Deep Prediction Of Investor Interest: a Supervised Clustering Approach

We propose a novel deep learning architecture suitable for the prediction of investor interest for a given asset in a given timeframe. This architecture performs both investor clustering and modelling at the same time. We first verify its superior performance on a simulated scenario inspired by real data and then apply it to a large proprietary database from BNP Paribas Corporate and Institutional Banking

Adaptive Collaborative Filtering with Personalized Time Decay Functions for Financial Product Recommendation

Classical recommender systems often assume that historical data are stationary and fail to account for the dynamic nature of user preferences, limiting their ability to provide reliable recommendations in time-sensitive settings. This assumption is particularly problematic in finance, where financial products exhibit continuous changes in valuations, leading to frequent shifts in client interests. These evolving interests, summarized in the past client-product interactions, see their utility fade over time with a degree that might differ from one client to another. To address this challenge, we propose a time-dependent collaborative filtering algorithm that can adaptively discount distant client-product interactions using personalized decay functions. Our approach is designed to handle the non-stationarity of financial data and produce reliable recommendations by modeling the dynamic collaborative signals between clients and products. We evaluate our method using a proprietary dataset from BNP Paribas and demonstrate significant improvements over state-of-the-art benchmarks from relevant literature. Our findings emphasize the importance of incorporating time explicitly in the model to enhance the accuracy of financial product recommendation.Comment: 10 pages, 1 figure, 2 tables, to be published in the Seventeenth ACM Conference on Recommender Systems (RecSys '23

Construct Validity and Cross Validity of a Test Battery Modeling Autism Spectrum Disorder (ASD) in Mice

International audienc

Memetic simulated annealing for data approximation with local-support curves

This paper introduces a new memetic optimization algorithm called MeSA (Memetic Simulated Annealing) to address the data fitting problem with local-support free-form curves. The proposed method hybridizes simulated annealing with the COBYLA local search optimization method. This approach is further combined with the centripetal parameterization and the Bayesian information criterion to compute all free variables of the curve reconstruction problem with B-splines. The performance of our approach is evaluated by its application to four different shapes with local deformations and different degrees of noise and density of data points. The MeSA method has also been compared to the non-memetic version of SA. Our results show that MeSA is able to reconstruct the underlying shape of data even in the presence of noise and low density point clouds. It also outperforms SA for all the examples in this paper.This work has been supported by the Spanish Ministry of Economy and Competitiveness (MINECO) under grants TEC2013-47141-C4-R (RACHEL) and #TIN2012-30768 (Computer Science National Program) and Toho University (Funabashi, Japan)

Postnatal Tshz3 Deletion Drives Altered Corticostriatal Function and Autism Spectrum Disorder–like Behavior

International audienceBACKGROUND: Heterozygous deletion of the TSHZ3 gene, encoding for the teashirt zinc-finger homeobox family member 3 (TSHZ3) transcription factor that is highly expressed in cortical projection neurons (CPNs), has been linked to an autism spectrum disorder (ASD) syndrome. Similarly, mice with Tshz3 haploinsufficiency show ASD-like behavior, paralleled by molecular changes in CPNs and corticostriatal synaptic dysfunctions. Here, we aimed at gaining more insight into "when" and "where" TSHZ3 is required for the proper development of the brain, and its deficiency crucial for developing this ASD syndrome. METHODS: We generated and characterized a novel mouse model of conditional Tshz3 deletion, obtained by crossing Tshz3 flox/flox with CaMKIIalpha-Cre mice, in which Tshz3 is deleted in CPNs from postnatal day 2 to 3 onward. We characterized these mice by a multilevel approach combining genetics, cell biology, electrophysiology, behavioral testing, and bioinformatics. RESULTS: These conditional Tshz3 knockout mice exhibit altered cortical expression of more than 1000 genes, w50% of which have their human orthologue involved in ASD, in particular genes encoding for glutamatergic syn-apse components. Consistently, we detected electrophysiological and synaptic changes in CPNs and impaired corticostriatal transmission and plasticity. Furthermore, these mice showed strong ASD-like behavioral deficits. CONCLUSIONS: Our study reveals a crucial postnatal role of TSHZ3 in the development and functioning of the corticostriatal circuitry and provides evidence that dysfunction in these circuits might be determinant for ASD pathogenesis. Our conditional Tshz3 knockout mouse constitutes a novel ASD model, opening the possibility for an early postnatal therapeutic window for the syndrome linked to TSHZ3 haploinsufficiency

Mild clinical presentation in KLHL40-related nemaline myopathy (NEM 8).

Nemaline myopathies are clinically and genetically heterogeneous muscle diseases characterized by the presence of nemaline bodies (rods) in muscle fibers. Mutations in the KLHL40 (kelch-like family member 40) gene (NEM 8) are common cause of severe/lethal nemaline myopathy. We report an 8-year-old girl born to consanguineous Moroccan parents, who presented with hypotonia and poor sucking at birth, delayed motor development, and further mild difficulties in walking and fatigability. A muscle biopsy revealed the presence of nemaline bodies. KLHL40 gene Sanger sequencing disclosed a never before reported pathogenic homozygous mutation which resulted in absent KLHL40 protein expression in the muscle. This further expands the phenotypical spectrum of KLHL40 related nemaline myopathy

Ownership and control in a competitive industry

We study a differentiated product market in which an investor initially owns a controlling stake in one of two competing firms and may acquire a non-controlling or a controlling stake in a competitor, either directly using her own assets, or indirectly via the controlled firm. While industry profits are maximized within a symmetric two product monopoly, the investor attains this only in exceptional cases. Instead, she sometimes acquires a noncontrolling stake. Or she invests asymmetrically rather than pursuing a full takeover if she acquires a controlling one. Generally, she invests indirectly if she only wants to affect the product market outcome, and directly if acquiring shares is profitable per se. --differentiated products,separation of ownership and control,private benefits of control