Memory Complexity Research Articles

Strongly perturbed bondorbital attractors for generalized systems.

This paper analyzes a generalized chaotic system of differential equations characterized by attractors with bondorbital structures. Both classical and fractional-order cases are examined analytically and numerically, with convergence and stability analyses provided. The numerical findings confirm the presence of bondorbital attractors in the classical system. In contrast, bondorbital attractors also emerge in the fractional model employing the Caputo-Fabrizio operator, albeit with significant perturbations for specific fractional orders. To validate these results, an electric circuit implementation of the fractional-order system using an field-programmable gate array board was conducted, yielding consistent outcomes. This study highlights the potential of fractional calculus, particularly the Caputo-Fabrizio operator, in capturing the memory effects and complex dynamics of chaotic systems. The work bridges theoretical modeling and practical hardware applications, offering valuable insights for modeling complex systems.

Viscoelastic, Shape Memory, and Fracture Characteristics of 3D-Printed Photosensitive Epoxy-Based Resin Under the Effect of Hydrothermal Ageing

Using 3D-printed (3DPd) polymers and their composites as shape memory materials in various smart engineering applications has raised the demand for such functionally graded sustainable materials. This study aims to investigate the viscoelastic, shape memory, and fracture toughness properties of the epoxy-based ultraviolet (UV)-curable resin. A UV-based DLP (Digital Light Processing) printer was employed for the 3D printing (3DPg) epoxy-based structures. The effect of the hydrothermal accelerated ageing on the various properties of the 3DPd components was examined. The viscoelastic performance in terms of glass transition temperature (Tg), storage modulus, and loss modulus was evaluated. The shape memory polymer (SMP) performance with respect to shape recovery and shape fixity (programming the shape) were calculated through dynamic mechanical thermal analysis (DMTA). DMTA is used to reveal the molecular mobility performance through three different regions, i.e., glass region, glass transition region, and rubbery region. The shape-changing region (within the glass transition region) between the Tg value from the loss modulus and the Tg value from the tan(δ) was analysed. The temperature memory behaviour was investigated for flat and circular 3DPd structures to achieve sequential deployment. The critical stress intensity factor values of the single-edge notch bending (SENB) specimens have been explored for different crack inclination angles to investigate mode I (opening) and mixed-mode I/III (opening and tearing) fracture toughness. This study can contribute to the development of highly complex shape memory 3DPd structures that can be reshaped several times with large deformation.

Deep learning techniques for atmospheric turbulence removal: a review

Atmospheric turbulence significantly complicates the interpretation and analysis of images by distorting them, making it hard to classify and track objects within a scene using traditional methods. This distortion arises from unpredictable, spatially varying disturbances, challenging the effectiveness of standard model-based techniques. These methods often become impractical due to their complexity and high memory demands, further complicating the task of restoring scenes affected by atmospheric turbulence. Deep learning approaches offer faster operation and are capable of implementation on small devices. This paper reviews the characteristics of atmospheric turbulence and its impact on acquired imagery. It compares performances of a range of state-of-the-art deep neural networks, including Transformers, SWIN and MAMBA, when used to mitigate spatio-temporal image distortions. Furthermore, this review presents: a list of available datasets; applicable metrics for evaluation of mitigation methods; an exhaustive list of state-of-the-art and historical mitigation methods. Finally, a critical statistical analysis of a range of example models is included. This review provides a roadmap of how datasets and metrics together with currently used and newly developed deep learning methods could be used to develop the next generation of turbulence mitigation techniques.

Analysis of RL electric circuits modeled by fractional Riccati IVP via Jacobi-Broyden Newton algorithm.

This paper focuses on modeling Resistor-Inductor (RL) electric circuits using a fractional Riccati initial value problem (IVP) framework. Conventional models frequently neglect the complex dynamics and memory effects intrinsic to actual RL circuits. This study aims to develop a more precise representation using a fractional-order Riccati model. We present a Jacobi collocation method combined with the Jacobi-Newton algorithm to address the fractional Riccati initial value problem. This numerical method utilizes the characteristics of Jacobi polynomials to accurately approximate solutions to the nonlinear fractional differential equation. We obtain the requisite Jacobi operational matrices for the discretization of fractional derivatives, therefore converting the initial value problem into a system of algebraic equations. The convergence and precision of the proposed algorithm are meticulously evaluated by error and residual analysis. The theoretical findings demonstrate that the method attains high-order convergence rates, dependent on suitable criteria related to the fractional-order parameters and the solution's smoothness. This study not only improves comprehension of RL circuit dynamics but also offers a solid numerical foundation for addressing intricate fractional differential equations.

Divided Memory: Dealing with the Past in the East German Town of Eisenhüttenstadt after the Upheaval of 1989–90

This paper explores perceptions of the German Democratic Republic (GDR) in Eisenhüttenstadt, the “first socialist town of Germany,” following the collapse of the state socialist dictatorship in East Germany. Despite its being the most well-known “socialist town” in Eastern Bloc, no systematic research has been done into how the town dealt with its troubled past. By analyzing discussion and representation of the town’s past in the public space through the year 2010, this study investigates how a town like Eisenhüttenstadt, which has no pre-socialist history, dealt with its past as East Germany transitioned away from state socialism. It also examines the impact of the town’s unique past on its current identity. The author argues that Diktaturgedächtnis [the memory of dictatorship], the lack of a pre-socialist past, and the town’s rejection of radical strategies for dealing with the past have led to complex collective memories and town identity in Eisenhüttenstadt. This complexity manifests itself in the embrace of different symbolic representations of history in different parts of the town and in splits in the public and private, and internal and external, collective memories.

Infinite Mixture Chaining: An Efficiency-Based Framework for the Dynamic Construction of Word Meaning.

The lexicon is an evolving symbolic system that expresses an unbounded set of emerging meanings with a limited vocabulary. As a result, words often extend to new meanings. Decades of research have suggested that word meaning extension is non-arbitrary, and recent work formalizes this process as cognitive models of semantic chaining whereby emerging meanings link to existing ones that are semantically close. Existing approaches have typically focused on a dichotomous formulation of chaining, couched in the exemplar or prototype theories of categorization. However, these accounts yield either memory-intensive or simplistic representations of meaning, while evidence for them is mixed. We propose a unified probabilistic framework, infinite mixture chaining, that derives different forms of chaining through the lens of cognitive efficiency. This framework subsumes the existing chaining models as a trade-off between representational accuracy and memory complexity, and it contributes a flexible class of models that supports the dynamic construction of word meaning by automatically forming semantic clusters informed by existing and novel usages. We demonstrate the effectiveness of this framework in reconstructing the historical development of the lexicon across multiple word classes and in different languages, and we also show that it correlates with human judgment of semantic change. Our study offers an efficiency-based view on the cognitive mechanisms of word meaning extension in the evolution of the lexicon.

Cumulative blood pressure load and cognitive decline in older adults: An observational analysis of two large cohorts.

Cumulative blood pressure metrics may provide greater precision for measuring temporal risk exposure, especially in later life where data are mixed regarding associations of high blood pressure (BP) on cognitive function. We examined the relationship between greater cumulative exposure to high BP in later life and several domains of cognitive function. Individual cognitive assessment scores and BP measurements in older adults (age ≥70 years) at baseline and over approximately 8 years of follow-up were available in the population-based Canadian Victoria Longitudinal Study (VLS) and Swedish Gothenburg H70 Birth Cohort Studies (H70). Linear mixed models were used to quantify associations between cumulative systolic and diastolic BP and change in cognitive scores. Each additional 100mmHg increase in cumulative BP was related to greater decline in the Rey Auditory Verbal Learning Test (RAVLT) List A, trials 1-5 total score over follow-up: -0.23 (95% confidence interval [CI] -0.32, -0.13) for systolic BP and -0.41 (95%CI -0.58, -0.23) for diastolic BP. Similarly increases cumulative systolic and diastolic BP were related to greater declines Digit Symbol Substitution Task (DSS) scores: -0.59 (95%CI -0.80, -0.38) and -1.04 (95% CI -1.40, -0.67), respectively. There were no associations of cumulative BP and temporal changes in general cognition, other measures of verbal episodic memory, or semantic fluency. Higher cumulative BP is associated with greater declines in RAVLT measured immediate memory span and complex attention, information processing speed and visuospatial scanning in older adults, but the scale of change is small. Additional research is required to further define these relationships and identify opportunities for prevention.

Efficient time-domain speech separation using short encoded sequence network

The key to single-channel time-domain speech separation lies in encoding the mixed speech into latent feature representations by encoder and obtaining the accurate estimation of the target speaker masks by the separation network. Despite the advanced separation network contribute to separate target speech, but due to the limitation of the time-domain encoder–decoder framework, these separation models commonly improve the separation performance by setting a small convolution kernel size of encoder to increase the length of the coded sequence, which will result in increased computational complexity and training costs for the model. Therefore, in this paper, we propose an efficient time-domain speech separation model using short-sequence encoder–decoder framework (ESEDNet). In this model, we construct a novel encoder–decoder framework to accommodate short encoded sequences, where the encoder consists of multiple convolution and downsampling operations to reduce length of high-resolution sequence, while the decoder utilizes the encoded features to reconstruct the fine-detailed speech sequence of the target speaker. Since the output sequence of the encoder is shorter, when combined with our proposed multi-temporal resolution Transformer separation network (MTRFormer), ESEDNet can efficiently obtains separation masks for the short encoded feature sequence. Experiments show that compared with previous state-of-the-art (SOTA) methods, ESEDNet is more efficient in terms of computational complexity, training speed and GPU memory usage, while maintaining competitive separation performance.

Historical Memory and Volumetric-Spatial Composition of Tsitsernakaberd Memorial Complex

The erection of memorials and monuments plays an important role in the historical memory of humanity, self-recognition of society and awareness of the social nature of memory. From this point of view, we looked at the architectural-spatial environment of Tsitsernakaberd, the memorial complex dedicated to the victims of the Genocide, the organization of which is subject to the perpetuation of historical memory and is one of the manifestations of the preservation of national identity. It finds its parallels in world practice. The motivation for the construction of the memorial complex was the fiftieth anniversary of Yeghern. The Tsitsernakaberd hill has been completely culturalized, the landscape has passed through four conceptual lenses: ''text'', ''arena'', ''performance'' and ''wound'', achieving the harmony of hand-made and non-handmade. In the work, a complete examination of the architecture of the memorial complex was carried out by the method of comparative analysis of professional literature and personal observations. In conclusion, it can be said that the memorial dedicated to the memory of the Genocide victims, apart from its historical and artistic significance, is also a universal value and a sacred place for all those who consider human life as an absolute value regardless of nationality, religion or race.

(Re)constructing ambivalent identities in Zahia Rahmani’s France: récit d’une enfance (2006) and Alice Zeniter’s L’Art de perdre (2017)

This article examines multivocal, intersubjective explorations of ambivalent identity in two literary works centring female descendants of harkis (Algerian auxiliary soldiers enlisted in the French army during the Algerian War). Zahia Rahmani’s France: récit d’une enfance (2006) and Alice Zeniter’s L’Art de perdre (2017) document their protagonists’ struggles to reconstruct, and simultaneously reconfigure or subvert, complex memories and identities fragmented by colonial history and its afterlives. Plural, often transgressive, forms of speech, dialogue, and storytelling are shown to facilitate explorations of ambivalent intergenerational and intercultural affect, also emerging as forces of critique, resistance, and interpellation. In their navigation of conflictual, ambiguous histories that place diverse memories and identities in productive tension, both texts complicate notions of belonging and inheritance. Their layered, dialogic narrative quests treat subjectivity, memory, and temporality as plural, fluid, and contingent, resisting straightforward identification with a singular identity marker, whether harki , Algerian, Amazigh (Berber), French, or otherwise. Crucially, through the real and imagined landscapes they traverse, they also turn outwards in their questioning of broader oppressive, traumatogenic structures and reductive narratives. ‘Pre-written’ narratives, whether involving colonial or contemporary Algeria, the experiences of the harkis or those of rural French communities, are thus deconstructed and rewritten. Drawing on theorizations and interrogations of postcolonial hybridity and witnessing, this article argues that these narrative (re)constructions privilege neither unified subjectivity nor potentially essentialized forms of fragmentary identity. Citing yet interrogating institutionalized narratives of memorialization, reconciliation, and reparation, Rahmani and Zeniter present ambivalent, fractured intergenerational transmission as a shifting locus of encounter, exchange, and contestation.

Numerical analysis of double-fractional PDEs in MHD hybrid nanofluid blood flow with slip velocity, heat source, and radiation effects

Abstract The study of blood flow in cylindrical geometries resembling small arteries is crucial for advancing drug delivery systems, cardiovascular health, and treatment methods. However, Conventional models have failed to capture the complex memory effects and non-local behavior inherent in blood flow dynamics, which hinders their accuracy in predicting critical flow and heat transfer properties for medical applications. To overcome these limitations, this research introduces a novel fractional-order magnetohydrodynamic model for blood flow, incorporating a $ZnO$ and $Fe_3O_4$ hybrid nanofluid. The model uniquely integrates boundary slip velocity effects within the double fractional Maxwell model (DFMM) rheology framework and utilizes the dual fractional phase lag bioheat model (DFPLM) applied to a porous cylindrical structure. Fractional-order time derivatives in the thermal and momentum equations are formulated using the Caputo approach, with numerical solutions derived via finite difference methods leveraging L1 and L2 approximations for Caputo fractional derivatives. The study examines the effects of fractional orders, relaxation time, and phase lags for heat and temperature, along with parameters such as thermal radiation, wall slip velocity, and porosity. These factors are analyzed for their impact on velocity, temperature, skin friction, and the Nusselt number. Results indicate that the hybrid nanofluid enhances heat transfer compared to blood or mono-hybrid nanofluids, while also reducing skin friction. Furthermore, fractional-order models provide more reliable and realistic predictions under varying flow conditions. The DFMM shows smoother transitions in velocity and friction, while the DFPLM predicts higher temperatures and greater heat transfer enhancement compared to classical and single-phase lag models. By integrating fractional calculus, this model offers improved simulation of complex transport phenomena in small arteries, contributing to the development of more effective cardiovascular treatments.

Avenida d. Pedro I: memória nacional e tensões na construção da São Paulo moderna

This text deals with the construction of the urban landscape in Colina do Ipiranga, São Paulo city, birthplace of the Brazilian nation; dealing in a particular way with one of its elements, avenida d. Pedro I, examining the disputes and the definition of its layout. The construction of the memorial complex for the independence of Brazil began in 1885, with the monumental building, and was completed with the works carried out in commemoration of the Centenary of Independence: the garden, the sculptural monument, and the avenue.

UMap: An application-oriented user level memory mapping library

Exploiting the prominent role of complex memories in exascale node architecture, the UMap page fault handler offers new capabilities to access large memory-mapped data sets directly. UMap provides flexible configuration options to customize page handling to each application, including analysis of massive observational and simulation data sets. The high-performance design features I/O decoupling, dynamic load balancing, and application-level controls. Page faults triggered by application threads and processes accessing data mapped to a UMapp’ed region are handled via the Linux userfaultfd protocol, an asynchronous message-oriented kernel-user communication mechanism that avoids the context switch penalty of traditional signal fault handlers. UMap is fully open source. In this paper, we give an overview of the UMap library architecture, its extensible plugin architecture, and the use/performance of UMap in emerging heterogeneous memory hierarchies such as near-node Non-volatile Memory (NVM) and network attached memories. We highlight new capabilities in two pagefault management plugins, the NetworkStore and SparseStore. We demonstrate the integration between UMap and multiple ECP products including Caliper, Metall, ZFP, Mochi, and Ripples.

Complex memories induced by intracranial electrical brain stimulation are related to complex networks.

The precise and fleeting moment of rich recollection triggered by an environmental cue is difficult to reproduce in the lab. However, epilepsy patients can experience sudden reminiscences after intracranial electrical brain stimulation (EBS). In these cases, the transient brain state related to the activation of the engram and its conscious perception can be recorded using intracerebral EEG (iEEG). We collected various EBS-induced reminiscences for iEEG analysis, classifying them as follows: no or weak details (familiarity); moderate details and context (semantic and personal semantic memories); high details and context (episodic). Nine brain areas were selected within the temporal lobes (including the hippocampus and temporal neocortex, ipsi- and contralateral) and the insula, defining a network (each area as a node). Functional connectivity was measured by estimating pair-wise non-linear correlations between signals recorded from these brain regions during different memory events. Seventeen reminiscences in six patients (2 episodic, 10 personal semantic, 2 semantic memories, 5 familiar objects, 1 déjà-rêvé) were compared to 18 control experiential phenomena (unrelated to reminiscence), 18 negative EBS (which failed to elicit memories or other phenomena) in the same locations, and pre-EBS baseline activity. The global functional connectivity in the network was higher following EBS-induced reminiscences than during baseline activity, control phenomena, or negative EBS. The degree of connectivity increased with the complexity of memories; it was higher for detailed and contextualized memories like episodic memories. More significant links compared to baseline (edges with higher non-linear correlation relative to baseline) were observed for episodic memories than for less contextualized memories. These increases in connectivity occurred in all frequency bands, except the delta band. Our results support understanding declarative memory retrieval as having a multiplexed organization. They also show that richer memories activated by intracranial EBS are related to more complex connectivity patterns across medial and neocortical temporal lobe structures.

A Mesh-based Simulation Framework using Automatic Code Generation

Optimized parallel implementations on GPU or CPU have dramatically enhanced the fidelity, resolution and accuracy of physical simulations and mesh-based algorithms. However, attaining optimal performance requires expert knowledge and might demand complex code and memory layout optimizations. This adds to the fact that physical simulation algorithms require the implementation of derivatives, which can be a tedious and error-prone process. In recent years, researchers and practitioners have investigated the concept of designing systems that allow for a more expressive definition of mesh-based simulation code. These systems leverage domain-specific languages (DSL), automatic differentiation or symbolic computing to enhance readability of implementations without compromising performance. We follow this line of work and propose a symbolic code generation approach tailored to mesh-based computations on parallel devices. Our system extends related work by incorporating collision handling and a data access synchronization approach, enabling rapid sparse matrix assembly.

АРХІТЕКТУРА ВІЙСЬКОВИХ МЕМОРІАЛІВ. ДОСІВІД ЇХ ПРОЄКТУВАННЯ В УКРАЇНІ І СВІТІ

The article provides a comprehensive analysis of the historical prerequisites for the formation of memorial architecture and modern trends in the design of memorial objects, in particular military cemeteries, which reflect the memory of historical events and fallen heroes. Special attention is paid to the experience of creating such objects in Ukraine and abroad, covering memorial complexes in Kyiv, Odesa, Cherkasy and Lviv, as well as in Serbia, Croatia, Bosnia and Herzegovina, Germany and the USA. The study covers architectural and artistic-aesthetic solutions of memorial complexes, which embody not only historical memory, but also function as symbols of national identity and cultural identity. Particular emphasis is placed on modern approaches, dominated by minimalist and supremacist solutions, which create an emotional and spiritual atmosphere for visitors, helping not only to honor the memory of the fallen, but also to gain a deeper understanding of one's own national belonging. The authors analyze the examples of design of memorial objects developed by the Lviv School of Architecture, in particular, the Department of Architectural Environment Design, in the form of educational, research and competition projects. These projects have the potential for the further development of the national architecture of memorial-monumental design, in particular in the context of commemorating the fallen military and civilians in Ukraine's current war against Russian aggression. Examples of the renovation of memorial objects, developed within the framework of bachelor's and master's programs, were studied, in particular, projects of military memorials in Lviv and other cities of Ukraine. Emphasis is placed on the combination of architectural, cultural and national aspects in the creation of memorial objects that serve not only as places of memory, but also as symbols of national unity and identity. This work emphasizes the importance of an architectural approach to the preservation of historical memory, contributing to the formation of new cultural symbols and the revival of national heritage through monumental and memorial design.

B-move: Faster Lossless Approximate Pattern Matching in a Run-Length Compressed Index.

Due to the increasing availability of high-quality genome sequences, pan-genomes are gradually replacing single consensus reference genomes in many bioinformatics pipelines to better capture genetic diversity. Traditional bioinformatics tools using the FM-index face memory limitations with such large genome collections. Recent advancements in run-length compressed indices like Gagie et al.'s r-index and Nishimoto and Tabei's move structure, alleviate memory constraints but focus primarily on backward search for MEM-finding. Arakawa et al.'s br-index initiates complete approximate pattern matching using bidirectional search in run-length compressed space, but with significant computational overhead due to complex memory access patterns. We introduce b-move, a novel bidirectional extension of the move structure, enabling fast, cache-efficient, lossless approximate pattern matching in run-length compressed space. It achieves bidirectional character extensions up to 7 times faster than the br-index, closing the performance gap with FM-index-based alternatives. For locating occurrences, b-move performs and operations up to 7 times faster than the br-index. At the same time, it maintains the favorable memory characteristics of the br-index, for example, all available complete E. coli genomes on NCBI's RefSeq collection can be compiled into a b-move index that fits into the RAM of a typical laptop. b-move proves practical and scalable for pan-genome indexing and querying. We provide a C++ implementation of b-move, supporting efficient lossless approximate pattern matching including locate functionality, available at https://github.com/biointec/b-move under the AGPL-3.0 license.

Speech Enhancement Algorithm Based on Microphone Array and Lightweight CRN for Hearing Aid

To address the performance and computational complexity issues in speech enhancement for hearing aids, a speech enhancement algorithm based on a microphone array and a lightweight two-stage convolutional recurrent network (CRN) is proposed. The algorithm consists of two main modules: a beamforming module and a post-filtering module. The beamforming module utilizes directional features and a complex time-frequency long short-term memory (CFT-LSTM) network to extract local representations and perform spatial filtering. The post-filtering module uses analogous encoding and two symmetric decoding structures, with stacked CFT-LSTM blocks in between. It further reduces residual noise and improves filtering performance by passing spatial information through an inter-channel masking module. Experimental results show that this algorithm outperforms existing methods on the generated hearing aid dataset and the CHIME-3 dataset, with fewer parameters and lower model complexity, making it suitable for hearing aid scenarios with limited computational resources.

Lateralization of memory function in temporal lobe epilepsy using scene memory fMRI.

Functional magnetic resonance imaging (fMRI) offers an alternative to the traditional Wada test for presurgical language and memory lateralization that carries almost no risk. However, fMRI lateralization of episodic memory remains challenging because the hippocampus, which is fundamental to episodic memory, is smaller, more prone to susceptibility artifact, and harder to functionally modulate than language regions. We previously showed that a complex scene memory task can lateralize memory function in the mesial temporal lobe. Using data acquired from N = 45 patients with temporal lobe epilepsy acquired with an improved stimulus paradigm and high-resolution fMRI, we now demonstrate that memory activation can be successfully lateralized within hippocampus proper. PLAIN LANGUAGE SUMMARY: Epilepsy surgery can improve seizure control in patients with temporal lobe epilepsy (TLE) that cannot be controlled with medications also, but ablation or removal of temporal lobe brain tissue can also cause cognitive deficits. Functional MRI (fMRI) can noninvasively map brain activation and perform well in lateralizing and localizing language function, but localizing and lateralizing memory function is more challenging. Building upon prior work using complex scene encoding to map memory function, we demonstrate that the use of high-resolution fMRI along with an optimized task paradigm allows memory activation to be detected within the hippocampus. Because the hippocampus is both a common site of TLE and a key region underlying memory, this approach is expected to contribute to presurgical evaluation of TLE.

The ECP SICM project: Managing complex memory hierarchies for exascale applications

The Exascale Computing Project (ECP)’s Simplified Interface to Complex Memories (SICM) effort focuses on developing universal interfaces for discovering, managing, and sharing data across complex memory hierarchies. These facilitate the exploitation of emerging memory technologies and support precise control over their various trade-offs such as high-bandwidth versus low-latency, persistent versus ephemeral, high-capacity versus low-capacity, and near-CPU versus near-GPU. SICM comprises three interrelated components: a low-level interface, a high-level interface, and a persistent-heap interface. The low-level SICM interface is intended for system and run-time developers as well as expert application developers who prefer full control of the memory objects used within their application. The high-level SICM interface builds upon the low-level interface, employing application-level profiling and analysis to optimize data management for complex memory hierarchies. The persistent-heap interface provides applications with a persistent memory allocator that can allocate custom C++ data structures in both block-storage and byte-addressable persistent memories.