Miracle-dz/UniFAR-FTUs huggingface dataset:
Using an expert deviation carrying the knowledge of climate data in usual clustering algorithms

In order to help physicists to expand their knowledge of the climate in the Lesser Antilles, we aim to identify the spatio-temporal configurations using clustering analysis on wind speed and cumulative rainfall datasets. But we show that using the L2 norm in conventional clustering methods as K-Means (KMS) and Hierarchical Agglomerative Clustering (HAC) can induce undesirable effects. So, we propose to replace Euclidean distance (L2) by a dissimilarity measure named Expert Deviation (ED). Based on the symmetrized Kullback-Leibler divergence, the ED integrates the properties of the observed physical parameters and climate knowledge. This measure helps comparing histograms of four patches, corresponding to geographical zones, that are influenced by atmospheric structures. The combined evaluation of the internal homogeneity and the separation of the clusters obtained using ED and L2 was performed. The results, which are compared using the silhouette index, show five clusters with high indexes. For the two available datasets one can see that, unlike KMS-L2, KMS-ED discriminates the daily situations favorably, giving more physical meaning to the clusters discovered by the algorithm. The effect of patches is observed in the spatial analysis of representative elements for KMS-ED. The ED is able to produce different configurations which makes the usual atmospheric structures clearly identifiable. Atmospheric physicists can interpret the locations of the impact of each cluster on a specific zone according to atmospheric structures. KMS-L2 does not lead to such an interpretability, because the situations represented are spatially quite smooth. This climatological study illustrates the advantage of using ED as a new approach.
PIVEN: A Deep Neural Network for Prediction Intervals with Specific Value Prediction

Improving the robustness of neural nets in regression tasks is key to their application in multiple domains. Deep learning-based approaches aim to achieve this goal either by improving the manner in which they produce their prediction of specific values (i.e., point prediction), or by producing prediction intervals (PIs) that quantify uncertainty. We present PIVEN, a deep neural network for producing both a PI and a prediction of specific values. Benchmark experiments show that our approach produces tighter uncertainty bounds than the current state-of-the-art approach for producing PIs, while managing to maintain comparable performance to the state-of-the-art approach for specific value-prediction. Additional evaluation on large image datasets further support our conclusions.
Integer Programming for Multi-Robot Planning: A Column Generation Approach

We consider the problem of coordinating a fleet of robots in a warehouse so as to maximize the reward achieved within a time limit while respecting problem and robot specific constraints. We formulate the problem as a weighted set packing problem where elements are defined as being the space-time positions a robot can occupy and the items that can be picked up and delivered. We enforce that robots do not collide, that each item is delivered at most once, and that the number of robots active at any time does not exceed the total number available. Since the set of robot routes is not enumerable, we attack optimization using column generation where pricing is a resource-constrained shortest-path problem.
Handover Rate and Sojourn Time Analysis in Mobile Drone-Assisted Cellular Networks

To improve capacity and overcome some of the limitations of traditional terrestrial cellular wireless networks, drones with aerial base stations can be deployed (e.g., to improve coverage, alleviate congestion, support overflow traffic). The mobility of drones allows flexible network reconfiguration to adapt to dynamic traffic and channel conditions. However, this is achieved at the expense of more handovers since even a static user may experience a handover when the drones are mobile. In this letter, we provide an exact analysis of the handover rate and sojourn time (time between two subsequent handovers) for a network of drone base stations. We also show that among different speed distributions with the same mean, the handover rate is minimum when all drone base stations move with the same speed.
The Evolution of Conspecific Acceptance Thresholds

The differential treatment of conspecifics (e.g., kin recognition) has three components. (1) The production component refers to the nature of the cues used by an individual (actor) to discriminate among classes of conspecifics (recipients) differing in their effects on the actor's fitness. (2) The perception component refers both to the internal template against which the actor matches these cues and to the matching algorithm that allows classification of a recipient (Sherman and Holmes 1985). I introduce a third component, (3) the action component, which refers to the determinants of an action taken by an actor that has calculated a particular degree of similarity between its internal template and a recipient's phenotype. I attempt to provide a theory of the action component of conspecific discrimination. In particular, I examine the factors that determine the optimal or evolutionarily stable acceptance threshold, that is, the level of dissimilarity between the actor's template and the recipient's phenotype below which recipients are accepted and above which recipients are rejected. Analyses of six different classes of recognition contexts show how the optimal or evolutionarily stable acceptance threshold is a function of the relative frequencies of interaction with the different classes of recipients, of the fitness consequences of accepting or rejecting different classes of recipients, and of the ways in which the latter fitness consequences combine. I discuss diverse behavioral phenomena that are elucidated by these models and suggest ways in which these models may be further tested.
An Overview of Deep Semi-Supervised Learning

Deep neural networks demonstrated their ability to provide remarkable performances on a wide range of supervised learning tasks (e.g., image classification) when trained on extensive collections of labeled data (e.g., ImageNet). However, creating such large datasets requires a considerable amount of resources, time, and effort. Such resources may not be available in many practical cases, limiting the adoption and the application of many deep learning methods. In a search for more data-efficient deep learning methods to overcome the need for large annotated datasets, there is a rising research interest in semi-supervised learning and its applications to deep neural networks to reduce the amount of labeled data required, by either developing novel methods or adopting existing semi-supervised learning frameworks for a deep learning setting. In this paper, we provide a comprehensive overview of deep semi-supervised learning, starting with an introduction to the field, followed by a summarization of the dominant semi-supervised approaches in deep learning.
Bayesian Probabilistic Numerical Integration with Tree-Based Models

Bayesian quadrature (BQ) is a method for solving numerical integration problems in a Bayesian manner, which allows user to quantify their uncertainty about the solution. The standard approach to BQ is based on Gaussian process (GP) approximation of the integrand. As a result, BQ approach is inherently limited to cases where GP approximations can be done in an efficient manner, thus often prohibiting high-dimensional or non-smooth target functions. This paper proposes to tackle this issue with a new Bayesian numerical integration algorithm based on Bayesian Additive Regression Trees (BART) priors, which we call BART-Int. BART priors are easy to tune and well-suited for discontinuous functions. We demonstrate that they also lend themselves naturally to a sequential design setting and that explicit convergence rates can be obtained in a variety of settings. The advantages and disadvantages of this new methodology are highlighted on a set of benchmark tests including the Genz functions, and on a Bayesian survey design problem.
Cloning, Expression, and Fatty Acid Regulation of the Human Δ-5 Desaturase*

Arachidonic (20:4(n-6)), eicosapentaenoic (20:5(n-3)), and docosahexaenoic (22:6(n-3)) acids are major components of brain and retina phospholipids, substrates for eicosanoid production, and regulators of nuclear transcription factors. One of the two rate-limiting steps in the production of these polyenoic fatty acids is the desaturation of 20:3(n-6) and 20:4(n-3) by Δ-5 desaturase. This report describes the cloning and expression of the human Δ-5 desaturase, and it compares the structural characteristics and nutritional regulation of the Δ-5 and Δ-6 desaturases. The open reading frame of the human Δ-5 desaturase encodes a 444-amino acid peptide which is identical in size to the Δ-6 desaturase and which shares 61% identity with the human Δ-6 desaturase. The Δ-5 desaturase contains two membrane-spanning domains, three histidine-rich regions, and a cytochrome b5 domain that all align perfectly with the same domains located in the Δ-6 desaturase. Expression of the open reading frame in Chinese hamster ovary cells instilled the ability to convert 20:3(n-6) to 20:4(n-6). Northern analysis revealed that many human tissues including skeletal muscle, lung, placenta, kidney, and pancreas expressed Δ-5 desaturase mRNA, but Δ-5 desaturase was most abundant in the liver, brain, and heart. However, in all tissues, the abundance of Δ-5 desaturase mRNA was much lower than that observed for the Δ-6 desaturase. When rats were fed a diet containing 10% safflower oil or menhaden fish oil, the level of hepatic mRNA for Δ-5 and Δ-6 desaturase was only 25% of that found in the liver of rats fed a fat-free diet or a diet containing triolein. Finally, a BLAST and Genemap search of the human genome revealed that the Δ-5 and Δ-6 desaturase genes reside in reverse orientation on chromosome 11 and that they are separated by <11,000 base pairs.
An Artificial Intelligence Solution for Electricity Procurement in Forward Markets

Retailers and major consumers of electricity generally purchase an important percentage of their estimated electricity needs years ahead in the forward market. This long-term electricity procurement task consists of determining when to buy electricity so that the resulting energy cost is minimised, and the forecast consumption is covered. In this scientific article, the focus is set on a yearly base load product from the Belgian forward market, named calendar (CAL), which is tradable up to three years ahead of the delivery period. This research paper introduces a novel algorithm providing recommendations to either buy electricity now or wait for a future opportunity based on the history of CAL prices. This algorithm relies on deep learning forecasting techniques and on an indicator quantifying the deviation from a perfectly uniform reference procurement policy. On average, the proposed approach surpasses the benchmark procurement policies considered and achieves a reduction in costs of 1.65% with respect to the perfectly uniform reference procurement policy achieving the mean electricity price. Moreover, in addition to automating the complex electricity procurement task, this algorithm demonstrates more consistent results throughout the years. Eventually, the generality of the solution presented makes it well suited for solving other commodity procurement problems.
Cross-Sensor Adversarial Domain Adaptation of Landsat-8 and Proba-V Images for Cloud Detection

The number of Earth observation satellites carrying optical sensors with similar characteristics is constantly growing. Despite their similarities and the potential synergies among them, derived satellite products are often developed for each sensor independently. Differences in retrieved radiances lead to significant drops in accuracy, which hampers knowledge and information sharing across sensors. This is particularly harmful for machine learning algorithms, since gathering new ground-truth data to train models for each sensor is costly and requires experienced manpower. In this work, we propose a domain adaptation transformation to reduce the statistical differences between images of two satellite sensors in order to boost the performance of transfer learning models. The proposed methodology is based on the cycle consistent generative adversarial domain adaptation framework that trains the transformation model in an unpaired manner. In particular, Landsat-8 and Proba-V satellites, which present different but compatible spatio-spectral characteristics, are used to illustrate the method. The obtained transformation significantly reduces differences between the image datasets while preserving the spatial and spectral information of adapted images, which is, hence, useful for any general purpose cross-sensor application. In addition, the training of the proposed adversarial domain adaptation model can be modified to improve the performance in a specific remote sensing application, such as cloud detection, by including a dedicated term in the cost function. Results show that, when the proposed transformation is applied, cloud detection models trained in Landsat-8 data increase cloud detection accuracy in Proba-V.
Checking marking reachability with the state equation in Petri net subclasses

Although decidable, the marking reachability problem for Petri nets is well-known to be intractable in general, and a non-elementary lower bound has been recently uncovered. In order to alleviate this difficulty, various structural and behavioral restrictions have been considered, allowing to relate reachability to properties that are easier to check. For a given initial marking, the set of potentially reachable markings is described by the state equation solutions and over-approximates the set of reachable markings. In this paper, we delineate several subclasses of weighted Petri nets in which the set of reachable markings equals the set of potentially reachable ones, a property we call the PR-R equality. When fulfilled, this property allows to use linear algebra to answer the reachability questions, avoiding a brute-force analysis of the state space. Notably, we provide conditions under which this equality holds in classes much more expressive than marked graphs, adding places with several ingoing and outgoing transitions, which allows to model real applications with shared buffers. To achieve it, we investigate the relationship between liveness, reversibility, boundedness and potential reachability in Petri nets. We also show that this equality does not hold in classes with close modeling capability when the conditions are relaxed.
Unified Characterization Platform for Emerging NVM Technology: Neural Network Application Benchmarking using off-the-Shelf NVM Chips

In this paper, we present a unified FPGA based electrical test-bench for characterizing different emerging NonVolatile Memory (NVM) chips. In particular, we present detailed electrical characterization and benchmarking of multiple commercially available, off-the-shelf, NVM chips viz.: MRAM, FeRAM, CBRAM, and ReRAM. We investigate important NVM parameters such as: (i) current consumption patterns, (ii) endurance, and (iii) error characterization. The proposed FPGA based testbench is then utilized for a Proof-of-Concept (PoC) Neural Network (NN) image classification application. Four emerging NVM chips are benchmarked against standard SRAM and Flash technology for the AI application as active weight memory during inference mode.
When is Particle Filtering Efficient for POMDP Sequential Planning?

Particle filtering is a popular method for inferring latent states in stochastic dynamical systems, whose theoretical properties have been well studied in machine learning and statistics communities. In sequential decision-making problems, e.g., partially observed Markov decision processes (POMDPs), oftentimes the inferred latent state is further used for planning at each step. This paper initiates a rigorous study on the efficiency of particle filtering for sequential planning, and gives the first particle complexity bounds. Though errors in past actions may affect the future, we are able to bound the number of particles needed so that the long-run reward of the policy based on particle filtering is close to that based on exact inference. In particular, we show that, in stable systems, polynomially many particles suffice. Key in our analysis is a coupling of the ideal sequence based on the exact planning and the sequence generated by approximate planning based on particle filtering. We believe this technique can be useful in other sequential decision-making problems.
Recurrent Flow Networks: A Recurrent Latent Variable Model for Spatio-Temporal Density Modelling

When modelling real-valued sequences, a typical approach in current RNN architectures is to use a Gaussian mixture model to describe the conditional output distribution. In this paper, we argue that mixture-based distributions could exhibit structural limitations when faced with highly complex data distributions such as for spatial densities. To address this issue, we introduce recurrent flow networks which combine deterministic and stochastic recurrent hidden states with conditional normalizing flows to form a probabilistic neural generative model capable of describing the kind of variability observed in highly structured spatio-temporal data. Inspired by the model's factorization, we further devise a structured variational inference network to approximate the intractable posterior distribution by exploiting a spatial representation of the data. We empirically evaluate our model against other generative models for sequential data on three real-world datasets for the task of spatio-temporal transportation demand modelling. Results show how the added flexibility allows our model to generate distributions matching potentially complex urban topologies.
A fully recurrent feature extraction for single channel speech enhancement

Convolutional neural network (CNN) modules are widely being used to build high-end speech enhancement neural models. However, the feature extraction power of vanilla CNN modules has been limited by the dimensionality constraint of the convolutional kernels integrated -- thereby has failed to adequately model the noise context information at the feature extraction stage. To this end, adding recurrency factor into the feature extracting CNN layers, we introduce a robust context-aware feature extraction strategy for single-channel speech enhancement. As being robust in capturing the local statistics of noise attributes at the extracted features, the suggested model is highly effective on differentiating speech cues, even at very noisy conditions. When evaluated against enhancement models using vanilla CNN modules, in unseen noise conditions, the suggested model with recurrency in the feature extraction layers has produced a Segmental SNR (SSNR) gain of up to 1.5 dB, while the parameters to be optimized are reduced by 25%.
Deep Drone Acrobatics

Performing acrobatic maneuvers with quadrotors is extremely challenging. Acrobatic flight requires high thrust and extreme angular accelerations that push the platform to its physical limits. Professional drone pilots often measure their level of mastery by flying such maneuvers in competitions. In this paper, we propose to learn a sensorimotor policy that enables an autonomous quadrotor to fly extreme acrobatic maneuvers with only onboard sensing and computation. We train the policy entirely in simulation by leveraging demonstrations from an optimal controller that has access to privileged information. We use appropriate abstractions of the visual input to enable transfer to a real quadrotor. We show that the resulting policy can be directly deployed in the physical world without any fine-tuning on real data. Our methodology has several favorable properties: it does not require a human expert to provide demonstrations, it cannot harm the physical system during training, and it can be used to learn maneuvers that are challenging even for the best human pilots. Our approach enables a physical quadrotor to fly maneuvers such as the Power Loop, the Barrel Roll, and the Matty Flip, during which it incurs accelerations of up to 3g.
Robustness of slow contraction to cosmic initial conditions

We present numerical relativity simulations of cosmological scenarios in which the universe is smoothed and flattened by undergoing a phase of slow contraction and test their sensitivity to a wide range of initial conditions. Our numerical scheme enables the variation of all freely specifiable physical quantities that characterize the initial spatial hypersurface, such as the initial shear and spatial curvature contributions as well as the initial field and velocity distributions of the scalar that drives the cosmological evolution. In particular, we include initial conditions that are far outside the perturbative regime of the well-known attractor scaling solution. We complement our numerical results by analytically performing a complete dynamical systems analysis and show that the two approaches yield consistent results.
Learning to Recover from Multi-Modality Errors for Non-Autoregressive Neural Machine Translation

Non-autoregressive neural machine translation (NAT) predicts the entire target sequence simultaneously and significantly accelerates inference process. However, NAT discards the dependency information in a sentence, and thus inevitably suffers from the multi-modality problem: the target tokens may be provided by different possible translations, often causing token repetitions or missing. To alleviate this problem, we propose a novel semi-autoregressive model RecoverSAT in this work, which generates a translation as a sequence of segments. The segments are generated simultaneously while each segment is predicted token-by-token. By dynamically determining segment length and deleting repetitive segments, RecoverSAT is capable of recovering from repetitive and missing token errors. Experimental results on three widely-used benchmark datasets show that our proposed model achieves more than 4 times speedup while maintaining comparable performance compared with the corresponding autoregressive model.
Eliciting Touristic Profiles: A User Study on Picture Collections

Eliciting the preferences and needs of tourists is challenging, since people often have difficulties to explicitly express them -- especially in the initial phase of travel planning. Recommender systems employed at the early stage of planning can therefore be very beneficial to the general satisfaction of a user. Previous studies have explored pictures as a tool of communication and as a way to implicitly deduce a traveller's preferences and needs. In this paper, we conduct a user study to verify previous claims and conceptual work on the feasibility of modelling travel interests from a selection of a user's pictures. We utilize fine-tuned convolutional neural networks to compute a vector representation of a picture, where each dimension corresponds to a travel behavioural pattern from the traditional Seven-Factor model. In our study, we followed strict privacy principles and did not save uploaded pictures after computing their vector representation. We aggregate the representations of the pictures of a user into a single user representation, i.e., touristic profile, using different strategies. In our user study with 81 participants, we let users adjust the predicted touristic profile and confirm the usefulness of our approach. Our results show that given a collection of pictures the touristic profile of a user can be determined.