Showing papers by "Katholieke Universiteit Leuven published in 2022"

Social media and body image: Recent trends and future directions

Abstract: This review presents recent trends in social media and body image research, with a particular focus on different social media platforms, features unique to social media, and potentially positive content for body image. First, it was found that visual platforms (e.g. Instagram) were more dysfunctional for body image than more textual platforms (e.g. Facebook). Second, taking and editing (but not posting) selfies resulted in negative effects on body image. Positive comments intensified the effects of exposure to idealized content. Third, of the forms of potentially positive content examined in recent research (i.e. fitspiration, disclaimer labels, and body positivity), only body positivity content had a positive effect on body image. Recommendations for future research are offered.

Thriving at work but insomniac at home: Understanding the relationship between supervisor bottom-line mentality and employee functioning

Abstract: Bottom-line mentality (BLM) describes a one-dimensional frame of mind revolving around bottom-line pursuits, which pervades most organizations today. But how does working with high BLM supervisors ...

Endurance-Aware Mapping of Spiking Neural Networks to Neuromorphic Hardware

Abstract: Neuromorphic computing systems are embracing memristors to implement high density and low power synaptic storage as crossbar arrays in hardware. These systems are energy efficient in executing Spiking Neural Networks (SNNs). We observe that long bitlines and wordlines in a memristive crossbar are a major source of parasitic voltage drops, which create current asymmetry. Through circuit simulations, we show the significant endurance variation that results from this asymmetry. Therefore, if the critical memristors (ones with lower endurance) are overutilized, they may lead to a reduction of the crossbar’s lifetime. We propose eSpine, a novel technique to improve lifetime by incorporating the endurance variation within each crossbar in mapping machine learning workloads, ensuring that synapses with higher activation are always implemented on memristors with higher endurance, and vice versa. eSpine works in two steps. First, it uses the Kernighan-Lin Graph Partitioning algorithm to partition a workload into clusters of neurons and synapses, where each cluster can fit in a crossbar. Second, it uses an instance of Particle Swarm Optimization (PSO) to map clusters to tiles, where the placement of synapses of a cluster to memristors of a crossbar is performed by analyzing their activation within the workload. We evaluate eSpine for a state-of-the-art neuromorphic hardware model with phase-change memory (PCM)-based memristors. Using 10 SNN workloads, we demonstrate a significant improvement in the effective lifetime.

Design and synthesis of chromophores with enhanced electro-optic activities in both bulk and plasmonic–organic hybrid devices

Abstract: This study demonstrates enhancement of in-device electro-optic activity via a series of theory-inspired organic electro-optic (OEO) chromophores based on strong (diarylamino)phenyl electron donating moieties. These chromophores are tuned to minimize trade-offs between molecular hyperpolarizability and optical loss. Hyper-Rayleigh scattering (HRS) measurements demonstrate that these chromophores, herein described as BAH, show >2-fold improvement in β versus standard chromophores such as JRD1, and approach that of the recent BTP and BAY chromophore families. Electric field poled bulk devices of neat and binary BAH chromophores exhibited significantly enhanced EO coefficients (r33) and poling efficiencies (r33/Ep) compared with state-of-the-art chromophores such as JRD1. The neat BAH13 devices with charge blocking layers produced very large poling efficiencies of 11.6 ± 0.7 nm2 V−2 and maximum r33 value of 1100 ± 100 pm V−1 at 1310 nm on hafnium dioxide (HfO2). These results were comparable to that of our recently reported BAY1 but with much lower loss (extinction coefficient, k), and greatly exceeding that of other previously reported OEO compounds. 3 : 1 BAH-FD : BAH13 blends showed a poling efficiency of 6.7 ± 0.3 nm2 V−2 and an even greater reduction in k. 1 : 1 BAH-BB : BAH13 showed a higher poling efficiency of 8.4 ± 0.3 nm2 V−2, which is approximately a 2.5-fold enhancement in poling efficiency vs. JRD1. Neat BAH13 was evaluated in plasmonic–organic hybrid (POH) Mach–Zehnder modulators with a phase shifter length of 10 μm and slot widths of 80 and 105 nm. In-device BAH13 achieved a maximum r33 of 208 pm V−1 at 1550 nm, which is ∼1.7 times higher than JRD1 under equivalent conditions.

Transfer learning in demand response: A review of algorithms for data-efficient modelling and control

Abstract: A number of decarbonization scenarios for the energy sector are built on simultaneous electrification of energy demand, and decarbonization of electricity generation through renewable energy sources. However, increased electricity demand due to heat and transport electrification and the variability associated with renewables have the potential to disrupt stable electric grid operation. To address these issues using demand response, researchers and practitioners have increasingly turned towards automated decision support tools which utilize machine learning and optimization algorithms. However, when applied naively, these algorithms suffer from high sample complexity, which means that it is often impractical to fit sufficiently complex models because of a lack of observed data. Recent advances have shown that techniques such as transfer learning can address this problem and improve their performance considerably - both in supervised and reinforcement learning contexts. Such formulations allow models to leverage existing domain knowledge and human expertise in addition to sparse observational data. More formally, transfer learning embodies all techniques where one aims to increase (learning) performance in a target domain or task, by using knowledge gained in a source domain or task. This paper provides a detailed overview of state-of-the-art techniques on applying transfer learning in demand response, showing improvements that can exceed 30% in a variety of tasks. We observe that most research to date has focused on transfer learning in the context of electricity demand prediction, although reinforcement learning based controllers have also seen increasing attention. However, a number of limitations remain in these studies, including a lack of benchmarks, systematic performance improvement tracking, and consensus on techniques that can help avoid negative transfer.