N=4 superYang-Mills theories using the automorphism twist approach. The conformal manifolds of these curves agree with those predicted by S-duality orbits of global structures, and we use this to identify which of the two S-duality orbits of the $so(5) \simeq sp(4)$ superYang-Mills theory the genus-2 curve corresponds to. We also compare the curves to earlier constructions of Seiberg-Witten curves for these theories as spectral curves of integrable systems. These spectral curves have genus greater than the rank, and so only give a Coulomb branch geometry upon projection to a sublattice of the homology lattice of the curves. We show how to determine the correct sublattice projection, and find that the integrable system curves do not apply to our theories.
Optimal coordination in Minority Game: A solution from reinforcement learning

Efficient allocation is important in nature and human society where individuals often compete for finite resources. The Minority Game is perhaps the simplest model that provides deep insights into how human coordinate to maximize the resource utilization. However, this model assumes the static strategies that are provided a priori, failing to capture their adaptive nature. Here, we turn to the paradigm of reinforcement learning, where individuals' strategies are evolving by evaluating both the past experience and rewards in the future. Specifically, we adopt the Q-learning algorithm, each player is endowed with a Q-table that guides their decision-making. We reveal that the population is able to reach the optimal allocation when individuals appreciate both the past experience and rewards in the future, and they are able to balance the exploitation of their Q-tables and the exploration by randomly acting. The optimal allocation is ruined when individuals tend to use either exploitation-only or exploration-only, where only partial coordination and even anti-coordination are observed. Mechanism analysis reveals that a moderate level of exploration can escape local minimums of metastable periodic states, and reaches the optimal coordination as the global minimum. Interestingly, the optimal coordination is underlined by a symmetry-breaking of action preferences, where nearly half of the population choose one side while the other half prefer the other side. The emergence of optimal coordination is robust to the population size and other game parameters. Our work therefore provides a natural solution to the Minority Game and sheds insights into the resource allocation problem in general. Besides, our work demonstrates the potential of the proposed reinforcement learning paradigm in deciphering many puzzles in the socio-economic context.
FoodLMM: A Versatile Food Assistant using Large Multi-modal Model

Large Multi-modal Models (LMMs) have made impressive progress in many vision-language tasks. Nevertheless, the performance of general LMMs in specific domains is still far from satisfactory. This paper proposes FoodLMM, a versatile food assistant based on LMMs with various capabilities, including food recognition, ingredient recognition, recipe generation, nutrition estimation, food segmentation and multi-round conversation. To facilitate FoodLMM to deal with tasks beyond pure text output, we introduce a series of novel task-specific tokens and heads, enabling the model to predict food nutritional values and multiple segmentation masks. We adopt a two-stage training strategy. In the first stage, we utilize multiple public food benchmarks for multi-task learning by leveraging instruct-following paradigm. In the second stage, we construct a multi-round conversation and a reasoning segmentation datasets to fine-tune the model, enabling it to conduct professional dialogues and generate segmentation masks based on complex reasoning in food domain. Our fine-tuned FoodLMM achieves state-of-the-art results across several food benchmarks. We will make our code, models and datasets publicly available.
Improved decoding of expander codes: fundamental trade-off between expansion ratio and minimum distance of inner code

Tanner codes are graph-based linear codes whose parity-check matrices can be characterized by a bipartite graph $G$ together with an inner code $C_0$. Expander codes are Tanner codes whose defining bipartite graph $G$ has good expansion property. The landmark work of Sipser and Spielman showed that every bipartite expander $G$ with expansion ratio $\delta>3/4$ together with a parity-check code defines an expander code which corrects $\Omega(n)$ errors in $O(n)$ time, where $n$ is the code length. Viderman showed that $\delta>2/3-\Omega(1)$ is already sufficient. Our paper is motivated by the following natural and fundamental problem in decoding expander codes: \textbf{Question:} What are the sufficient and necessary conditions that $\delta$ and $d_0$ should satisfy so that {\it every} bipartite expander $G$ with expansion ratio $\delta$ and {\it every} inner code $C_0$ with minimum distance $d_0$ together define an expander code which corrects $\Omega(n)$ errors in $O(n)$ time? We give a near-optimal solution to the question above, showing that $\delta d_0>3$ is sufficient and $\delta d_0>1$ is necessary. Our result significantly improves the previously known result of Dowling and Gao, who showed that $d_0=\Omega(c\delta^{-2})$ is sufficient, where $c$ is the left-degree of $G$. We suspect that $\delta d_0>1$ is also sufficient to solve the question above.
From Text to Multimodal: A Comprehensive Survey of Adversarial Example Generation in Question Answering Systems

Integrating adversarial machine learning with Question Answering (QA) systems has emerged as a critical area for understanding the vulnerabilities and robustness of these systems. This article aims to comprehensively review adversarial example-generation techniques in the QA field, including textual and multimodal contexts. We examine the techniques employed through systematic categorization, providing a comprehensive, structured review. Beginning with an overview of traditional QA models, we traverse the adversarial example generation by exploring rule-based perturbations and advanced generative models. We then extend our research to include multimodal QA systems, analyze them across various methods, and examine generative models, seq2seq architectures, and hybrid methodologies. Our research grows to different defense strategies, adversarial datasets, and evaluation metrics and illustrates the comprehensive literature on adversarial QA. Finally, the paper considers the future landscape of adversarial question generation, highlighting potential research directions that can advance textual and multimodal QA systems in the context of adversarial challenges.
A Comparison of Image and Scalar-Based Approaches in Preconditioner Selection

Within high-performance computing (HPC), solving large sparse linear systems efficiently remains paramount, with iterative methods being the predominant choice. However, the performance of these methods is tightly coupled to the aptness of the chosen preconditioner. The multifaceted nature of sparse matrices makes the universal prescription of preconditioners elusive. Notably, the key attribute of sparsity is not precisely captured by scalar metrics such as bandwidth or matrix dimensions. Advancing prior methodologies, this research introduces matrix sparsity depiction via RGB images. Utilizing a convolutional neural network (CNN), the task of preconditioner selection turns into a multi-class classification problem. Extensive tests on 126 SuiteSparse matrices emphasize the enhanced prowess of the CNN model, noting a 32% boost in accuracy and a 25% reduction in computational slowdown.
End-to-End 3D Object Detection using LiDAR Point Cloud

There has been significant progress made in the field of autonomous vehicles. Object detection and tracking are the primary tasks for any autonomous vehicle. The task of object detection in autonomous vehicles relies on a variety of sensors like cameras, and LiDAR. Although image features are typically preferred, numerous approaches take spatial data as input. Exploiting this information we present an approach wherein, using a novel encoding of the LiDAR point cloud we infer the location of different classes near the autonomous vehicles. This approach does not implement a bird's eye view approach, which is generally applied for this application and thus saves the extensive pre-processing required. After studying the numerous networks and approaches used to solve this approach, we have implemented a novel model with the intention to inculcate their advantages and avoid their shortcomings. The output is predictions about the location and orientation of objects in the scene in form of 3D bounding boxes and labels of scene objects.
UVAGaze: Unsupervised 1-to-2 Views Adaptation for Gaze Estimation

Gaze estimation has become a subject of growing interest in recent research. Most of the current methods rely on single-view facial images as input. Yet, it is hard for these approaches to handle large head angles, leading to potential inaccuracies in the estimation. To address this issue, adding a second-view camera can help better capture eye appearance. However, existing multi-view methods have two limitations. 1) They require multi-view annotations for training, which are expensive. 2) More importantly, during testing, the exact positions of the multiple cameras must be known and match those used in training, which limits the application scenario. To address these challenges, we propose a novel 1-view-to-2-views (1-to-2 views) adaptation solution in this paper, the Unsupervised 1-to-2 Views Adaptation framework for Gaze estimation (UVAGaze). Our method adapts a traditional single-view gaze estimator for flexibly placed dual cameras. Here, the "flexibly" means we place the dual cameras in arbitrary places regardless of the training data, without knowing their extrinsic parameters. Specifically, the UVAGaze builds a dual-view mutual supervision adaptation strategy, which takes advantage of the intrinsic consistency of gaze directions between both views. In this way, our method can not only benefit from common single-view pre-training, but also achieve more advanced dual-view gaze estimation. The experimental results show that a single-view estimator, when adapted for dual views, can achieve much higher accuracy, especially in cross-dataset settings, with a substantial improvement of 47.0%. Project page: https://github.com/MickeyLLG/UVAGaze.