Miracle-dz/UniFAR-FTUs huggingface dataset:
Attention-based Region of Interest (ROI) Detection for Speech Emotion Recognition

Automatic emotion recognition for real-life appli-cations is a challenging task. Human emotion expressions aresubtle, and can be conveyed by a combination of several emo-tions. In most existing emotion recognition studies, each audioutterance/video clip is labelled/classified in its entirety. However,utterance/clip-level labelling and classification can be too coarseto capture the subtle intra-utterance/clip temporal dynamics. Forexample, an utterance/video clip usually contains only a fewemotion-salient regions and many emotionless regions. In thisstudy, we propose to use attention mechanism in deep recurrentneural networks to detection the Regions-of-Interest (ROI) thatare more emotionally salient in human emotional speech/video,and further estimate the temporal emotion dynamics by aggre-gating those emotionally salient regions-of-interest. We comparethe ROI from audio and video and analyse them. We comparethe performance of the proposed attention networks with thestate-of-the-art LSTM models on multi-class classification task ofrecognizing six basic human emotions, and the proposed attentionmodels exhibit significantly better performance. Furthermore, theattention weight distribution can be used to interpret how anutterance can be expressed as a mixture of possible emotions.
Covariate-Balancing-Aware Interpretable Deep Learning Models for Treatment Effect Estimation

Estimating treatment effects is of great importance for many biomedical applications with observational data. Particularly, interpretability of the treatment effects is preferable for many biomedical researchers. In this paper, we first provide a theoretical analysis and derive an upper bound for the bias of average treatment effect (ATE) estimation under the strong ignorability assumption. Derived by leveraging appealing properties of the weighted energy distance, our upper bound is tighter than what has been reported in the literature. Motivated by the theoretical analysis, we propose a novel objective function for estimating the ATE that uses the energy distance balancing score and hence does not require the correct specification of the propensity score model. We also leverage recently developed neural additive models to improve interpretability of deep learning models used for potential outcome prediction. We further enhance our proposed model with an energy distance balancing score weighted regularization. The superiority of our proposed model over current state-of-the-art methods is demonstrated in semi-synthetic experiments using two benchmark datasets, namely, IHDP and ACIC, as well as is examined through the study of the effect of smoking on the blood level of cadmium using NHANES.
Minimum Cost Multicuts for Incorrect Landmark Edge Detection in Pose-graph SLAM

Pose-graph SLAM is the de facto standard framework for constructing large-scale maps from multi-session experiences of relative observations and motions during visual robot navigation. It has received increasing attention in the context of recent advanced SLAM frameworks such as graph neural SLAM. One remaining challenge is landmark misrecognition errors (i.e., incorrect landmark edges) that can have catastrophic effects on the inferred pose-graph map. In this study, we present comprehensive criteria to maximize global consistency in the pose graph using a new robust graph cut technique. Our key idea is to formulate the problem as a minimum-cost multi-cut that enables us to optimize not only landmark correspondences but also the number of landmarks while allowing for a varying number of landmarks. This makes our proposed approach invariant against the type of landmark measurement, graph topology, and metric information, such as the speed of the robot motion. The proposed graph cut technique was integrated into a practical SLAM framework and verified experimentally using the public NCLT dataset.
The efficiency of Quantum Mechanical Carnot Engine using the Woods Saxon model

The quantum engine cycle serves as an analogous representation of classical heat engines for microscopic systems and the quantum regime of thermal devices is composed of a single element. In this work, the Quantum-Mechanical properties of a non-linear quantum oscillator described by the Woods-Saxon [WS] model are examined. The Quantum-Mechanical analogue of the Carnot cycle was constructed using changes in both the width 𝐿 of the well and the quantum state of the potential well. The efficiency of the quantum engine, consisting of adiabatic and isothermal processes based on the Woods-Saxon [WS] potential is derived. The result is shown to be analogous to that of the classical engine and found to agree, within an appropriate limit, with existing results obtained from other potential models. This implies that the [WS] potential can be used as an alternative model in quantum engines.
Multi-CPR: A Multi Domain Chinese Dataset for Passage Retrieval

Passage retrieval is a fundamental task in information retrieval (IR) research, which has drawn much attention recently. In the English field, the availability of large-scale annotated dataset (e.g, MS MARCO) and the emergence of deep pre-trained language models (e.g, BERT) has resulted in a substantial improvement of existing passage retrieval systems. However, in the Chinese field, especially for specific domains, passage retrieval systems are still immature due to quality-annotated dataset being limited by scale. Therefore, in this paper, we present a novel multi-domain Chinese dataset for passage retrieval (Multi-CPR). The dataset is collected from three different domains, including E-commerce, Entertainment video and Medical. Each dataset contains millions of passages and a certain amount of human annotated query-passage related pairs. We implement various representative passage retrieval methods as baselines. We find that the performance of retrieval models trained on dataset from general domain will inevitably decrease on specific domain. Nevertheless, a passage retrieval system built on in-domain annotated dataset can achieve significant improvement, which indeed demonstrates the necessity of domain labeled data for further optimization. We hope the release of the Multi-CPR dataset could benchmark Chinese passage retrieval task in specific domain and also make advances for future studies.
How to Train Unstable Looped Tensor Network

A rising problem in the compression of Deep Neural Networks is how to reduce the number of parameters in convolutional kernels and the complexity of these layers by low-rank tensor approximation. Canonical polyadic tensor decomposition (CPD) and Tucker tensor decomposition (TKD) are two solutions to this problem and provide promising results. However, CPD often fails due to degeneracy, making the networks unstable and hard to fine-tune. TKD does not provide much compression if the core tensor is big. This motivates using a hybrid model of CPD and TKD, a decomposition with multiple Tucker models with small core tensor, known as block term decomposition (BTD). This paper proposes a more compact model that further compresses the BTD by enforcing core tensors in BTD identical. We establish a link between the BTD with shared parameters and a looped chain tensor network (TC). Unfortunately, such strongly constrained tensor networks (with loop) encounter severe numerical instability, as proved by y (Landsberg, 2012) and (Handschuh, 2015a). We study perturbation of chain tensor networks, provide interpretation of instability in TC, demonstrate the problem. We propose novel methods to gain the stability of the decomposition results, keep the network robust and attain better approximation. Experimental results will confirm the superiority of the proposed methods in compression of well-known CNNs, and TC decomposition under challenging scenarios
Online Learning of Reusable Abstract Models for Object Goal Navigation

In this paper, we present a novel approach to incrementally learn an Abstract Model of an unknown environment, and show how an agent can reuse the learned model for tackling the Object Goal Navigation task. The Abstract Model is a finite state machine in which each state is an abstraction of a state of the environment, as perceived by the agent in a certain position and orientation. The perceptions are high-dimensional sensory data (e.g., RGB-D images), and the abstraction is reached by exploiting image segmentation and the Taskonomy model bank. The learning of the Abstract Model is accomplished by executing actions, observing the reached state, and updating the Abstract Model with the acquired information. The learned models are memorized by the agent, and they are reused whenever it recognizes to be in an environment that corresponds to the stored model. We investigate the effectiveness of the proposed approach for the Object Goal Navigation task, relying on public benchmarks. Our results show that the reuse of learned Abstract Models can boost performance on Object Goal Navigation.
Machine Learning Simulates Agent-Based Model Towards Policy

Public Policies are not intrinsically positive or negative. Rather, policies provide varying levels of effects across different recipients. Methodologically, computational modeling enables the application of multiple influences on empirical data, thus allowing for heterogeneous response to policies. We use a random forest machine learning algorithm to emulate an agent-based model (ABM) and evaluate competing policies across 46 Metropolitan Regions (MRs) in Brazil. In doing so, we use input parameters and output indicators of 11,076 actual simulation runs and one million emulated runs. As a result, we obtain the optimal (and non-optimal) performance of each region over the policies. Optimum is defined as a combination of GDP production and the Gini coefficient inequality indicator for the full ensemble of Metropolitan Regions. Results suggest that MRs already have embedded structures that favor optimal or non-optimal results, but they also illustrate which policy is more beneficial to each place. In addition to providing MR-specific policies' results, the use of machine learning to simulate an ABM reduces the computational burden, whereas allowing for a much larger variation among model parameters. The coherence of results within the context of larger uncertainty--vis-\`a-vis those of the original ABM--reinforces robustness of the model. At the same time the exercise indicates which parameters should policymakers intervene on, in order to work towards precise policy optimal instruments.
Geometric Singular Perturbation Analysis of the Multiple-Timescale Hodgkin-Huxley Equations

We present a novel and global three-dimensional reduction of a non-dimensionalised version of the four-dimensional Hodgkin-Huxley equations [J. Rubin and M. Wechselberger, Giant squid--hidden canard: the 3D geometry of the Hodgkin-Huxley model, Biological Cybernetics, 97 (2007), pp. 5--32] that is based on geometric singular perturbation theory (GSPT). We investigate the dynamics of the resulting three-dimensional system in two parameter regimes in which the flow evolves on three distinct timescales. Specifically, we demonstrate that the system exhibits bifurcations of oscillatory dynamics and complex mixed-mode oscillations (MMOs), in accordance with the geometric mechanisms introduced in [P. Kaklamanos, N. Popovi\'c, and K. U. Kristiansen, Bifurcations of mixed--mode oscillations in three--timescale systems: An extended prototypical example, Chaos: An Interdisciplinary Journal of Nonlinear Science, 32 (2022), p. 013108], and we classify the various firing patterns in terms of the external applied current. While such patterns have been documented in [S. Doi, S. Nabetani, and S. Kumagai, Complex nonlinear dynamics of the Hodgkin-Huxley equations induced by time scale changes, Biological Cybernetics, 85 (2001), pp. 51--64] for the multiple-timescale Hodgkin-Huxley equations, we elucidate the geometry that underlies the transitions between them, which had not been previously emphasised.
Simulating Authenticated Broadcast in Networks of Bounded Degree

The authenticated broadcast is simulated in the bounded-degree networks to provide efficient broadcast primitives for building efficient higher-layer Byzantine protocols. A general abstraction of the relay-based broadcast system is introduced, in which the properties of the relay-based broadcast primitives are generalized. With this, fault-tolerant propagation is proposed as a building block of the broadcast primitives. Meanwhile, complementary systems are proposed in complementing fault-tolerant propagation and localized communication. Analysis shows that efficient fault-tolerant propagation can be built with sufficient initiation areas. Meanwhile, by integrating fault-tolerant propagation and localized communication, efficient broadcast primitives can be built in bounded-degree networks.
A Survey of Implicit Discourse Relation Recognition

A discourse containing one or more sentences describes daily issues and events for people to communicate their thoughts and opinions. As sentences are normally consist of multiple text segments, correct understanding of the theme of a discourse should take into consideration of the relations in between text segments. Although sometimes a connective exists in raw texts for conveying relations, it is more often the cases that no connective exists in between two text segments but some implicit relation does exist in between them. The task of implicit discourse relation recognition (IDRR) is to detect implicit relation and classify its sense between two text segments without a connective. Indeed, the IDRR task is important to diverse downstream natural language processing tasks, such as text summarization, machine translation and so on. This article provides a comprehensive and up-to-date survey for the IDRR task. We first summarize the task definition and data sources widely used in the field. We categorize the main solution approaches for the IDRR task from the viewpoint of its development history. In each solution category, we present and analyze the most representative methods, including their origins, ideas, strengths and weaknesses. We also present performance comparisons for those solutions experimented on a public corpus with standard data processing procedures. Finally, we discuss future research directions for discourse relation analysis.
RumorLens: Interactive Analysis and Validation of Suspected Rumors on Social Media

With the development of social media, various rumors can be easily spread on the Internet and such rumors can have serious negative effects on society. Thus, it has become a critical task for social media platforms to deal with suspected rumors. However, due to the lack of effective tools, it is often difficult for platform administrators to analyze and validate rumors from a large volume of information on a social media platform efficiently. We have worked closely with social media platform administrators for four months to summarize their requirements of identifying and analyzing rumors, and further proposed an interactive visual analytics system, RumorLens, to help them deal with the rumor efficiently and gain an in-depth understanding of the patterns of rumor spreading. RumorLens integrates natural language processing (NLP) and other data processing techniques with visualization techniques to facilitate interactive analysis and validation of suspected rumors. We propose well-coordinated visualizations to provide users with three levels of details of suspected rumors: an overview displays both spatial distribution and temporal evolution of suspected rumors; a projection view leverages a metaphor-based glyph to represent each suspected rumor and further enable users to gain a quick understanding of their overall characteristics and similarity with each other; a propagation view visualizes the dynamic spreading details of a suspected rumor with a novel circular visualization design, and facilitates interactive analysis and validation of rumors in a compact manner. By using a real-world dataset collected from Sina Weibo, one case study with a domain expert is conducted to evaluate RumorLens. The results demonstrated the usefulness and effectiveness of our approach.
On the Conjugacy Search Problem in Extraspecial p-Groups

In the recently emerging field of group-based cryptography, the Conjugacy Search Problem (CSP) has gained traction as a non-commutative replacement of the Discrete Log Problem (DLP). The problem of finding a secure class of nonabelian groups for use as platforms is open and a subject of active research. This paper demonstrates a polynomial time solution of the CSP in an important class of nonabelian groups, the extraspecial $p$-groups. For this purpose, and as a further result, we provide a reduction of the CSP in certain types of central products. The consequences of our results are practically relevant for ruling out several groups as platforms, since several nonabelian groups are constructed by combining smaller groups by taking direct and central products.
AI-based Approach for Safety Signals Detection from Social Networks: Application to the Levothyrox Scandal in 2017 on Doctissimo Forum

Social media can be an important source of information facilitating the detection of new safety signals in pharmacovigilance. Various approaches have investigated the analysis of social media data using AI such as NLP techniques for detecting adverse drug events. Existing approaches have focused on the extraction and identification of Adverse Drug Reactions, Drug-Drug Interactions and drug misuse. However, non of the works tackled the detection of potential safety signals by taking into account the evolution in time of relevant indicators. Moreover, despite the success of deep learning in various healthcare applications, it was not explored for this task. We propose an AI-based approach for the detection of potential pharmaceutical safety signals from patients' reviews that can be used as part of the pharmacovigilance surveillance process to flag the necessity of an in-depth pharmacovigilance investigation. We focus on the Levothyrox case in France which triggered huge attention from the media following the change of the medication formula, leading to an increase in the frequency of adverse drug reactions normally reported by patients. Our approach is two-fold. (1) We investigate various NLP-based indicators extracted from patients' reviews including words and n-grams frequency, semantic similarity, Adverse Drug Reactions mentions, and sentiment analysis. (2) We propose a deep learning architecture, named Word Cloud Convolutional Neural Network (WC-CNN) which trains a CNN on word clouds extracted from the patients comments. We study the effect of different time resolutions and different NLP pre-processing techniques on the model performance. Our results show that the proposed indicators could be used in the future to effectively detect new safety signals. The WC-CNN model trained on word clouds extracted at monthly resolution outperforms the others with an accuracy of 75%.
Process for Developing Asynchronous Online Training for Transportation Agency Professionals: Case Study

Efficient training is an essential component of work force development for transportation agencies. The South Carolina Department of Transportation (DOT) recognized one such training need in its management of contracts for professional services consultants. This recognition led to a research effort to identify standardized procedures in the procurement and administration of these contracts, resulting in the development of a training manual and daylong pilot training session. Although the session received positive feedback, it limited the time and location for delivery. This traditional, in-class method is no longer the only available option. Asynchronous online training presents agencies with the option of providing training to employees regardless of their spatial or schedule variabilities while minimizing the need for instructor effort and availability. In light of this change, the South Carolina DOT commissioned a subsequent research effort to study best practices for development, delivery, and assessment of online training and to use these findings to create training modules of the previously completed manual and training session. The focus of this paper is to present the process for developing asynchronous online training employed for the 10 modules, which total roughly 5 h. Although this process is based on best practices, they are not discussed at length; rather, references to findings are made where they pertain. The process, unique lessons learned, and advisable practices are discussed. The paper’s presentation of the online development process can be applied to other transportation agencies intending to implement asynchronous online training for professional development.
Stochastic homogenization of random walks on point processes

We consider random walks on the support of a random purely atomic measure on $\mathbb{R}^d$ with random jump probability rates. The jump range can be unbounded. The purely atomic measure is reversible for the random walk and stationary for the action of the group $\mathbb{G}=\mathbb{R}^d$ or $\mathbb{G}=\mathbb{Z}^d$. By combining two-scale convergence and Palm theory for $\mathbb{G}$-stationary random measures and by developing a cut-off procedure, under suitable second moment conditions we prove for almost all environments the homogenization for the massive Poisson equation of the associated Markov generators. In addition, we obtain the quenched convergence of the $L^2$-Markov semigroup and resolvent of the diffusively rescaled random walk to the corresponding ones of the Brownian motion with covariance matrix $2D$. For symmetric jump rates, the above convergence plays a crucial role in the derivation of hydrodynamic limits when considering multiple random walks with site-exclusion or zero range interaction. We do not require any ellipticity assumption, neither non-degeneracy of the homogenized matrix $D$. Our results cover a large family of models, including e.g. random conductance models on $\mathbb{Z}^d$ and on general lattices (possibly with long conductances), Mott variable range hopping, simple random walks on Delaunay triangulations, simple random walks on supercritical percolation clusters.
Asymptotic in a class of network models with an increasing sub-Gamma degree sequence

For the differential privacy under the sub-Gamma noise, we derive the asymptotic properties of a class of network models with binary values with a general link function. In this paper, we release the degree sequences of the binary networks under a general noisy mechanism with the discrete Laplace mechanism as a special case. We establish the asymptotic result including both consistency and asymptotically normality of the parameter estimator when the number of parameters goes to infinity in a class of network models. Simulations and a real data example are provided to illustrate asymptotic results.
Reinforcement Learning in Modern Biostatistics: Constructing Optimal Adaptive Interventions

In recent years, reinforcement learning (RL) has acquired a prominent position in the space of health-related sequential decision-making, becoming an increasingly popular tool for delivering adaptive interventions (AIs). However, despite potential benefits, its real-life application is still limited, partly due to a poor synergy between the methodological and the applied communities. In this work, we provide the first unified survey on RL methods for learning AIs, using the common methodological umbrella of RL to bridge the two AI areas of dynamic treatment regimes and just-in-time adaptive interventions in mobile health. We outline similarities and differences between these two AI domains and discuss their implications for using RL. Finally, we leverage our experience in designing case studies in both areas to illustrate the tremendous collaboration opportunities between statistical, RL, and healthcare researchers in the space of AIs.