Miracle-dz/UniFAR-FTUs huggingface dataset:
Markovian Gaussian Process Variational Autoencoders

Sequential VAEs have been successfully considered for many high-dimensional time series modelling problems, with many variant models relying on discrete-time mechanisms such as recurrent neural networks (RNNs). On the other hand, continuous-time methods have recently gained attraction, especially in the context of irregularly-sampled time series, where they can better handle the data than discrete-time methods. One such class are Gaussian process variational autoencoders (GPVAEs), where the VAE prior is set as a Gaussian process (GP). However, a major limitation of GPVAEs is that it inherits the cubic computational cost as GPs, making it unattractive to practioners. In this work, we leverage the equivalent discrete state space representation of Markovian GPs to enable linear time GPVAE training via Kalman filtering and smoothing. For our model, Markovian GPVAE (MGPVAE), we show on a variety of high-dimensional temporal and spatiotemporal tasks that our method performs favourably compared to existing approaches whilst being computationally highly scalable.
Analysis of Dynamic load management of Virtual Machinesin a Cloud architectures

— Cloud infrastructures ought to accommodate altering needs for different forms of processing with heterogeneous workloads and time constraints. In a identical context, dynamic management of virtualized application environments is becoming very important to exploit computing resources, particularly with up to date virtualization capabilities that permit live sessions to be moved transparently between servers. This paper proposes novel management algorithms to decide about reallocations of virtual machines in a cloud context characterised by way of huge numbers of hosts. The unconventional algorithms determine just the true critical instances and take choices without recurring to ordinary thresholds. Moreover, they consider load development conduct of the resources alternatively of instantaneous or natural measures.
Squarefree values of polynomial discriminants II

Abstract We determine the density of integral binary forms of given degree that have squarefree discriminant, proving for the first time that the lower density is positive. Furthermore, we determine the density of integral binary forms that cut out maximal orders in number fields. The latter proves, in particular, an “arithmetic Bertini theorem” conjectured by Poonen for P Z . Our methods also allow us to prove that there are ≫ X number fields of degree n having associated Galois group Sn and absolute discriminant less than X , improving the best previously known lower bound of ≫ X. Finally, our methods correct an error in and thus resurrect earlier (retracted) results of Nakagawa on lower bounds for the number of totally unramified An-extensions of quadratic number fields of bounded discriminant.
FedHAP: Federated Hashing With Global Prototypes for Cross-Silo Retrieval

Deep hashing has been widely applied in large-scale data retrieval due to its superior retrieval efficiency and low storage cost. However, data are often scattered in data silos with privacy concerns, so performing centralized data storage and retrieval is not always possible. Leveraging the approach of federated learning (FL) to perform deep hashing is a recent research trend. However, existing frameworks mostly rely on the aggregation of the local deep hashing models, which are trained by performing similarity learning with local skewed data only. Therefore, they cannot work well for non-IID clients in a real federated environment. To overcome these challenges, we propose a novel federated hashing framework that enables participating clients to jointly train a shared deep hashing model by leveraging the class-wise prototypical hash codes. Globally, sharing global prototypes with only one prototypical hash code per class assists in learning consistent code distributions across clients while minimizing the cost of communication and privacy. Locally, the use of the global prototypical hash codes are maximized by jointly training a discriminator network and the local hashing network. Extensive experiments on benchmark datasets are conducted to demonstrate that our method can significantly improve the performance of the deep hashing model in the federated environments with non-IID data distributions.
Outpainting by Queries

Image outpainting, which is well studied with Convolution Neural Network (CNN) based framework, has recently drawn more attention in computer vision. However, CNNs rely on inherent inductive biases to achieve effective sample learning, which may degrade the performance ceiling. In this paper, motivated by the flexible self-attention mechanism with minimal inductive biases in transformer architecture, we reframe the generalised image outpainting problem as a patch-wise sequence-to-sequence autoregression problem, enabling query-based image outpainting. Specifically, we propose a novel hybrid vision-transformer-based encoder-decoder framework, named \textbf{Query} \textbf{O}utpainting \textbf{TR}ansformer (\textbf{QueryOTR}), for extrapolating visual context all-side around a given image. Patch-wise mode's global modeling capacity allows us to extrapolate images from the attention mechanism's query standpoint. A novel Query Expansion Module (QEM) is designed to integrate information from the predicted queries based on the encoder's output, hence accelerating the convergence of the pure transformer even with a relatively small dataset. To further enhance connectivity between each patch, the proposed Patch Smoothing Module (PSM) re-allocates and averages the overlapped regions, thus providing seamless predicted images. We experimentally show that QueryOTR could generate visually appealing results smoothly and realistically against the state-of-the-art image outpainting approaches.
Robotic Detection of a Human-Comprehensible Gestural Language for Underwater Multi-Human-Robot Collaboration

In this paper, we present a motion-based robotic communication framework that enables non-verbal communication among autonomous underwater vehicles (AUVs) and human divers. We design a gestural language for AUV-to-AUV communication which can be easily understood by divers observing the conversation - unlike typical radio frequency, light, or audio-based AUV communication. To allow AUVs to visually understand a gesture from another AUV, we propose a deep network (RRCommNet) which exploits a self-attention mechanism to learn to recognize each message by extracting maximally discriminative spatio-temporal features. We train this network on diverse simulated and real-world data. Our experimental evaluations, both in simulation and in closed-water robot trials, demonstrate that the proposed RRCommNet architecture is able to decipher gesture-based messages with an average accuracy of 88-94% on simulated data and 73-83% on real data (depending on the version of the model used). Further, by performing a message transcription study with human participants, we also show that the proposed language can be understood by humans with an overall transcription accuracy of 88 %. Finally, we discuss the inference runtime of RRCommNet on embedded GPU hardware, for real-time use on board AUVs in the field.
Envelopes and principal component regression

Envelope methods offer targeted dimension reduction for various models. The overarching goal is to improve efficiency in multivariate parameter estimation by projecting the data onto a lower-dimensional subspace known as the envelope. Envelope approaches have advantages in analyzing data with highly correlated variables, but their iterative Grassmannian optimization algorithms do not scale very well with ultra high-dimensional data. While the connections between envelopes and partial least squares in multivariate linear regression have promoted recent progress in high-dimensional studies of envelopes, we propose a more straightforward way of envelope modeling from a novel principal components regression perspective. The proposed procedure, Non-Iterative Envelope Component Estimation (NIECE), has excellent computational advantages over the iterative Grassmannian optimization alternatives in high dimensions. We develop a unified NIECE theory that bridges the gap between envelope methods and principal components in regression. The new theoretical insights also shed light on the envelope subspace estimation error as a function of eigenvalue gaps of two symmetric positive definite matrices used in envelope modeling. We apply the new theory and algorithm to several envelope models, including response and predictor reduction in multivariate linear models, logistic regression, and Cox proportional hazard model. Simulations and illustrative data analysis show the potential for NIECE to improve standard methods in linear and generalized linear models significantly.
Identification of Substation Configurations in Modern Power Systems using Artificial Intelligence

Power system transmission network topology is utilized in energy management system applications. Substation configurations are fundamental to transmission network topology processing. Modern power systems consisting of renewable energy sources require reliable and fast network topology processing due to the variable nature of wind and solar power plants. Currently used transmission network topology processing, which is based on the relay signals communicated through SCADA is not highly reliable or highly accurate. Substation configuration identification (SCI) for different substation arrangements including main and transfer bus arrangement (MTBA), ring bus arrangement (RBA), and single bus arrangement (SBA) is investigated. Synchrophasor measurement based SCI for functional arrangements (FA) using artificial intelligence (AI) approaches is proposed in this paper. This method improves monitoring FA. Typical results for MTBA, RBA and SBA substation configuration identification is presented. A modified two-area four-machine power system model with two grid connected solar PV plants consisting of MTBA, RBA and SBA is simulated on real-time digital simulator. AI based SCI is shown to accurately identify all possible FAs for the three substation arrangements under any operating condition.
Western Mediterranean wetlands bird species classification: evaluating small-footprint deep learning approaches on a new annotated dataset

The deployment of an expert system running over a wireless acoustic sensors network made up of bioacoustic monitoring devices that recognise bird species from their sounds would enable the automation of many tasks of ecological value, including the analysis of bird population composition or the detection of endangered species in areas of environmental interest. Endowing these devices with accurate audio classification capabilities is possible thanks to the latest advances in artificial intelligence, among which deep learning techniques excel. However, a key issue to make bioacoustic devices a ff ordable is the use of small footprint deep neural networks that can be embedded in resource and battery constrained hardware platforms. For this reason, this work presents a critical comparative analysis between two heavy and large footprint deep neural networks (VGG16 and ResNet50) and a lightweight alternative, MobileNetV2. Our experimental results reveal that MobileNetV2 achieves an average F1-score less than a 5% lower than ResNet50 (0.789 vs. 0.834), performing better than VGG16 with a footprint size nearly 40 times smaller. Moreover, to compare the models, we have created and made public the Western Mediterranean Wetland Birds dataset, consisting of 201.6 minutes and 5,795 audio excerpts of 20 endemic bird species of the Aiguamolls de l’Emporda` Natural Park.
Paint and Distill: Boosting 3D Object Detection with Semantic Passing Network

3D object detection task from lidar or camera sensors is essential for autonomous driving. Pioneer attempts at multi-modality fusion complement the sparse lidar point clouds with rich semantic texture information from images at the cost of extra network designs and overhead. In this work, we propose a novel semantic passing framework, named SPNet, to boost the performance of existing lidar-based 3D detection models with the guidance of rich context painting, with no extra computation cost during inference. Our key design is to first exploit the potential instructive semantic knowledge within the ground-truth labels by training a semantic-painted teacher model and then guide the pure-lidar network to learn the semantic-painted representation via knowledge passing modules at different granularities: class-wise passing, pixel-wise passing and instance-wise passing. Experimental results show that the proposed SPNet can seamlessly cooperate with most existing 3D detection frameworks with 1$\sim$5% AP gain and even achieve new state-of-the-art 3D detection performance on the KITTI test benchmark. Code is available at: https://github.com/jb892/SPNet.
PSR J0952−0607: The Fastest and Heaviest Known Galactic Neutron Star

We describe Keck-telescope spectrophotometry and imaging of the companion of the “black widow” pulsar PSR J0952−0607, the fastest known spinning neutron star (NS) in the disk of the Milky Way. The companion is very faint at minimum brightness, presenting observational challenges, but we have measured multicolor light curves and obtained radial velocities over the illuminated “day” half of the orbit. The model fits indicate system inclination i = 59. °8 ± 1. °9 and a pulsar mass M NS = 2.35 ± 0.17 M ⊙, the largest well-measured mass found to date. Modeling uncertainties are small, since the heating is not extreme; the companion lies well within its Roche lobe and a simple direct-heating model provides the best fit. If the NS started at a typical pulsar birth mass, nearly 1 M ⊙ has been accreted; this may be connected with the especially low intrinsic dipole surface field, estimated at 6 × 107 G. Joined with reanalysis of other black widow and redback pulsars, we find that the minimum value for the maximum NS mass is Mmax>2.19M⊙ (2.09 M ⊙) at 1σ (3σ) confidence. This is ∼ 0.15 M ⊙ heavier than the lower limit on Mmax implied by the white dwarf–pulsar binaries measured via radio Shapiro-delay techniques.
PAC Reinforcement Learning for Predictive State Representations

In this paper we study online Reinforcement Learning (RL) in partially observable dynamical systems. We focus on the Predictive State Representations (PSRs) model, which is an expressive model that captures other well-known models such as Partially Observable Markov Decision Processes (POMDP). PSR represents the states using a set of predictions of future observations and is defined entirely using observable quantities. We develop a novel model-based algorithm for PSRs that can learn a near optimal policy in sample complexity scaling polynomially with respect to all the relevant parameters of the systems. Our algorithm naturally works with function approximation to extend to systems with potentially large state and observation spaces. We show that given a realizable model class, the sample complexity of learning the near optimal policy only scales polynomially with respect to the statistical complexity of the model class, without any explicit polynomial dependence on the size of the state and observation spaces. Notably, our work is the first work that shows polynomial sample complexities to compete with the globally optimal policy in PSRs. Finally, we demonstrate how our general theorem can be directly used to derive sample complexity bounds for special models including $m$-step weakly revealing and $m$-step decodable tabular POMDPs, POMDPs with low-rank latent transition, and POMDPs with linear emission and latent transition.
Shapley Computations Using Surrogate Model-Based Trees

Shapley-related techniques have gained attention as both global and local interpretation tools because of their desirable properties. However, their computation using conditional expectations is computationally expensive. Approximation methods suggested in the literature have limitations. This paper proposes the use of a surrogate model-based tree to compute Shapley and SHAP values based on conditional expectation. Simulation studies show that the proposed algorithm provides improvements in accuracy, unifies global Shapley and SHAP interpretation, and the thresholding method provides a way to trade-off running time and accuracy.
Symbolic Runtime Verification for Monitoring under Uncertainties and Assumptions

Runtime Verification deals with the question of whether a run of a system adheres to its specification. This paper studies runtime verification in the presence of partial knowledge about the observed run, particularly where input values may not be precise or may not be observed at all. We also allow declaring assumptions on the execution which permits to obtain more precise verdicts also under imprecise inputs. To this end, we show how to understand a given correctness property as a symbolic formula and explain that monitoring boils down to solving this formula iteratively, whenever more and more observations of the run are given. We base our framework on stream runtime verification, which allows to express temporal correctness properties not only in the Boolean but also in richer logical theories. While in general our approach requires to consider larger and larger sets of formulas, we identify domains (including Booleans and Linear Algebra) for which pruning strategies exist, which allows to monitor with constant memory (i.e. independent of the length of the observation) while preserving the same inference power as the monitor that remembers all observations. We empirically exhibit the power of our technique using a prototype implementation under two important cases studies: software for testing car emissions and heart-rate monitoring.
Temporal Disentanglement of Representations for Improved Generalisation in Reinforcement Learning

Reinforcement Learning (RL) agents are often unable to generalise well to environment variations in the state space that were not observed during training. This issue is especially problematic for image-based RL, where a change in just one variable, such as the background colour, can change many pixels in the image. The changed pixels can lead to drastic changes in the agent's latent representation of the image, causing the learned policy to fail. To learn more robust representations, we introduce TEmporal Disentanglement (TED), a self-supervised auxiliary task that leads to disentangled image representations exploiting the sequential nature of RL observations. We find empirically that RL algorithms utilising TED as an auxiliary task adapt more quickly to changes in environment variables with continued training compared to state-of-the-art representation learning methods. Since TED enforces a disentangled structure of the representation, our experiments also show that policies trained with TED generalise better to unseen values of variables irrelevant to the task (e.g. background colour) as well as unseen values of variables that affect the optimal policy (e.g. goal positions).
Test cell data-based predictive modelling to determine HVAC energy consumption for three façade solutions in Madrid

This study aims to narrow the gap between predicted and actual energy performance in buildings. Predictive models were established that relate the electric consumption by HVAC systems to maintain certain indoor environmental conditions in variable weather to the type of facade. The models were developed using data gathered from test cells with adiabatic envelopes on all but the facade to be tested. Three facade types were studied. The first, the standard solution, consisted in a double wythe brick wall with an intermediate air space, the configuration most commonly deployed in multi-family dwellings built in Spain between 1940 and 1980 (prior to the enactment of the first building codes that limited overall energy demand in buildings). The other two were retrofits frequently found in such buildings: ventilated facades and ETICS (external thermal insulation composite systems). Two predictive models were designed for each type of facade, one for summer and the other for winter. The linear regression equations and the main statistical parameters are reported.
1st Place Solution to the EPIC-Kitchens Action Anticipation Challenge 2022

In this report, we describe the technical details of our submission to the EPIC-Kitchens Action Anticipation Challenge 2022. In this competition, we develop the following two approaches. 1) Anticipation Time Knowledge Distillation using the soft labels learned by the teacher model as knowledge to guide the student network to learn the information of anticipation time; 2) Verb-Noun Relation Module for building the relationship between verbs and nouns. Our method achieves state-of-the-art results on the testing set of EPIC-Kitchens Action Anticipation Challenge 2022.
Indoor optical fiber eavesdropping approach and its avoidance

The optical fiber network has become a worldwide infrastructure. In addition to the basic functions in telecommunication, its sensing ability has attracted more and more attention. In this paper, we discuss the risk of household fiber being used for eavesdropping and demonstrate its performance in the lab. Using a 3-meter tail fiber in front of the household optical modem, voices of normal human speech can be eavesdropped by a laser interferometer and recovered 1.1 km away. The detection distance limit and system noise are analyzed quantitatively. We also give some practical ways to prevent eavesdropping through household fiber.
A Survey on Table Question Answering: Recent Advances

Table Question Answering (Table QA) refers to providing precise answers from tables to answer a user's question. In recent years, there have been a lot of works on table QA, but there is a lack of comprehensive surveys on this research topic. Hence, we aim to provide an overview of available datasets and representative methods in table QA. We classify existing methods for table QA into five categories according to their techniques, which include semantic-parsing-based, generative, extractive, matching-based, and retriever-reader-based methods. Moreover, as table QA is still a challenging task for existing methods, we also identify and outline several key challenges and discuss the potential future directions of table QA.
Edge Augmentation on Disconnected Graphs via Eigenvalue Elevation

The graph-theoretical task of determining most likely inter-community edges based on disconnected subgraphs’ intra-community connectivity is proposed. An algorithm is developed for this edge augmentation task, based on elevating the zero eigenvalues of graph’s spectrum. Upper bounds for eigenvalue elevation amplitude and for the corresponding augmented edge density are derived and are authenticated with simulation on random graphs. The algorithm works consistently across synthetic and real networks, yielding desirable performance at connecting graph components. Edge augmentation reverse-engineers graph partition under diﬀerent community detection methods (Girvan-Newman method, greedy modularity maximization, label propagation, Louvain method, and ﬂuid community), in most cases producing inter-community edges at > 50% frequency.