Black-Box Anomaly Attribution

When the prediction of a black-box machine learning model deviates from the true observation, what can be said about the reason behind that deviation? This is a fundamental and ubiquitous question that the end user in a business or industrial AI application often asks. The deviation may be due to a sub-optimal black-box model, or it may be simply because the sample in question is an outlier. In either case, one would ideally wish to obtain some form of attribution score -- a value indicative of the extent to which an input variable is responsible for the anomaly. In the present paper we address this task of ``anomaly attribution,'' particularly in the setting in which the model is black-box and the training data are not available. Specifically, we propose a novel likelihood-based attribution framework we call the ``likelihood compensation (LC),'' in which the responsibility score is equated with the correction on each input variable needed to attain the highest possible likelihood. We begin by showing formally why mainstream model-agnostic explanation methods, such as the local linear surrogate modeling and Shapley values, are not designed to explain anomalies. In particular, we show that they are ``deviation-agnostic,'' namely, that their explanations are blind to the fact that there is a deviation in the model prediction for the sample of interest. We do this by positioning these existing methods under the unified umbrella of a function family we call the ``integrated gradient family.'' We validate the effectiveness of the proposed LC approach using publicly available data sets. We also conduct a case study with a real-world building energy prediction task and confirm its usefulness in practice based on expert feedback

Thierry Lefebvre et Sébastien Poulain (dirs), Les radios locales : histoires, territoires et réseaux

Après Les radios libres, 30 ans de FM. La parole libérée ? (2016), Thierry Lefebvre, spécialiste reconnu des radios libres en France, et Sébastien Poulain, cofondateur de la revue RadioMorphoses et administrateur du Groupe de recherches et d’études sur la radio (Grer) interrogent l’histoire des radios locales, leurs acteurs et leurs modes de fonctionnement en France et dans d’autres territoires francophones, notamment en Belgique et en République démocratique du Congo (RDC). L’étude couvre l..

Integrated Water Resources Vulnerability Assessment: A Multidimensional Approach and Geographic Information System Based in Fès, Meknès, and Ifrane Perimeters, Morocco

Water resources are disproportionately distributed, and more and more problems related to this precious resource are being reported around the world due to anthropogenic pressures and global environmental changes. This paper focuses on assessing the vulnerability of water resources in an integrated way, by taking into account hydrological, environmental, socio-economic and pollution factors, in order to delineate sensitive areas of water resources under a geographic information system. The framework for assessing the water resources vulnerability in the Fès, Meknès, and Ifrane perimeters was based on a participatory approach through a survey. The data collected on the identified factors are then processed under ArcGIS tool to aggregate the normalized value into a water resources vulnerability index. The result shows that the degree of vulnerability of water resources in most of the study area is considered to be at the "threshold" to "non-vulnerable". However, three (3) main areas were considered to be "moderately vulnerable" to "highly vulnerable" precisely in the South of the city of Meknes (Zone 1), from the West of the city of Fès (Zone 2), and finally the Dayet Ifrah area (Zone 3). The sensitivity analysis showed that five factors have more impact on the overall water resources vulnerability map: topography, poverty, water withdrawal, population density, and access to drinking water. The result of this study could help integrated water resources management planners take action to improve the overall water quantity and quality in the area, and it can be extended to a larger scale like regional, national or cross-country

Assessment of Surface Water Quality using Indices and Geographic Information System in the Sebou River Basin, Morocco

The aim of this study is to access the quality of monitored rivers and to map the polluted river sections in the Sebou basin using Geographic Information System (GIS). The potential causes of water quality variation will also be added for suitable measures to be taken. A Water Quality Index (WQI) which developed in Morocco was applied to 17 river water quality monitoring stations with data on 6 parameters (Dissolved oxygen (DO), ammonium ion (NH4 + ), 5-day biochemical oxygen demand (BOD5), chemical oxygen demand (COD), fecal coliforms (FC) and total phosphorus (TP)) collected twice during the wet and dry season over 1990-2017 period. The result shows that river water quality is classified as bad, very bad and medium at 59% of the monitoring stations, while 41% are considered as good to excellent. Interpolation of mean values of overall WQI of the 17 river water quality monitoring stations, revealed evidence of quality degradation along several kilometers of most river sections in the Sebou basin. The correlation matrix between the sub-indices of water quality parameters and the overall WQI showed high positive correlation coefficients and highlights the contribution to water quality degradation as follows: TP ( = +0.96 ) ≥ NH4 + ( = +0.96 ) > BOD5 ( = +0.94) > COD ( = +0.86) > FC ( = +0.83) > DO ( = +0.79). The sections of Fès, Innaounene Rivers, and an extended stretch of Tizguit River must no longer be used for irrigation. River water quality is overall of better quality in the wet season compared to the dry season. Simple linear regressions between the seasonal water quality variation and the overall WQI showed higher coefficients of determination R 2 (0.67 and 0.60) between dry season WQI and the overall WQI and between wet season WQI and the overall WQI respectively. It is clear that discharges of industrial and domestic wastewater during the dry season and agricultural activities are most likely to be the causes of the degradation of river water quality

L’argumentation radiophonique au service de la promotion des femmes au Niger

Cet article propose d’analyser l’argumentation d’une militante associative diffusée sur les antennes de la radio publique du Niger (Voix du Sahel), dans une émission qui invite généralement des femmes : « Femme et développement ». Il a vocation à faire comprendre le fonctionnement discursif d’un dispositif radiophonique qui vise à faire la promotion sociale des femmes. En tenant compte de la situation sociopolitique et économique des femmes ainsi que de l’orientation idéologique de la station, l’article s’attache aux stratégies d’argumentation radiophonique mises au service d’un discours féministe et entend contribuer à la recherche scientifique actuelle sur l’analyse du discours médiatique en contexte africain.This article proposes to analyze the argumentation of an associative activist broadcast on the public radio of Niger (Voix du Sahel), in a program which generally invites women: « Woman and development ». It aims at showcasing the discursive functioning of a radio device that aims at the social recognition of women. Taking into account the social, political and economic situation of women in Niger as well as the ideological orientation of the station, the article focuses on the strategies of radio argumentation in the promotion of a feminist discourse and intends to contribute to the current scientific research on the analysis of media discourse in the African context

Targeted Advertising on Social Networks Using Online Variational Tensor Regression

This paper is concerned with online targeted advertising on social networks. The main technical task we address is to estimate the activation probability for user pairs, which quantifies the influence one user may have on another towards purchasing decisions. This is a challenging task because one marketing episode typically involves a multitude of marketing campaigns/strategies of different products for highly diverse customers. In this paper, we propose what we believe is the first tensor-based contextual bandit framework for online targeted advertising. The proposed framework is designed to accommodate any number of feature vectors in the form of multi-mode tensor, thereby enabling to capture the heterogeneity that may exist over user preferences, products, and campaign strategies in a unified manner. To handle inter-dependency of tensor modes, we introduce an online variational algorithm with a mean-field approximation. We empirically confirm that the proposed TensorUCB algorithm achieves a significant improvement in influence maximization tasks over the benchmarks, which is attributable to its capability of capturing the user-product heterogeneity.Comment: 18 pages, 7 figure

Origin of Charge Separation at Organic Photovoltaic Heterojunctions: A Mesoscale Quantum Mechanical View

The high efficiency of charge generation within organic photovoltaic blends apparently contrasts with the strong “classical” attraction between newly formed electron–hole pairs. Several factors have been identified as possible facilitators of charge dissociation, such as quantum mechanical coherence and delocalization, structural and energetic disorder, built-in electric fields, and nanoscale intermixing of the donor and acceptor components of the blends. Our mesoscale quantum-chemical model allows an unbiased assessment of their relative importance, through excited-state calculations on systems containing thousands of donor and acceptor sites. The results on several model heterojunctions confirm that the classical model severely overestimates the binding energy of the electron–hole pairs, produced by vertical excitation from the electronic ground state. Using physically sensible parameters for the individual materials, we find that the quantum mechanical energy difference between the lowest interfacial charge transfer states and the fully separated electron and hole is of the order of the thermal energy

Anomaly Attribution with Likelihood Compensation

This paper addresses the task of explaining anomalous predictions of a black-box regression model. When using a black-box model, such as one to predict building energy consumption from many sensor measurements, we often have a situation where some observed samples may significantly deviate from their prediction. It may be due to a sub-optimal black-box model, or simply because those samples are outliers. In either case, one would ideally want to compute a ``responsibility score'' indicative of the extent to which an input variable is responsible for the anomalous output. In this work, we formalize this task as a statistical inverse problem: Given model deviation from the expected value, infer the responsibility score of each of the input variables. We propose a new method called likelihood compensation (LC), which is founded on the likelihood principle and computes a correction to each input variable. To the best of our knowledge, this is the first principled framework that computes a responsibility score for real valued anomalous model deviations. We apply our approach to a real-world building energy prediction task and confirm its utility based on expert feedback.Comment: 8 pages, 7 figure

Momentum-resolved charge excitations in high-Tc cuprates studied by resonant inelastic x-ray scattering

We report a Cu K-edge resonant inelastic x-ray scattering (RIXS) study of high-Tc cuprates. Momentum-resolved charge excitations in the CuO2 plane are examined from parent Mott insulators to carrier-doped superconductors. The Mott gap excitation in undoped insulators is found to commonly show a larger dispersion along the [pi,pi] direction than the [pi,0] direction. On the other hand, the resonance condition displays material dependence. Upon hole doping, the dispersion of the Mott gap excitation becomes weaker and an intraband excitation appears as a continuum intensity below the gap at the same time. In the case of electron doping, the Mott gap excitation is prominent at the zone center and a dispersive intraband excitation is observed at finite momentum transfer

Diagnostic Spatio-temporal Transformer with Faithful Encoding

This paper addresses the task of anomaly diagnosis when the underlying data generation process has a complex spatio-temporal (ST) dependency. The key technical challenge is to extract actionable insights from the dependency tensor characterizing high-order interactions among temporal and spatial indices. We formalize the problem as supervised dependency discovery, where the ST dependency is learned as a side product of multivariate time-series classification. We show that temporal positional encoding used in existing ST transformer works has a serious limitation in capturing higher frequencies (short time scales). We propose a new positional encoding with a theoretical guarantee, based on discrete Fourier transform. We also propose a new ST dependency discovery framework, which can provide readily consumable diagnostic information in both spatial and temporal directions. Finally, we demonstrate the utility of the proposed model, DFStrans (Diagnostic Fourier-based Spatio-temporal Transformer), in a real industrial application of building elevator control