Miracle-dz/UniFAR-FTUs huggingface dataset:
Templating Shuffles

Cloud data centers are evolving fast. At the same time, today's large-scale data analytics applications require non-trivial performance tuning that is often specific to the applications, workloads, and data center infrastructure. We propose TeShu, which makes network shuffling an extensible unified service layer common to all data analytics. Since an optimal shuffle depends on a myriad of factors, TeShu introduces parameterized shuffle templates, instantiated by accurate and efficient sampling that enables TeShu to dynamically adapt to different application workloads and data center layouts. Our preliminary experimental results show that TeShu efficiently enables shuffling optimizations that improve performance and adapt to a variety of data center network scenarios.
Tell Me How You Feel: Designing Emotion-Aware Voicebots to Ease Pandemic Anxiety In Aging Citizens

The feeling of anxiety and loneliness among aging population has been recently amplified by the COVID-19 related lockdowns. Emotion-aware multimodal bot application combining voice and visual interface was developed to address the problem in the group of older citizens. The application is novel as it combines three main modules: information, emotion selection and psychological intervention, with the aim of improving human well-being. The preliminary study with target group confirmed that multimodality improves usability and that the information module is essential for participating in a psychological intervention. The solution is universal and can also be applied to areas not directly related to COVID-19 pandemic.
On Controller Tuning with Time-Varying Bayesian Optimization

Changing conditions or environments can cause system dynamics to vary over time. To ensure optimal control performance, controllers should adapt to these changes. When the underlying cause and time of change is unknown, we need to rely on online data for this adaptation. In this paper, we will use time-varying Bayesian optimization (TVBO) to tune controllers online in changing environments using appropriate prior knowledge on the control objective and its changes. Two properties are characteristic of many online controller tuning problems: First, they exhibit incremental and lasting changes in the objective due to changes to the system dynamics, e.g., through wear and tear. Second, the optimization problem is convex in the tuning parameters. Current TVBO methods do not explicitly account for these properties, resulting in poor tuning performance and many unstable controllers through over-exploration of the parameter space. We propose a novel TVBO forgetting strategy using Uncertainty-Injection (UI), which incorporates the assumption of incremental and lasting changes. The control objective is modeled as a spatio-temporal Gaussian process (GP) with UI through a Wiener process in the temporal domain. Further, we explicitly model the convexity assumptions in the spatial dimension through GP models with linear inequality constraints. In numerical experiments, we show that our model outperforms the state-of-the-art method in TVBO, exhibiting reduced regret and fewer unstable parameter configurations.
E2N: Error Estimation Networks for Goal-Oriented Mesh Adaptation

Given a partial di ﬀ erential equation (PDE), goal-oriented error estimation allows us to under-stand how errors in a diagnostic quantity of interest (QoI), or goal , occur and accumulate in a numerical approximation, for example using the ﬁnite element method. By decomposing the error estimates into contributions from individual elements, it is possible to formulate adaptation methods, which modify the mesh with the objective of minimising the resulting QoI error. How-ever, the standard error estimate formulation involves the true adjoint solution, which is unknown in practice. As such, it is common practice to approximate it with an ‘enriched’ approximation (e.g. in a higher order space or on a reﬁned mesh). Doing so generally results in a signiﬁcant increase in computational cost, which can be a bottleneck compromising the competitiveness of (goal-oriented) adaptive simulations. The central idea of this paper is to develop a “data-driven” goal-oriented mesh adaptation approach through the selective replacement of the expensive error estimation step with an appropriately conﬁgured and trained neural network. In doing so, the error estimator may be obtained without even constructing the enriched spaces. An element-by-element construction is employed here, whereby local values of various parameters related to the mesh geometry and underlying problem physics are taken as inputs, and the corresponding contribution to the error estimator is taken as output. We demonstrate that this approach is able to obtain the same accuracy with a reduced computational cost, for adaptive mesh test cases related to ﬂow around tidal turbines, which interact via their downstream wakes, and where the overall power output of the farm is taken as the QoI. Moreover, we demonstrate that the element-by-element approach implies reasonably low training costs.
Spectral rounding and image segmentation

The task of assigning labels to pixels is central to computer vision. In automatic segmentation an algorithm assigns a label to each pixel where labels connote a shared property across pixels (e.g. color, bounding contour, texture). Recent approaches to image segmentation have formulated this labeling task as partitioning a graph derived from the image. We use spectral segmentation to denote the family of algorithms that seek a partitioning by processing the eigenstructure associated with image graphs. In this thesis we analyze current spectral segmentation algorithms and explain their performance, both practically and theoretically, on the Normalized Cuts (NCut) criterion. Further, we introduce a novel family of spectral graph partitioning methods, spectral rounding, and apply them to image segmentation tasks. Edge separators of a graph are produced by iteratively reweighting the edges until the graph disconnects into the prescribed number of components. At each iteration a small number of eigenvectors with small eigenvalue are computed and used to determine the reweighting. In this way spectral rounding directly produces discrete solutions where as current spectral algorithms must map the continuous eigenvectors to discrete solutions by employing a heuristic geometric separator (e.g. k-means). We show that spectral rounding compares favorably to current spectral approximations on the NCut criterion in natural image segmentation. Quantitative evaluations are performed on multiple image databases including the Berkeley Segmentation Database. These experiments demonstrate that segmentations with improved NCut value (obtained using the SR-Algorithm) are more highly correlated with human hand-segmentations.
Algebraic Multilevel Preconditioning for Helmholtz Equation

We propose efficient algebraic multilevel preconditioning for the Helmholtz equation with high wave numbers. Our method is mainly based on using new multilevel ILU techniques for symmetric indefinite systems. The method is mainly based on three major ingredients: 1. symmetric maximum weight matchings to increase the block di- agonal dominance of the system, 2. inverse-based pivoting to drive the coarsening process and finally 3. filtering techniques to handle frequencies near zero eigenvalues. We will illustrate the resulting multilevel method of this approach for selected numerical examples.
PowerFDNet: Deep Learning-Based Stealthy False Data Injection Attack Detection for AC-Model Transmission Systems

Smart grids are vulnerable to stealthy false data injection attacks (SFDIAs), as SFDIAs can bypass residual-based bad data detection mechanisms. Methods based on deep learning technology have shown promising accuracy in the detection of SFDIAs. However, most existing methods rely on the temporal structure of a sequence of measurements but do not take account of the spatial structure between buses and transmission lines. To address this issue, we propose a spatiotemporal deep network, PowerFDNet, for the SFDIA detection in AC-model power grids. The PowerFDNet consists of two sub-architectures: spatial architecture (SA) and temporal architecture (TA). The SA is aimed at extracting representations of bus/line measurements and modeling the spatial structure based on their representations. The TA is aimed at modeling the temporal structure of a sequence of measurements. Therefore, the proposed PowerFDNet can effectively model the spatiotemporal structure of measurements. Case studies on the detection of SFDIAs on the benchmark smart grids show that the PowerFDNet achieved significant improvement compared with the state-of-the-art SFDIA detection methods. In addition, an IoT-oriented lightweight prototype of size 52 MB is implemented and tested for mobile devices, which demonstrates the potential applications on mobile devices. The trained model will be available at [Online]. Available: https://github.com/FrankYinXF/PowerFDNet.
An Ensemble Approach for Multiple Emotion Descriptors Estimation Using Multi-task Learning

This paper illustrates our submission method to the fourth Affective Behavior Analysis in-the-Wild (ABAW) Competition. The method is used for the Multi-Task Learning Challenge. Instead of using only face information, we employ full information from a provided dataset containing face and the context around the face. We utilized the InceptionNet V3 model to extract deep features then we applied the attention mechanism to refine the features. After that, we put those features into the transformer block and multi-layer perceptron networks to get the final multiple kinds of emotion. Our model predicts arousal and valence, classifies the emotional expression and estimates the action units simultaneously. The proposed system achieves the performance of 0.917 on the MTL Challenge validation dataset.
Layer-Wise Partitioning and Merging for Efficient and Scalable Deep Learning

Deep Neural Network (DNN) models are usually trained sequentially from one layer to another, which causes forward, backward and update locking's problems, leading to poor performance in terms of training time. The existing parallel strategies to mitigate these problems provide suboptimal runtime performance. In this work, we have proposed a novel layer-wise partitioning and merging, forward and backward pass parallel framework to provide better training performance. The novelty of the proposed work consists of 1) a layer-wise partition and merging model which can minimise communication overhead between devices without the memory cost of existing strategies during the training process; 2) a forward pass and backward pass parallelisation and optimisation to address the update locking problem and minimise the total training cost. The experimental evaluation on real use cases shows that the proposed method outperforms the state-of-the-art approaches in terms of training speed; and achieves almost linear speedup without compromising the accuracy performance of the non-parallel approach.
High Dimensional Stochastic Linear Contextual Bandit with Missing Covariates

Recent works in bandit problems adopted lasso convergence theory in the sequential decision-making setting. Even with fully observed contexts, there are technical challenges that hinder the application of existing lasso convergence theory: 1) proving the restricted eigenvalue condition under conditionally sub-Gaussian noise and 2) accounting for the dependence between the context variables and the chosen actions. This paper studies the effect of missing covariates on regret for stochastic linear bandit algorithms. Our work provides a high-probability upper bound on the regret incurred by the proposed algorithm in terms of covariate sampling probabilities, showing that the regret degrades due to missingness by at most $\zeta_{min}^{2}$, where $\zeta_{min}$ is the minimum probability of observing covariates in the context vector. We illustrate our algorithm for the practical application of experimental design for collecting gene expression data by a sequential selection of class discriminating DNA probes.
A deep spectromorphological study of the gamma-ray emission surrounding the young massive stellar cluster Westerlund 1

Young massive stellar clusters are extreme environments and potentially provide the means for efficient particle acceleration. Indeed, they are increasingly considered as being responsible for a significant fraction of cosmic rays (CRs) accelerated within the Milky Way. Westerlund 1, the most massive known young stellar cluster in our Galaxy is a prime candidate for studying this hypothesis. While the very-high-energy $\gamma$-ray source HESS J1646-458 has been detected in the vicinity of Westerlund 1 in the past, its association could not be firmly identified. We aim to identify the physical processes responsible for the $\gamma$-ray emission around Westerlund 1 and thus to better understand the role of massive stellar clusters in the acceleration of Galactic CRs. Using 164 hours of data recorded with the High Energy Stereoscopic System (H.E.S.S.), we carried out a deep spectromorphological study of the $\gamma$-ray emission of HESS J1646-458. We furthermore employed H I and CO observations of the region to infer the presence of gas that could serve as target material for interactions of accelerated CRs. We detected large-scale ($\sim 2^\circ$ diameter) $\gamma$-ray emission with a complex morphology, exhibiting a shell-like structure and showing no significant variation with $\gamma$-ray energy. The combined energy spectrum of the emission extends to several tens of TeV, and is uniform across the entire source region. We did not find a clear correlation of the $\gamma$-ray emission with gas clouds as identified through H I and CO observations. We conclude that, of the known objects within the region, only Westerlund 1 can explain the bulk of the $\gamma$-ray emission. Several CR acceleration sites and mechanisms are conceivable, and discussed in detail. (abridged)
A Convolutional Attention Based Deep Network Solution for UAV Network Attack Recognition over Fading Channels and Interference

— When users exchange data with Unmanned Aerial vehicles - (UAVs) over air-to-ground (A2G) wireless communication networks, they expose the link to attacks that could increase packet loss and might disrupt connectivity. For example, in emergency deliveries, losing control information (i.e data related to the UAV control communication) might result in accidents that cause UAV destruction and damage to buildings or other elements in a city. To prevent these problems, these issues must be addressed in 5G and 6G scenarios. This research offers a deep learning (DL) approach for detecting attacks in UAVs equipped with orthogonal frequency division multiplexing (OFDM) receivers on Clustered Delay Line (CDL) channels in highly complex scenarios involving authenticated terrestrial users, as well as attackers in unknown locations. We use the two observable parameters available in 5G UAV connections: the Received Signal Strength Indicator (RSSI) and the Signal to Interference plus Noise Ratio (SINR). The prospective algorithm is generalizable regarding attack identification, which does not occur during training. Further, it can identify all the attackers in the environment with 20 terrestrial users. A deeper investigation into the timing requirements for recognizing attacks show that after training, the minimum time necessary after the attack begins is 100 ms, and the minimum attack power is 2 dBm, which is the same power that the authenticated UAV uses. Our algorithm also detects moving attackers from a distance of 500 m.
Decentralized scheduling through an adaptive, trading-based multi-agent system

In multi-agent reinforcement learning systems, the actions of one agent can have a negative impact on the rewards of other agents. One way to combat this problem is to let agents trade their rewards amongst each other. Motivated by this, this work applies a trading approach to a simulated scheduling environment, where the agents are responsible for the assignment of incoming jobs to compute cores. In this environment, reinforcement learning agents learn to trade successfully. The agents can trade the usage right of computational cores to process high-priority, high-reward jobs faster than low-priority, low-reward jobs. However, due to combinatorial effects, the action and observation spaces of a simple reinforcement learning agent in this environment scale exponentially with key parameters of the problem size. However, the exponential scaling behavior can be transformed into a linear one if the agent is split into several independent sub-units. We further improve this distributed architecture using agent-internal parameter sharing. Moreover, it can be extended to set the exchange prices autonomously. We show that in our scheduling environment, the advantages of a distributed agent architecture clearly outweigh more aggregated approaches. We demonstrate that the distributed agent architecture becomes even more performant using agent-internal parameter sharing. Finally, we investigate how two different reward functions affect autonomous pricing and the corresponding scheduling.
Correcting Model Bias with Sparse Implicit Processes

Model selection in machine learning (ML) is a crucial part of the Bayesian learning procedure. Model choice may impose strong biases on the resulting predictions, which can hinder the performance of methods such as Bayesian neural networks and neural samplers. On the other hand, newly proposed approaches for Bayesian ML exploit features of approximate inference in function space with implicit stochastic processes (a generalization of Gaussian processes). The approach of Sparse Implicit Processes (SIP) is particularly successful in this regard, since it is fully trainable and achieves flexible predictions. Here, we expand on the original experiments to show that SIP is capable of correcting model bias when the data generating mechanism differs strongly from the one implied by the model. We use synthetic datasets to show that SIP is capable of providing predictive distributions that reflect the data better than the exact predictions of the initial, but wrongly assumed model.
Benchmarks for a Global Extraction of Information from Deeply Virtual Exclusive Scattering

We develop a framework to establish benchmarks for machine learning and deep neural networks analyses of exclusive scattering cross sections (FemtoNet). Within this framework we present an extraction of Compton form factors for deeply virtual Compton scattering from an unpolarized proton target. Critical to this effort is a study of the effects of physics constraint built into machine learning (ML) algorithms. We use the Bethe-Heitler process, which is the QED radiative background to deeply virtual Compton scattering, to test our ML models and, in particular, their ability to generalize information extracted from data. We then use these techniques on the full cross section and compare the results to analytic model calculations. We propose a quantification technique, the random targets method, to begin understanding the separation of aleatoric and epistemic uncertainties as they are manifest in exclusive scattering analyses. We propose a set of both physics driven and machine learning based benchmarks providing a stepping stone towards applying explainable machine learning techniques with controllable uncertainties in a wide range of deeply virtual exclusive processes.
Learning Human Kinematics by Modeling Temporal Correlations between Joints for Video-based Human Pose Estimation

Estimating human poses from videos is critical in human-computer interaction. By precisely estimating human poses, the robot can provide an appropriate response to the human. Most existing approaches use the optical flow, RNNs, or CNNs to extract temporal features from videos. Despite the positive results of these attempts, most of them only straightforwardly integrate features along the temporal dimension, ignoring temporal correlations between joints. In contrast to previous methods, we propose a plug-and-play kinematics modeling module (KMM) based on the domain-cross attention mechanism to model the temporal correlation between joints across different frames explicitly. Specifically, the proposed KMM models the temporal correlation between any two joints by calculating their temporal similarity. In this way, KMM can learn the motion cues of each joint. Using the motion cues (temporal domain) and historical positions of joints (spatial domain), KMM can infer the initial positions of joints in the current frame in advance. In addition, we present a kinematics modeling network (KIMNet) based on the KMM for obtaining the final positions of joints by combining pose features and initial positions of joints. By explicitly modeling temporal correlations between joints, KIMNet can infer the occluded joints at present according to all joints at the previous moment. Furthermore, the KMM is achieved through an attention mechanism, which allows it to maintain the high resolution of features. Therefore, it can transfer rich historical pose information to the current frame, which provides effective pose information for locating occluded joints. Our approach achieves state-of-the-art results on two standard video-based pose estimation benchmarks. Moreover, the proposed KIMNet shows some robustness to the occlusion, demonstrating the effectiveness of the proposed method.
Constraining the evolution of Newton’s constant with slow inspirals observed from spaceborne gravitational-wave detectors

Spaceborne gravitational-wave (GW) detectors observing at milli-Hz and deci-Hz frequencies are expected to detect large numbers of quasi-monochromatic signals. The first and second time-derivative of the GW frequency ($\dot f_0$ and $\ddot f_0$) can be measured for the most favourable sources and used to look for negative post-Newtonian corrections, which can be induced by the source's environment or modifications of general relativity. We present an analytical, Fisher-matrix-based approach to estimate how precisely such corrections can be constrained. We use this method to estimate the bounds attainable on the time evolution of the gravitational constant $G(t)$ with different classes of quasi-monochromatic sources observable with LISA and DECIGO, two representative spaceborne detectors for milli-Hz and deci-Hz GW frequencies. We find that the most constraining source among a simulated population of LISA galactic binaries could yield $\dot G/G_0 \lesssim 10^{-6}\text{yr}^{-1}$, while the best currently known verification binary will reach $\dot G/G_0 \lesssim 10^{-4}\text{yr}^{-1}$. We also perform Monte-Carlo simulations using quasi-monochromatic waveforms to check the validity of our Fisher-matrix approach, as well as inspiralling waveforms to analyse binaries that do not satisfy the quasi-monochromatic assumption. We find that our analytical Fisher matrix produces good order-of-magnitude constraints even for sources well beyond its regime of validity. Monte-Carlo investigations also show that chirping stellar-mass compact binaries detected by DECIGO-like detectors at cosmological distances of tens of Mpc can yield constraints as tight as $\dot G/G_0 \lesssim 10^{-11}\text{yr}^{-1}$.