Miracle-dz/UniFAR-FTUs huggingface dataset:
Multiplicative topological indices: Analytical properties and application to random networks

We consider two general classes of multiplicative degree-based topological indices (MTIs), denoted by $ X_{\Pi, F_V}(G) = \prod_{u \in V(G)} F_V(d_u) $ and $ X_{\Pi, F_E}(G) = \prod_{uv \in E(G)} F_E(d_u, d_v) $, where $ uv $ indicates the edge of $ G $ connecting the vertices $ u $ and $ v $, $ d_u $ is the degree of the vertex $ u $, and $ F_V(x) $ and $ F_E(x, y) $ are functions of the vertex degrees. This work has three objectives: First, we follow an analytical approach to deal with a classical topic in the study of topological indices: to find inequalities that relate two MTIs between them, but also to their additive versions $ X_\Sigma(G) $. Second, we propose some statistical analysis of MTIs as a generic tool for studying average properties of random networks, extending these techniques for the first time to the context of MTIs. Finally, we perform an innovative scaling analysis of MTIs which allows us to state a scaling law that relates different random graph models.
Impact of the Covid 19 outbreaks on the italian twitter vaccination debat: a network based analysis

Vaccine hesitancy, or the reluctance to be vaccinated, is a phenomenon that has recently become particularly significant, in conjunction with the vaccination campaign against COVID-19. During the lockdown period, necessary to control the spread of the virus, social networks have played an important role in the Italian debate on vaccination, generally representing the easiest and safest way to exchange opinions and maintain some form of sociability. Among social network platforms, Twitter has assumed a strategic role in driving the public opinion, creating compact groups of users sharing similar views towards the utility, uselessness or even dangerousness of vaccines. In this paper, we present a new, publicly available, dataset of Italian tweets, TwitterVax, collected in the period January 2019--May 2022. Considering monthly data, gathered into forty one retweet networks -- where nodes identify users and edges are present between users who have retweeted each other -- we performed community detection within the networks, analyzing their evolution and polarization with respect to NoVax and ProVax users through time. This allowed us to clearly discover debate trends as well as identify potential key moments and actors in opinion flows, characterizing the main features and tweeting behavior of the two communities.
Adaptive Identification of Populations with Treatment Benefit in Clinical Trials: Machine Learning Challenges and Solutions

We study the problem of adaptively identifying patient subpopulations that benefit from a given treatment during a confirmatory clinical trial. This type of adaptive clinical trial has been thoroughly studied in biostatistics, but has been allowed only limited adaptivity so far. Here, we aim to relax classical restrictions on such designs and investigate how to incorporate ideas from the recent machine learning literature on adaptive and online experimentation to make trials more flexible and efficient. We find that the unique characteristics of the subpopulation selection problem -- most importantly that (i) one is usually interested in finding subpopulations with any treatment benefit (and not necessarily the single subgroup with largest effect) given a limited budget and that (ii) effectiveness only has to be demonstrated across the subpopulation on average -- give rise to interesting challenges and new desiderata when designing algorithmic solutions. Building on these findings, we propose AdaGGI and AdaGCPI, two meta-algorithms for subpopulation construction. We empirically investigate their performance across a range of simulation scenarios and derive insights into their (dis)advantages across different settings.
Online Learning with Feedback Graphs: The True Shape of Regret

Sequential learning with feedback graphs is a natural extension of the multi-armed bandit problem where the problem is equipped with an underlying graph structure that provides additional information - playing an action reveals the losses of all the neighbors of the action. This problem was introduced by \citet{mannor2011} and received considerable attention in recent years. It is generally stated in the literature that the minimax regret rate for this problem is of order $\sqrt{\alpha T}$, where $\alpha$ is the independence number of the graph, and $T$ is the time horizon. However, this is proven only when the number of rounds $T$ is larger than $\alpha^3$, which poses a significant restriction for the usability of this result in large graphs. In this paper, we define a new quantity $R^*$, called the \emph{problem complexity}, and prove that the minimax regret is proportional to $R^*$ for any graph and time horizon $T$. Introducing an intricate exploration strategy, we define the \mainAlgorithm algorithm that achieves the minimax optimal regret bound and becomes the first provably optimal algorithm for this setting, even if $T$ is smaller than $\alpha^3$.
Fully-Dynamic All-Pairs Shortest Paths: Likely Optimal Worst-Case Update Time

The All-Pairs Shortest Paths (APSP) problem is one of the fundamental problems in theoretical computer science. It asks to compute the distance matrix of a given $n$-vertex graph. We revisit the classical problem of maintaining the distance matrix under a fully dynamic setting undergoing vertex insertions and deletions with a fast worst-case running time and efficient space usage. Although an algorithm with amortized update-time $\tilde O(n ^ 2)$ has been known for nearly two decades [Demetrescu and Italiano, STOC 2003], the current best algorithm for worst-case running time with efficient space usage runs is due to [Gutenberg and Wulff-Nilsen, SODA 2020], which improves the space usage of the previous algorithm due to [Abraham, Chechik, and Krinninger, SODA 2017] to $\tilde O(n ^ 2)$ but fails to improve their running time of $\tilde O(n ^ {2 + 2 / 3})$. It has been conjectured that no algorithm in $O(n ^ {2.5 - \epsilon})$ worst-case update time exists. For graphs without negative cycles, we meet this conjectured lower bound by introducing a Monte Carlo algorithm running in randomized $\tilde O(n ^ {2.5})$ time while keeping the $\tilde O(n ^ 2)$ space bound from the previous algorithm. Our breakthrough is made possible by the idea of ``hop-dominant shortest paths,'' which are shortest paths with a constraint on hops (number of vertices) that remain shortest after we relax the constraint by a constant factor.
Ergonomic Collaboration between Humans and Robots: An Energy-Aware Signal Temporal Logic Perspective

This paper presents a method for designing energy-aware collaboration tasks between humans and robots, and generating corresponding trajectories to carry out those tasks. The method involves using high-level specifications expressed as Signal Temporal Logic (STL) specifications to automatically synthesize task assignments and trajectories. The focus is on a specific task where a Multi-Rotor Aerial Vehicle (MRAV) performs object handovers in a power line setting. The motion planner takes into account constraints such as payload capacity and refilling, while ensuring that the generated trajectories are feasible. The approach also allows users to specify robot behaviors that prioritize human comfort, including ergonomics and user preferences. The method is validated through numerical analyses in MATLAB and realistic Gazebo simulations in a mock-up scenario.
Causal Discovery using Bayesian Model Selection

With only observational data on two variables, and without other assumptions, it is not possible to infer which one causes the other. Much of the causal literature has focused on guaranteeing identifiability of causal direction in statistical models for datasets where strong assumptions hold, such as additive noise or restrictions on parameter count. These methods are then subsequently tested on realistic datasets, most of which violate their assumptions. Building on previous attempts, we show how to use causal assumptions within the Bayesian framework. This allows us to specify models with realistic assumptions, while also encoding independent causal mechanisms, leading to an asymmetry between the causal directions. Identifying causal direction then becomes a Bayesian model selection problem. We analyse why Bayesian model selection works for known identifiable cases and flexible model classes, while also providing correctness guarantees about its behaviour. To demonstrate our approach, we construct a Bayesian non-parametric model that can flexibly model the joint. We then outperform previous methods on a wide range of benchmark datasets with varying data generating assumptions showing the usefulness of our method.
Exploring semantic information in disease: Simple Data Augmentation Techniques for Chinese Disease Normalization

Disease name normalization is an important task in the medical domain. It classifies disease names written in various formats into standardized names, serving as a fundamental component in smart healthcare systems for various disease-related functions. Nevertheless, the most significant obstacle to existing disease name normalization systems is the severe shortage of training data. While data augmentation is a powerful approach for addressing data scarcity, our findings reveal that conventional data augmentation techniques often impede task performance, primarily due to the multi-axis and multi-granularity nature of disease names. Consequently, we introduce a set of customized data augmentation techniques designed to leverage the semantic information inherent in disease names. These techniques aim to enhance the model's understanding of the semantic intricacies and classification structure of disease names. Through extensive experimentation, we illustrate that our proposed plug-and-play methods not only surpass general data augmentation techniques but also exhibit significant performance improvements across various baseline models and training objectives, particularly in scenarios with limited training data. This underscores its potential for widespread application in medical language processing tasks.
Data Science in an Agent-Based Simulation World

In data science education, the importance of learning to solve real-world problems has been argued. However, there are two issues with this approach: (1) it is very costly to prepare multiple real-world problems (using real data) according to the learning objectives, and (2) the learner must suddenly tackle complex real-world problems immediately after learning from a textbook using ideal data. To solve these issues, this paper proposes data science teaching material that uses agent-based simulation (ABS). The proposed teaching material consists of an ABS model and an ABS story. To solve issue 1, the scenario of the problem can be changed according to the learning objectives by setting the appropriate parameters of the ABS model. To solve issue 2, the difficulty level of the tasks can be adjusted by changing the description in the ABS story. We show that, by using this teaching material, the learner can simulate the typical tasks performed by a data scientist in a step-by-step manner (causal inference, data understanding, hypothesis building, data collection, data wrangling, data analysis, and hypothesis testing). The teaching material described in this paper focuses on causal inference as the learning objectives and infectious diseases as the model theme for ABS, but ABS is used as a model to reproduce many types of social phenomena, and its range of expression is extremely wide. Therefore, we expect that the proposed teaching material will inspire the construction of teaching material for various objectives in data science education.
Learning from AI: An Interactive Learning Method Using a DNN Model Incorporating Expert Knowledge as a Teacher

Visual explanation is an approach for visualizing the grounds of judgment by deep learning, and it is possible to visually interpret the grounds of a judgment for a certain input by visualizing an attention map. As for deep-learning models that output erroneous decision-making grounds, a method that incorporates expert human knowledge in the model via an attention map in a manner that improves explanatory power and recognition accuracy is proposed. In this study, based on a deep-learning model that incorporates the knowledge of experts, a method by which a learner"learns from AI"the grounds for its decisions is proposed. An"attention branch network"(ABN), which has been fine-tuned with attention maps modified by experts, is prepared as a teacher. By using an interactive editing tool for the fine-tuned ABN and attention maps, the learner learns by editing the attention maps and changing the inference results. By repeatedly editing the attention maps and making inferences so that the correct recognition results are output, the learner can acquire the grounds for the expert's judgments embedded in the ABN. The results of an evaluation experiment with subjects show that learning using the proposed method is more efficient than the conventional method.
DOS: Diverse Outlier Sampling for Out-of-Distribution Detection

Modern neural networks are known to give overconfident prediction for out-of-distribution inputs when deployed in the open world. It is common practice to leverage a surrogate outlier dataset to regularize the model during training, and recent studies emphasize the role of uncertainty in designing the sampling strategy for outlier dataset. However, the OOD samples selected solely based on predictive uncertainty can be biased towards certain types, which may fail to capture the full outlier distribution. In this work, we empirically show that diversity is critical in sampling outliers for OOD detection performance. Motivated by the observation, we propose a straightforward and novel sampling strategy named DOS (Diverse Outlier Sampling) to select diverse and informative outliers. Specifically, we cluster the normalized features at each iteration, and the most informative outlier from each cluster is selected for model training with absent category loss. With DOS, the sampled outliers efficiently shape a globally compact decision boundary between ID and OOD data. Extensive experiments demonstrate the superiority of DOS, reducing the average FPR95 by up to 25.79% on CIFAR-100 with TI-300K.
Ear Structure Feature Extraction Based on Principal Curvature of Surface

A novel automatic ear anatomy feature extraction method was proposed. Firstly, the side face image including an ear was preprocessed to eliminate the noise and spur. Secondly, we regarded the 2D gray image as a surface because the characteristics of surface are that gray level changes in edge area is sharp and the curvature is larger than that of the smooth area. In accordance with the characteristics, the maximum and minimum principal curvatures of surface were calculated, and then, automatic threshold was used to segment curvature images. Finally, two thresholded images were combined to get the feature edge image. The experiments demonstrate that extracted feature edge curve is smooth and connected. Experiment shows that our method is robust to illumination intensity and edge curve can meet the requirements of next process.
Enhanced Robust Motion Control based on Unknown System Dynamics Estimator for Robot Manipulators

To achieve high-accuracy manipulation in the presence of unknown disturbances, we propose two novel efficient and robust motion control schemes for high-dimensional robot manipulators. Both controllers incorporate an unknown system dynamics estimator (USDE) to estimate disturbances without requiring acceleration signals and the inverse of inertia matrix. Then, based on the USDE framework, an adaptive-gain controller and a super-twisting sliding mode controller are designed to speed up the convergence of tracking errors and strengthen anti-perturbation ability. The former aims to enhance feedback portions through error-driven control gains, while the latter exploits finite-time convergence of discontinuous switching terms. We analyze the boundedness of control signals and the stability of the closed-loop system in theory, and conduct real hardware experiments on a robot manipulator with seven degrees of freedom (DoF). Experimental results verify the effectiveness and improved performance of the proposed controllers, and also show the feasibility of implementation on high-dimensional robots.
Layer-Wise Feedback Alignment is Conserved in Deep Neural Networks

In the quest to enhance the efficiency and bio-plausibility of training deep neural networks, Feedback Alignment (FA), which replaces the backward pass weights with random matrices in the training process, has emerged as an alternative to traditional backpropagation. While the appeal of FA lies in its circumvention of computational challenges and its plausible biological alignment, the theoretical understanding of this learning rule remains partial. This paper uncovers a set of conservation laws underpinning the learning dynamics of FA, revealing intriguing parallels between FA and Gradient Descent (GD). Our analysis reveals that FA harbors implicit biases akin to those exhibited by GD, challenging the prevailing narrative that these learning algorithms are fundamentally different. Moreover, we demonstrate that these conservation laws elucidate sufficient conditions for layer-wise alignment with feedback matrices in ReLU networks. We further show that this implies over-parameterized two-layer linear networks trained with FA converge to minimum-norm solutions. The implications of our findings offer avenues for developing more efficient and biologically plausible alternatives to backpropagation through an understanding of the principles governing learning dynamics in deep networks.
BeyondPixels: A Comprehensive Review of the Evolution of Neural Radiance Fields

Neural rendering combines ideas from classical computer graphics and machine learning to synthesize images from real-world observations. NeRF, short for Neural Radiance Fields, is a recent innovation that uses AI algorithms to create 3D objects from 2D images. By leveraging an interpolation approach, NeRF can produce new 3D reconstructed views of complicated scenes. Rather than directly restoring the whole 3D scene geometry, NeRF generates a volumetric representation called a ``radiance field,'' which is capable of creating color and density for every point within the relevant 3D space. The broad appeal and notoriety of NeRF make it imperative to examine the existing research on the topic comprehensively. While previous surveys on 3D rendering have primarily focused on traditional computer vision-based or deep learning-based approaches, only a handful of them discuss the potential of NeRF. However, such surveys have predominantly focused on NeRF's early contributions and have not explored its full potential. NeRF is a relatively new technique continuously being investigated for its capabilities and limitations. This survey reviews recent advances in NeRF and categorizes them according to their architectural designs, especially in the field of novel view synthesis.
Inverse boundary value problems for discrete Schrödinger operators on the multi-dimensional square lattice

We consider an inverse boundary value problem for a discrete Schrodinger operator $-\Delta + \hat{q} $ on a bounded domain in the square lattice. We define an analogue of the Dirichlet-to-Neumann map, and give a reconstruction procedure of the potential $\hat{q} $ from the D-to-N map for all energies.
Planetary nebulae as tracers of the kinematic structure of the starburst galaxy IC 10

Abstract The Local Group dwarf irregular galaxy IC 10 is the nearest starburst galaxy (at 725 kpc) and presents extended and complex HI kinematics. It is believed that IC 10 is still experiencing an intense and very recent burst of star formation, likely triggered by infalling gas from an extended envelope which is counter-rotating with respect to the galaxy proper rotation. In this contribution we summarize our preliminary results (Gonçalves et al. 2011, in prep.) obtained by performing a kinematic analysis of the PN population of IC 10. Our aim is to explore the kinematic connection of its intermediate to old stellar populations with that obtained from HI observations. Some PNe were found at large galactocentric distances and could therefore represent a trace of past tidal interactions.
Taylor vortices versus Taylor columns

Abstract Taylor–Couette flow is inevitably associated with the visually appealing toroidal vortices, waves, and spirals that are instigated by linear instability. The linearly stable regimes, however, pose a new challenge: do they undergo transition to turbulence and if so, what is its mechanism? Maretzke et al. (J. Fluid Mech., vol. 742, 2014, pp. 254–290) begin to address this question by determining the transient growth over the entire parameter space. They find that in the quasi-Keplerian regime, the optimal perturbations take the form of Taylor columns and that the maximum energy achieved depends only on the shear.
Combining Abstract Tasks and Haptic Material to Foster Computational Thinking in Computer Science Students

Students of Computer Science often struggle with the abstract nature of the content of their studies, especially in the student entry phase. This is partly due to the fact that on the one hand, many first year students possess only moderate abstract thinking skills, while on the other hand, Computer Science lecturers often take the existence of abstract thinking skills for granted, as they themselves are well versed in this area. To bridge this gap and foster the students’ abstract thinking skills, we employ haptic material to make abstract tasks and the corresponding solution processes more tangible. This supports the development of abstract thinking skills, and at the same time helps to actively engage students in our classes on software development. In this paper we describe our experiences with active classroom learning units based on haptic materials, as part of our inverted classroom teaching.
A Multi-Paradigm Programming Language for Education

An introductory CS1 course at universities has to meet different requirements. In addition to computational thinking, students have to learn a programming language and its underlying concepts. Furthermore, they should be educated in different paradigms such as procedural, object-oriented, and functional programming. Industrial-strength languages are optimized towards the requirements of professional software developers and tend to offer an overwhelming and complex set of language constructs. Using such a language in a CS1 course, students find themselves mainly struggling with learning the language, being primarily exposed to simple programs for demonstrating its constructs. In contrast, MuLE is an educational language that supports multiple paradigms with a small set of carefully designed constructs. Using MuLE, beginners may develop programming skills more easily, while they still learn underlying concepts systematically that may be transferred to other languages in their further education.