Miracle-dz/UniFAR-FTUs huggingface dataset:
Energy Demand Forecasting Using Deep Learning: Applications for the French Grid

This paper investigates the use of deep learning techniques in order to perform energy demand forecasting. To this end, the authors propose a mixed architecture consisting of a convolutional neural network (CNN) coupled with an artificial neural network (ANN), with the main objective of taking advantage of the virtues of both structures: the regression capabilities of the artificial neural network and the feature extraction capacities of the convolutional neural network. The proposed structure was trained and then used in a real setting to provide a French energy demand forecast using Action de Recherche Petite Echelle Grande Echelle (ARPEGE) forecasting weather data. The results show that this approach outperforms the reference Réseau de Transport d’Electricité (RTE, French transmission system operator) subscription-based service. Additionally, the proposed solution obtains the highest performance score when compared with other alternatives, including Autoregressive Integrated Moving Average (ARIMA) and traditional ANN models. This opens up the possibility of achieving high-accuracy forecasting using widely accessible deep learning techniques through open-source machine learning platforms.
Multi-Step Adversarial Perturbations on Recommender Systems Embeddings

Recommender systems (RSs) have attained exceptional performance in learning users' preferences and helping them in finding the most suitable products. Recent advances in adversarial machine learning (AML) in the computer vision domain have raised interests in the security of state-of-the-art model-based recommenders. Recently, worrying deterioration of recommendation accuracy has been acknowledged on several state-of-the-art model-based recommenders (e.g., BPR-MF) when machine-learned adversarial perturbations contaminate model parameters. However, while the single-step fast gradient sign method (FGSM) is the most explored perturbation strategy, multi-step (iterative) perturbation strategies, that demonstrated higher efficacy in the computer vision domain, have been highly under-researched in recommendation tasks. In this work, inspired by the basic iterative method (BIM) and the projected gradient descent (PGD) strategies proposed in the CV domain, we adapt the multi-step strategies for the item recommendation task to study the possible weaknesses of embedding-based recommender models under minimal adversarial perturbations. Letting the magnitude of the perturbation be fixed, we illustrate the highest efficacy of the multi-step perturbation compared to the single-step one with extensive empirical evaluation on two widely adopted recommender datasets. Furthermore, we study the impact of structural dataset characteristics, i.e., sparsity, density, and size, on the performance degradation issued by presented perturbations to support RS designer in interpreting recommendation performance variation due to minimal variations of model parameters. Our implementation and datasets are available at https://anonymous.4open. science/r/9f27f909-93d5-4016-b01c-8976b8c14bc5/.
The Naming Game in Social Networks : Community Formation and Consensus Engineering

We study the dynamics of the Naming Game [Baronchelli et al., (2006) J. Stat. Mech. : Theory Exp. P06014] in empirical social networks. This stylized agent-based model captures essential features of agreement dynamics in a network of autonomous agents, corresponding to the development of shared classification schemes in a network of artificial agents or opinion spreading and social dynamics in social networks. Our study focuses on the impact that communities in the underlying social graphs have on the outcome of the agreement process. We find that networks with strong community structure hinder the system from reaching global agreement; the evolution of the Naming Game in these networks maintains clusters of coexisting opinions indefinitely. Further, we investigate agent-based network strategies to facilitate convergence to global consensus.
Real-Time Stochastic Optimal Control for Multi-Agent Quadrotor Systems

This paper presents a novel method for controlling teams of unmanned aerial vehicles using Stochastic Optimal Control (SOC) theory. The approach consists of a centralized high-level planner that computes optimal state trajectories as velocity sequences, and a platform-specific low-level controller which ensures that these velocity sequences are met. The planning task is expressed as a centralized path-integral control problem, for which optimal control computation corresponds to a probabilistic inference problem that can be solved by efficient sampling methods. Through simulation we show that our SOC approach (a) has significant benefits compared to deterministic control and other SOC methods in multimodal problems with noise-dependent optimal solutions, (b) is capable of controlling a large number of platforms in real-time, and (c) yields collective emergent behaviour in the form of flight formations. Finally, we show that our approach works for real platforms, by controlling a team of three quadrotors in outdoor conditions.
MULTILEVEL PRECONDITIONERS FOR DISCONTINUOUS GALERKIN APPROXIMATIONS OF ELLIPTIC PROBLEMS WITH JUMP COEFFICIENTS By

In this article we develop and analyze two-level and multi-level methods for the family of Interior Penalty (IP) discontinuous Galerkin (DG) discretizations of second order elliptic problems with rough coefficients (exhibiting large jumps across interfaces in the domain). These methods are based on a decomposition of the DG finite element space that inherently hinges on the diffusion coefficient of the problem. Our analysis of the proposed preconditioners is presented for both symmetric and non-symmetric IP schemes, and we establish both robustness with respect to the jump in the coefficient and near-optimality with respect to the mesh size. Following the analysis, we present a sequence of detailed numerical results which verify the theory and illustrate the performance of the methods.
In Proceedings of NIPS ’ 01 Efficiency versus Convergence of Boolean Kernels for On-Line Learning Algorithms

We study online learning in Boolean domains using kernels wh ich capture feature expansions equivalent to using conjunctions o ver basic features. We demonstrate a tradeoff between the computational efficiency with which these kernels can be computed and the generalizat ion bility of the resulting classifier. We first describe several ker nel functions which capture either limited forms of conjunctions or all co njunctions. We show that these kernels can be used to efficiently run the Pe rceptron algorithm over an exponential number of conjunctions; however we also prove that using such kernels the Perceptron algorithm can make an exponential number of mistakes even when learning simple functions. We also consider an analogous use of kernel functions o run the multiplicative-update Winnow algorithm over an expanded f ature space of exponentially many conjunctions. While known upper boun ds imply that Winnow can learn DNF formulae with a polynomial mistake bound in this setting, we prove that it is computationally hard to s imulate Winnow’s behavior for learning DNF over such a feature set, and t hus that such kernel functions for Winnow are not efficiently computa ble.
RESEARCH OUTPUTS / RÉSULTATS DE RECHERCHE

We propose an opinion dynamics model that combines processes of vanity and opinion propagation. The interactions take place between randomly chosen pairs. During an interaction, the agents propagate their opinions about themselves and about other people they know. Moreover, each individual is subject to vanity: if her interlocutor seems to value her highly, then she increases her opinion about this interlocutor. On the contrary she tends to decrease her opinion about those who seem to undervalue her. The combination of these dynamics with the hypothesis that the opinion propagation is more eﬃcient when coming from highly valued individuals, leads to diﬀerent patterns when varying the parameters. For instance, for some parameters the positive opinion links between individuals generate a small world network. In one of the patterns, absolute dominance of one agent alternates with a state of generalised distrust, where all agents have a very low opinion of all the others (including themselves). We provide some explanations of the mechanisms behind these emergent behaviors and ﬁnally propose a discussion about their interest.
On the Growth Rate of the Weight Distribution of Irregular Doubly Generalized LDPC Codes

In this paper, the asymptotic growth rate of the weight distribution of irregular doubly generalized LDPC (D-GLDPC) codes is derived. The analysis yields a compact expression which accurately approximates the growth rate function for the case of small linear-weight codewords. This paper generalizes existing results for LDPC and generalized LDPC (GLDPC) codes. Ensembles with smallest check or variable node minimum distance greater than 2 are shown to have good growth-rate behavior, while for other ensembles a fundamental parameter is identified which discriminates between an asymptotically small and an asymptotically large expected number of small linear-weight codewords. Also, in the latter case it is shown that the growth rate depends only on the check and variable nodes with minimum distance 2. An important connection between this new result and the stability condition of D-GLDPC codes over the BEC is highlighted. Such a connection, previously observed for LDPC and GLDPC codes, is now extended to the case of D-GLDPC codes. Finally, it is shown that the analysis may be extended to include the growth rate of the stopping set size distribution of irregular D-GLDPC codes.
Seasonal variations in the activity budgets of the white-headed langur

We collected behavioral data from a group of white-headed langurs ( Trachypitheucs leucocephalus) between August 2007 and July 2008 at Fusui Rare Animal Reserve,Guangxi Province,China using the instantaneous scan sampling method. Our goal is to investigate the influences of temporal and spatial distribution of temperature and food resources on diurnal activity budgets of white-headed langurs. Our results indicated that white-headed langurs showed morning and afternoon feeding peaks,with a midday resting peak in accordance of changes of diurnal ambient temperature. There were marked seasonal changes in activity cycles: more time was spent resting in the midday resting peak in the rainy season than in the dry season. On the contrary,there was a minor midday feeding peak in the dry season. Langurs spent ca. 46. 4% of their daytime resting,29. 0% moving,20. 3% feeding,4. 3% for other behaviors. Their activity budgets varied seasonally: the langurs spent more time in moving + feeding and less time on resting in the dry season than in the rainy season. There were significant correlations between the monthly percentage of time spent on various main activities and the monthly proportion of young leaves and mature leaves in the diet.
WAGNER with millisecond to microsecond motions extended surface patch with multiple conformations fluctuating The counterreceptor binding site of human CD 2 exhibits an data

We have used I5N NMR relaxation experiments to probe, for the glycosylated human CD2 adhesion domain, the overall molecular motion, as well as very fast nanosecond-picosecond (ns-ps) and slow millisecond-microsecond (ms-ps) internal motions. Using a novel analysis method that considers all residues, we obtained a correlation time for the overall motion of 9.5 + 0.3 ns. Surprisingly, we found a large contiguous patch of residues in the counterreceptor (CD58) binding site of human CD2 exhibiting slow conformational exchange motions (ms-ps). On the other hand, almost none of the residues of the CD58 binding site display fast (ns-ps) internal motions of amplitudes larger than what is seen for well-ordered regions of the structure. Residues close to the N-glycosylation site, and the first N-acetylglucosamine of the high mannose glycan are as rigid as the protein core. Residues conserved in the immunoglobulin superfamily V-set domain are generally very rigid.
rideApp RideSharing Application smsApp SMS Application mapApp Map Application SearchActivity MsgActivity action : VIEW dataScheme : geo action

We address the problem of discovering communication links between applications in the popular Android mobile operating system, an important problem for security and privacy in Android. Any scalable static analysis in this complex setting is bound to produce an excessive amount of false-positives, rendering it impractical. To improve precision, we propose to augment static analysis with a trained neural-network model that estimates the probability that a communication link truly exists. We describe a neural-network architecture that encodes abstractions of communicating objects in two applications and estimates the probability with which a link indeed exists. At the heart of our architecture are type-directed encoders (tde), a general framework for elegantly constructing encoders of a compound data type by recursively composing encoders for its constituent types. We evaluate our approach on a large corpus of Android applications, and demonstrate that it achieves very high accuracy. Further, we conduct thorough interpretability studies to understand the internals of the learned neural networks.
Sistema de Apoyo a la Visión mediante una Retina Artificial Neuromórfica a través de Realidad Aumentada TIC2003-09557

The construction of a system for aiding some kind of blindness is proposed. It consists in a bioinspired retina model into a reconfigurable architecture which is connected to an augmented reality device based on see-through technology. This device will permit the integration of real world images to the ones obtained by the neuromorphic retina. This system will provide a local gain control, in addition of a bioinspired preprocessing, FPGAs plasticity, and the miniaturization of the components, including low power devices. These characteristics are highly desirable in some low vision supporting systems.
Proportional switching in FIFO networks

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Influence of cognitive, geographical, and collaborative proximity on knowledge production of Canadian nanotechnology

We address the question of whether or not geographical, cognitive, and collaborative proximity have an impact on citation probability in the scientific writings of Canadian nanotechnology. Even though a number of studies in the proximity literature deal with the effects of spatial distance, scientific specialization, and social network structure, to our knowledge no one has combined all three to explore the production of academic information. We generate a feature framework based on measurements from these factors, relying on statistical and classification approaches to assess their influence on effective citations. Specifically, by means of applying binary regression models along with tree-based machine learning algorithms, we found statistical significance proving that these features have both a verifiable impact and predictive potential. Importantly, our work is the first one that we have seen combining these techniques to infer the establishment of positive citation links. Moreover, we employed inductive network analysis comprehensively to examine the co-authorship links between authors publishing in nanoscience, considering additional network metrics to the ones usually adopted in the literature. Our findings reveal that cognitive proximity, closely followed by the collaborative aspect, are the most important elements inducing Canadian scholars to cite, with geography sometimes acting as their base. Our results enable us to reach better understanding related to the citation behavior of the nanoresearch community in Canada, making our work a valuable contribution to scholarly literature, also giving us ground to make policy recommendations.
Non-Uniform Stochastic Average Gradient Method for Training Conditional Random Fields

This paper explores using a stochastic average gradient (SAG) algorithm for training conditional random fields (CRFs). The SAG algorithm is the first general stochastic gradient algorithm to have a linear convergence rate. However, despite its success on simple classification problems, when applied to CRFs the algorithm requires too much memory because it requires storing a previous gradient with respect to every training example. In this work we show that SAG algorithms can be tractably applied to large-scale CRFs by tracking the marginals over vertices and edges in the graphical model. We also incorporate a simple non-uniform adaptive sampling scheme that learns how often we should sample each training point. Our experimental results reveal that this method significantly outperforms existing methods. 1 Conditional Random Fields Conditional random fields (CRFs) [9] are a ubiquitous tool in natural language processing. They are used for part-of-speech tagging [12], semantic role labeling [1], topic modeling [27], information extraction [16], shallow parsing [19], named-entity recognition [18], and a variety of applications in other fields such as computer vision [15]. Despite the widespread use of CRFs and major advances in fitting them, the time needed for numerical optimization in CRF models remains a bottleneck in many application. Lafferty et al. [9] proposed training CRFs with an iterative scaling algorithm (2), but this proved to be inferior to generic optimization strategies like the limited-memory quasi-Newton algorithm L-BFGS [24, 19]. The bottleneck in quasi-Newton methods is that we must do inference on all training examples on every iteration. Due to the high cost of inference on even a single training example example, it is now common to train CRFs using stochastic gradient methods, which work online and only process one example at a time [23, 5]. However, stochastic gradient methods have sub-linear convergence rates. Collins et al. [2] proposed an online exponentiated gradient algorithm with a linear convergence rate in the dual problem, but the practical performance of this method degrades without strong regularization, see [2, Figures 5-6 and Table 3] and [8, Figure 1]. Hybrid stochastic/quasi-Newton methods have also been explored [6], but these methods eventually process every example on every iteration. In this work, we consider training CRFs with the stochastic average gradient (SAG) algorithm of Le Roux et al. [11], a general stochastic gradient method that achieves a linear convergence rate for finite training sets and has shown impressive empirical performance for binary classification. Beyond the faster convergence rate, the SAG method also has effective heuristics for issues that have traditionally frustrated users of stochastic gradient methods: setting the step-size and deciding when to stop. Our results indicate that the SAG algorithm with a simple adaptive step-size and non-uniform sampling strategy can outperform previous training methods by an order of magnitude.
Shark : SQL and Rich Analytics at Scale

Shark is a new data analysis system that marries query processing with complex analytics on large clusters. It leverages a novel distributed memory abstraction to provide a unified engine that can run SQL queries and sophisticated analytics functions (e.g., iterative machine learning) at scale, and efficiently recovers from failures mid-query. This allows Shark to run SQL queries up to 100× faster than Apache Hive, and machine learning programs more than 100× faster than Hadoop. Unlike previous systems, Shark shows that it is possible to achieve these speedups while retaining a MapReducelike execution engine, and the fine-grained fault tolerance properties that such engine provides. It extends such an engine in several ways, including column-oriented in-memory storage and dynamic mid-query replanning, to effectively execute SQL. The result is a system that matches the speedups reported for MPP analytic databases over MapReduce, while offering fault tolerance properties and complex analytics capabilities that they lack.
High Breakdown Point Robust Regression with Censored Data High Breakdown Point Robust Regression with Censored Data

In this paper we propose a class of high breakdown point estimators for the linear regression model when the response variable contains censored observations. These estimators are robust against high-leverage outliers and they generalize the LMS, S, MM and τ-estimators for linear regression. An important contribution of this paper is that we can define consistent estimators using a bounded loss function (or equivalently, a re-descending score function). Since the calculation of these estimators can be computationally costly we propose an efficient algorithm to compute them. We illustrate their use on an example and present simulation studies that show that these estimators also have good finite sample properties.
The cores of random hypergraphs with a given degree sequence

We study random r‐uniform n vertex hypergraphs with fixed degree sequence d = (d1…,dn), maximum degree Δ = o(n1/24) and total degree θn, where θ is bounded. We give the size, number of edges and degree sequence of the κ ≥ 2) up to a whp error of O(n2/3 Δ4/3 log n). In the case of graphs (r = 2) we give further structural details such as the number of tree components and, for the case of smooth degree sequences, the size of the mantle. We give various examples, such as the cores of r‐uniform hypergraphs with a near Poisson degree sequence, and an improved upper bound for the first linear dependence among the columns in the independent column model of random Boolean matrices. © 2004 Wiley Periodicals, Inc. Random Struct. Alg. , 25, 2004
Effective Search of Logical Forms for Weakly Supervised Knowledge-Based Question Answering

Many algorithms for Knowledge-Based Question Answering (KBQA) depend on semantic parsing, which translates a question to its logical form. When only weak supervision is provided, it is usually necessary to search valid logical forms for model training. However, a complex question typically involves a huge search space, which creates two main problems: 1) the solutions limited by computation time and memory usually reduce the success rate of the search, and 2) spurious logical forms in the search results degrade the quality of training data. These two problems lead to a poorly-trained semantic parsing model. In this work, we propose an effective search method for weakly supervised KBQA based on operator prediction for questions. With search space constrained by predicted operators, sufficient search paths can be explored, more valid logical forms can be derived, and operators possibly causing spurious logical forms can be avoided. As a result, a larger proportion of questions in a weakly supervised training set are equipped with logical forms, and fewer spurious logical forms are generated. Such high-quality training data directly contributes to a better semantic parsing model. Experimental results on one of the largest KBQA datasets (i.e., CSQA) verify the effectiveness of our approach and deliver a new state-of-the-art performance.