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Introduction The studies about developmental bases of attachment in healthy children with high intellectual potential (HIP) are rare.Moreover, the literature underline socio-emotional disorders in HIP and difficulties with behavioral adjustment of parents. Objectives We aimed to explore the developmental trajectory of attachment in HIP children without psychological or learning disorders. Methods The french version of the Adolescent-Unresolved-Attachment-Questionnaire (QANRA: internal consistency=0.74-0.82; test-retest =0.58-0.83) was analyzed in 80 healthy children (50 HIP with IQ>130 vs. 30 non-HIP), aged 7-to-13-years-old (mean 10y; SD 1.8). All children were recruited in private and public schools in Paris. Results There was no significant difference between the groups. However, when we looked at the developmental trajectory by distinguishing the period of adolescence [7-10 years (56% in the HIP group vs. 53% in the non-HIP); 11-13 years (44% in the HIP group vs. 47% in the non-HIP)], we have noted a significantly early integration of resolved attachment in the HIP children that seems to remain stable in adolescence. Conclusions Our findings highlight the early onset of attachment with a harmony of intellectual/psycho-affective development in HIP children without skipping stages, but more quickly and effectively. This could potentially be explained by their cognitive abilities, particularly the theory of mind and the executive functions, known to be significantly more efficient in HIP children without neurodevelopmental disorders. Disclosure of Interest None Declared

Introduction The majority of studies on the HIP IQ attest a heterogeneity of the IQ profile as specific to HIP. However, the samples are recruited in clinical consultations. Thus, it is important to investigate new samples from schools. Objectives We aimed to analyze the index scores of the IQ profile of children without disorders or specific school assistance. Methods The WISC-V was conducted and analyzed in 80 healthy children (50 HIP vs. 30 non-HIP), aged 7-to-13 years-old (mean 10y; SD 1.8). All children were recruited in private and public schools in Paris. Results All IQ index scores were significantly higher in the HIP vs. non-HIP. In both groups, the Verbal Comprehension Index was the highest index while the Processing Speed Index was the lowest. There are significantly (p=0.02) more heterogeneous IQ profiles in HIP (64%) vs. non-HIP (47%), with a significantly larger gap between the highest and lowest index (respectively: median = 29.5 vs. 21.5). There was a significant-positive correlation between IQ level in general and the heterogeneity of the profile (r = 0.42; p

Introduction Few studies have analyzed the psychometric profile (Homogenous/Heterogenous), established by the Wechsler scale in high intellectual potential children (HIP, IQ>130), regarding the psycho-socio-emotional characteristics. Objectives We aimed to look at the links between the IQ-profile and the psycho-socio-emotional characteristics in HIP. Methods Anamnestic questionnaire and Wechsler-Intelligence-Scale for children (WISC-V) were conducted and analyzed in 58 healthy children with HIP, aged 7-to-13 years-old (mean 10y; SD 1.8). It was possible to distinguished 27 Homogenousvs 30 Heterogenous IQ-profile. Results No significant difference between homogenous/heterogonous groups, FIQ was positively significantly correlated with “Reacting very little emotionally”, “Tendency to isolation”. Visual-Spatial-Index (VSI) with “Ability to adapt to new people” (r=-0.4, p=0.02), “few interests” (r=0.5, p=0.008). Verbal-Comprehension-Index (VCI) with “Reacting strongly to frustration”, “Difficulties to understand limits”, “Separation anxiety”. A significant difference between homogenous/heterogonous groups was shown regarding “few interests” with high rate in the heterogonous group (t= -2.34, p=0.023). Conclusions HIP seems to cover specific psycho-socio-emotional characteristics linked to IQ index distribution. Thus, it appears interesting to assess more the emotional and socio-cognitive field to understand these characteristics in HIP children. Disclosure No significant relationships.

Energy efficiency remains one of the main factors for improving the key performance markers of RRAMs to support IoT edge devices. This paper proposes a simple and feasible low power design scheme which can be used as a powerful tool for energy reduction in RRAM circuits. The design scheme is exclusively based on current control during write and read operations and ensures that write operations are completed without wasted energy. Self-adaptive write termination circuits are proposed to control the RRAM current during FORMING, RESET and SET operations. The termination circuits sense the programming current and stop the write pulse as soon as a preferred programming current is reached. Simulation results demonstrate that an appropriate choice of the programming currents can help obtain 4.1X improvement in FORMING, 9.1X improvement in SET and 1.12X improvement in RESET energy. Also, the possibility to have a tight control over the RESET resistance is demonstrated. READ energy optimization is also covered by leveraging on a differential sense amplifier offering a programmable current reference. Finally, an optimal trade-off between energy consumption during SET/RESET operations and an acceptable read margin is established according to the final application requirements.

Introduction Developmental studies in infancy remain rare. Studies measuring depressive symptoms in gifted children are contradictory, considering more anxiety or depression than in non-gifted children. Furthermore, questionnaires or anxiety scales are used without taking into account all aspects of mood disorders and thus, rarely depression scales have been conducted. Objectives To refine the developmental trajectory of depression in a national sample of French gifted children by identification of the specific risk and protective factors. Methods A self-reported depression scale MDI-C (Multiscore-Depression-Inventory-for-Children) were sent to families to be administered to their gifted children from preschool to high school, aged from 4 to 20 years-old (IQ >125) looking for help from gifted associations. A larger wave of data collection on different aspects of child and family history was collected (pregnancy, term and delivery mode, neonatal period, psychomotor development, health, schooling, interpersonal relationships with family and friends, personality, parental socio-economic status). Results 438 children (> 130) were eligible. Regarding anamnestic fields, Exploratory-Factor-Analysis highlighted six predictive factors of depression with eigenvalues from 1.09 to 3.17. Major factors explaining 62.96% of total variance are: Factor-1 “motor skills disorder” (14.53%). Factor-2 “positive family relationships” (14.04%). Factor-3 “positive social relationships with peers” (14.02%). Factor-4 “integration of social codes” (11.23%). Factor-6 “Learning disabilities and rehabilitation” (10.1%). Conclusions Our findings highlight specifics risk factors of depression in the field of learning disabilities or social cognition, while a good quality of social relationships since childhood seems to be a preventive factor. Disclosure No significant relationships.

Introduction Handwriting disorder is commonly observed in Developmental Coordination Disorder (DCD) (87-88%) and is often noted in children with high Intellectual Quotient (HIQ).Two mainly pure DCD subtypes: ideomotor-DCD (IM), visuospatial/or visuoconstructional-DCD (VSC) and a mixed subtype (MX) were identified in the literature but nothing is known regarding IQ and dysgraphia. Objectives To refine the specific clinical features of dysgraphia related to DCD subtypes regarding IQ levels. Methods Neurovisual, neuropsychological, neuropsychomotor functions, and handwriting performances of 38 children (6-to-12 years-old: mean 9y, SD 2.7) diagnosed with DCD (DSM-5 criteria) were collected. Two matched groups were analyzed according to their IQ: 19 (TC) typical children (IQ=90-110) and 19 HIQ children (IQ> 120). Results IQ scores were not significantly associated with dysgraphia. There isa significant difference between TC vs HIQ with a lower rate of IM-DCD respectively 11% vs 5% (p=.035) and 68% vs 37% for VSC-DCD (p=.03) but 21% vs 58% in MX-DCD (p=.41). Dysgraphia was significantly more present in TC group with MX-DCD and in HIQ with VSC-DCD. A negative correlation between Kho’s’ cubes test failure (p=.006), visual-spatial memory (p=.05) and VSC-DCD was noted in HIQ group. The deficit of visual spatial memory was significantly related to dysgraphia in HIQ children (p=.01) associated to visual gnosis impairment (p=.03). Conclusions Dysgraphia was significantly found with VSC-DCD subgroup in FIQ>120 with specific features of visual perception disorders suggesting more involvement of the right cortex. These results suggest that VSC-DCD in HIQ could be a neurovisual impairment rather than a pure VSC-DCD.

Neuromorphic processors promise low-latency and energy-efficient processing by adopting novel brain-inspired design methodologies. Yet, current neuromorphic solutions still struggle to rival conventional deep learning accelerators' performance and area efficiency in practical applications. Event-driven data-flow processing and near/in-memory computing are the two dominant design trends of neuromorphic processors. However, there remain challenges in reducing the overhead of event-driven processing and increasing the mapping efficiency of near/in-memory computing, which directly impacts the performance and area efficiency. In this work, we discuss these challenges and present our exploration of optimizing event-based neural network inference on SENECA, a scalable and flexible neuromorphic architecture. To address the overhead of event-driven processing, we perform comprehensive design space exploration and propose spike-grouping to reduce the total energy and latency. Furthermore, we introduce the event-driven depth-first convolution to increase area efficiency and latency in convolutional neural networks (CNNs) on the neuromorphic processor. We benchmarked our optimized solution on keyword spotting, sensor fusion, digit recognition and high resolution object detection tasks. Compared with other state-of-the-art large-scale neuromorphic processors, our proposed optimizations result in a 6× to 300× improvement in energy efficiency, a 3× to 15× improvement in latency, and a 3× to 100× improvement in area efficiency. Our optimizations for event-based neural networks can be potentially generalized to a wide range of event-based neuromorphic processors., Competing Interests: YX, KS, G-JS, AD, SW, RM, PN, CA, PM, PD, ST, MK, KV, MS, GT, and AY were employed by IMEC. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2024 Xu, Shidqi, van Schaik, Bilgic, Dobrita, Wang, Meijer, Nembhani, Arjmand, Martinello, Gebregiorgis, Hamdioui, Detterer, Traferro, Konijnenburg, Vadivel, Sifalakis, Tang and Yousefzadeh.)

Advances in materials science and memory devices work in tandem for the evolution of Artificial Intelligence systems. Energy-efficient computation is the ultimate goal of emerging memristor technology, in which the storage and computation can be done in the same memory crossbar. In this work, an analog memristor device is fabricated utilizing the unique characteristics of single-wall carbon nanotubes (SWCNTs) to act as the switching medium of the device. Via the planar structure, the memristor device exhibits analog switching ability with high state stability. The device's conductance and capacitance can be tuned simultaneously, increasing the device's potential and broadening its applications' horizons. The multi-state storage capability and long-term memory are the key factors that make the device a promising candidate for bio-inspired computing applications. As a demonstrator, the fabricated memristor is deployed in spiking neural networks (SNN) to exploit its analog switching feature for energy-efficient classification operation. Results reveal that the computation-in-memory implementation performs Vector Matrix Multiplication with 95% inference accuracy and few femtojoules per spike energy efficiency. The memristor device presented in this work opens new insights towards utilizing the outstanding features of SWCNTs for efficient analog computation in deep learning systems., (© 2023. The Author(s).)

We aimed to explore the developmental trajectory of depressive symptoms in a national sample of French children with a high intellectual potential (HIP) seeking help from gifted organizations. Participants were drawn from a national retrospective survey sent to 1200 families with HIP children (IQ ≥ 130) from primary to high school and they answered a self-report questionnaire of a depression scale (MDI-C). The children's parents completed a self-report questionnaire collected on different stages of the child's school level, perinatality, psychomotor development, health, family's history, behavior, interpersonal relationships and daily activities, school performance, presence of learning disorders and remediation. Four hundred and twenty HIP children were eligible with an IQ ≥ 130 aged from 8 to 17 years-old, 49% with depressive symptoms and 51% with no depressive symptoms. Analysis of 136 variables from anamnestic fields based on the use of Spearman's ρ test (ρ) with a non-parametric correlations showed that "learning disabilities" are significantly related to depressive symptoms in different groups (primary p = 0.001, middle p = 0.02, high school p = 0.001) as well as "difficulties in psychomotor skills" during primary ( p = 0.003) and middle school ( p = 0.02). Good relationships with family as well as with peers are significantly negatively correlated with depressive symptoms from childhood to primary ( p = 0.003) and high school ( p = 0.02). Certain details of correlations between the MDI-C scale's subfactors and anamnestic variables were analyzed. The ANOVA test about the MDI-C scale showed provocation as a significant marker at middle school (F (1, 418) = 3.487, p = 0.03) and low self-esteem at high school (F (1, 418) = 3.337, p = 0.03). A holistic developmental approach allowed us to highlight the risk factors of depression with a developmental trajectory origin linked to disorders of social adjustment and psychomotor skills and to the importance of misdiagnosed learning disabilities because of giftedness. Our findings support the interest in an early identification of and intervention in depression risk to improve clinical decision making on the effect of giftedness on mental health outcomes.

Timely detection of cardiac arrhythmia characterized by abnormal heartbeats can help in the early diagnosis and treatment of cardiovascular diseases. Wearable healthcare devices typically use neural networks to provide the most convenient way of continuously monitoring heart activity for arrhythmia detection. However, it is challenging to achieve high accuracy and energy efficiency in these smart wearable healthcare devices. In this work, we provide architecture-level solutions to deploy neural networks for cardiac arrhythmia classification. We have created a hierarchical structure after analyzing various neural network topologies where only required network components are activated to improve energy efficiency while maintaining high accuracy. In our proposed architecture, we introduce a severity-based classification approach to directly help the users of the wearable healthcare device as well as the medical professionals. Additionally, we have employed computation-in-memory based hardware to improve energy efficiency and area consumption by leveraging in-situ data processing and scalability of emerging memory technologies such as resistive random access memory (RRAM). Simulation experiments conducted using the MIT-BIH arrhythmia dataset show that the proposed architecture provides high accuracy while consuming average energy of 0.11 μJ per heartbeat classification and 0.11 mm 2 area, thereby achieving 25× improvement in average energy consumption and 12× improvement in area compared to the state-of-the-art.