Miracle-dz/UniFAR-FTUs huggingface dataset:
Supersymmetry, Part I (Theory)

This is a review of the theoretical aspects of the supersymmetric extension of the Standard Model of particle physics, extracted from Chapter 88 of the 2023 update of the Review of Particle Physics, which appears in R.L. Workman et al. (Particle Data Group), Prog. Theor. Exp. Phys. 2022, 083C01 (2022) and 2023 update. The companion review,"Supersymmetry, Part II (Experiment)", can be found in Chapter 89 of the Review of Particle Physics (op. cit.).
UGGNet: Bridging U-Net and VGG for Advanced Breast Cancer Diagnosis

In the field of medical imaging, breast ultrasound has emerged as a crucial diagnostic tool for early detection of breast cancer. However, the accuracy of diagnosing the location of the affected area and the extent of the disease depends on the experience of the physician. In this paper, we propose a novel model called UGGNet, combining the power of the U-Net and VGG architectures to enhance the performance of breast ultrasound image analysis. The U-Net component of the model helps accurately segment the lesions, while the VGG component utilizes deep convolutional layers to extract features. The fusion of these two architectures in UGGNet aims to optimize both segmentation and feature representation, providing a comprehensive solution for accurate diagnosis in breast ultrasound images. Experimental results have demonstrated that the UGGNet model achieves a notable accuracy of 78.2\% on the "Breast Ultrasound Images Dataset. "
Computational Argumentation-based Chatbots: a Survey

Chatbots are conversational software applications designed to interact dialectically with users for a plethora of different purposes. Surprisingly, these colloquial agents have only recently been coupled with computational models of arguments (i.e. computational argumentation), whose aim is to formalise, in a machine-readable format, the ordinary exchange of information that characterises human communications. Chatbots may employ argumentation with different degrees and in a variety of manners. The present survey sifts through the literature to review papers concerning this kind of argumentation-based bot, drawing conclusions about the benefits and drawbacks that this approach entails in comparison with standard chatbots, while also envisaging possible future development and integration with the Transformer-based architecture and state-of-the-art Large Language models.
Enhanced Automated Code Vulnerability Repair using Large Language Models

This research addresses the complex challenge of automated repair of code vulnerabilities, vital for enhancing digital security in an increasingly technology-driven world. The study introduces a novel and efficient format for the representation of code modification, using advanced Large Language Models (LLMs) such as Code Llama and Mistral. These models, fine-tuned on datasets featuring C code vulnerabilities, significantly improve the accuracy and adaptability of automated code repair techniques. A key finding is the enhanced repair accuracy of these models when compared to previous methods such as VulRepair, which underscores their practical utility and efficiency. The research also offers a critical assessment of current evaluation metrics, such as perfect predictions, and their limitations in reflecting the true capabilities of automated repair models in real-world scenarios. Following this, it underscores the importance of using test datasets devoid of train samples, emphasizing the need for dataset integrity to enhance the effectiveness of LLMs in code repair tasks. The significance of this work is its contribution to digital security, setting new standards for automated code vulnerability repair and paving the way for future advancements in the fields of cybersecurity and artificial intelligence. The study does not only highlight the potential of LLMs in enhancing code security but also fosters further exploration and research in these crucial areas.
AST-T5: Structure-Aware Pretraining for Code Generation and Understanding

Large language models (LLMs) have made significant advancements in code-related tasks, yet many LLMs treat code as simple sequences, neglecting its structured nature. We introduce AST-T5, a novel pretraining paradigm that leverages the Abstract Syntax Tree (AST) for enhanced code generation, transpilation, and understanding. Using dynamic programming, our AST-Aware Segmentation retains code structure, while our AST-Aware Span Corruption objective equips the model to reconstruct various code structures. Unlike other models, AST-T5 avoids intricate program analyses or architectural changes, so it integrates seamlessly with any encoder-decoder Transformer. Evaluations show that AST-T5 consistently outperforms similar-sized LMs across various code-related tasks. Structure-awareness makes AST-T5 particularly powerful in code-to-code tasks, surpassing CodeT5 by 2 points in exact match score for the Bugs2Fix task and by 3 points in exact match score for Java-C# Transpilation in CodeXGLUE. Our code and model are publicly available at https://github.com/gonglinyuan/ast_t5.
Self-similar and Universal Dynamics in Drainage of Mobile Soap Films

Vertical soap films drain under the influence of gravity, as indicated by the downward motion of colorful horizontal interference fringes observed on their surfaces. In this study conducted with rectangular soap films, we experimentally characterize the descent dynamics of these isothickness fringes and report its self-similar nature. We also show that this result is equivalent to thickness profiles exhibiting a separation of space and time. By integrating new measurements with data from the literature across various conditions, we validate these properties and establish the universality of the dynamics, governed by a single physical scalar. Our findings provide new insights for proposing a drainage model and understanding the mechanism of marginal regeneration at the origin of the process.
Well-balanced convex limiting for finite element discretizations of steady convection-diffusion-reaction equations

We address the numerical treatment of source terms in algebraic flux correction schemes for steady convection-diffusion-reaction (CDR) equations. The proposed algorithm constrains a continuous piecewise-linear finite element approximation using a monolithic convex limiting (MCL) strategy. Failure to discretize the convective derivatives and source terms in a compatible manner produces spurious ripples, e.g., in regions where the coefficients of the continuous problem are constant and the exact solution is linear. We cure this deficiency by incorporating source term components into the fluxes and intermediate states of the MCL procedure. The design of our new limiter is motivated by the desire to preserve simple steady-state equilibria exactly, as in well-balanced schemes for the shallow water equations. The results of our numerical experiments for two-dimensional CDR problems illustrate potential benefits of well-balanced flux limiting in the scalar case.
In-Database Data Imputation

Missing data is a widespread problem in many domains, creating challenges in data analysis and decision making. Traditional techniques for dealing with missing data, such as excluding incomplete records or imputing simple estimates (e.g., mean), are computationally efficient but may introduce bias and disrupt variable relationships, leading to inaccurate analyses. Model-based imputation techniques offer a more robust solution that preserves the variability and relationships in the data, but they demand significantly more computation time, limiting their applicability to small datasets. This work enables efficient, high-quality, and scalable data imputation within a database system using the widely used MICE method. We adapt this method to exploit computation sharing and a ring abstraction for faster model training. To impute both continuous and categorical values, we develop techniques for in-database learning of stochastic linear regression and Gaussian discriminant analysis models. Our MICE implementations in PostgreSQL and DuckDB outperform alternative MICE implementations and model-based imputation techniques by up to two orders of magnitude in terms of computation time, while maintaining high imputation quality.
DiarizationLM: Speaker Diarization Post-Processing with Large Language Models

In this paper, we introduce DiarizationLM, a framework to leverage large language models (LLM) to post-process the outputs from a speaker diarization system. Various goals can be achieved with the proposed framework, such as improving the readability of the diarized transcript, or reducing the word diarization error rate (WDER). In this framework, the outputs of the automatic speech recognition (ASR) and speaker diarization systems are represented as a compact textual format, which is included in the prompt to an optionally finetuned LLM. The outputs of the LLM can be used as the refined diarization results with the desired enhancement. As a post-processing step, this framework can be easily applied to any off-the-shelf ASR and speaker diarization systems without retraining existing components. Our experiments show that a finetuned PaLM 2-S model can reduce the WDER by rel. 55.5% on the Fisher telephone conversation dataset, and rel. 44.9% on the Callhome English dataset.
Neural Networks with Kernel-Weighted Corrective Residuals for Solving Partial Differential Equations

Physics-informed machine learning (PIML) has emerged as a promising alternative to conventional numerical methods for solving partial differential equations (PDEs). PIML models are increasingly built via deep neural networks (NNs) whose architecture and training process are designed such that the network satisfies the PDE system. While such PIML models have substantially advanced over the past few years, their performance is still very sensitive to the NN's architecture and loss function. Motivated by this limitation, we introduce kernel-weighted Corrective Residuals (CoRes) to integrate the strengths of kernel methods and deep NNs for solving nonlinear PDE systems. To achieve this integration, we design a modular and robust framework which consistently outperforms competing methods in solving a broad range of benchmark problems. This performance improvement has a theoretical justification and is particularly attractive since we simplify the training process while negligibly increasing the inference costs. Additionally, our studies on solving multiple PDEs indicate that kernel-weighted CoRes considerably decrease the sensitivity of NNs to factors such as random initialization, architecture type, and choice of optimizer. We believe our findings have the potential to spark a renewed interest in leveraging kernel methods for solving PDEs.
Dynamic parallel transmit diffusion MRI at 7T

Diffusion MRI (dMRI) is inherently limited by SNR. Scanning at 7T increases intrinsic SNR but 7T MRI scans suffer from regions of signal dropout, especially in the temporal lobes and cerebellum. We applied dynamic parallel transmit (pTx) to allow whole-brain 7T dMRI and compared with circularly polarized (CP) pulses in 6 subjects. Subject-specific 2-spoke dynamic pTx pulses were designed offline for 8 slabs covering the brain. We used vendor-provided B0 and B1+ mapping. Spokes positions were set using the Fourier difference approach, and RF coefficients optimized with a Jacobi-matrix high-flip-angle optimizer. Diffusion data were analyzed with FSL. Comparing whole-brain averages for pTx against CP scans: mean flip angle error improved by 15% for excitation (2-spoke-VERSE 15.7° vs CP 18.4°, P=0.012) and improved by 14% for refocusing (2-spoke-VERSE 39.7° vs CP 46.2°, P=0.008). Computed spin-echo signal standard deviation improved by 14% (2-spoke-VERSE 0.185 vs 0.214 CP, P=0.025). Temporal SNR increased by 5.4% (2-spoke-VERSE 8.47 vs CP 8.04, P=0.004) especially in the inferior temporal lobes. Diffusion fitting uncertainty decreased by 6.2% for first fibres (2-spoke VERSE 0.0655 vs CP 0.0703, P<0.001) and 1.3% for second fibers (2-spoke VERSE 0.139 vs CP 0.141, P=0.01). In conclusion, dynamic parallel transmit improves the uniformity of 7T diffusion-weighted imaging. In future, less restrictive SAR limits for parallel transmit scans are expected to allow further improvements.
The RoSiD Tool: Empowering Users to Design Multimodal Signals for Human-Robot Collaboration

Robots that cooperate with humans must be effective at communicating with them. However, people have varied preferences for communication based on many contextual factors, such as culture, environment, and past experience. To communicate effectively, robots must take those factors into consideration. In this work, we present the Robot Signal Design (RoSiD) tool to empower people to easily self-specify communicative preferences for collaborative robots. We show through a participatory design study that the RoSiD tool enables users to create signals that align with their communicative preferences, and we illuminate how this tool can be further improved.
Constructing cospectral graphs by unfolding non-bipartite graphs

In 2010, Butler introduced the unfolding operation on a bipartite graph to produce two bipartite graphs, which are cospectral for the adjacency and the normalized Laplacian matrices. In this article, we describe how the idea of unfolding a bipartite graph with respect to another bipartite graph can be extended to nonbipartite graphs. In particular, we describe how unfoldings involving reflexive bipartite, semi-reflexive bipartite, and multipartite graphs are used to obtain cospectral nonisomorphic graphs for the adjacency matrix.
GLISP: A Scalable GNN Learning System by Exploiting Inherent Structural Properties of Graphs

As a powerful tool for modeling graph data, Graph Neural Networks (GNNs) have received increasing attention in both academia and industry. Nevertheless, it is notoriously difficult to deploy GNNs on industrial scale graphs, due to their huge data size and complex topological structures. In this paper, we propose GLISP, a sampling based GNN learning system for industrial scale graphs. By exploiting the inherent structural properties of graphs, such as power law distribution and data locality, GLISP addresses the scalability and performance issues that arise at different stages of the graph learning process. GLISP consists of three core components: graph partitioner, graph sampling service and graph inference engine. The graph partitioner adopts the proposed vertex-cut graph partitioning algorithm AdaDNE to produce balanced partitioning for power law graphs, which is essential for sampling based GNN systems. The graph sampling service employs a load balancing design that allows the one hop sampling request of high degree vertices to be handled by multiple servers. In conjunction with the memory efficient data structure, the efficiency and scalability are effectively improved. The graph inference engine splits the $K$-layer GNN into $K$ slices and caches the vertex embeddings produced by each slice in the data locality aware hybrid caching system for reuse, thus completely eliminating redundant computation caused by the data dependency of graph. Extensive experiments show that GLISP achieves up to $6.53\times$ and $70.77\times$ speedups over existing GNN systems for training and inference tasks, respectively, and can scale to the graph with over 10 billion vertices and 40 billion edges with limited resources.
Amirkabir campus dataset: Real-world challenges and scenarios of Visual Inertial Odometry (VIO) for visually impaired people

Visual Inertial Odometry (VIO) algorithms estimate the accurate camera trajectory by using camera and Inertial Measurement Unit (IMU) sensors. The applications of VIO span a diverse range, including augmented reality and indoor navigation. VIO algorithms hold the potential to facilitate navigation for visually impaired individuals in both indoor and outdoor settings. Nevertheless, state-of-the-art VIO algorithms encounter substantial challenges in dynamic environments, particularly in densely populated corridors. Existing VIO datasets, e.g., ADVIO, typically fail to effectively exploit these challenges. In this paper, we introduce the Amirkabir campus dataset (AUT-VI) to address the mentioned problem and improve the navigation systems. AUT-VI is a novel and super-challenging dataset with 126 diverse sequences in 17 different locations. This dataset contains dynamic objects, challenging loop-closure/map-reuse, different lighting conditions, reflections, and sudden camera movements to cover all extreme navigation scenarios. Moreover, in support of ongoing development efforts, we have released the Android application for data capture to the public. This allows fellow researchers to easily capture their customized VIO dataset variations. In addition, we evaluate state-of-the-art Visual Inertial Odometry (VIO) and Visual Odometry (VO) methods on our dataset, emphasizing the essential need for this challenging dataset.
Machine-Learning Performance on Higgs-Pair Production associated with Dark Matter at the LHC

Di-Higgs production at the LHC associated with missing transverse energy is explored in the context of simplified models that generically parameterize a large class of models with heavy scalars and dark matter candidates. Our aim is to figure out the improvement capability of machine-learning tools over traditional cut-based analyses. In particular, boosted decision trees and neural networks are implemented in order to determine the parameter space that can be tested at the LHC demanding four $b$-jets and large missing energy in the final state. We present a performance comparison between both machine-learning algorithms, based on the maximum significance reached, by feeding them with different sets of kinematic features corresponding to the LHC at a center-of-mass energy of 14 TeV. Both algorithms present very similar performances and substantially improve traditional analyses, being sensitive to most of the parameter space considered for a total integrated luminosity of 1 ab$^{-1}$, with significances at the evidence level, and even at the discovery level, depending on the masses of the new heavy scalars. A more conservative approach with systematic uncertainties on the background of 30\% has also been contemplated, again providing very promising significances.
conv_einsum: A Framework for Representation and Fast Evaluation of Multilinear Operations in Convolutional Tensorial Neural Networks

Modern ConvNets continue to achieve state-of-the-art results over a vast array of vision and image classification tasks, but at the cost of increasing parameters. One strategy for compactifying a network without sacrificing much expressive power is to reshape it into a tensorial neural network (TNN), which is a higher-order tensorization of its layers, followed by a factorization, such as a CP-decomposition, which strips a weight down to its critical basis components. Passes through TNNs can be represented as sequences of multilinear operations (MLOs), where the evaluation path can greatly affect the number of floating point operations (FLOPs) incurred. While functions such as the popular einsum can evaluate simple MLOs such as contractions, existing implementations cannot process multi-way convolutions, resulting in scant assessments of how optimal evaluation paths through tensorized convolutional layers can improve training speed. In this paper, we develop a unifying framework for representing tensorial convolution layers as einsum-like strings and a meta-algorithm conv_einsum which is able to evaluate these strings in a FLOPs-minimizing manner. Comprehensive experiments, using our open-source implementation, over a wide range of models, tensor decompositions, and diverse tasks, demonstrate that conv_einsum significantly increases both computational and memory-efficiency of convolutional TNNs.
RoBERTurk: Adjusting RoBERTa for Turkish

We pretrain RoBERTa on a Turkish corpora using BPE tokenizer. Our model outperforms BERTurk family models on the BOUN dataset for the POS task while resulting in underperformance on the IMST dataset for the same task and achieving competitive scores on the Turkish split of the XTREME dataset for the NER task - all while being pretrained on smaller data than its competitors. We release our pretrained model and tokenizer.
Spontaneous activity of striatal projection neurons supports maturation of striatal inputs to substantia nigra dopaminergic neurons

Spontaneous activity of neurons during early ontogenesis is instrumental for stabilization and refinement of developing neuronal connections. The role of spontaneous activity in synaptic development has been described in detail for cortical-like structures. Yet, very little is known about activity-dependent development of long-range inhibitory projections, such as projections from striatum. Here, we show that striatal projection neurons (SPNs) in dorsal striatum are spontaneously active in P4-P14 mice. Spontaneous activity was detected in both direct-pathway SPNs (dSPNs) and indirect-pathway SPNs (iSPNs). Most of the spontaneously active cells were in striosomes – a chemical compartment in striatum defined by expression of µ-opioid receptor. Higher excitability of both striosomal dSPNs and iSPNs was related to their intrinsic excitability properties (higher action potential half-width and IV slope). Tonic activation of muscarinic M1 receptor maintains the spontaneous activity of striosomal SPNs, the effect being stronger in iSPNs and weaker in dSPNs. To investigate if the neonatal spontaneous activity is needed for the stabilization of SPN long-range projections, we chemogenetically inhibited striosomal SPNs in neonatal animals and studied the efficiency of striatonigral projections in adult animals. Inhibition of striosomal SPNs by chronic CNO administration to P6-14 pups caused a reduction in the functional GABAergic innervation and in the density of gephyrin puncta in dopaminergic neurons of substantia nigra pars compacta of the adult (P52-79) animals. Chronic administration of CNO later in development (P21-29), on the contrary, resulted in higher mIPSC frequency in dopaminergic cells of the adult animals. Thus, the activity-dependent stabilization of striosomal projections has different developmental phases, and the long-term outcome of perturbations in these processes depends on the developmental period when they occur. Taken together, our results demonstrate that spontaneous activity of SPNs is essential for the maturation and stabilization of striatal efferents.