Miracle-dz/UniFAR-FTUs huggingface dataset:
Multivariate Data Explanation by Jumping Emerging Patterns Visualization

Multivariate or multidimensional visualization plays an essential role in exploratory data analysis by allowing users to derive insights and formulate hypotheses. Despite their popularity, it is usually users’ responsibility to (visually) discover the data patterns, which can be cumbersome and time-consuming. Visual Analytics (VA) and machine learning techniques can be instrumental in mitigating this problem by automatically discovering and representing such patterns. One example is the integration of classification models with (visual) interpretability strategies, where models are used as surrogates for data patterns so that understanding a model enables understanding the phenomenon represented by the data. Although useful and inspiring, the few proposed solutions are based on visual representations of so-called black-box models, so the interpretation of the patterns captured by the models is not straightforward, requiring mechanisms to transform them into human-understandable pieces of information. This paper presents multiVariate dAta eXplanation (VAX), a new VA method to support identifying and visual interpreting patterns in multivariate datasets. Unlike the existing similar approaches, VAX uses the concept of Jumping Emerging Patterns, inherent interpretable logic statements representing class-variable relationships (patterns) derived from random Decision Trees. The potential of VAX is shown through use cases employing two real-world datasets covering different scenarios where intricate patterns are discovered and represented, something challenging to be done using usual exploratory approaches.
Learning to Reach, Swim, Walk and Fly in One Trial: Data-Driven Control with Scarce Data and Side Information

We develop a learning-based control algorithm for unknown dynamical systems under very severe data limitations. Speciﬁcally, the algorithm has access to streaming and noisy data only from a single and ongoing trial. It accomplishes such performance by effectively leveraging various forms of side information on the dynamics to reduce the sample complexity. Such side information typically comes from elementary laws of physics and qualitative properties of the system. More precisely, the algorithm approximately solves an optimal control problem encoding the system’s desired be-havior. To this end, it constructs and iteratively reﬁnes a data-driven differential inclusion that contains the unknown vector ﬁeld of the dynamics. The differential inclusion, used in an interval Taylor-based method, enables to over-approximate the set of states the system may reach. Theo-retically, we establish a bound on the suboptimality of the approximate solution with respect to the optimal control with known dynamics. We show that the longer the trial or the more side information is available, the tighter the bound. Empirically, experiments in a high-ﬁdelity F-16 aircraft simulator and MuJoCo’s environments illustrate that, despite the scarcity of data, the algorithm can provide performance comparable to reinforcement learning algorithms trained over millions of environment interactions. Besides, we show that the algorithm outperforms existing techniques combining system identiﬁcation and model predictive control.
Two-Faced Humans on Twitter and Facebook: Harvesting Social Multimedia for Human Personality Profiling

Human personality traits are the key drivers behind our decision-making, influencing our life path on a daily basis. Inference of personality traits, such as Myers-Briggs Personality Type, as well as an understanding of dependencies between personality traits and users' behavior on various social media platforms is of crucial importance to modern research and industry applications. The emergence of diverse and cross-purpose social media avenues makes it possible to perform user personality profiling automatically and efficiently based on data represented across multiple data modalities. However, the research efforts on personality profiling from multi-source multi-modal social media data are relatively sparse, and the level of impact of different social network data on machine learning performance has yet to be comprehensively evaluated. Furthermore, there is not such dataset in the research community to benchmark. This study is one of the first attempts towards bridging such an important research gap. Specifically, in this work, we infer the Myers-Briggs Personality Type indicators, by applying a novel multi-view fusion framework, called "PERS" and comparing the performance results not just across data modalities but also with respect to different social network data sources. Our experimental results demonstrate the PERS's ability to learn from multi-view data for personality profiling by efficiently leveraging on the significantly different data arriving from diverse social multimedia sources. We have also found that the selection of a machine learning approach is of crucial importance when choosing social network data sources and that people tend to reveal multiple facets of their personality in different social media avenues. Our released social multimedia dataset facilitates future research on this direction.
In situ atomic force microscopy study of hectorite and nontronite dissolution: Implications for phyllosilicate edge surface structures and dissolution mechanisms

Abstract The dissolution behavior of two smectite minerals, hectorite (trioctahedral) and nontronite (dioctahedral), was observed in situ, in acid solutions, using atomic force microscopy. As expected, the crystallites dissolved inward from the edges, and the basal surfaces appeared to be unreactive during the timescale of the experiments. The hectorite (010) faces appeared to dissolve about 6× more slowly than the lath ends, usually broken edges. The edges visibly dissolved on all sides, and appeared to roughen somewhat. On the other hand, the (010), (110), and (11̄0) faces on nontronite crystals were exceptionally stable, so that any dissolution fronts originating at broken edges or defects would quickly become pinned along these faces, after which no more dissolution was observable. These observations can be explained by using periodic bond chain theory to predict the topology of the surface functional groups on the edge faces of these minerals. If a certain amount of predicted surface relaxation is allowed on the (110) and (11̄0) faces of nontronite, an important difference between the exceptionally stable faces and the others becomes apparent. That is, the oxygen sites connecting the octahedral and tetrahedral sheets are all fully bonded on the nontronite (010), (110), and (11̄0) edge faces, whereas all hectorite edge faces and nontronite broken edges would have coordinatively unsaturated connecting O atoms. This explanation for the differential reactivity of these crystal faces implies that the rate limiting step of the dissolution process is the breaking of bonds to connecting O atoms.
Finding critical edges in complex networks through local information

In transportation, communication, social and other real networks, there are some critical edges play an important role in the delivery of traﬃc ﬂow and information packets. Identifying critical edges in complex networks is of great signiﬁcance in both theoretical studies and practical applications. Considering the overlap of communities in the neighborhood of edges, a novel and eﬀective index named subgraph overlap (SO) is proposed in this paper. Experimental
TinyML: Analysis of Xtensa LX6 microprocessor for Neural Network Applications by ESP32 SoC

In recent decades, Machine Learning (ML) has become extremely important for many computing applications. The pervasiveness of ultra-low-power embedded devices such as ESP32 or ESP32 Cam with tiny Machine Learning (tinyML) applications will enable the mass proliferation of Artificial Intelligent powered Embedded IoT Devices. In the last few years, the microcontroller device (Espressif ESP32) became powerful enough to be used for small/tiny machine learning (tinyML) tasks. The ease of use of platforms like Arduino IDE, MicroPython and TensorFlow Lite (TF) with tinyML application make it an indispensable topic of research for mobile robotics, modern computer science and electrical engineering. The goal of this paper is to analyze the speed of the Xtensa dual core 32-bit LX6 microprocessor by running a neural network application. The different number of inputs (9, 36, 144 and 576) inputted through the different number of neurons in neural networks with one and two hidden layers. Xtensa LX6 microprocessor has been analyzed because it comes inside with Espressif ESP32 and ESP32 Cam which are very easy to use, plug and play IoT device. In this paper speed of the Xtensa LX6 microprocessor in feed-forward mode has been analyzed.
FloorPP-Net: Reconstructing Floor Plans using Point Pillars for Scan-to-BIM

This paper presents a deep learning-based point cloud processing method named FloorPP-Net for the task of Scan-to-BIM (building information model). FloorPP-Net first converts the input point cloud of a building story into point pillars (PP), then predicts the corners and edges to output the floor plan. Altogether, FloorPP-Net establishes an end-to-end supervised learning framework for the Scan-to-Floor-Plan (Scan2FP) task. In the 1st International Scan-to-BIM Challenge held in conjunction with CVPR 2021, FloorPP-Net was ranked the second runner-up in the floor plan reconstruction track. Future work includes general edge proposals, 2D plan regularization, and 3D BIM reconstruction.
Chiral magic-angle twisted bilayer graphene in a magnetic field: Landau level correspondence, exact wave functions, and fractional Chern insulators

We show that the flat bands in the chiral model of magic-angle twisted bilayer graphene remain exactly flat in the presence of a perpendicular magnetic field. This is shown by an exact mapping between the model and the lowest Landau level wavefunctions at an effective magnetic field, in which the external field is either augmented or reduced by one flux quantum per unit cell. When the external field reaches one flux quantum per unit cell, the model exhibits a topological phase transition. These findings allow us to analyze a Jain-series of Fractional Chern Insulators states in the exactly flat band, and to point out an unconventional dependence of the energy gap on the magnetic field.
A Neural Frequency-Severity Model and Its Application to Insurance Claims

This paper proposes a flexible and analytically tractable class of frequency and severity models for predicting insurance claims. The proposed model is able to capture nonlinear relationships in explanatory variables by characterizing the logarithmic mean functions of frequency and severity distributions as neural networks. Moreover, a potential dependence between the claim frequency and severity can be incorporated. In particular, the paper provides analytic formulas for mean and variance of the total claim cost, making our model ideal for many applications such as pricing insurance contracts and the pure premium. A simulation study demonstrates that our method successfully recovers nonlinear features of explanatory variables as well as the dependency between frequency and severity. Then, this paper uses a French auto insurance claim dataset to illustrate that the proposed model is superior to the existing methods in fitting and predicting the claim frequency, severity, and the total claim loss. Numerical results indicate that the proposed model helps in maintaining the competitiveness of an insurer by accurately predicting insurance claims and avoiding adverse selection.
QUBO transformation using Eigenvalue Decomposition

Quadratic Unconstrained Binary Optimization (QUBO) is a general-purpose modeling framework for combinatorial optimization problems and is a requirement for quantum annealers. This paper utilizes the eigenvalue decomposition of the underlying Q matrix to alter and improve the search process by extracting the information from dominant eigenvalues and eigenvectors to implicitly guide the search towards promising areas of the solution landscape. Computational results on benchmark datasets illustrate the efficacy of our routine demonstrating significant performance improvements on problems with dominant eigenvalues.
Long Time Behavior of an Age- and Leaky Memory-Structured Neuronal Population Equation

We study the asymptotic stability of a two-dimensional mean-field equation, which takes the form of a nonlocal transport equation and generalizes the time-elapsed neuron network model by the inclusion of a leaky memory variable. This additional variable can represent a slow fatigue mechanism, like spike frequency adaptation or short-term synaptic depression. Even though two-dimensional models are known to have emergent behaviors, like population bursts, which are not observed in standard one-dimensional models, we show that in the weak connectivity regime, two-dimensional models behave like one-dimensional models, i.e. they relax to a unique stationary state. The proof is based on an application of Harris' ergodic theorem and a perturbation argument, adapted to the case of a multidimensional equation with delays.
Force Sensing in an Optomechanical System with Feedback-Controlled In-Loop Light

F. Bemani,1, ∗ O. Černotı́k,1, † L. Ruppert,1, ‡ D. Vitali,2, 3, 4, § and R. Filip1, ¶ 1Department of Optics, Palacký University, 17. listopadu 1192/12, 77146 Olomouc, Czechia 2Physics Division, School of Science and Technology, University of Camerino, I-62032 Camerino (MC), Italy 3INFN, Sezione di Perugia, via A. Pascoli, Perugia, Italy 4CNR-INO, L.go Enrico Fermi 6, I-50125 Firenze, Italy (Dated: June 22, 2021)
Conversational Agents in Software Engineering: Survey, Taxonomy and Challenges

The use of natural language interfaces in the field of human-computer interaction is undergoing intense study through dedicated scientific and industrial research. The latest contributions in the field, including deep learning approaches like recurrent neural networks, the potential of context-aware strategies and user-centred design approaches, have brought back the attention of the community to software-based dialogue systems, generally known as conversational agents or chatbots. Nonetheless, and given the novelty of the field, a generic, context-independent overview on the current state of research of conversational agents covering all research perspectives involved is missing. Motivated by this context, this paper reports a survey of the current state of research of conversational agents through a systematic literature review of secondary studies. The conducted research is designed to develop an exhaustive perspective through a clear presentation of the aggregated knowledge published by recent literature within a variety of domains, research focuses and contexts. As a result, this research proposes a holistic taxonomy of the different dimensions involved in the conversational agents’ field, which is expected to help researchers and to lay the groundwork for future research in the field of natural language interfaces.
Improving Dialog Systems for Negotiation with Personality Modeling

In this paper, we explore the ability to model and infer personality types of opponents, predict their responses, and use this information to adapt a dialog agent’s high-level strategy in negotiation tasks. Inspired by the idea of incorporating a theory of mind (ToM) into machines, we introduce a probabilistic formulation to encapsulate the opponent’s personality type during both learning and inference. We test our approach on the CraigslistBargain dataset (He et al. 2018) and show that our method using ToM inference achieves a 20% higher dialog agreement rate compared to baselines on a mixed population of opponents. We also demonstrate that our model displays diverse negotiation behavior with different types of opponents.
Combining gravitational and electromagnetic waves observations to investigate local structure and the Hubble tension

Recent estimations of the Hubble parameter $H_0$ based on gravitational waves (GW) observations can be used to shed some light on the discrepancy between the value of the Hubble parameter $H_0^P$ obtained from large scale observations such as the Planck mission, and the small scale value $H_0^R$, obtained from low redshift supernovae (SNe). In order to investigate the origin of this discrepancy we perform a combined analysis of the luminosity distance of SNe and GW sources, using different methods, finding that the impact of the GW data is very limited, due to the small number of data points, and their large errors. We analyze separately data from the Pantheon and the Union 2.1 catalogues, finding that a model with $H_0^P$ and a small local void can fit the data as well as a homogeneous model with $H_0^R$, resolving the apparent $H_0$ tension. We find that there is a significant difference between the size and depth of the inhomogeneity obtained using the two datasets, which could be due to the different sky coverage of the two catalogues. For Pantheon we obtain evidence of a local inhomogeneity with a density contrast $\delta_v=-0.155 \pm 0.026$, extending up to a redshift of $z_v = 0.056 \pm 0.0002$, while for Union 2.1 we obtain $\delta_v=-0.461 \pm 0.032$ and $z_v= 0.081\pm 0.008$. We also perform some analysis using redshift shell averaged data, and obtain approximately the same results, hinting to the fact that the effects of the monopole component of the local inhomogeneity are the dominant ones
A Systematic Survey of Regularization and Normalization in GANs

Generative Adversarial Networks (GANs) have been widely applied in different scenarios thanks to the development of deep neural networks. The original GAN was proposed based on the non-parametric assumption of the infinite capacity of networks. However, it is still unknown whether GANs can fit the target distribution without any prior information. Due to the overconfident assumption, many issues remain unaddressed in GANs training, such as non-convergence, mode collapses, and gradient vanishing. Regularization and normalization are common methods of introducing prior information to stabilize training and improve discrimination. Although a handful number of regularization and normalization methods have been proposed for GANs, to the best of our knowledge, there exists no comprehensive survey that primarily focuses on objectives and development of these methods, apart from some incomprehensive and limited-scope studies. In this work, we conduct a comprehensive survey on the regularization and normalization techniques from different perspectives of GANs training. First, we systematically describe different perspectives of GANs training and thus obtain the different objectives of regularization and normalization. Based on these objectives, we propose a new taxonomy. Furthermore, we compare the performance of the mainstream methods on different datasets and investigate the applications of regularization and normalization techniques that have been frequently employed in state-of-the-art GANs. Finally, we highlight potential future directions of research in this domain. Code and studies related to the regularization and normalization of GANs in this work are summarized at https://github.com/iceli1007/GANs-Regularization-Review.
Prepared for submission to JCAP Inflationary attractors from a non-canonical kinetic term

We show explicitly how the T-model, E-model, and Hilltop inflations are obtained from the inflation models with a non-canonical kinetic term and an arbitrary potential. By this method, any attractor of observables ns and r is possible. The presence of attractors poses a challenge to differentiate inflation models. 1Corresponding author. ar X iv :2 10 6. 10 30 3v 1 [ gr -q c] 1 8 Ju n 20 21
A Max-Min Entropy Framework for Reinforcement Learning

In this paper, we propose a max-min entropy framework for reinforcement learning (RL) to overcome the limitation of the soft actor-critic (SAC) algorithm implementing the maximum entropy RL in model-free sample-based learning. Whereas the maximum entropy RL guides learning for policies to reach states with high entropy in the future, the proposed max-min entropy framework aims to learn to visit states with low entropy and maximize the entropy of these low-entropy states to promote better exploration. For general Markov decision processes (MDPs), an efﬁcient algorithm is constructed under the proposed max-min entropy framework based on disentanglement of exploration and exploitation. Numerical results show that the proposed algorithm yields drastic performance improvement over the current state-of-the-art RL algorithms. in model-free sample-based learning with function approximation. In order to overcome such limitations associated with implementation of the maximum entropy RL, we propose a max-min entropy framework for RL, which aims to learn policies reaching states with low entropy and maximizing the entropy of these low-entropy states, whereas the conventional maximum entropy RL optimizes for policies that aim to visit states with high entropy and maximize the entropy of those high-entropy states for high entropy of the entire trajectory. We implemented the proposed max-min entropy framework into a practical iterative actor-critic algorithm based on policy iteration with disentangled exploration and exploitation. It is demonstrated that the proposed algorithm signiﬁcantly enhances exploration capability due to the fairness across states induced by the max-min framework and yields drastic performance improvement over existing RL algorithms including maximum-entropy SAC on difﬁcult control tasks.
Self-healing mechanism of lithium metal

Li metal as an ideal anode material meets the requirements of state-of-the-art secondary batteries. However, the dendrite growth of Li causes safety concerns and results in a low coulombic efficiency, which has significantly restricted the commercial applications of Li secondary batteries. Owing to the intrinsic limitations of even the most advanced experimental and computational techniques, a mechanistic understanding of Li deposition (growth) on the atomic scale is lacking. Here, we construct a Li potential model by machine learning with an accuracy of quantum mechanical computations. Our molecular dynamics simulations based on this potential model reveal two self-healing mechanisms in a large Li metal system, surface self-healing and bulk self-healing, and identify three Li dendrite morphologies in different conditions, “needle,” “mushroom,” and “hemisphere. ” We finally propose the concepts of local current density and variance of local current density as a supplement to critical current density to determine the probability of self-healing triggered.
Attend What You Need: Motion-Appearance Synergistic Networks for Video Question Answering

Video Question Answering is a task which requires an AI agent to answer questions grounded in video. This task entails three key challenges: (1) understand the intention of various questions, (2) capturing various elements of the input video (e.g., object, action, causality), and (3) cross-modal grounding between language and vision information. We propose Motion-Appearance Synergistic Networks (MASN), which embed two cross-modal features grounded on motion and appearance information and selectively utilize them depending on the question’s intentions. MASN consists of a motion module, an appearance module, and a motion-appearance fusion module. The motion module computes the action-oriented cross-modal joint representations, while the appearance module focuses on the appearance aspect of the input video. Finally, the motion-appearance fusion module takes each output of the motion module and the appearance module as input, and performs question-guided fusion. As a result, MASN achieves new state-of-the-art performance on the TGIF-QA and MSVD-QA datasets. We also conduct qualitative analysis by visualizing the inference results of MASN.