Memory Reduction Research Articles

Association of chronic arsenic exposure with cellular immune profile in MINIMat adolescents: A birth cohort in Bangladesh

Chronic arsenic exposure is known to affect the immune system. We aimed to evaluate the association between arsenic exposure and immune cell profile in 15 years old adolescents (n=389) in rural Bangladesh, with chronic exposure to groundwater arsenic. Single blood and urine were collected. Urinary arsenic (U-As) concentration was measured using atomic absorption spectrometry. Peripheral blood mononuclear cells (PBMC) were analyzed by flow cytometry. Non-linear association was found between U-As (median, 24.9 µg/L) and immune cells with a cut-off at U-As 20 µg/L. U-As (<20 µg/L) were significantly associated with increases in CD8+T (21 %), naïve CD8+T (42 %) and early B cells (40 %), and classical monocytes (55 %), but reduction in CD3+T cells (37%) and intermediate-monocytes (56 %). U-As (>20 µg/L) were associated with a 3 % reduction in memory B cells. Arsenic exposure was associated with altered immune cell profile in adolescents likely rendering them vulnerable to adverse health effects in later life.

Benchmarking In-Sensor Machine Learning Computing: An Extension to the MLCommons-Tiny Suite

This paper proposes a new benchmark specifically designed for in-sensor digital machine learning computing to meet an ultra-low embedded memory requirement. With the exponential growth of edge devices, efficient local processing is essential to mitigate economic costs, latency, and privacy concerns associated with the centralized cloud processing. Emerging intelligent sensors equipped with computing assets to run neural network inferences and embedded in the same package, which hosts the sensing elements, present new challenges due to their limited memory resources and computational skills. This benchmark evaluates models trained with Quantization Aware Training (QAT) and compares their performance with Post-Training Quantization (PTQ) across three use cases: Human Activity Recognition (HAR) by means of the SHL dataset, Physical Activity Monitoring (PAM) by means of the PAMAP2 dataset, and superficial electromyography (sEMG) regression with the NINAPRO DB8 dataset. The results demonstrate the effectiveness of QAT over PTQ in most scenarios, highlighting the potential for deploying advanced AI models on highly resource-constrained sensors. The INT8 versions of the models always outperformed their FP32, regarding memory and latency reductions, except for the activations for CNN. The CNN model exhibited reduced memory usage and latency with respect to its Dense counterpart, allowing it to meet the stringent 8KiB data RAM and 32 KiB program RAM limits of the ISPU. The TCN model proved to be too large to fit within the memory constraints of the ISPU, primarily due to its greater capacity in terms of number of parameters, designed for processing more complex signals like EMG. This benchmark aims to guide the development of efficient AI solutions for In-Sensor Machine Learning Computing, fostering innovation in the field of Edge AI benchmarking, such as the one conducted by the MLCommons-Tiny working group.

ASIP Performance Enhancement by Hazard Control through Scoreboard

The application-specific instruction set processor (ASIP) has been gradually accepted in AI, communication, media, game and industry control. The digital signal processor (DSP) is a typical ASIP, whose benefits include high performance in specific domains, low power consumption, high flexibility and low silicon consumption. One of the challenges for DSP design is to handle problems induced by datapath acceleration. The datapath acceleration (instruction fusion, black box instructions) induces control complexities. To most efficiently utilize hardware, control challenges can be summarized as RAW (Read-After-Write) handling, hardware hazard handling, and WAW (Write-After-Write) handling. Both an advanced compiler and hardware hazard handler can be used as solutions. In this paper, we introduced both solutions and exposed the benefits from the hardware solution. The benefits include utilizing low silicon to achieve higher performance and program memory reduction on chip. In summary, our solution only uses 0.91% extra silicon area yet achieves 32.75% performance improvement. So, the overall performance-to-cost ratio could be evaluated as 32%.

Effect of a cash transfer intervention on memory decline and dementia probability in older adults in rural South Africa

Evidence on cash transfers as a population-level intervention to support healthy cognitive aging in low-income settings is sparse. We assessed the effect of a cash transfer intervention on cognitive aging outcomes in older South African adults. We leveraged the overlap in the sampling frames of a Phase 3 randomized cash transfer trial [HIV Prevention Trial Network (HPTN) 068, 2011-2015] and an aging cohort [Health and Aging in Africa: A Longitudinal Study of an INDEPTH Community (HAALSI), 2014-2022] in rural Mpumalanga Province, South Africa. In 2011/12, young women and their primary caregivers were randomly assigned 1:1 to receive a monthly cash transfer or control. In 2014/2015, 862 adults aged 40+ y living in trial households were enrolled in the HAALSI cohort, with cognitive data collected in three waves over 7 y. We estimated the impact of the intervention on rate of memory decline and dementia probability scores. Memory decline in the cash transfer arm was 0.03 SD units (95% CI: 0.002, 0.05) slower per year than in the control arm. Dementia probability scores were three percentage points lower in the cash transfer arm than the control arm (β = -0.03; 95% CI: -0.05, -0.001). Effects were consistent across subgroups. A modestly sized household cash transfer delivered over a short period in mid- to later-life led to a meaningful slowing of memory decline and reduction in dementia probability 7 y later. Cash transfer programs could help stem the tide of new dementia cases in economically vulnerable populations in the coming decades.

Cognitive reserve as residual variance in cognitive performance: Latent dimensionality, correlates, and dementia prediction.

Cognitive reserve (CR) is typically operationalized as episodic memory residualized on brain health indices. The dimensionality of more generalized models of CR has rarely been examined. In a sample of N = 113 dementia-free older adults (ages 62-86 years at MRI scan; 58.4% women), the domain-specific representation of general cognition (COG) before vs. after residualization on brain indices (brain volume loss, cerebral blood flow, white matter hyperintensities) was compared (i.e., COG vs. CR). COG and CR were assessed by 15 tasks spanning five domains: processing speed, verbal memory, visuospatial memory, fluid reasoning, and vocabulary. Measurement invariance and item-construct representation were tested in a series of structural factor analyses. COG and CR were then examined in relation to 22 risk and protective factors and dementia status at time of death. Item-factor loadings differed such that CR more strongly emphasized fluid reasoning. More years of education, higher occupational class, more hobbies/interests, and fewer difficulties with personal mobility similarly predicted better COG and CR. Only the sub-domain of visuospatial memory (both before and after residualization) was associated with conversion to dementia by end-of-life (r = -.30; p = .01). Results provide tentative support for the role of fluid reasoning (intelligence) as a potential compensatory factor for age- and/or neuropathology-related reductions in processing speed and memory. Intellectually stimulating work, efforts to preserve personal mobility, and a diversity of hobbies and interests may attenuate age- and/or pathology-related reductions in cognitive functioning prior to dementia onset.

Optimizing Spatial Complexity of the Hard Decision Decoder Based on Hash and Syndrome Decoding (HSDec)

In this article, we propose an optimized version of the Hard Decision Decoder based on Hash and Syndrome Decoding (HSDec) decoder, named Reduced Memory Space of HSDec (RMS-HSDec), which uses less memory space. In this article, we aim to reduce the spatial complexity of the HSDec decoding algorithm while preserving its error correction capabilities. Our methodology involves allocating only the essential memory space for correctable error patterns and optimizing the hashing mechanism to effectively handle potential collisions. While maintaining the integrity of error correction, this new method guarantees memory reduction rates of over 96 % for the BCH(63, 39, 9) code and over 84 % for the QR(47, 24, 11) code compared to HSDec. Simulations were conducted to evaluate the performance of RMS-HSDec on various BCH and QR codes over AWGN and Rayleigh channels. The results demonstrated significant memory reduction rates and coding gains ranging from 0.8 dB to 2.8 dB over the AWGN channel and from 14 dB to 32 dB over the Rayleigh channel, confirming the robustness of the algorithm under different channel conditions. Comparative analyses showed that RMS-HSDec maintains competitive performance with existing decoders while offering effective error correction. These findings confirm the robustness of the RMS-HSDec algorithm under different channel conditions. Overall, the proposed decoder proves to be an effective solution, optimizing memory usage without compromising error correction capabilities, making it ideal for high-density data applications and environments with limited memory resources.

SpQuant-SNN: ultra-low precision membrane potential with sparse activations unlock the potential of on-device spiking neural networks applications.

Spiking neural networks (SNNs) have received increasing attention due to their high biological plausibility and energy efficiency. The binary spike-based information propagation enables efficient sparse computation in event-based and static computer vision applications. However, the weight precision and especially the membrane potential precision remain as high-precision values (e.g., 32 bits) in state-of-the-art SNN algorithms. Each neuron in an SNN stores the membrane potential over time and typically updates its value in every time step. Such frequent read/write operations of high-precision membrane potential incur storage and memory access overhead in SNNs, which undermines the SNNs' compatibility with resource-constrained hardware. To resolve this inefficiency, prior works have explored the time step reduction and low-precision representation of membrane potential at a limited scale and reported significant accuracy drops. Furthermore, while recent advances in on-device AI present pruning and quantization optimization with different architectures and datasets, simultaneous pruning with quantization is highly under-explored in SNNs. In this work, we present SpQuant-SNN, a fully-quantized spiking neural network with ultra-low precision weights, membrane potential, and high spatial-channel sparsity, enabling the end-to-end low precision with significantly reduced operations on SNN. First, we propose an integer-only quantization scheme for the membrane potential with a stacked surrogate gradient function, a simple-yet-effective method that enables the smooth learning process of quantized SNN training. Second, we implement spatial-channel pruning with membrane potential prior, toward reducing the layer-wise computational complexity, and floating-point operations (FLOPs) in SNNs. Finally, to further improve the accuracy of low-precision and sparse SNN, we propose a self-adaptive learnable potential threshold for SNN training. Equipped with high biological adaptiveness, minimal computations, and memory utilization, SpQuant-SNN achieves state-of-the-art performance across multiple SNN models for both event-based and static image datasets, including both image classification and object detection tasks. The proposed SpQuant-SNN achieved up to 13× memory reduction and >4.7× FLOPs reduction with < 1.8% accuracy degradation for both classification and object detection tasks, compared to the SOTA baseline.

A combinatorial model reduction method for the finite element analysis of wind instruments

AbstractA high‐fidelity finite element model is proposed for the complete simulation of the time‐harmonic acoustic propagation in wind instruments. The challenge is to meet the extremely high accuracy required by professional musicians, in a complex domain, for all fingerings and over a wide frequency range, within an affordable computational time. Several modelling assumptions are made to limit the numerical complexity of the problem while preserving all relevant physics. A dedicated high‐performance solution strategy is also proposed, based on partitioning, condensation and model order reduction, exploiting the combinatorial nature of wind instrument fingerings. Finally, the proposed approach is applied to the simulation of an alto saxophone. An order of magnitude reduction in memory and computational cost is achieved.

Immunophenotyping and Therapeutic Insights from Chronic Mucocutaneous Candidiasis Cases with STAT1 Gain-of-Function Mutations.

Heterozygous STAT1 Gain-of-Function (GOF) mutations are the most common cause of chronic mucocutaneous candidiasis (CMC) among Inborn Errors of Immunity. Clinically, these mutations manifest as a broad spectrum of immune dysregulation, including autoimmune diseases, vascular disorders, and malignancies. The pathogenic mechanisms of immune dysregulation and its impact on immune cells are not yet fully understood. In treatment, JAK inhibitors have shown therapeutic effectiveness in some patients. We analyzed clinical presentations, cellular phenotypes, and functional impacts in five Taiwanese patients with STAT1 GOF. We identified two novel GOF mutations in 5 patients from 2 Taiwanese families, presenting with symptoms of CMC, late-onset rosacea, and autoimmunity. The enhanced phosphorylation and delayed dephosphorylation were displayed by the patients' cells. There are alterations in both innate and adaptive immune cells, including expansion of CD38+HLADR +CD8+ T cells, a skewed activated Tfh cells toward Th1, reduction of memory, marginal zone and anergic B cells, all main functional dendritic cell lineages, and a reduction in classical monocyte. Baricitinib showed therapeutic effectiveness without side effects. Our study provides the first comprehensive clinical and molecular characteristics in STAT1 GOF patient in Taiwan and highlights the dysregulated T and B cells subsets which may hinge the autoimmunity in STAT1 GOF patients. It also demonstrated the therapeutic safety and efficacy of baricitinib in pediatric patient. Further research is needed to delineate how the aberrant STAT1 signaling lead to the changes in cellular populations as well as to better link to the clinical manifestations of the disease.

DELTA: Memory-Efficient Training via Dynamic Fine-Grained Recomputation and Swapping

To accommodate the increasingly large-scale models within limited-capacity GPU memory, various coarse-grained techniques, such as recomputation and swapping, have been proposed to optimize memory usage. However, these methods have encountered limitations, either in terms of inefficient memory reduction or diminished training performance. In response to this, our paper introduces DELTA, an innovative approach for memory-efficient large-scale model training that combines fine-grained memory optimization and prefetching technology to reduce memory usage while maintaining high training throughput concurrently. Initially, we formulate the problem of memory-throughput joint optimization as an easy-solving 0/1 Knapsack problem. Leveraging this formalization, we use an improving polynomial complexity heuristic algorithm to address the problem effectively. Furthermore, we introduce a novel bidirectional prefetching technology into dynamic memory management, which significantly accelerates the model training when compared to relying solely on recomputation or swapping. Finally, DELTA offers users an automated training execution library, eliminating the need for manual configuration or specialized expertise. Experimental results demonstrate the effectiveness of DELTA in reducing GPU memory consumption. Compared to state-of-the-art methods, DELTA achieves substantial memory savings ranging from 40% to 72%, while maintaining comparable convergence performance for various models, including ResNet-50, ResNet-101, and BERT-Large. Notably, DELTA enables the training of GPT2-Large and GPT2-XL with batch sizes increased by 5.5 × and 6 ×, respectively, showcasing its versatility and practicality in enabling large-scale model training on GPU hardware.

Rapid prediction for the transient dispersion of leaked airborne pollutant in urban environment based on graph neural networks

Rapidly predicting airborne pollutant dispersion in urban is vital for ventilation design and evacuation planning. Computational fluid dynamics (CFD) simulations are commonly used to provide accurate predictions, but the computational cost is too high. Although graph neural networks (GNNs) provide fast predictions of flow fields by manipulating unstructured mesh on GPU, they suffer from high memory usage and accuracy decreases when applied to large-scale urban scenes. Moreover, it is difficult for GNNs to learn the coupled relationship between wind field and pollutant concentration field. We propose a multi-objective GNN model as CFD surrogate to rapidly predict the transient dispersion of airborne pollutant under the influence of complex wind field patterns in urban environment. Based on random urban layouts generated by a 2D bin packing algorithm, we employ a validated CFD model to construct a sample dataset of wind fields and concentration fields. We leverage graph pooling and multi-scale feature fusion to improve prediction accuracy, and subgraph partitioning of both wind field and concentration field to reduce GPU memory usage. The results show that our GNN model at its best runs 1–2 orders of magnitude faster than CFD simulation with accuracy evaluation metrics R2=0.92, and achieves 70 % GPU memory reduction.

Obesity as Inducer of Cognitive Function Decline via Dysbiosis of Gut Microbiota in Rats.

Diet-induced obesity is a global phenomenon that affects the population worldwide with manifestations at both the phenotypic and genotypic levels. Cognitive function decline is a major global health challenge. The relation between obesity and cognitive function is a debatable issue. The main goal of the current research was to study the implications of obesity on cognitive function and gut microbiota diversity and its impact on plasma and brain metabolic parameters in rats. Obesity was induced in rats by feeding on a high-fat (HF) or a high-fat/high-sucrose (HFHS) diet. The results reveal that both the HF (0.683) and HFHS (0.688) diets were effective as obesity inducers, which was confirmed by a significant increase in the body mass index (BMI). Both diet groups showed dyslipidemia and elevation of oxidative stress, insulin resistance (IR), and inflammatory markers with alterations in liver and kidney functions. Obesity led to a reduction in cognitive function through a reduction in short-term memory by 23.8% and 30.7% in the rats fed HF and HFHS diets, respectively, and learning capacity and visuo-spatial memory reduced by 8.9 and 9.7 s in the rats fed an HF or HFHS diet, respectively. Bacteroidetes, Firmicutes, Proteobacteria, Fusobacteria, and Spirochaetes phyla were detected. The Firmicutes/Bacteroidetes ratio (F/B) significantly decreased in the HF group, while it increased in the HFHS group compared to the normal control. The two species, Bacteroides acidifaciens and Bacteroides ovatus, which are associated with IR, were drastically compromised by the high-fat/high-sucrose diet. Some species that have been linked to reduced inflammation showed a sharp decrease in the HFHS group, while Prevotella copri, which is linked to carbohydrate metabolism, was highly enriched. In conclusion: Obesity led to cognitive impairment through changes in short-term and visuo-spatial memory. A metagenomic analysis revealed alterations in the abundance of some microbial taxa associated with obesity, inflammation, and insulin resistance in the HF and HFHS groups.

Olfactory Ecto-Mesenchymal Stem Cells in Modeling and Treating Alzheimer's Disease.

Alzheimer's disease (AD) is a condition in the brain that is marked by a gradual and ongoing reduction in memory, thought, and the ability to perform simple tasks. AD has a poor prognosis but no cure yet. Therefore, the need for novel models to study its pathogenesis and therapeutic strategies is evident, as the brain poorly recovers after injury and neurodegenerative diseases and can neither replace dead neurons nor reinnervate target structures. Recently, mesenchymal stem cells (MSCs), particularly those from the human olfactory mucous membrane referred to as the olfactory ecto-MSCs (OE-MSCs), have emerged as a potential avenue to explore in modeling AD and developing therapeutics for the disease due to their lifelong regeneration potency and facile accessibility. This review provides a comprehensive summary of the current literature on isolating OE-MSCs and delves into whether they could be reliable models for studying AD pathogenesis. It also explores whether healthy individual-derived OE-MSCs could be therapeutic agents for the disease. Despite being a promising tool in modeling and developing therapies for AD, some significant issues remain, which are also discussed in the review.

Montelukast Ameliorates Scopolamine-induced Alzheimer's Disease: Role on Cholinergic Neurotransmission, Antioxidant Defence System, Neuroinflammation and Expression of BDNF.

Alzheimer's disease (AD) is an overwhelming neurodegenerative disease with progressive loss of memory. AD is characterized by the deposition of the senile plaques mainly composed of β-amyloid (Aβ) fragment, BDNF decline, Cholinergic system overactivity and neuroinflammation. Montelukast (MTK), a leukotriene receptor antagonist, showed astounding neuroprotective effects in a variety of neurodegenerative disorders. This study aims to investigate the ameliorative effects of Montelukast in the scopolamineinduced Alzheimer's disease (AD) model in rats and evaluate its activity against neuroinflammation. Thirty rats were split into five groups: Control group (1 mL/kg normal saline, i.p.), Montelukast perse (10 mg/kg, i.p.), Disease group treated with Scopolamine (3 mg/kg, i.p.), Donepezil group (3 mg/kg, i.p.), Montelukast treatment group (10 mg/kg, i.p.) and behavioural and biochemical tests were carried out to assess the neuro protective effect. Scopolamine treatment led to a significant reduction in learning and memory and an elevation in cholinesterase levels when compared with the control group (p < 0.01). Additionally, elevated oxidative stress and Amyloid-β levels were associated with enhanced neuroinflammation (p < 0.05, p < 0.01). Furthermore, the decline in neurotrophic factor BDNF is also observed when compared with the normal control group (p < 0.01). Montelukast pre-treatment significantly attenuated learning and memory impairment and cholinesterase levels. Besides, Montelukast and standard drug donepezil administration significantly suppressed the oxidative stress markers (p < 0.01), Amyloid-β levels, neuroinflammatory mediators (p < 0.05) and caused a significant increase in BDNF levels (p < 0.05). Montelukast bestowed ameliorative effects in scopolamine-induced AD animal models as per the previous studies via attenuation of memory impairment, cholinesterase neurotransmission, oxidative stress, Amyloid-β levels, neuroinflammatory mediators and enhanced BDNF levels.

Neural Appearance Model for Cloth Rendering

AbstractThe realistic rendering of woven and knitted fabrics has posed significant challenges throughout many years. Previously, fiber‐based micro‐appearance models have achieved considerable success in attaining high levels of realism. However, rendering such models remains complex due to the intricate internal scatterings of hundreds of fibers within a yarn, requiring vast amounts of memory and time to render. In this paper, we introduce a new framework to capture aggregated appearance by tracing many light paths through the underlying fiber geometry. We then employ lightweight neural networks to accurately model the aggregated BSDF, which allows for the precise modeling of a diverse array of materials while offering substantial improvements in speed and reductions in memory. Furthermore, we introduce a novel importance sampling scheme to further speed up the rate of convergence. We validate the efficacy and versatility of our framework through comparisons with preceding fiber‐based shading models as well as the most recent yarn‐based model.

IgA class-switched CD27-CD21+ B cells in IgA nephropathy.

IgA nephropathy (IgAN) is characterised by the production of galactose-deficient IgA1 (GdIgA1) antibodies. As the source of pathogenic antibodies, B cells are central to IgAN pathogenesis, but the B cell activation pathways as well as the potential B cell source of dysregulated IgA-secretion remain unknown. We carried out flow cytometry analysis of peripheral blood B cells in patients with IgA nephropathy and control subjects with a focus on IgA-expressing B cells to uncover the pathways of B cell activation in IgAN and how these could give rise to pathogenic GdIgA1 antibodies. In addition to global changes in the B cell landscape - expansion of naive and reduction in memory B cells - IgAN patients present with an increased frequency of IgA-expressing B cells that lack the classical memory marker CD27, but are CD21pos. IgAN patients further have an expanded population of IgApos antibody-secreting cells, which correlate with serum IgA levels. Both IgApos plasmabalsts and CD27neg B cells co-express GdIgA1. Implicating dysregulation at mucosal surfaces as the driver of such B cell differentiation, we found a correlation between lipopolysaccharide (LPS) in the serum and IgAposCD27neg B cell frequency. We propose that dysregulated immunity in the mucosa may drive de novo B cell activation within germinal centres, giving rise to IgAposCD27neg B cells and subsequently IgA-producing plasmablasts. These data integrate B cells into the paradigm of IgAN pathogenesis and allow to further investigate this pathway to uncover biomarkers and develop therapeutic interventions.

Re2Pair: Increasing the Scalability of RePair by Decreasing Memory Usage.

The RePair compression algorithm produces a context-free grammar by iteratively substituting the most frequently occurring pair of consecutive symbols with a new symbol until all consecutive pairs of symbols appear only once in the compressed text. It is widely used in the settings of bioinformatics, machine learning, and information retrieval where random access to the original input text is needed. For example, in pangenomics, RePair is used for random access to a population of genomes. BigRePair improves the scalability of the original RePair algorithm by using Prefix-Free Parsing (PFP) to preprocess the text prior to building the RePair grammar. Despite the efficiency of PFP on repetitive text, there is a scalability issue with the size of the parse which causes a memory bottleneck in BigRePair. In this paper, we design and implement recursive RePair (denoted as ), which builds the RePair grammar using recursive PFP. Our novel algorithm faces the challenge of constructing the RePair grammar without direct access to the parse of text, relying solely on the dictionary of the text and the parse and dictionary of the parse of the text. We compare to BigRePair using SARS-CoV-2 haplotypes and haplotypes from the 1000 Genomes Project. We show that our method achieves over a 40% peak memory reduction and a speed up ranging between 12% to 79% compared to BigRePair when compressing the largest input texts in all experiments. is made publicly available under the GNU public license here: https://github.com/jkim210/Recursive-RePair.

Targeting fear memories: Examining pharmacological disruption in a generalized fear framework

Labilization-reconsolidation, which relies on retrieval, has been considered an opportunity to attenuate the negative aspects of traumatic memories. A therapeutic strategy based on reconsolidation blockade is deemed more effective than current therapies relying on memory extinction. Nevertheless, extremely stressful memories frequently prove resistant to this process. Here, after inducing robust fear memory in mice through strong fear conditioning, we examined the possibility of rendering it susceptible to pharmacological modulation based on the degree of generalized fear (GF). To achieve this, we established an ordered gradient of GF, determined by the perceptual similarity between the associated context (CA) and non-associated contexts (CB, CC, CD, and CE) to the aversive event. We observed that as the exposure context became less similar to CA, the defensive pattern shifted from passive to active behaviors in both male and female mice. Subsequently, in conditioned animals, we administered propranolol after exposure to the different contexts (CA, CB, CC, CD or CE). In males, propranolol treatment resulted in reduced freezing time and enhanced risk assessment behaviors when administered following exposure to CA or CB, but not after CC, CD, or CE, compared to the control group. In females, a similar change in behavioral pattern was observed with propranolol administered after exposure to CC, but not after the other contexts. These results highlight the possibility of indirectly manipulating a robust contextual fear memory by controlling the level of generalization during recall. Additionally, it was demonstrated that the effect of propranolol on reconsolidation would not lead to a reduction in fear memory per se, but rather to its reorganization resulting in greater behavioral flexibility (from passive to active behaviors). Finally, from a clinical viewpoint, this would be of considerable relevance since following this strategy could make the treatment of psychiatric disorders associated with traumatic memory formation more effective and less stressful.

How Maximizers Estimate Their Decision Time: The Mediating Effect of Memory Reduction

ABSTRACTPrevious studies have divided people into maximizers and satisficers based on their tendency to seek the best in decision‐making. In the present research, we aim to unravel the time estimation process of maximizers in decision‐making through four studies. The results indicate that maximizers tend to underestimate the time spent in decision‐making, which is due to the difference in their memory reduction for decision‐related information compared to that of satisficers. Specifically, maximizers' memories of special information (rather than common information) become worse than those of satisficers, which leads to their underestimation of decision time. These findings provide a deeper understanding of how maximizers estimate their decision time, which offers important insights into how maximizers make their decisions. Overall, this research contributes to the literature by shedding new light on maximization from the perspective of memory.

Direct simulations of external flow and noise radiation using the generalized interpolation-supplemented cascaded lattice Boltzmann method

Direct simulations of external flow and the associated noise radiation are studied by an improved lattice Boltzmann method, i.e., the generalized interpolation-supplemented cascaded lattice Boltzmann method (GICLBM). In this method, the cascaded collision scheme is used to improve the numerical stability of the conventional collision schemes, and the generalized interpolation approach is used in the particle streaming process so as to allow a non-uniform and body-fitted mesh partition. With that, both near- and far-field flow dynamics and noise radiation are resolved simultaneously. In order to capture sound waves, the perfectly matched layer is also implemented so as to avoid waves reflecting to and polluting the inner acoustic field. Moreover, a novel index technique is developed for the GICLBM to enable implicit streaming, which brings an efficient memory reduction. Three cases are then performed to showcase the feasibility, accuracy, extensibility, and efficiency of the present framework, including flow past a square cylinder, flow past an elliptic cylinder, and flow past a NACA 0012 airfoil, each implemented with a type of body-fitted mesh. Both the fluid dynamic and noise radiation are found to be in good agreement with results using the Navier–Stokes solvers. This study demonstrates the potential of the GICLBM for accurately and efficiently simulating external problems as well as sound generation and propagation.