Recent advances in deep learning architectures, driven by Artificial Intelligence (AI), have significantly enhanced time-series forecasting accuracy. However, the inherent lack of interpretability and causal reasoning in black-box models limits their adoption in high-impact applications such as traffic management and urban mobility. To address this challenge, we propose$\mathrm{C}^{2}$-STORM (Causal Counterfactual Spatio-Temporal TransfORmer Model), a novel framework that integrates deep learning to achieve both accurate and interpretable predictions. Our approach uses frontdoor-adjusted causal extraction to reduce confounding bias, grounded in structural causal principles. This is coupled with a Dynamic Causality Generator that learns context-aware Spatio-Temporal (ST) dependencies via graphstructured attention, enhancing node-specific feature prioritization. Furthermore, we develop a transformer-based counterfactual reasoning module that simulates actionable “what-if” scenarios, aligning interventions with domain-specific logic. Experiments conducted on real-world datasets (PeMSD7, LondonBike) demonstrate the effectiveness of$\mathrm{C}^{2}$-STORM, achieving state-of-the-art performance with improvements of 4.2- 6.6% in MAE/MAPE over baseline methods. Ablation studies confirm the significance of each component, while visual explanations, including causal graphs and feature importance maps, enhance the interpretability and transparency of predictions. By bridging causal inference with ST learning, C²-STORM represents a significant advancement in trustworthy forecasting for complex systems.

Nowadays, Normalized Difference Vegetation Index (NDVI) time-series has been successfully used in research regarding global environmental change. NDVI time series have proven to be a useful means of indicating drought-related vegetation conditions, due to their real-time coverage across the globe at relatively high spatial resolution. In this paper, we propose a method for detecting rare events in NDVI series. These events are particularly rare and infrequent which increases their complexity of detecting and analyzing them. The proposed method is based on the analysis of the random component by Jarque-Bera test to verify the presence of rare events and obtain their features (time and amplitude). For validation, we have used a database for regions in Northwestern of Tunisia. These data come from MODIS for a period from 18 February 2000 to 17 November 2013 at a spatial resolution of 250 m by 250m.

The recent technological progress contribute to a huge increase of 3D models available in digital forms. Numerous applications were developed to deal with this amount of information, especially for 3D shape retrieval. One of the main issues is to break the semantic gap between shapes wanted by users and shapes returned by retrieval methods. In this paper, we propose an algorithm to address this question. 3D shapes that match a semantic query are retrieved in three main steps. First, the semantic query is described by a sample of 3D shapes designed under uncertainty. Second, we learn the concept behind this sample. Then, we use a state-of-the-art retrieval method to return real-world 3D shapes that match this semantic query. We present results from an experiment. Three semantic concepts are learned and 3D shapes from SHREC’07 database that match each concept are retrieved using our algorithm. The result are good and promising.

Nowadays, the process of change detection is regarded as an outstanding way for urban planning and design. The major concern of this paper is to investigate the non-stationary character of multi-temporal time series. To overcome this problem, we propose an adaptive multiplicative decomposition of non-stationary multi-temporal satellite image, which allows to decompose the series into three components: trend, seasonal and random, to properly model the evolution of land cover. We carried several experiments to validate our approach based on Landsat images covering the region of “Tres Cantos-Madrid” in Spain. The obtained results show the effectiveness of our proposed method comparing to some conventional methods.

Subdividing surfaces into cylinders is a significant question in various applications. Even if specific approaches have been described in several domains, most of the time topological properties are not explicitly handled, and the segmentation remains mainly driven by geometry. We present here an original approach to describe the topological and combinatorial nature of a tiling with cylinders. We first introduce m-cellular complexes, a framework allowing the flexible description of cuttings and tilings. Then we describe n-loops, an extension of the loops for producing tilings with cylinders. Computational issues of n-loops are then addressed, using both topological and geometrical properties of the surface. Finally, we propose two applications, first tiling a surface with large quadrangles patches, and then segmenting surfaces with possible protrusions.

Accurate means of quantifying the respective contributions of different structures to the solar irradiance is now a key issue in Solar Physics, with implications to Sun-Earth relationships and space weather study. In this paper, we propose a three-step fusion scheme, that allows to aggregate (17.1 nm, 19.5 nm) data stemming from the solar EIT instrument onboard the SoHO mission, and that is flexible enough to allow the integration of other type of information. The method is based on both a spatially constrained possibilistic clustering algorithm and a context dependent fusion operator. It aggregates the complementary and redundant information coming from the input sources. The results obtained on a 9-year dataset are consistent with those found in the solar physics literature. Unlike previous algorithms used in solar physics, our method has the ability to add further heterogeneous sources and sensors (e.g. human knowledge, images in other bandpasses, ratio of images) to the process, in order to postpone the decision step (here the segmentation of structures of interest) until sufficient information is available.

Electric chronic stimulation of the human motor cortex (ECSM) has been reported to alleviate chronic severe pain. However the mechanism of action of ECSM is still hypothetical. This is due mainly to the poor knowledge of 1) the electric diffusion through the multiple structures beneath the epidural contacts (i.e. dura matter, cerebrospinal fluid space, arachnoid membrane, grey and white matter layers, pie mere and vascular tree), 2) the absence of consensus concerning the stimulation parameters (mono versus bipolar stimulation, cathodic or anodic current) and 3) the detailed cortical topography of the contacts. In this study we focused on the precise identification of the cortical areas covered by the electric contacts in a series of twelve patients operated on for ECSM. We propose a new automatic tool for topographic analysis able to compute 2D maps from the 3D anatomic MRI with bijective transformation (point- to-point correspondance). Anatomical regions of interest (AROIs) were visually identified, manually outlined and extracted (Iplan, BrainLab, Germany) for further analysis: 1) for the anatomic structures, on pre operative Tl-weigthed magnetic resonance imaging (MRI), the frontal (superior or Fl, intermediate or F2 and inferior or F3), the pre central and the post central gyrus; 2) for the electrode contacts (Resume, Medtronic, USA), on post operative computerized tomography (CT). After getting white and gray matter membership maps by automatic segmentation, we produced a cortical mask to build a triangular mesh. We defined a homeomorphism between the 3D mesh and a subset of R2 and could apply in consequence the circle packing algorithm. We built depth maps (distance to the skull), distance- to-contact maps (distance to a given electrode contact) and anatomic structure maps. Results showed that it was easier to accurately define the location of the contact projection on the cortex allowing physicians to correlate the benefit with the topography. In particular, because of the unfolding, it was easier to integrate the cytoarchitectonics (i.e. the manually identified AROIs) knowledge in the analysis. Beyond the better understanding of ECSM and indirectly of the pathophysiologic process of chronic pain, this new tool might be used in the future for image guided electrode positioning.

MOTIVATION: Microbial diversity is still largely unknown in most environments, such as soils. In order to get access to this microbial 'black-box', the development of powerful tools such as microarrays are necessary. However, the reliability of this approach relies on probe efficiency, in particular sensitivity, specificity and explorative power, in order to obtain an image of the microbial communities that is close to reality. RESULTS: We propose a new probe design algorithm that is able to select microarray probes targeting SSU rRNA at any phylogenetic level. This original approach, implemented in a program called 'PhylArray', designs a combination of degenerate and non-degenerate probes for each target taxon. Comparative experimental evaluations indicate that probes designed with PhylArray yield a higher sensitivity and specificity than those designed by conventional approaches. Applying the combined PhyArray/GoArrays strategy helps to optimize the hybridization performance of short probes. Finally, hybridizations with environmental targets have shown that the use of the PhylArray strategy can draw attention to even previously unknown bacteria.

This paper reports a new automated method for the segmentation of internal cerebral structures using an information fusion technique. The information is provided both by images and expert knowledge, and consists in morphological, topological, and tissue constitution data. All this ambiguous, complementary and redundant information is managed using a three-step fusion scheme based on fuzzy logic. The information is first modeled into a common theoretical frame managing its imprecision and incertitude. The models are then fused and a decision is taken in order to reduce the imprecision and to increase the certainty in the location of the structures. The whole process is illustrated on the segmentation of thalamus, putamen, and head of the caudate nucleus from expert knowledge and magnetic resonance images, in a protocol involving 14 healthy volunteers. The quantitative validation is achieved by comparing computed, manually segmented structures and published data by means of indexes assessing the accuracy of volume estimation and spatial location. Results suggest a consistent volume estimation with respect to the expert quantification and published data, and a high spatial similarity of the segmented and computed structures. This method is generic and applicable to any structure that can be defined by expert knowledge and morphological images.