Miracle-dz/UniFAR-FTUs huggingface dataset:
BOTTER: A framework to analyze social bots in Twitter

Social networks have triumphed in communicating people online, but they have also been exploited to launch influence operations for manipulating society. The deployment of software-controlled accounts (e.g., social bots) has proven to be one of the most effective enablers for that purpose, and tools for their detection have been developed and widely adopted. However, the way to analyze these accounts and measure their impact is heterogeneous in the literature, where each case study performs unique measurements. To unify these efforts, we propose a common framework to analyze the interference of social bots in Twitter. The methodology compares the non-authentic actors with the rest of users from different perspectives, thus building objective metrics to measure their actual impact. We validate the framework by applying it to a dataset of Twitter iterations dated in the weeks preceding the 2019 Spanish general election. In this sense, we check that our framework facilitates the quantitative evaluation of unauthentic groups, particularly discovering that social bots changed the natural dynamics of the network in these days, but did not have a significant impact. We also consider this methodology as a practical tool for the qualitative interpretation of experimental results, particularly suggesting within the aforementioned electoral context that semi-automated accounts are potentially more threatening than fully automated ones.
A Hodge Theoretic Extension of Shapley Axioms

Lloyd S. Shapley introduced a set of axioms in 1953, now called the Shapley axioms, and showed that the axioms characterize a natural allocation among the players who are in grand coalition of a cooperative game. Recently, Stern and Tettenhorst showed that a cooperative game can be decomposed into a sum of component games, one for each player, whose value at the grand coalition coincides with the Shapley value. The component games are defined by the solutions to the naturally defined system of least squares linear equations via the framework of the Hodge decomposition on the hypercube graph. In this paper we propose a new set of axioms which characterizes the component games given by Stern and Tettenhorst, thereby suggesting that the component values for every coalition state may also serve for a valid measure of fair allocation among the players in each coalition. Our axioms may be seen as a completion of Shapley's in view of this characterization of the Hodge-theoretic component games. In addition, we provide a path integral representation of the component games which may be seen as an extension of the Shapley formula.
O2O-Afford: Annotation-Free Large-Scale Object-Object Affordance Learning

Contrary to the vast literature in modeling, perceiving, and understanding agent-object (e.g., human-object, hand-object, robot-object) interaction in computer vision and robotics, very few past works have studied the task of object-object interaction, which also plays an important role in robotic manipulation and planning tasks. There is a rich space of object-object interaction scenarios in our daily life, such as placing an object on a messy tabletop, fitting an object inside a drawer, pushing an object using a tool, etc. In this paper, we propose a unified affordance learning framework to learn object-object interaction for various tasks. By constructing four object-object interaction task environments using physical simulation (SAPIEN) and thousands of ShapeNet models with rich geometric diversity, we are able to conduct large-scale object-object affordance learning without the need for human annotations or demonstrations. At the core of technical contribution, we propose an object-kernel point convolution network to reason about detailed interaction between two objects. Experiments on large-scale synthetic data and real-world data prove the effectiveness of the proposed approach. Please refer to the project webpage for code, data, video, and more materials: https://cs.stanford. edu/~kaichun/o2oafford
Unsupervised Technique To Conversational Machine Reading

Conversational machine reading (CMR) tools have seen a rapid progress in the recent past. The current existing tools rely on the supervised learning technique which require labeled dataset for their training. The supervised technique necessitates that for every new rule text, a manually labeled dataset must be created. This is tedious and error prone. This paper introduces and demonstrates how unsupervised learning technique can be applied in the development of CMR. Specifically, we demonstrate how unsupervised learning can be used in rule extraction and entailment modules of CMR. Compared to the current best CMR tool, our developed framework reports 3.3% improvement in micro averaged accuracy and 1.4 % improvement in macro averaged accuracy.
Multiple Graph Learning for Scalable Multi-view Clustering

Graph-based multi-view clustering has become an active topic due to the efficiency in characterizing both the complex structure and relationship between multimedia data. However, existing methods have the following shortcomings: (1) They are inefficient or even fail for graph learning in large scale due to the graph construction and eigen-decomposition. (2) They cannot well exploit both the complementary information and spatial structure embedded in graphs of different views. To well exploit complementary information and tackle the scalability issue plaguing graph-based multi-view clustering, we propose an efficient multiple graph learning model via a small number of anchor points and tensor Schatten p-norm minimization. Specifically, we construct a hidden and tractable large graph by anchor graph for each view and well exploit complementary information embedded in anchor graphs of different views by tensor Schatten p-norm regularizer. Finally, we develop an efficient algorithm, which scales linearly with the data size, to solve our proposed model. Extensive experimental results on several datasets indicate that our proposed method outperforms some state-of-the-art multi-view clustering algorithms.
The Sejong Open Cluster Survey (SOS). VII. A Photometric Study of the Young Open Cluster IC 1590

Young open clusters are ideal laboratories to understand the star formation process. We present deep UBVI and Hα photometry for the young open cluster IC 1590 in the center of the H ii region NGC 281. Early-type members are selected from UBV photometric diagrams, and low-mass pre-main-sequence (PMS) members are identified by using Hα photometry. In addition, the published X-ray source list and Gaia astrometric data are also used to isolate probable members. A total of 408 stars are selected as members. The mean reddening obtained from early-type members is 〈E(B − V)〉 = 0.40 ± 0.06 (s.d.). We confirm the abnormal extinction law for the intracluster medium. The distance modulus to the cluster determined from the zero-age main-sequence fitting method is 12.3 ± 0.2 mag (d = 2.88 ± 0.28 kpc), which is consistent with the distance d=2.70−0.20+0.24 kpc from the recent Gaia parallaxes. We also estimate the ages and masses of individual members by means of stellar evolutionary models. The mode of the age of PMS stars is about 0.8 Myr. The initial mass function of IC 1590 is derived. It appears to be a steeper shape (Γ = −1.49 ± 0.14) than that of the Salpeter/Kroupa initial mass function for the high-mass regime (m > 1 M ⊙). The signature of mass segregation is detected from the difference in the slopes of the initial mass functions for the inner ( r<2.′5 ) and outer regions of this cluster. We finally discuss the star formation history in NGC 281.
NMPO: Near-Memory Computing Profiling and Offloading

Real-world applications are now processing big-data sets, often bottlenecked by the data movement between the compute units and the main memory. Near-memory computing (NMC), a modern data-centric computational paradigm, can alleviate these bottlenecks, thereby improving the performance of applications. The lack of NMC system availability makes simulators the primary evaluation tool for performance estimation. However, simulators are usually time-consuming, and methods that can reduce this overhead would accelerate the early-stage design process of NMC systems. This work proposes Near-Memory computing Profiling and Offloading (NMPO), a high-level framework capable of predicting NMC offloading suitability employing an ensemble machine learning model. NMPO predicts NMC suitability with an accuracy of 85.6% and, compared to prior works, can reduce the prediction time by using hardware-dependent applications features by up to 3 order of magnitude.
Multimodal Semantic Scene Graphs for Holistic Modeling of Surgical Procedures

From a computer science viewpoint, a surgical domain model needs to be a conceptual one incorporating both behavior and data. It should therefore model actors, devices, tools, their complex interactions and data flow. To capture and model these, we take advantage of the latest computer vision methodologies for generating 3D scene graphs from camera views. We then introduce the Multimodal Semantic Scene Graph (MSSG) which aims at providing a unified symbolic, spatiotemporal and semantic representation of surgical procedures. This methodology aims at modeling the relationship between different components in surgical domain including medical staff, imaging systems, and surgical devices, opening the path towards holistic understanding and modeling of surgical procedures. We then use MSSG to introduce a dynamically generated graphical user interface tool for surgical procedure analysis which could be used for many applications including process optimization, OR design and automatic report generation. We finally demonstrate that the proposed MSSGs could also be used for synchronizing different complex surgical procedures. While the system still needs to be integrated into real operating rooms before getting validated, this conference paper aims mainly at providing the community with the basic principles of this novel concept through a first prototypal partial realization based on MVOR dataset.
Rethinking the Evaluation of Neural Machine Translation

The evaluation of neural machine translation systems is usually built upon generated translation of a certain decoding method (e.g., beam search) with evaluation metrics over the generated translation (e.g., BLEU). However, this evaluation framework suffers from high search errors brought by heuristic search algorithms and is limited by its nature of evaluation over one best candidate. In this paper, we propose a novel evaluation protocol, which not only avoids the effect of search errors but provides a system-level evaluation in the perspective of model ranking. In particular, our method is based on our newly proposed exact top-k decoding instead of beam search. Our approach evaluates model errors by the distance between the candidate spaces scored by the references and the model respectively. Extensive experiments on WMT’14 English-German demonstrate that bad ranking ability is connected to the well-known beam search curse, and state-of-the-art Transformer models are facing serious ranking errors. By evaluating various model architectures and techniques, we provide several interesting ﬁndings. Finally, to effectively approximate the exact search algorithm with same time cost as original beam search, we present a minimum heap augmented beam search algorithm.
Coach2vec: autoencoding the playing style of soccer coaches

Capturing the playing style of professional soccer coaches is a complex, and yet barely explored, task in sports analytics. Nowadays, the availability of digital data describing every relevant spatio-temporal aspect of soccer matches, allows for capturing and analyzing the playing style of players, teams, and coaches in an automatic way. In this paper, we present coach2vec, a workflow to capture the playing style of professional coaches using match event streams and artificial intelligence. Coach2vec extracts ball possessions from each match, clusters them based on their similarity, and reconstructs the typical ball possessions of coaches. Then, it uses an autoencoder, a type of artificial neural network, to obtain a concise representation (encoding) of the playing style of each coach. Our experiments, conducted on soccer-logs describing the last four seasons of the Italian first division, reveal interesting similarities between prominent coaches, paving the road to the simulation of playing styles and the quantitative comparison of professional coaches.
Limited depth bandit-based strategy for Monte Carlo planning in continuous action spaces

This paper addresses the problem of optimal control using search trees. We start by considering multi-armed bandit problems with continuous action spaces and propose LD-HOO, a limited depth variant of the hierarchical optimistic optimization (HOO) algorithm. We provide a regret analysis for LD-HOO and show that, asymptotically, our algorithm exhibits the same cumulative regret as the original HOO while being faster and more memory efficient. We then propose a Monte Carlo tree search algorithm based on LD-HOO for optimal control problems and illustrate the resulting approach's application in several optimal control problems.
Neural Machine Translation for Low-resource Languages: A Survey

Neural Machine Translation (NMT) has seen tremendous growth in the last ten years since the early 2000s and has already entered a mature phase. While considered the most widely used solution for Machine Translation, its performance on low-resource language pairs remains sub-optimal compared to the high-resource counterparts due to the unavailability of large parallel corpora. Therefore, the implementation of NMT techniques for low-resource language pairs has been receiving the spotlight recently, thus leading to substantial research on this topic. This article presents a detailed survey of research advancements in low-resource language NMT (LRL-NMT) and quantitative analysis to identify the most popular techniques. We provide guidelines to select the possible NMT technique for a given LRL data setting based on our findings. We also present a holistic view of the LRL-NMT research landscape and provide recommendations to enhance the research efforts further.
Spatio-Temporal Context for Action Detection

Research in action detection has grown in the recentyears, as it plays a key role in video understanding. Modelling the interactions (either spatial or temporal) between actors and their context has proven to be essential for this task. While recent works use spatial features with aggregated temporal information, this work proposes to use non-aggregated temporal information. This is done by adding an attention based method that leverages spatio-temporal interactions between elements in the scene along the clip.The main contribution of this work is the introduction of two cross attention blocks to effectively model the spatial relations and capture short range temporal interactions.Experiments on the AVA dataset show the advantages of the proposed approach that models spatio-temporal relations between relevant elements in the scene, outperforming other methods that model actor interactions with their context by +0.31 mAP.
Model selection for 
K+Σ−
 photoproduction within an isobar model

We utilise an isobar model to investigate theK+Σ− photoproduction off a neutron in the resonance region. Except for the Born terms, we include high-spin (spin-3/2 and spin-5/2) nucleon resonances in the consistent formalism together with a few ∆ and kaon resonances to achieve an acceptable agreement with data. Interestingly, we reveal that no hyperon resonances are needed to achieve a reasonable description of data. On the other hand the N(1720)3/2 resonance was found to be very important for correct description of data. The free parameters of the model were fitted to experimental data from the LEPS and CLAS Collaborations on either differential cross sections or photon beam asymmetry. The novel feature of the fitting procedure is the use of a regularization method, the Least Absolute Shrinkage Selection Operator, and information criteria in order to choose the best fit.
Decentralized Blockchain-based model for Edge Computing

Blockchain technology is among the fastest-growing technologies in the world today. It has been adopted in diverse areas but mostly in financial systems, such as Bitcoin cryptocurrency. Therefore, it is a niche that has attracted interest from researchers from various fields, including computer science. Other areas where Blockchain is being embraced are the Smart Grid and Internet of Things (IoT) technologies, among others. While it is all good and improving many areas of applications, Blockchain still has some shortcomings. For example, it is not designed for high scalability when accommodating normal transactions. On the other hand, a parallel technology that has diverse applications in distributed networks better known as edge computing has emerged. Its main advantage is that it increases the speed of pf processes within those networks. However, like Blockchain, edge computing has its shortcomings. Its security systems and management systems have been found to be wanting. Hence the idea to integrate the two technologies and take advantage of their strengths. A blend of the two would lead to advanced network servers, huge data storage, and heightened security in transactions. However, this integration will best happen when some measures are taken. For example, there is a need to address scalability, resource management satisfactorily, and the security of the systems. To solve the integration problem, a decentralized Blockchain-based model of Edge computing is proposed in this paper.
Structure and electronic properties of pure and nitrogen doped nanocrystalline tungsten oxide thin films

vii TABLE OF CONTENTS . ix LIST OF TABLES . xiv LIST OF FIGURES . xv CHAPTER 1: INTRODUCTION . 1 1.1 Tungsten oxide (WO3) . 5 1.1.1 Crystal structure . 5 1.1.2 Electronic structure . 7 1.1. 3 Charge transport mechanism.10 1.2 Technical background . 11 1.2. 1 Photocatalytic water splitting . 12 1.2. 2. Requirements of photoelectrodes for water splitting . 13 1.2. 3 Titanium oxide (TiO2) as a photoelectrode . 14 Doped TiO2 . 15 1.2. 4 Tungsten oxide (WO3) as a photocatalyst . 16 1.2. 5 Nitrogen doping of WO3 . 16
The Axial Anomaly in Lorentz Violating Theories: Towards the Electromagnetic Response of Weakly Tilted Weyl Semimetals

Using the path integral formulation in Euclidean space, we extended the calculation of the abelian chiral anomalies in the case of Lorentz violating theories by considering a new fermionic correction term provided by the standard model extension, which arises in the continuous Hamiltonian of a weakly tilted Weyl semimetal, and whose cones have opposite tilting. We found that this anomaly is insensitive to the tilting parameter, retaining its well-known covariant form. This independence on the Lorentz violating parameters is consistent with other findings reported in the literature. The initially imposed gauge invariant regularization was consistently recovered at the end of the calculation by the appearance of highly non-trivial combinations of the covariant derivatives, which ultimately managed to give only terms containing the electromagnetic tensor. We emphasize that the value of the anomaly with an arbitrary parameter is not automatically related to the effective action describing the electromagnetic response of such materials.
Optimal Rates for Random Order Online Optimization

We study online convex optimization in the random order model, recently proposed by \citet{garber2020online}, where the loss functions may be chosen by an adversary, but are then presented to the online algorithm in a uniformly random order. Focusing on the scenario where the cumulative loss function is (strongly) convex, yet individual loss functions are smooth but might be non-convex, we give algorithms that achieve the optimal bounds and significantly outperform the results of \citet{garber2020online}, completely removing the dimension dependence and improving their scaling with respect to the strong convexity parameter. Our analysis relies on novel connections between algorithmic stability and generalization for sampling without-replacement analogous to those studied in the with-replacement i.i. d. ~setting, as well as on a refined average stability analysis of stochastic gradient descent.
Spin-orbit torques and magnetotransport properties of 
α−Sn
 and 
β−Sn
 heterostructures

Topological insulators have emerged as an important material class for efficient spin-charge interconversion. Most topological insulators considered to date are binary or ternary compounds, with the exception of α-Sn. Here we report a comprehensive characterization of the growth, magnetotransport properties, and current-induced spin-orbit torques of α-Sn and β-Sn-based ferromagnetic heterostructures. We show that α-Sn grown with a Bi surfactant on CdTe(001) promotes large spin-orbit torques in a ferromagnetic FeCo layer at room temperature, comparable to Pt, whereas α-Sn grown without Bi surfactant and the non-topological phase, β-Sn, induce lower torques. The dampinglike and fieldlike spin-orbit torque efficiency in α-Sn with Bi are 0.12 and 0.18, respectively. Further, we show that α-Sn grown with and without Bi presents a spin Hall-like magnetoresistance comparable to that found in heavy metal/ferromagnet bilayers. Our work demonstrates direct and efficient charge-to-spin conversion in α-Sn ferromagnetic heterostructures, showing that α-Sn is a promising material for current-induced magnetization control in spintronic devices.
A microlensing search of 700 million VVV light curves

The VISTA Variables in the VÄśa Lactea (VVV) survey and its extension, have been monitoring about 560 square degrees of sky centred on the Galactic bulge and inner disc for nearly a decade. The photometric catalogue contains of order 109 sources monitored in the Ks band down to 18 mag over hundreds of epochs from 2010-2019. Using these data we develop a decision tree classifier to identify microlensing events. As inputs to the tree, we extract a few physically motivated features as well as simple statistics ensuring a good fit to a microlensing model both on and off the event amplification. This produces a fast and efficient classifier trained on a set of simulated microlensing events and catacylsmic variables, together with flat baseline light curves randomly chosen from the VVV data. The classifier achieves 97 per cent accuracy in identifying simulated microlensing events in a validation set. We run the classifier over the VVV data set and then visually inspect the results, which produces a catalogue of 1 959 microlensing events. For these events, we provide the Einstein radius crossing time via a Bayesian analysis. The spatial dependence on recovery efficiency of our classifier is well characterised, and this allows us to compute spatially resolved completeness maps as a function of Einstein crossing time over the VVV footprint. We compare our approach to previous microlensing searches of the VVV. We highlight the importance of Bayesian fitting to determine the microlensing parameters for events with surveys like VVV with sparse data.