Miracle-dz/UniFAR-FTUs huggingface dataset:
Sarcasm: Mock Politeness Performed In The Big Bang Theory

Sarcasm has become an uncommon way to treat other’s faces in communication. However, the study of sarcasm has not yet been investigated thoroughly in the case of such daily illocutionary acts of the conversations. One of the sarcasm phenomena is mostly seen in the comedy situation of The Big Bang Theory. This research aims at analyzing mock politeness or sarcasm in the utterances of each character. The analysis focuses on describing the use of mock politeness in pragmatics context. The data was obtained from informal speech acts’ situations. The research carries out the descriptive qualitative method. The data was analyzed by using politeness strategy in examining the functions of mock politeness. The results show that sarcasm caused by the violation of pragmatic aspects in The Big Bang Theory releases in several types of utterances, they are: declarative, representative, and expressive. The function of sarcasm in this comedy situation mostly to create humors which are divided into several functions such as humor as a means of social critics, humor to satirize, and humor to mock.
Speech Emotion Recognition Using Support Vector Machine

Speech Emotion Recognition is the act of attempting to recognize human emotion from the input audio file and the associated affective states from speech. This is capitalizing on the fact that voice often reflects underlying emotion through tone, frequency and pitch. Here we use python modules and machine learning algorithms for detecting the emotion from the audio file. Here we are predicting the emotion and display it to the user on the screen. We have mainly three different steps to detect the emotion and they are like training the model, extracting feature sets and providing input test audio. We use machine learning techniques like Multilayer perceptron Classifier (MLP Classifier) which is used to categorize the given data into respective groups which are non-linearly separated. We use python modules like librosa, pyaudio, sklearn, numpy to detect the emotion from audio file.
Performance Health Index for Complex Cyber Infrastructures

Most IT systems depend on a set of configuration variables (CVs), expressed as a name/value pair that collectively defines the resource allocation for the system. While the ill effects of misconfiguration or improper resource allocation are well-known, there are no effective a priori metrics to quantify the impact of the configuration on the desired system attributes such as performance, availability, etc. In this paper, we propose a Configuration Health Index (CHI) framework specifically attuned to the performance attribute to capture the influence of CVs on the performance aspects of the system. We show how CHI, which is defined as a configuration scoring system, can take advantage of the domain knowledge and the available (but rather limited) performance data to produce important insights into the configuration settings. We compare the CHI with both well-advertised segmented non-linear models and state-of-the-art data-driven models, and show that the CHI not only consistently provides better results but also avoids the dangers of a pure data drive approach which may predict incorrect behavior or eliminate some essential configuration variables from consideration.
Boltzmann electronic dc transport in multiorbital weakly disordered crystals

Motivated by the increasing number of systems featuring multiple bands at low energy, we address the Boltzmann approach to transport in a multiband weakly disordered non-interacting crystal subject to a small electric field. In general, the multiband structure leads to a considerable complication of the Boltzmann equation. Indeed, even in the presence of elastic impurity scattering one needs to compute for each band and momentum the dressed velocities, which account for scattering events. Here we provide a semi-analytical solution to the Boltzmann equation that reduces such a challenging numerical task to the much simpler numerical computation of a small tensor whose dimension is set by the number of bands at the Fermi level. This approach further allows us to discuss the interplay of symmetry and disorder for different impurity types, including those originating from random-matrix Wigner ensembles. As an example of application we consider the 2D isotropic Rashba metal and we discuss, in a full analytical fashion, how different types of disorder may break the exactness of the relaxation-time approximation and induce transport anisotropy, and may allow one to identify the presence of spin-orbit coupling as deviations of the conductivity from Drude behavior.
Black Holes Hint towards De Sitter Matrix Theory

De Sitter black holes and other non-perturbative configurations can be used to probe the holographic degrees of freedom of de Sitter space. For small black holes, evidence was first provided in the seminal work of Banks, Fiol, and Morrise and follow-ups by Banks and Fischler, showing that dS is described by a form of matrix theory. For large black holes, the evidence provided here is new: Gravitational calculations and matrix theory calculations of the rates of exponentially rare fluctuations match one another in surprising detail. The occurrences of Nariai geometry and the “inside-out” transition are particularly interesting examples, which I explain in this paper.
A Multi-Sensor Interface to Improve the Teaching and Learning Experience in Arc Welding Training Tasks

—This paper presents the development of a multi-sensor extended reality platform to improve the teaching and learning experience of arc welding tasks. Traditional methods to acquire hand-eye welding coordination skills are typically conducted through one-to-one instruction where trainees/trainers must wear protective helmets and conduct several hands-on tests with metal workpieces. This approach is inefﬁcient as the harmful light emitted from the electric arc impedes the close monitoring of the welding process (practitioners can only observe a small bright spot and most geometric information cannot be perceived). To tackle these problems, some recent training approaches have leveraged on virtual reality (VR) as a way to safely simulate the process and visualize the geometry of the workpieces. However, the synthetic nature of the virtual simulation reduces the effectiveness of the platform; It fails to comprise actual interactions with the welding environment, which may hinder the learning process of a trainee. To incorporate a real welding experience, in this work we present a new automated multi-sensor extended reality platform for arc welding training. It consists of three components: (1) An HDR camera, monitoring the real welding spot in real-time; (2) A depth sensor, capturing the 3D geometry of the scene; and (3) A head-mounted VR display, visualizing the process safely. Our innovative platform provides trainees with a “bot trainer”, virtual cues of the seam geometry, automatic spot tracking, and a performance score. To validate the platform’s feasibility, we conduct extensive experiments with several welding training tasks. We show that compared with the traditional training practice and recent virtual reality approaches, our automated method achieves better performances in terms of accuracy, learning curve, and effectiveness.
Identification of Potential Biomarkers Associated with Acute Myocardial Infarction by Weighted Gene Coexpression Network Analysis

Background In the general population, acute myocardial infarction (AMI) represents a significant cause of mortality. This study is aimed at identifying novel diagnostic biomarkers to aid in treating and diagnosing AMI. Methods The Gene Expression Omnibus (GEO) database was explored to extract two microarray datasets, GSE66360 and GSE48060, which were subsequently merged into a single cohort. Both AMI and control samples were analyzed for differentially expressed genes (DEGs), which were subsequently subjected to weighed gene coexpression network analysis (WGCNA) to identify the most significant module. Gene Ontology (GO) and pathway analyses subsequently carried out the most significant gene modules along with construction of a protein-protein interaction network (PPI). Cytoscape plugin cytoHubba allowed for the prediction of the top 4 key genes according to the network maximal clique centrality (MCC) algorithm. The expression levels and diagnostic value of the four key genes were additionally verified in the GSE62646 dataset. Results A WCGNA analysis revealed 878 DEGs which were clustered into 6 modules. The module with the most significance in AMI was colored blue. Subsequent GO and KEGG pathway enrichment analysis on blue module genes revealed that they were primarily enriched in the inflammation-related pathways. These findings, in combination with PPI and coexpression networks, resulted in the identification of the top four genes by cytoHubba, which included leukocyte immunoglobulin-like receptor B2 (LILRB2), toll-like receptor 2 (TLR2), neutrophil cytosolic factor 2 (NCF2), and S100A9. Among them, LILRB2, NCF2, and S100A9 were validated in the GSE62646 dataset. Conclusions The results suggested that LILRB2, NCF2, and S100A9 could be potential gene biomarkers for AMI.
Optimal Target Shape for LiDAR Pose Estimation

Targets are essential in problems such as object tracking in cluttered or textureless environments, camera (and multi-sensor) calibration tasks, and simultaneous localization and mapping (SLAM). Target shapes for these tasks typically are symmetric (square, rectangular, or circular) and work well for structured, dense sensor data such as pixel arrays (i.e., image). However, symmetric shapes lead to pose ambiguity when using sparse sensor data such as LiDAR point clouds and suffer from the quantization uncertainty of the LiDAR. This letter introduces the concept of optimizing target shape to remove pose ambiguity for LiDAR point clouds. A target is designed to induce large gradients at edge points under rotation and translation relative to the LiDAR to ameliorate the quantization uncertainty associated with point cloud sparseness. Moreover, given a target shape, we present a means that leverages the target’s geometry to estimate the target’s vertices while globally estimating the pose. Both the simulation and the experimental results (verified by a motion capture system) confirm that by using the optimal shape and the global solver, we achieve centimeter error in translation and a few degrees in rotation even when a partially illuminated target is placed 30 meters away. All the implementations and datasets are available at https://github.com/UMich-BipedLab/global_pose_estimation_for_optimal_shape.
The notions of inertial balanced viscosity and inertial virtual viscosity solution for rate-independent systems

Abstract The notion of inertial balanced viscosity (IBV) solution to rate-independent evolutionary processes is introduced. Such solutions are characterized by an energy balance where a suitable, rate-dependent, dissipation cost is optimized at jump times. The cost is reminiscent of the limit effect of small inertial terms. Therefore, this notion proves to be a suitable one to describe the asymptotic behavior of evolutions of mechanical systems with rate-independent dissipation in the limit of vanishing inertia and viscosity. It is indeed proved, in finite dimension, that these evolutions converge to IBV solutions. If the viscosity operator is neglected, or has a nontrivial kernel, the weaker notion of inertial virtual viscosity (IVV) solutions is introduced, and the analogous convergence result holds. Again in a finite-dimensional context, it is also shown that IBV and IVV solutions can be obtained via a natural extension of the minimizing movements algorithm, where the limit effect of inertial terms is taken into account.
Learning from What's Right and Learning from What's Wrong

The concept of updating (or conditioning or revising) a probability distribution is fundamental in (machine) learning and in predictive coding theory. The two main approaches for doing so are called Pearl's rule and Jeffrey's rule. Here we make, for the first time, mathematically precise what distinguishes them: Pearl's rule increases validity (expected value) and Jeffrey's rule decreases (Kullback-Leibler) divergence. This forms an instance of a more general distinction between learning from what's right and learning from what's wrong. The difference between these two approaches is illustrated in a mock cognitive scenario.
Effective Debye screening mass in an anisotropic quark gluon plasma

Due to the rapid longitudinal expansion of the quark-gluon plasma created in heavy-ion collisions, large local-rest-frame momentum-space anisotropies are generated during the system’s evolution. These momentum-space anisotropies complicate the modeling of heavy-quarkonium dynamics in the quark-gluon plasma due to the fact that the resulting inter-quark potentials are spatially anisotropic, requiring real-time solution of the 3D Schrödinger equation. Herein, we introduce a method for reducing anisotropic heavy-quark potentials to isotropic ones by introducing an effective screening mass that depends on the quantum numbers l and m of a given state. We demonstrate that, using the resulting effective Debye screening masses, one can solve a 1D Schrödinger equation and reproduce the full 3D results for the energies and binding energies of low-lying heavy-quarkonium bound states to relatively high accuracy. The resulting effective isotropic potential models could provide an efficient method for including momentum-anisotropy effects in open quantum system simulations of heavy-quarkonium dynamics in the quark-gluon plasma.
Learning from Multiple Noisy Augmented Data Sets for Better Cross-Lingual Spoken Language Understanding

Lack of training data presents a grand challenge to scaling out spoken language understanding (SLU) to low-resource languages. Although various data augmentation approaches have been proposed to synthesize training data in low-resource target languages, the augmented data sets are often noisy, and thus impede the performance of SLU models. In this paper we focus on mitigating noise in augmented data. We develop a denoising training approach. Multiple models are trained with data produced by various augmented methods. Those models provide supervision signals to each other. The experimental results show that our method outperforms the existing state of the art by 3.05 and 4.24 percentage points on two benchmark datasets, respectively. The code will be made open sourced on github.
Riemannian preconditioned coordinate descent for low multi-linear rank approximation

This paper presents a memory efficient, first-order method for low multi-linear rank approximation of high-order, high-dimensional tensors. In our method, we exploit the second-order information of the cost function and the constraints to suggest a new Riemannian metric on the Grassmann manifold. We use a Riemmanian coordinate descent method for solving the problem, and also provide a global convergence analysis matching that of the coordinate descent method in the Euclidean setting. We also show that each step of our method with the unit step-size is actually a step of the orthogonal iteration algorithm. Experimental results show the computational advantage of our method for high-dimensional tensors.
Empirical Study of Named Entity Recognition Performance Using Distribution-aware Word Embedding

With the fast development of Deep Learning techniques, Named Entity Recognition (NER) is becoming more and more important in the information extraction task. The greatest difficulty that the NER task faces is to keep the detectability even when types of NE and documents are unfamiliar. Realizing that the specificity information may contain potential meanings of a word and generate semantic-related features for word embedding, we develop a distribution-aware word embedding and implement three different methods to make use of the distribution information in a NER framework. And the result shows that the performance of NER will be improved if the word specificity is incorporated into existing NER methods.
Order-
N
 orbital-free density-functional calculations with machine learning of functional derivatives for semiconductors and metals

Orbital-free density functional theory (OFDFT) offers a challenging way of electronic-structure calculations scaled asO(N) computation for system sizeN . We here develop a scheme of the OFDFT calculations based on the accurate and transferrable kinetic-energy density functional (KEDF) which is created in an unprecedented way using appropriately constructed neural network (NN). We show that our OFDFT scheme reproduces the electron density obtained in the state-of-the-art DFT calculations and then provides accurate structural properties of 24 different systems, ranging from atoms, molecules, metals, semiconductors and an ionic material. The accuracy and the transferability of our KEDF is achieved by our NN training system in which the kinetic-energy functional derivative (KEFD) at each real-space grid point is used. The choice of the KEFD as a set of training data is essentially important, because first it appears directly in the Euler equation which one should solve and second, its learning assists in reproducing the physical quantity expressed as the first derivative of the total energy. More generally, the present development of KEDF T [ρ] is in the line of systematic expansion in terms of the functional derivatives δ1T/δρ1 through progressive increase of l1. The present numerical success demonstrates the validity of this approach. The computational cost of the present OFDFT scheme indeed shows the O(N) scaling, as is evidenced by the computations of the semiconductor SiC used in power electronics.
Theory of Mind Based Assistive Communication in Complex Human Robot Cooperation

When cooperating with a human, a robot should not only care about its environment and task but also develop an understanding of the partner's reasoning. To support its human partner in complex tasks, the robot can share information that it knows. However simply communicating everything will annoy and distract humans since they might already be aware of and not all information is relevant in the current situation. The questions when and what type of information the human needs, are addressed through the concept of Theory of Mind based Communication which selects information sharing actions based on evaluation of relevance and an estimation of human beliefs. We integrate this into a communication assistant to support humans in a cooperative setting and evaluate performance benefits. We designed a human robot Sushi making task that is challenging for the human and generates different situations where humans are unaware and communication could be beneficial. We evaluate the influence of the human centric communication concept on performance with a user study. Compared to the condition without information exchange, assisted participants can recover from unawareness much earlier. The approach respects the costs of communication and balances interruptions better than other approaches. By providing information adapted to specific situations, the robot does not instruct but enable the human to make good decision.
On the similarities between Control Barrier Functions (CBFs) and Behavior Control Lyapunov Functions (BCLFs)

Control Barrier Functions (CBFs) is an important tool used to address situations with multiple concurrent control objectives, such as safety and goal convergence. In this paper we investigate the similarities between CBFs and so-called Behavior Control Lyapunov Functions (BCLFs) that have been proposed to address the same type of problems in the aeronautics domain. The key results of both CBFs and BCLFs is the description of the set of controls that render a given set invariant. We compare the corresponding theorems, and show that if we restrict the general class K function in CBFs to be the general linear function of BCLFs, and restrict the number of objectives as well as the number of priority levels to be just one in BCLFs, the results in terms of admissible control sets are equivalent. Furthermore, both papers show that the invariant set is made asymptotically stable.
Hosting Industry Centralization and Consolidation

There have been growing concerns about the concentration and centralization of Internet infrastructure. In this work, we scrutinize the hosting industry on the Internet by using active measurements, covering 19 Top-Level Domains (TLDs). We show how the market is heavily concentrated: 1/3 of the domains are hosted by only 5 hosting providers, all US-based companies. For the country-code TLDs (ccTLDs), however, hosting is primarily done by local, national hosting providers and not by the large American cloud and content providers. We show how shared languages (and borders) shape the hosting market — German hosting companies have a notable presence in Austrian and Swiss markets, given they all share German as official language. While hosting concentration has been relatively high and stable over the past four years, we see that American hosting companies have been continuously increasing their presence in the market related to high traffic, popular domains within ccTLDs — except for Russia, notably.
Modeling core-collapse supernovae gravitational-wave memory in laser interferometric data

We study the properties of the gravitational wave (GW) emission between $10^{-5}$ Hz and $50$ Hz (which we refer to as low-frequency emission) from core-collapse supernovae, in the context of studying such signals in laser interferometric data as well as performing multi-messenger astronomy. We pay particular attention to the GW linear memory, which is when the signal amplitude does not return to zero after the GW burst. Based on the long term simulation of a core-collapse supernova of a solar-metallicity star with a zero-age main sequence mass of 15 solar masses, we discuss the spectral properties, the memory's dependence on observer position and the polarization of low-frequency GWs from slowly non (or slowly) rotating core-collapse supernovae. We make recommendations on the angular spacing of the orientations needed to properly produce results that are averaged over multiple observer locations by investigating the angular dependence of the GW emission. We propose semi-analytical models that quantify the relationship between the bulk motion of the supernova shock-wave and the GW memory amplitude. We discuss how to extend neutrino generated GW signals from numerical simulations that were terminated before the neutrino emission has subsided. We discuss how the premature halt of simulations and the non-zero amplitude of the GW memory can induce artefacts during the data analysis process. Lastly, we also investigate potential solutions and issues in the use of taperings for both ground and space-based interferometers.