FairBranch: Fairness Conflict Correction on Task-group Branches for Fair Multi-Task Learning

The generalization capacity of Multi-Task Learning (MTL) becomes limited when unrelated tasks negatively impact each other by updating shared parameters with conflicting gradients, resulting in negative transfer and a reduction in MTL accuracy compared to single-task learning (STL). Recently, there has been an increasing focus on the fairness of MTL models, necessitating the optimization of both accuracy and fairness for individual tasks. Similarly to how negative transfer affects accuracy, task-specific fairness considerations can adversely influence the fairness of other tasks when there is a conflict of fairness loss gradients among jointly learned tasks, termed bias transfer. To address both negative and bias transfer in MTL, we introduce a novel method called FairBranch. FairBranch branches the MTL model by assessing the similarity of learned parameters, grouping related tasks to mitigate negative transfer. Additionally, it incorporates fairness loss gradient conflict correction between adjoining task-group branches to address bias transfer within these task groups. Our experiments in tabular and visual MTL problems demonstrate that FairBranch surpasses state-of-the-art MTL methods in terms of both fairness and accuracy

Evaluation of Granger causality measures for constructing networks from multivariate time series

Granger causality and variants of this concept allow the study of complex dynamical systems as networks constructed from multivariate time series. In this work, a large number of Granger causality measures used to form causality networks from multivariate time series are assessed. These measures are in the time domain, such as model-based and information measures, the frequency domain and the phase domain. The study aims also to compare bivariate and multivariate measures, linear and nonlinear measures, as well as the use of dimension reduction in linear model-based measures and information measures. The latter is particular relevant in the study of high-dimensional time series. For the performance of the multivariate causality measures, low and high dimensional coupled dynamical systems are considered in discrete and continuous time, as well as deterministic and stochastic. The measures are evaluated and ranked according to their ability to provide causality networks that match the original coupling structure. The simulation study concludes that the Granger causality measures using dimension reduction are superior and should be preferred particularly in studies involving many observed variables, such as multi-channel electroencephalograms and financial markets.Comment: 24 pages, 5 figures, to be published in Entrop

VERITE: A Robust Benchmark for Multimodal Misinformation Detection Accounting for Unimodal Bias

Multimedia content has become ubiquitous on social media platforms, leading to the rise of multimodal misinformation (MM) and the urgent need for effective strategies to detect and prevent its spread. In recent years, the challenge of multimodal misinformation detection (MMD) has garnered significant attention by researchers and has mainly involved the creation of annotated, weakly annotated, or synthetically generated training datasets, along with the development of various deep learning MMD models. However, the problem of unimodal bias in MMD benchmarks -- where biased or unimodal methods outperform their multimodal counterparts on an inherently multimodal task -- has been overlooked. In this study, we systematically investigate and identify the presence of unimodal bias in widely-used MMD benchmarks (VMU-Twitter, COSMOS), raising concerns about their suitability for reliable evaluation. To address this issue, we introduce the "VERification of Image-TExtpairs" (VERITE) benchmark for MMD which incorporates real-world data, excludes "asymmetric multimodal misinformation" and utilizes "modality balancing". We conduct an extensive comparative study with a Transformer-based architecture that shows the ability of VERITE to effectively address unimodal bias, rendering it a robust evaluation framework for MMD. Furthermore, we introduce a new method -- termed Crossmodal HArd Synthetic MisAlignment (CHASMA) -- for generating realistic synthetic training data that preserve crossmodal relations between legitimate images and false human-written captions. By leveraging CHASMA in the training process, we observe consistent and notable improvements in predictive performance on VERITE; with a 9.2% increase in accuracy. We release our code at: https://github.com/stevejpapad/image-text-verificatio

InDistill: Information flow-preserving knowledge distillation for model compression

In this paper we introduce InDistill, a model compression approach that combines knowledge distillation and channel pruning in a unified framework for the transfer of the critical information flow paths from a heavyweight teacher to a lightweight student. Such information is typically collapsed in previous methods due to an encoding stage prior to distillation. By contrast, InDistill leverages a pruning operation applied to the teacher's intermediate layers reducing their width to the corresponding student layers' width. In that way, we force architectural alignment enabling the intermediate layers to be directly distilled without the need of an encoding stage. Additionally, a curriculum learning-based training scheme is adopted considering the distillation difficulty of each layer and the critical learning periods in which the information flow paths are created. The proposed method surpasses state-of-the-art performance on three standard benchmarks, i.e. CIFAR-10, CUB-200, and FashionMNIST by 3.08%, 14.27%, and 1% mAP, respectively, as well as on more challenging evaluation settings, i.e. ImageNet and CIFAR-100 by 1.97% and 5.65% mAP, respectively

FRCSyn Challenge at WACV 2024:Face Recognition Challenge in the Era of Synthetic Data

Despite the widespread adoption of face recognition technology around the world, and its remarkable performance on current benchmarks, there are still several challenges that must be covered in more detail. This paper offers an overview of the Face Recognition Challenge in the Era of Synthetic Data (FRCSyn) organized at WACV 2024. This is the first international challenge aiming to explore the use of synthetic data in face recognition to address existing limitations in the technology. Specifically, the FRCSyn Challenge targets concerns related to data privacy issues, demographic biases, generalization to unseen scenarios, and performance limitations in challenging scenarios, including significant age disparities between enrollment and testing, pose variations, and occlusions. The results achieved in the FRCSyn Challenge, together with the proposed benchmark, contribute significantly to the application of synthetic data to improve face recognition technology.Comment: 10 pages, 1 figure, WACV 2024 Workshop

The Effect of a Hidden Source on the Estimation of Connectivity Networks from Multivariate Time Series

Many methods of Granger causality, or broadly termed connectivity, have been developed to assess the causal relationships between the system variables based only on the information extracted from the time series. The power of these methods to capture the true underlying connectivity structure has been assessed using simulated dynamical systems where the ground truth is known. Here, we consider the presence of an unobserved variable that acts as a hidden source for the observed high-dimensional dynamical system and study the effect of the hidden source on the estimation of the connectivity structure. In particular, the focus is on estimating the direct causality effects in high-dimensional time series (not including the hidden source) of relatively short length. We examine the performance of a linear and a nonlinear connectivity measure using dimension reduction and compare them to a linear measure designed for latent variables. For the simulations, four systems are considered, the coupled Hénon maps system, the coupled Mackey–Glass system, the neural mass model and the vector autoregressive (VAR) process, each comprising 25 subsystems (variables for VAR) at close chain coupling structure and another subsystem (variable for VAR) driving all others acting as the hidden source. The results show that the direct causality measures estimate, in general terms, correctly the existing connectivity in the absence of the source when its driving is zero or weak, yet fail to detect the actual relationships when the driving is strong, with the nonlinear measure of dimension reduction performing best. An example from finance including and excluding the USA index in the global market indices highlights the different performance of the connectivity measures in the presence of hidden source

Granger causality and multi-variate time series analysis with applications on neurophysiology

The aim of the present thesis is the valuation and also the development of new methods, regarding the research field of connectivity analysis of complex dynamical systems. Also, this thesis is strongly multidisciplinary, given that we use the methods that we developed in order to shed light to problems of the field of neurophysiology and finance. After extensive literature review, we used a large number of causality measures, popular in the research of multi-variate time series analysis. We also used dynamical systems from different categories, such as non-linear difference equations, delay differential equations, a system that simulates the electroencephalogram and the stochastic process of vector autoregressive model in many scenarios regarding the dynamical regime, the number of variables and the coupling strength. The main conclusion of this study is that the dimension reduction measures are more efficient in the estimation of the true coupling structure. The causality network characteristics provide useful information for the topology of the causal effects among the system's variables. We evaluate the causality network characteristics in terms of their ability to classify multi-variate time series in different coupling structure categories. For this we use different setups in terms of the dynamical regime and coupling strength of the system under study. The conclusion is that network characteristics related to the strength distribution and the clustering of the nodes hold the crucial discriminative information. The estimation of causality effects is a hard task when important variables of the system are not observed. In this case non-existing causal effects are estimated as existing. Also, existing causal effects are not estimated as such when the impact of the hidden variable is strong, because of the common input that introduces correlations to the system. We developed a method for the estimation of causality and instantaneous causality from multi-variate time series, that shows great performance. We also showed through an analytical example, that the additional causal effects that arise when latent confounders are present are not necessarily false. Concerning instantaneous causality, we applied the proposed method on electroencephalogram recordings with epileptiform discharges. The results showed that hidden variables are present during the electroencephalogram recordings, the impact of which increases during the epileptiform discharges. In the electroencephalogram applications we used uni-variate and multi-variate means to analyse the multi-variate time series. The results showed that the duration of an induced (with transcranial magnetic stimulation) epileptiform discharge is, to some extent, correlated with the dynamical regime of certain brain regions in the early stage of the epileptiform discharge. Also, the connectivity analysis showed that the brain network undergoes critical changes before, during and after the epileptiform discharge. More precisely, there exist three discrete stages during the epileptiform discharge. Using the same recordings we applied uni-variate methods of time series analysis and we inferred that the epileptic brain has many covert states of excitability during the interictal period.Η παρούσα διδακτορική διατριβή έχει ως στόχο την αξιολόγηση αλλά και την ανάπτυξη νέων μεθόδων οι οποίες αφορούν το επιστημονικό πεδίο της ανάλυσης συνδεσιμότητας πολυπλοκων δυναμικών συστημάτων. Επίσης, η διατριβή έχει έντονα διεπιστημονικό χαρακτήρα, δεδομένου ότι χρησιμοποιούμε τις μεθόδους που αναπτύξαμε για την εξαγωγή συμπερασμάτων στον τομέα της νευροφυσιολογίας. Ύστερα από εκτενή βιβλιογραφική ανασκόπηση, συμπεριλάβαμε ένα μεγάλο πλήθος από μέτρα αιτιοτήτας, τα οποία είναι ευρέως διαδεδομένα στη διεθνή έρευνα των πολυ-μεταβλητών χρονοσειρών. Επίσης, χρησιμοποιήσαμε δυναμικά συστήματα από μεγάλο εύρος κατηγοριών όπως στοχαστικές διαδικασίες, μη-γραμμικές εξισώσεις διαφορών, διαφορικές εξισώσεις υστέρησης και ένα σύστημα που προσομοιώνει τη συμπεριφορά του ηλεκτροεγκεφαλογραφήμματος σε πολλά σενάρια δυναμικών καταστάσεων, μεγέθους και δύναμης σύζευξης. Το βασικό συμπέρασμα αυτής της μελέτης είναι ότι τα μέτρα μείωσης διάστασης είναι αποδοτικότερα στην εκτίμηση της δομής σύζευξης. Τα χαρακτηριστικά των δικτύων αιτιότητας που μπορούμε να εκτιμήσουμε από πολυ-μεταβλητές χρονοσειρές με τα μέτρα αιτιότητας μπορούν να δώσουν χρήσιμες πληροφορίες σχετικά με την τοπολογία των συνδέσεων μεταξύ των παρατηρήσιμων μεταβλητών του συστήματος. Σε διαφορετικούς σχεδιασμούς ως προς το δυναμικό σύστημα, τη δυναμική κατάσταση του συστήματος και τη δύναμη σύζευξης μεταξύ των μεταβλητών αξιολογούμε τα χαρακτηριστικά των δικτύων ως προς τη δυνατότητά τους να κατηγοριοποιούν σωστά τις πολυ-μεταβλητές χρονοσειρές ως προς τη δομή συνδεσιμότητας. Το συμπέρασμα είναι ότι δείκτες που σχετίζονται με την κατανομή της ισχύος στους κόμβους του δικτύου αλλά και με την ιδιότητα της συσταδοποίησης κατέχουν την κρίσιμη πληροφορία για τη διάκριση των δομών σύζευξης που υπάρχει πίσω από τις πολυ-μεταβλητές χρονοσειρές. Στην περίπτωση κατά την οποία μεταβλητές ενός συστήματος παραμένουν κρυφές, η ανάλυση συνδεσιμότητας γίνεται μία δύσκολη υπόθεση. Σχέσεις οι οποίες δεν υπάρχουν στο πραγματικό δίκτυο συζεύξεων αναδεικνύονται ως υπαρκτές. Επίσης, στην περίπτωση που η επίδραση των μη-παρατηρήσιμων μεταβλητών είναι ισχυρή, σχέσεις που υπάρχουν μπορεί να μην αναγνωριστούν από τα μέτρα εξ' αιτίας των συσχετίσεων που εισάγονται από την κοινή επίδραση. Αναπτύξαμε μία μέθοδο εκτίμησης της αιτιότητας και της στιγμιαίας αιτιότητας από πολυ-μεταβλητές χρονοσειρές, η οποία παρουσιάζει εξαιρετική απόδοση. Μάλιστα, μέσα από ένα αναλυτικό παράδειγμα δείξαμε ότι οι επιπρόσθετες σχέσεις που εκτιμώνται, όταν υπάρχουν κρυφές επιδράσεις, δεν είναι κατ' ανάγκην ψευδείς. Όσον αφορά τη στιγμιαία αιτιότητα, εφαρμόσαμε τη νέα μέθοδο σε δεδομένα ηλεκτροεγκεφαλογραφικών καταγραφών, στις οποίες περιέχονται επιληπτικές εκφορτίσεις. Τα αποτελέσματα έδειξαν ότι κατά τη διάρκεια της καταγραφής της δραστηριότητας του εγκεφάλου υπάρχουν κρυφές μεταβλητές των οποίων η ισχύς εντείνεται κατά την επιληπτική περίοδο. Στις εφαρμογές σε ηλεκτροεγκεφαλογραφήματα χρησιμοποιήσαμε μονομεταβλητές και πολυμεταβλητές μεθόδους ανάλυσης πολυ-μεταβλητών χρονοσειρών. Τα αποτελέσματα έδειξαν ότι η διάρκεια της επιληπτικής εκφόρτιση, η οποία προκαλείται από διακρανιακό μαγνητικό ερεθισμό καθορίζεται σε κάποιο βαθμό από τη δυναμική κατάσταση συγκεκριμένων περιοχών του εγκεφάλου στην αρχική φάση της επιληπτικής εκφόρτισης. Επίσης, η ανάλυση συνδεσιμότητας έδειξε ότι το δίκτυο του εγκεφάλου υφίσταται κρίσιμες αλλαγές πριν, κατά τη διάρκεια και μετά την επιληπτική εκφόρτιση και πιο συγκεκριμένα αναδείχθηκαν τρεις διακριτές φάσεις της επιληπτικής εκφόρτισης. Στα ίδια δεδομένα εφαρμόζοντας μονο-μεταβλητές μεθόδους της ανάλυσης χρονοσειρών συμπεραίνουμε ότι κατά τη διάρκεια της φυσιολογικής λειτουργίας του, ο εγκέφαλος μπορεί να βρίσκεται σε διαφορετικές δυναμικές καταστάσεις διεγερσιμότητας

Discriminating dynamic regimes using measures of causality networks from multivariate time series

Abstract-In many applications ranging from neurophysiology to finance, the dynamics of the underlying mechanism to observed multivariate time series is believed to change and this is reflected to the inter-dependence structure of the observed variables. We consider a Granger causality index for estimating the inter-dependence structure and form causality networks with nodes the observed variables and directed connections given by the selected Granger causality index. The focus of the study is on assessing the different network measures as to their ability in discriminating different dynamic regimes of the system underlying the multivariate time series. For this, we first compute the network measures on many realizations of the coupled Mackey-Glass system under different coupling structures, and then to electroencephalogram recordings containing episodes of epileptiform discharges. The ranking of the network measures on the simulated and real data revealed the same subset of measures performing best

GAP: Geometric Aggregation of Popularity Metrics

Estimating and analyzing the popularity of an entity is an important task for professionals in several areas, e.g., music, social media, and cinema. Furthermore, the ample availability of online data should enhance our insights into the collective consumer behavior. However, effectively modeling popularity and integrating diverse data sources are very challenging problems with no consensus on the optimal approach to tackle them. To this end, we propose a non-linear method for popularity metric aggregation based on geometrical shapes derived from the individual metrics’ values, termed Geometric Aggregation of Popularity metrics (GAP). In this work, we particularly focus on the estimation of artist popularity by aggregating web-based artist popularity metrics. Finally, even though the most natural choice for metric aggregation would be a linear model, our approach leads to stronger rank correlation and non-linear correlation scores compared to linear aggregation schemes. More precisely, our approach outperforms the simple average method in five out of seven evaluation measures