Miracle-dz/UniFAR-FTUs huggingface dataset:
A Review of Evaluation Practices of Gesture Generation in Embodied Conversational Agents

Embodied conversational agents (ECAs) are often designed to produce nonverbal behavior to complement or enhance their verbal communication. One such form of the nonverbal behavior is co-speech gesturing, which involves movements that the agent makes with its arms and hands that are paired with verbal communication. Co-speech gestures for ECAs can be created using different generation methods, divided into rule-based and data-driven processes, with the latter, gaining traction because of the increasing interest from the applied machine learning community. However, reports on gesture generation methods use a variety of evaluation measures, which hinders comparison. To address this, we present a systematic review on co-speech gesture generation methods for iconic, metaphoric, deictic, and beat gestures, including reported evaluation methods. We review 22 studies that have an ECA with a human-like upper body that uses co-speech gesturing in social human-agent interaction. This includes studies that use human participants to evaluate performance. We found most studies use a within-subject design and rely on a form of subjective evaluation, but without a systematic approach. We argue that the field requires more rigorous and uniform tools for co-speech gesture evaluation, and formulate recommendations for empirical evaluation, including standardized phrases and example scenarios to help systematically test generative models across studies. Furthermore, we also propose a checklist that can be used to report relevant information for the evaluation of generative models, as well as to evaluate co-speech gesture use.
Constraining the fast radio burst properties using the joint distributions of dispersion measure and fluence of the events detected at Parkes, ASKAP, CHIME, and UTMOST

The Parkes, ASKAP, CHIME and UTMOST telescopes, which have all detected FRBs, each works at a different frequency and has a different detection criteria. Using simulations, we have combined the constraints from all four telescopes to identify an allowed range of model parameters (U, 33) for the FRB source population. Here U is the spectral index and 33 is the mean FRB energy in units of 10 J across a 2128 − 2848 MHz band in the FRB rest frame. We have considered several different FRB energy distributions, and also different scenarios for the scattering pulse broadening, the event rate density variation with I and the host dispersion measure. We find that in all cases, the common allowed region includes the range −3.9 ≤ U ≤ −1.3 and 0.42 ≤ 33 ≤ 1. In all case, large values U > 4 and 33 > 60 are ruled out. Considering the allowed (U, 33) parameter range, we predict that CHIME is unlikely to detect an FRB with extra-galactic dispersion measure ( " G) exceeding 3700 pc cm−3. A substantially larger " G in the large FRB sample anticipated from CHIME would falsify the assumptions of the present analysis. Our analysis is expected to yield tighter parameter constraints with the advent of more FRB data.
On the Implementation Complexity of Digital Full-Duplex Self-Interference Cancellation

In-band full-duplex systems promise to further increase the throughput of wireless systems, by simultaneously transmitting and receiving on the same frequency band. However, concurrent transmission generates a strong self-interference signal at the receiver, which requires the use of cancellation techniques. A wide range of techniques for analog and digital self-interference cancellation have already been presented in the literature. However, their evaluation focuses on cases where the underlying physical parameters of the full-duplex system do not vary significantly. In this paper, we focus on adaptive digital cancellation, motivated by the fact that physical systems change over time. We examine some of the different cancellation methods in terms of their performance and implementation complexity, considering the cost of both cancellation and training. We then present a comparative analysis of all these methods to determine which perform better under different system performance requirements. We demonstrate that with a neural network approach, the reduction in arithmetic complexity for the same cancellation performance relative to a state-of-the-art polynomial model is several orders of magnitude.
An Optimization Framework for Power Infrastructure Planning

The ubiquitous expansion and transformation of the energy supply system involves large-scale power infrastructure construction projects. In view of investments of more than a million dollars per kilometre, planning authorities aim to minimize the resistances posed by multiple stakeholders. Mathematical optimization research offers efficient algorithms to compute globally optimal routes based on geographic input data. We propose a framework that utilizes a graph model where vertices represent possible locations of transmission towers, and edges are placed according to the feasible distance between neighbouring towers. In order to cope with the specific challenges arising in linear infrastructure layout, we first introduce a variant of the Bellman-Ford algorithm that efficiently computes the minimal-angle shortest path. Secondly, an iterative procedure is proposed that yields a locally optimal path at considerably lower memory requirements and runtime. Third, we discuss and analyse methods to output $k$ diverse path alternatives. Experiments on real data show that compared to previous work, our approach reduces the resistances by more than 10% in feasible time, while at the same time offering much more flexibility and functionality. Our methods are demonstrated in a simple and intuitive graphical user interface, and an open-source package (LION), available at https://pypi.org/project/lion-sp/
Generating function for tensor network diagrammatic summation

The understanding of complex quantum many-body systems has been vastly boosted by tensor network (TN) methods. Among others, excitation spectrum and long-range interacting systems can be studied using TNs, where one however confronts the intricate summation over an extensive number of tensor diagrams. Here, we introduce a set of generating functions, which encode the diagrammatic summations as leading order series expansion coefﬁcients. Combined with automatic differentiation, the generating function allows us to solve the problem of TN diagrammatic summation. We illustrate this scheme by computing variational excited states and dynamical structure factor of a quantum spin chain, and further investigating entanglement properties of excited states. Extensions to inﬁnite size systems and higher dimension are outlined.
The IITM Earth System Model (IITM ESM)

Earth System Models (ESM) are important tools that allow us to understand and quantify the physical, chemical&biological mechanisms governing the rates of change of elements of the Earth System, comprising of the atmosphere, ocean, land, cryosphere and biosphere (terrestrial and marine) and related components. ESMs are essentially coupled numerical models which incorporate processes within and across the different Earth system components and are expressed as set of mathematical equations. ESMs are useful for enhancing our fundamental understanding of the climate system, its multi-scale variability, global and regional climatic phenomena and making projections of future climate change. In this chapter, we briefly describe the salient aspects of the Indian Institute of Tropical Meteorology ESM (IITM ESM), that has been developed recently at the IITM, Pune, India, for investigating long-term climate variability and change with focus on the South Asian monsoon.
SDA: Improving Text Generation with Self Data Augmentation

Data augmentation has been widely used to improve deep neural networks in many research fields, such as computer vision. However, less work has been done in the context of text, partially due to its discrete nature and the complexity of natural languages. In this paper, we propose to improve the standard maximum likelihood estimation (MLE) paradigm by incorporating a self-imitation-learning phase for automatic data augmentation. Unlike most existing sentence-level augmentation strategies, which are only applied to specific models, our method is more general and could be easily adapted to any MLE-based training procedure. In addition, our framework allows task-specific evaluation metrics to be designed to flexibly control the generated sentences, for example, in terms of controlling vocabulary usage and avoiding nontrivial repetitions. Extensive experimental results demonstrate the superiority of our method on two synthetic and several standard real datasets, significantly improving related baselines.
Variational Embeddings for Community Detection and Node Representation

In this paper, we study how to simultaneously learn two highly correlated tasks of graph analysis, i.e., community detection and node representation learning. We propose an efficient generative model called VECoDeR for jointly learning Variational Embeddings for COmmunity DEtection and node Representation. VECoDeR assumes that every node can be a member of one or more communities. The node embeddings are learned in such a way that connected nodes are not only ``closer" to each other but also share similar community assignments. A joint learning framework leverages community-aware node embeddings for better community detection. We demonstrate on several graph datasets that VECoDeR effectively outperforms many competitive baselines on all three tasks i.e. node classification, overlapping community detection and non-overlapping community detection. We also show that VECoDeR is computationally efficient and has quite robust performance with varying hyperparameters.
Predicting polarizabilities of silicon clusters using local chemical environments

Calculating polarizabilities of large clusters with first-principles techniques is challenging because of the unfavorable scaling of computational cost with cluster size. To address this challenge, we demonstrate that polarizabilities of large hydrogenated silicon clusters containing thousands of atoms can be efficiently calculated with machine learning methods. Specifically, we construct machine learning models based on the smooth overlap of atomic positions (SOAP) descriptor and train the models using a database of calculated random-phase approximation polarizabilities for clusters containing up to 110 silicon atoms. We first demonstrate the ability of the machine learning models to fit the data and then assess their ability to predict cluster polarizabilities using k-fold cross validation. Finally, we study the machine learning predictions for clusters that are too large for explicit first-principles calculations and find that they accurately describe the dependence of the polarizabilities on the ratio of hydrogen to silicon atoms and also predict a bulk limit that is in good agreement with previous studies.
Evaluation of User Dynamics Created by Weak Ties among Divided Communities

Flaming phenomena represent the divergence in the strength of user dynamics as created by user interactions in online social networks (OSNs). Although it has been known that flaming phenomena occur when the Laplacian matrix of the OSN has non-real eigenvalues, it was recently shown that flaming phenomena may occur even if all the eigenvalues are real numbers. This effect appears only in the situation that some eigenvalues are degenerate, and a special unitary transformation is applied to the equations representing user dynamics; whether actual OSNs satisfy this condition has not been fully discussed. In this paper, we clarify that the user dynamics caused by the degeneration of eigenvalue 0 is one specific example of the above condition. We also investigate the mechanism and characteristics of flaming phenomena generated by degenerated eigenvalues. Furthermore, we demonstrate through numerical simulations that the degeneration of eigenvalues can cause divergence.
Downlink SCMA Codebook Design With Low Error Rate by Maximizing Minimum Euclidean Distance of Superimposed Codewords

Sparse code multiple access (SCMA), as a codebook-based non-orthogonal multiple access (NOMA) technique, has received research attention in recent years. The codebook design problem for SCMA has also been studied to some extent since codebook choices are highly related to the system’s error rate performance. In this paper, we approach the SCMA codebook design problem by formulating an optimization problem to maximize the minimum Euclidean distance (MED) of superimposed codewords under power constraints. While SCMA codebooks with a larger MED are expected to obtain a better BER performance, no optimal SCMA codebook in terms of MED maximization, to the authors’ best knowledge, has been reported in the SCMA literature yet. In this paper, a new iterative algorithm based on alternating maximization with exact penalty is proposed for the MED maximization problem. The proposed algorithm, when supplied with appropriate initial points and parameters, achieves a set of codebooks of all users whose MED is larger than any previously reported results. A Lagrange dual problem is derived which provides an upper bound of MED of any set of codebooks. Even though there is still a nonzero gap between the achieved MED and the upper bound given by the dual problem, simulation results demonstrate clear advantages in error rate performances of the proposed set of codebooks over all existing ones not only in AWGN channels but also in some downlink scenarios that fit in 5G/NR applications, making it a good codebook candidate thereof. The proposed set of SCMA codebooks, however, are not shown to outperform existing ones in uplink channels or in the case where non-consecutive OFDMA subcarriers are used. The correctness and accuracy of error curves in the simulation results are further confirmed by the coincidences with the theoretical upper bounds of error rates derived for any given set of codebooks.
Land Use Detection & Identification using Geo-tagged Tweets

Geo-tagged tweets can potentially help with ‘sensing’ the interaction of people with their surrounding environment. Based on this hypothesis, this paper makes use of geotagged tweets in order to ascertain various land uses with a broader goal to help with urban/city planning. The proposed method utilises supervised learning to reveal spatial land use within cities with the help of Twitter activity signatures. Specifically, the technique involves using tweets from three cities of Australia namely Brisbane, Melbourne and Sydney. Analytical results are checked against the zoning data provided by respective city councils and a good match is observed between the predicted land use and existing land zoning by the city councils. We show that geo-tagged tweets contain features that can be useful for land use identification.
Channel Boosting Feature Ensemble for Radar-based Object Detection

Autonomous vehicles are conceived to provide safe and secure services by validating the safety standards as indicated by SOTIF-ISO/PAS-21448 (Safety of the intended functionality)11https://www.daimler. com/innovation/case/autonomous/safety-first-for-automated-driving-2.htm. Keeping in this context, the perception of the environment plays an instrumental role in conjunction with localization, planning and control modules. As a pivotal algorithm in the perception stack, object detection provides extensive insights into the autonomous vehicle's surroundings. Camera and Lidar are extensively utilized for object detection among different sensor modalities, but these exteroceptive sensors have limitations in resolution and adverse weather conditions. In this work, radar-based object detection is explored provides a counterpart sensor modality to be deployed and used in adverse weather conditions. The radar gives complex data; for this purpose, a channel boosting feature ensemble method with transformer encoder-decoder network is proposed. The object detection task using radar is formulated as a set prediction problem and evaluated on the publicly available dataset [1] in both good and good-bad weather conditions. The proposed method's efficacy is extensively evaluated using the COCO evaluation metric, and the best-proposed model surpasses its state-of-the-art counterpart method by 12.55% and 12.48% in both good and good-bad weather conditions.
An Empirical Study on Serverless Workflow Service

Along with the wide-adoption of Serverless Computing, more and more applications are developed and deployed on cloud platforms. Major cloud providers present their serverless workflow services to orchestrate serverless functions, making it possible to perform complex applications effectively. A comprehensive instruction is necessary to help developers understand the pros and cons, and make better choices among these serverless workflow services. However, the characteristics of these serverless workflow services have not been systematically analyzed. To fill the knowledge gap, we survey four mainstream serverless workflow services, investigating their characteristics and performance. Specifically, we review their official documents and compare them in terms of seven dimensions including programming model, state management, etc. Then, we compare the performance (i.e., execution time of functions, execution time of workflows, orchestration overhead of workflows) under various experimental settings considering activity complexity and data-flow complexity of workflows, as well as function complexity of serverless functions. Finally, we discuss and verify the service effectiveness for two actual workloads. Our findings could help application developers and serverless providers to improve the development efficiency and user experience.
Kinematics and multiband period–luminosity–metallicity relation of RR Lyrae stars via statistical parallax

This paper presents results from photometric and statistical-parallax analysis of a sample of 850 field RR Lyrae (RRL) variables. The photometric and spectroscopic data for sample RRLs are obtained from (1) our new spectroscopic observations (for 448 RRLs) carried out with the Southern African Large Telescope (SALT); (2) our photometric observations using the 1.0-m telescope of the South African Astronomical Observatory (SAAO), and (3) literature. These are combined with accurate proper motion data from the second release of Gaia mission (DR2). This study primarily determines the velocity distribution of solar neighborhood RRLs, and it also calibrates the zero points of the RRLs visual V-band luminosity-metallicity (LZ or MV−[Fe/H]) relation and their period-luminosity-metallicity (PLZ) relations in the WISE W1− and 2MASS Ks−band. We find the bulk velocity of the halo RRLs relative to the Sun to be (U0, V0,W0)Halo = (−16±7,−219±7,−6±5) km s−1 in the direction of Galactic center, Galactic rotation and North Galactic pole, respectively, with velocity-dispersion ellipsoids (σVR, σVφ, σVθ)Halo = (153±7, 106±4, 101±4) km s−1. The corresponding parameters for the disk component are found to be (U0, V0,W0)Disk = (−19 ± 5,−46 ± 5,−14 ± 3) km s−1 and (σVR, σVφ, σVθ)Disk = (49± 4, 38± 4, 25± 3) km s−1. The calibrated PLZ in W1−, Ks−band, and Vband LZ relations are 〈MW1〉 = −0.824 + 0.124[Fe/H]− 2.381 log PF , 〈MKs〉 = −0.804 + 0.101[Fe/H]− 2.33 log PF and 〈MV 〉 = 1.041 + 0.232[Fe/H], respectively. The calibrated PLZ and LZ relations are used to estimate the Galactic Center distance and the distance modulus of the Large Magellanic Cloud (LMC), which are found to be 7.99±0.49 kpc and 18.46±0.09 mag, respectively. All our results are in excellent agreement with available literature based on statistical parallax analysis, but are considerably more accurate and precise. Moreover, the zero-points of our calibrated PLZ and LZ relations are quite consistent with current results found by other techniques and yield the LMC distance modulus that is within 0.04 mag of the current most precise estimate.
Context-aware Target Apps Selection and Recommendation for Enhancing Personal Mobile Assistants

Users install many apps on their smartphones, raising issues related to information overload for users and resource management for devices. Moreover, the recent increase in the use of personal assistants has made mobile devices even more pervasive in users’ lives. This article addresses two research problems that are vital for developing effective personal mobile assistants: target apps selection and recommendation. The former is the key component of a unified mobile search system: a system that addresses the users’ information needs for all the apps installed on their devices with a unified mode of access. The latter, instead, predicts the next apps that the users would want to launch. Here we focus on context-aware models to leverage the rich contextual information available to mobile devices. We design an in situ study to collect thousands of mobile queries enriched with mobile sensor data (now publicly available for research purposes). With the aid of this dataset, we study the user behavior in the context of these tasks and propose a family of context-aware neural models that take into account the sequential, temporal, and personal behavior of users. We study several state-of-the-art models and show that the proposed models significantly outperform the baselines.
Bayesian Surrogate Analysis and Uncertainty Propagation with Explicit Surrogate Uncertainties and Implicit Spatio-temporal Correlations

We introduce Bayesian Probability Theory to investigate uncertainty propagation based on meta-models. We approach the problem from the perspective of data analysis, with a given (however almost-arbitrary) input probability distribution and a given ”training” set of computer simulations. While proven mathematically to be the unique consistent probability calculus, the subject of this paper is not to demonstrate beauty but usefulness. We explicitly list all propositions and lay open the general structure of any uncertainty propagation based on meta-models. The former allows rigorous treatment at any stage, while the latter allows us to quantify the interaction of the surrogate uncertainties with the usual parameter uncertainties. Additionally, we show a simple way to implicitly include spatio-temporal correlations. We then apply the framework jointly to a family of generalized linear meta-model that implicitly includes Polynomial Chaos Expansions as a special case. While we assume a Gaussian surrogate-uncertainty, we do not assume a scale for the surrogate uncertainty to be known, i.e. a Student-t. We end up with semi-analytic formulas for surrogate uncertainties and uncertainty propagation.