Miracle-dz/UniFAR-FTUs huggingface dataset:
A Taxonomy of Live Migration Management in Cloud Computing

Cloud Data Centers have become the key infrastructure for providing services. Instance migration across different computing nodes in edge and cloud computing is essential to guarantee the quality of service in dynamic environments. Many studies have been conducted on dynamic resource management involving migrating Virtual Machines to achieve various objectives, such as load balancing, consolidation, performance, energy-saving, and disaster recovery. Some have investigated to improve and predict the performance of single live migration. Recently, several research studies service migration in edge-centric computing paradigms. However, there is a lack of taxonomy and survey that focuses on the management of live migration in edge and cloud computing environments. In this article, we examine the characteristics of each field and propose a migration management-centric taxonomy to provide a holistic framework and guideline for researchers on the topic, including the performance and cost model, migration generations in resource management algorithms, migration planning and scheduling, and migration lifecycle management and orchestration. We also identify research gaps and opportunities to improve the performance of resource management with live migrations.
One-shot Talking Face Generation from Single-speaker Audio-Visual Correlation Learning

Audio-driven one-shot talking face generation methods are usually trained on video resources of various persons. However, their created videos often suffer unnatural mouth shapes and asynchronous lips because those methods struggle to learn a consistent speech style from different speakers. We observe that it would be much easier to learn a consistent speech style from a specific speaker, which leads to authentic mouth movements. Hence, we propose a novel one-shot talking face generation framework by exploring consistent correlations between audio and visual motions from a specific speaker and then transferring audio-driven motion fields to a reference image. Specifically, we develop an Audio-Visual Correlation Transformer (AVCT) that aims to infer talking motions represented by keypoint based dense motion fields from an input audio. In particular, considering audio may come from different identities in deployment, we incorporate phonemes to represent audio signals. In this manner, our AVCT can inherently generalize to audio spoken by other identities. Moreover, as face keypoints are used to represent speakers, AVCT is agnostic against appearances of the training speaker, and thus allows us to manipulate face images of different identities readily. Considering different face shapes lead to different motions, a motion field transfer module is exploited to reduce the audio-driven dense motion field gap between the training identity and the one-shot reference. Once we obtained the dense motion field of the reference image, we employ an image renderer to generate its talking face videos from an audio clip. Thanks to our learned consistent speaking style, our method generates authentic mouth shapes and vivid movements. Extensive experiments demonstrate that our synthesized videos outperform the state-of-the-art in terms of visual quality and lip-sync.
DemoGrasp: Few-Shot Learning for Robotic Grasping with Human Demonstration

The ability to successfully grasp objects is crucial in robotics, as it enables several interactive downstream applications. To this end, most approaches either compute the full 6D pose for the object of interest or learn to predict a set of grasping points. While the former approaches do not scale well to multiple object instances or classes yet, the latter require large annotated datasets and are hampered by their poor generalization capabilities to new geometries. To overcome these shortcomings, we propose to teach a robot how to grasp an object with a simple and short human demonstration. Hence, our approach neither requires many annotated images nor is it restricted to a specific geometry. We first present a small sequence of RGB-D images displaying a human-object interaction. This sequence is then leveraged to build associated hand and object meshes that represent the depicted interaction. Subsequently, we complete missing parts of the reconstructed object shape and estimate the relative transformation between the reconstruction and the visible object in the scene. Finally, we transfer the a-priori knowledge from the relative pose between object and human hand with the estimate of the current object pose in the scene into necessary grasping instructions for the robot. Exhaustive evaluations with Toyota’s Human Support Robot (HSR) in real and synthetic environments demonstrate the applicability of our proposed methodology and its advantage in comparison to previous approaches.
Utilizing Multiple Objectives for the Optimization of the Pultrusion Process Based on a Thermo-Chemical Simulation

Pultrusion is one of the most effective manufacturing processes for producing composites with constant cross-sectional profiles. This obviously makes it more attractive for both researchers and practitioners to investigate the optimum process parameters, i.e. pulling speed, power and dimensions of the heating platens, length and width of the heating die, design of the resin injection chamber, etc., to provide better understanding of the process, consequently to improve the efficiency of the process as well the product quality. Numerous simulation approaches have been presented until now. However, optimization studies had been limited with either experimental cases or determining only one objective to improve one aspect of the performance of the process. This objective is either augmented by other process related criteria or subjected to constraints which might have had the same importance of being treated as objectives. In essence, these approaches convert a true multi-objective optimization problem (MOP) into a single-objective optimization problem (SOP). This transformation obviously results in only one optimum solution and it does not support the efforts to get more out of an optimization study, such as relations between variables and objectives or constraints. In this study, an MOP considering thermo-chemical aspects of the pultrusion process (e.g. cure degree, temperatures), in which the pulling speed is maximized and the heating power is minimized simultaneously (without defining any preference between them), has been formulated. An evolutionary multi-objective optimization (EMO) algorithm, non-dominated sorting genetic algorithm (NSGA-II [Deb et al., 2002]), has been used to solve this MOP in an ideal way where the outcome is the set of multiple solutions (i.e. Pareto-optimal solutions) and each solution is theoretically an optimal solution corresponding to a particular trade-off among objectives. Following the solution process, in other words obtaining the Pareto-optimal front, a further postprocessing study has been performed to unveil some common principles existing between the variables, the objectives and the constraints either along the whole front or in some portion of it. These relationships will reveal a design philosophy not only for the improvement of the process efficiency, but also a methodology to design a pultrusion die for different operating conditions.
The local PNG bias of neutral Hydrogen, HI

We use separate universe simulations with the IllustrisTNG galaxy formation model to predict the local PNG bias parameters bΦ and bΦδ of atomic neutral hydrogen, HI. These parameters and their relation to the linear density bias parameter b 1 play a key role in observational constraints of the local PNG parameter f NL using the HI power spectrum and bispectrum. Our results show that the popular calculation based on the universality of the halo mass function overpredicts the bΦ (b 1) and bΦδ (b 1) relations measured in the simulations. In particular, our results show that at z ≲ 1 the HI power spectrum is more sensitive to f NL compared to previously thought (bΦ is more negative), but is less sensitive at other epochs (bΦ is less positive). We discuss how this can be explained by the competition of physical effects such as that large-scale gravitational potentials with local PNG (i) accelerate the conversion of hydrogen to heavy elements by star formation, (ii) enhance the effects of baryonic feedback that eject the gas to regions more exposed to ionizing radiation, and (iii) promote the formation of denser structures that shield the HI more efficiently. Our numerical results can be used to revise existing forecast studies on f NL using 21 cm line-intensity mapping data. Despite this first step towards predictions for the local PNG bias parameters of HI, we emphasize that more work is needed to assess their sensitivity on the assumed galaxy formation physics and HI modeling strategy.
Convergence of a class of fully non-linear parabolic equations on Hermitian manifolds

Abstract. We consider a class of fully non-linear parabolic equations on compact Hermitian manifolds involving symmetric functions of partial Laplacians. Under fairly general assumptions, we show the long time existence and convergence of solutions. We also derive a Harnack inequality for the linearized equation which is used in the proof of convergence. Mathematical Subject Classification (MSC2020): 35K10, 35K55, 58J35.
Extracting and Measuring Uncertain Biomedical Knowledge from Scientific Statements

Abstract Purpose Given the information overload of scientific literature, there is an increasing need for computable biomedical knowledge buried in free text. This study aimed to develop a novel approach to extracting and measuring uncertain biomedical knowledge from scientific statements. Design/methodology/approach Taking cardiovascular research publications in China as a sample, we extracted subject–predicate–object triples (SPO triples) as knowledge units and unknown/hedging/conflicting uncertainties as the knowledge context. We introduced information entropy (IE) as potential metric to quantify the uncertainty of epistemic status of scientific knowledge represented at subject-object pairs (SO pairs) levels. Findings The results indicated an extraordinary growth of cardiovascular publications in China while only a modest growth of the novel SPO triples. After evaluating the uncertainty of biomedical knowledge with IE, we identified the Top 10 SO pairs with highest IE, which implied the epistemic status pluralism. Visual presentation of the SO pairs overlaid with uncertainty provided a comprehensive overview of clusters of biomedical knowledge and contending topics in cardiovascular research. Research limitations The current methods didn’t distinguish the specificity and probabilities of uncertainty cue words. The number of sentences surrounding a given triple may also influence the value of IE. Practical implications Our approach identified major uncertain knowledge areas such as diagnostic biomarkers, genetic polymorphism and co-existing risk factors related to cardiovascular diseases in China. These areas are suggested to be prioritized; new hypotheses need to be verified, while disputes, conflicts, and contradictions need to be settled. Originality/value We provided a novel approach by combining natural language processing and computational linguistics with informetric methods to extract and measure uncertain knowledge from scientific statements.
Conditional Deep Hierarchical Variational Autoencoder for Voice Conversion

Variational autoencoder-based voice conversion (VAE-VC) has the advantage of requiring only pairs of speeches and speaker labels for training. Unlike the majority of the research in VAE-VC which focuses on utilizing auxiliary losses or discretizing latent variables, this paper investigates how an increasing model expressiveness has benefits and impacts on the VAE-VC. Specifically, we first analyze VAE-VC from a rate-distortion perspective, and point out that model expressiveness is significant for VAE-VC because rate and distortion reflect similarity and naturalness of converted speeches. Based on the analysis, we propose a novel V C method using a deep hierarchical VAE, which has high model expressiveness as well as having fast conversion speed thanks to its non-autoregressive decoder. Also, our analysis reveals another problem that similarity can be degraded when the latent variable of VAEs has redundant infor-mation. We address the problem by controlling the information contained in the latent variable using $\beta$ - VAE objective. In the experiment using VCTK corpus, the proposed method achieved mean opinion scores higher than 3.5 on both naturalness and similarity in inter-gender settings, which are higher than the scores of existing auto encoder-based VC methods.
Feature-based Recognition Framework for Super-resolution Images

In practical application, the performance of recognition network usually decreases when being applied on super-resolution images. In this paper, we propose a feature-based recognition network combined with GAN (FGAN). Our network improves the recognition accuracy by extracting more features that benefit recognition from SR images. In the experiment, we build three datasets using three different super-resolution algorithm, and our network increases the recognition accuracy by more than 6% comparing with ReaNet50 and DenseNet121.
Investigating the Impact of 9/11 on The Simpsons through Natural Language Processing

The impact of real-world events on fictional media is particularly apparent in the American cartoon series The Simpsons. While there are often very direct pop-culture references evident in the dialogue and visual gags of the show, subtle changes in tone or sentiment may not be so obvious. Our aim was to use Natural Language Processing to attempt to search for changes in word frequency, topic, and sentiment before and after the September 11 terrorist attacks in New York. No clear trend change was seen, there was a slight decrease in the average sentiment over time around the relevant 2000− 2002 period, but the scripts still maintained an overall positive value, indicating that the comedic nature of The Simpsons did not wane particularly significantly. The exploration of other social issues and even character-specific statistics is needed to bolster the findings here.
Using Static and Dynamic Malware features to perform Malware Ascription

Malware ascription is a relatively unexplored area, and it is rather difficult to attribute malware and detect authorship. In this paper, we employ various Static and Dynamic features of malicious executables to classify malware based on their family. We leverage Cuckoo Sandbox and machine learning to make progress in this research. Post analysis, classification is performed using various deep learning and machine learning algorithms. Using the features gathered from VirusTotal (static) and Cuckoo (dynamic) reports, we ran the vectorized data against Multinomial Naive Bayes, Support Vector Machine, and Bagging using Decision Trees as the base estimator. For each classifier, we tuned the hyper-parameters using exhaustive search methods. Our reports can be extremely useful in malware ascription.
Stage Conscious Attention Network (SCAN) : A Demonstration-Conditioned Policy for Few-Shot Imitation

In few-shot imitation learning (FSIL), using behavioral cloning (BC) to solve unseen tasks with few expert demonstrations becomes a popular research direction. The following capabilities are essential in robotics applications: (1) Behaving in compound tasks that contain multiple stages. (2) Retrieving knowledge from few length-variant and misalignment demonstrations. (3) Learning from an expert different from the agent. No previous work can achieve these abilities at the same time. In this work, we conduct FSIL problem under the union of above settings and introduce a novel stage conscious attention network (SCAN) to retrieve knowledge from few demonstrations simultaneously. SCAN uses an attention module to identify each stage in length-variant demonstrations. Moreover, it is designed under demonstration-conditioned policy that learns the relationship between experts and agents. Experiment results show that SCAN can perform in complicated compound tasks without fine-tuning and provide the explainable visualization. Project page is at https://sites.google. com/view/scan-aaai2022.
Real-time Informative Surgical Skill Assessment with Gaussian Process Learning

Endoscopic Sinus and Skull Base Surgeries (ESSBSs) is a challenging and potentially dangerous surgical procedure, and objective skill assessment is the key components to improve the effectiveness of surgical training, to re-validate surgeons’ skills, and to decrease surgical trauma and the complication rate in operating rooms. Because of the complexity of surgical procedures, the variation of operation styles, and the fast development of new surgical skills, the surgical skill assessment remains a challenging problem. This work presents a novel Gaussian Process Learning-based heuristic automatic objective surgical skill assessment method for ESSBSs. Different with classical surgical skill assessment algorithms, the proposed method 1) utilizes the kinematic features in surgical instrument relative movements, instead of using specific surgical tasks or the statistics to assess skills in real-time; 2) provide informative feedback, instead of a summative scores; 3) has the ability to incrementally learn from new data, instead of depending on a fixed dataset. The proposed method projects the instrument movements into the endoscope coordinate to reduce the data dimensionality. It then extracts the kinematic features of the projected data and learns the relationship between surgical skill levels and the features with the Gaussian Process learning technique. The proposed method was verified in full endoscopic skull base and sinus surgeries on cadavers. These surgeries have different pathology, requires different treatment and has different complexities. The experimental results show that the proposed method reaches 100% prediction precision for complete surgical procedures and 90% precision for real-time prediction assessment.
Complex Langevin boundary terms in lattice models

Michael Westh Hansen, Erhard Seiler, Dénes Sextya,∗ and Ion-Olimpiu Stamatescu Institute of Physics, NAWI Graz, University of Graz, Universitätsplatz 5, Graz, Austria Institut für Theoretische Physik, Universität Heidelberg, Philosophenweg 16, 69120 Heidelberg, Germany Max-Planck-Institut für Physik (Werner-Heisenberg-Institut), Föhringer Ring 6, München, Germany E-mail: michael.hansen@uni-graz.at, ehs@mpp.mpg. de,
Automation Usage Pattern During Complex Electro Mechanical Product Development

Project Focus: This research is focused on assessing the state of the art of design automation usage in the aerospace and automotive industries. This report presents design automation usage data from the perspective of their impact on time savings over the entire product development process. In doing so, it explores the following three questions: Where do development time or resources go? Where might significant chunks of product development time be saved? And what are some promising design automation technologies in the research and commercial off the shelf (COTS) software domains? Findings: 1. Product development effort is split between local and system level tasks. Local task executions, local iterations, and system level task executions take on average 32%, 23%, and 14% of the elapsed time respectively. Rest of the development time (30%) is spent in set ups and waiting for information releases at the local and system level. 2. On average, design automation usage impacts the development process as follows: speeds up design process (30%); speeds up design, only if the process is restructured (33%); and improves design fidelity, but does not speed up the process (18%). Rest of the development process is associated with rolling out IT/infrastructure (19%). Based on these data, potential opportunities for saving development time are identified. A framework for assessing the task execution effectiveness is presented. 3. Designers use their automation tools in a “greedy” manner: they push for higher design fidelity and at the same time seek productivity gains. Further development of promising design and design management technologies is recommended. Acknowledgement This report is prepared under contract number F33615-94-C-4429 from the Materials and Manufacturing Directorate of the US Air Force Research Laboratory (AFRL), Dayton, OH. This work is conducted under the direction of Mr. George Orzel. The authors thank Mr. Orzel, Dr. Gene Himes (AFRL), and Mr. Cliff Harris, Co-Director, Lean Aircraft Initiative at MIT for help in setting up this study. Many colleagues from academia and industry have provided information and opinions that are incorporated in this report. Initial findings were presented during the Operations Management Seminar series at MIT in April, 1999. Contents are offered for stimulation of ideas only, and do not represent the opinions of AFRL, MIT, or any of the participants in the survey.
Variational Autoencoder with CCA for Audio–Visual Cross-modal Retrieval

Cross-modal retrieval is to utilize one modality as a query to retrieve data from another modality, which has become a popular topic in information retrieval, machine learning, and databases. Finding a method to effectively measure the similarity between different modality data is the major challenge of cross-modal retrieval. Although several research works have calculated the correlation between different modality data via learning a common subspace representation, the encoder’s ability to extract features from multi-modal information is not satisfactory. In this article, we present a novel variational autoencoder architecture for audio–visual cross-modal retrieval by learning paired audio–visual correlation embedding and category correlation embedding as constraints to reinforce the mutuality of audio–visual information. On the one hand, audio encoder and visual encoder separately encode audio data and visual data into two different latent spaces. Further, two mutual latent spaces are respectively constructed by canonical correlation analysis. On the other hand, probabilistic modeling methods are used to deal with possible noise and missing information in the data. Additionally, in this way, the cross-modal discrepancies from intra-modal and inter-modal information are simultaneously eliminated in the joint embedding subspace. We conduct extensive experiments over two benchmark datasets. The experimental results confirm that the proposed architecture is effective in learning audio–visual correlation and is appreciably better than the existing cross-modal retrieval methods.