bluuebunny/crossref_metadata_2025 huggingface dataset:
Multi-GPU k-Nearest Neighbor Search in the Context of Data Embedding
Authors: Kłusek Adrian  , Dzwinel Witold
Published: February 2025

The k-nearest neighbor (k-NN) search is the rudimentary procedure widely used in machine learning and data embedding techniques. Herein we present a new multi-GPU/CUDA implementation of the brute-force (BF) k-NN algorithm. We demonstrate its advantages over currently the fastest GPU/CUDA implementations of BF k-NN, e.g., [1] both in terms of computational time and memory requirements. Unlike its competitors, our code scales linearly with the number of GPUs (up to 8 units) what allows for scrutinizing much larger high-dimensional datasets. We also present a new GPU implementation of the approximate k-NN algorithm used in the LargeVis data embedding algorithm [2], which is more than two orders of magnitude faster than its original CPU version. We discuss its limitations in terms of accuracy, efficiency and usefulness in data embedding.
Optimal Nine Node Diffusion for Special Torus Graphs
Authors: Dimitrakopoulou K.
Published: February 2025

The local Extrapolated Diffusion (EDF) method was studied in [5] for a torus network using communication among 4 adjacent neighbors. In the present paper we study the performance of the same method with the extension that we add four additional edges to the central node in position (i,j) of the torus network. We develop the EDF method for a 8-regular torus network. The method uses two sets of parameters and for each node in order to increase its rate of convergence. The conventional way to analyze the convergence of the Diffusion method is to use matrix analysis. This approach depends heavily upon the property of the Laplacian matrix being circulant. However, the Laplacian matrix of our method does not have this property. To circumvent this problem we use Fourier analysis to determine optimum values for the set of parameters via a closed form formulae resulting in the maximization of its rate of convergence. It is shown that the optimum value of the convergence factor depends only upon the dimensions of the torus. Moreover, by keeping fixed the one dimension and increasing the other dimension of the torus the rate of convergence of the EDF method with 8 adjacent neighbors is also increased compared to the EDF method with 4 adjacent neighbors.
Porting of the DBCSR Library for Sparse Matrix-Matrix Multiplications to Intel Xeon Phi Systems
Authors: Bethune Iain  , Glöss Andreas  , Hutter Jürg  , Lazzaro Alfio  , Pabst Hans  , Reid Fiona
Published: February 2025

Multiplication of two sparse matrices is a key operation in the simulation of the electronic structure of systems containing thousands of atoms and electrons. The highly optimized sparse linear algebra library DBCSR (Distributed Block Compressed Sparse Row) has been specifically designed to efficiently perform such sparse matrix-matrix multiplications. This library is the basic building block for linear scaling electronic structure theory and low scaling correlated methods in CP2K. It is parallelized using MPI and OpenMP, and can exploit GPU accelerators by means of CUDA. We describe a performance comparison of DBCSR on systems with Intel Xeon Phi Knights Landing (KNL) processors, with respect to systems with Intel Xeon CPUs (including systems with GPUs).
Implementing Deep Neural Networks on Fresh Breeze
Authors: Dennis Jack B.  , Huang Lei  , Lim Willie  , Wu Hsiang-Huang  , Yan Yuzhong
Published: February 2025

This paper discusses how deep neural network simulations can be implemented and executed on a proposed computer system inspired by data flow principles. In particular, we use the cycle accurate Kiva simulator developed by the MIT CSAIL Fresh Breeze project to simulate the data flow machine used for running the neural networks. An example of a deep neural network and its simulation using Kiva is presented and used to demonstrate the benefits of the tree-of-chunks representation, hardware task scheduling, memory unification, and modular programming. The neural network is written in funJava, a functional subset of Java. Our compiler for funJava programs converts funJava methods to data flow graphs, identifies opportunities for data parallel implementation, and generates a graph of machine executable code blocks called codelets which are executed as tasks by the data flow machine. The behavior and performance of the neural network as the input data size and the number of cores in the data flow machine are varied are reported and discussed.
Distributed Event-Based Computing
Authors: Brown Andrew  , Thomas David  , Reeve Jeff  , Tarawneh Ghaith  , De Gennaro Alessandro  , Mokhov Andrey  , Naylor Matthew  , Kazmierski Tom
Published: February 2025

As computing systems get larger in capability – a good thing – they also get larger in ways less desirable: cost, volume, power requirements and so on. Further, as the datastructures necessary to support large computations grow physically, the proportion of wallclock time spent communicating increases dramatically at the expense of the time spent calculating. This state of affairs is currently unacceptable and will only get worse as exa-scale machines move from the esoteric to the commonplace. As the unit cost of non-trivial cores continues to fall, one powerful approach is to build systems that have immense numbers of relatively small cores embedded (both geometrically and topologically) in a vast distributed network of stored state data: take the compute to the data, rather than the other way round. In this paper, we describe POETS – Partially Ordered Event Triggered Systems. This is a novel kind of computing architecture, built upon the neuromorphic concept that has inspired such machines as SpiNNaker[1,2] and BrainScaleS[3]. The central idea is that a problem is broken down into a large set of interacting devices, which communicate asynchronously via small, hardware brokered packets (the arrival of which is an event). The set of devices is the task graph. You cannot take a conventional codebase and port it to a POETS architecture; it is necessary to strip the application back to the underlying mathematics and reconstruct the algorithm in a manner sympathetic to the solution capabilities of the machine. However, for the class of problems for which this approach is suitable, POETS has already demonstrated solution speedups of a factor of 200 over conventional techniques.
ParaFPGA 2017: Enlarging the Scope of Parallel Programming with FPGAs
Authors: D'Hollander Erik H.  , Touhafi Abdellah
Published: February 2025

The biennial mini-symposium “Parallel computing with FPGAs” brings together research on applications and tools fostering the use of field programmable gate arrays. Key aspects are the efficiency, programmability, scalability and portability of high-level synthesis languages and tools. In particular this year's contributions present productivity and programmability results of using HLS languages OpenCL, OmpSs, MATLAB/Octave, OpenSPL and Vivado HLS. The current state and future challenges of HLS within the FPGA landscape is covered in a special keynote on bridging the gap between software and hardware designers.
Higher-Level Parallelism Abstractions for Video Applications with SPar
Authors: Griebler Dalvan  , Hoffmann Renato B.  , Danelutto Marco  , Fernandes Luiz G.
Published: February 2025

SPar is a Domain-Specific Language (DSL) designed to provide high-level parallel programming abstractions for streaming applications. Video processing application domain requires parallel processing to extract and analyze information quickly. When using state-of-the-art frameworks such as FastFlow and TBB, the application programmer has to manage source code re-factoring and performance optimization to implement parallelism efficiently. Our goal is to make this process easier for programmers through SPar. Thus we assess SPar's programming language and its performance in traditional video applications. We also discuss different implementations compared to the ones of SPar. Results demonstrate that SPar maintains the sequential code structure, is less code intrusive, and provides higher-level programming abstractions without introducing notable performance losses. Therefore, it represents a good choice for application programmers from the video processing domain.
Energy-Efficiency Evaluation of Intel KNL for HPC Workloads
Authors: Calore Enrico  , Gabbana Alessandro  , Schifano Sebastiano Fabio  , Tripiccione Raffaele
Published: February 2025

In this work we focus on energy performance of the Knights Landing Xeon Phi, the latest many-core architecture processor introduced by Intel for the HPC market. We take into account the 64-core Xeon Phi 7230, and analyze the computing and energy efficiency using both the on-chip MCDRAM and the off-chip DDR4 memory as main storage for the application data domain. As a benchmark application we use a Lattice Boltzmann code heavily optimized for this architecture, and implemented using different memory data layouts to store the data-domain. We then assess the energy consumption using different data-layouts, memory configurations (DDR4 or MCDRAM), and number of threads per core.
Solving Sparse Linear Systems of Equations Using Fortran Coarrays
Authors: Abdullahi Hassan Ambra  , Cardellini Valeria  , Filippone Salvatore
Published: February 2025

Coarrays have been part of the Fortran standard since Fortran 2008 and provide a syntactic extension of Fortran to support parallel programming, often called Coarray Fortran (CAF). Although MPI is the de facto standard for parallel programs running on distributed memory systems and little scientific software is written in CAF, many scientific applications could benefit from the use of CAF. We present the migration from MPI to CAF of the libraries PSBLAS and MLD2P4 for the solution of large systems of equations using iterative methods and preconditioners. In this paper, we describe some investigations for implementing the necessary communication steps in PSBLAS and MLD2P4 and provide performance results obtained on linear systems arising from discretization of 2D and 3D PDEs.
Clinical Trials for Disease-Modifying Therapies in Alzheimer's Disease: A Primer, Lessons Learned, and a Blueprint for the Future
Authors: Cummings Jeffrey  , Ritter Aaron  , Zhong Kate
Published: February 2025

Alzheimer's disease (AD) has no currently approved disease-modifying therapies (DMTs), and treatments to prevent, delay the onset, or slow the progression are urgently needed. A delay of 5 years if available by 2025 would decrease the total number of patients with AD by 50% in 2050. To meet the definition of DMT, an agent must produce an enduring change in the course of AD; clinical trials of DMTs have the goal of demonstrating this effect. AD drug discovery entails target identification followed by high throughput screening and lead optimization of drug-like compounds. Once an optimized agent is available and has been assessed for efficacy and toxicity in animals, it progresses through Phase I testing with healthy volunteers, Phase II learning trials to establish proof-of-mechanism and dose, and Phase III confirmatory trials to demonstrate efficacy and safety in larger populations. Phase III is followed by Food and Drug Administration review and, if appropriate, market access. Trial populations include cognitively normal at-risk participants in prevention trials, mildly impaired participants with biomarker evidence of AD in prodromal AD trials, and subjects with cognitive and functional impairment in AD dementia trials. Biomarkers are critical in trials of DMTs, assisting in participant characterization and diagnosis, target engagement and proof-of-pharmacology, demonstration of disease-modification, and monitoring side effects. Clinical trial designs include randomized, parallel group; delayed start; staggered withdrawal; and adaptive. Lessons learned from completed trials inform future trials and increase the likelihood of success.
Three Decades of Dementia Research: Insights from One Small Community of Indomitable Rotterdammers
Authors: Wolters Frank J.  , Adams Hieab H.H.  , Bos Daniel  , Licher Silvan  , Ikram M. Arfan
Published: February 2025

The most commonly encountered opening sentence in scientific publications about dementia undoubtedly relates to the overwhelming burden of disease. Finding an effective preventive or therapeutic intervention against dementia has been considered the most important unmet need in contemporary medicine. While efforts on tackling this devastating disease have increased exponentially, it is difficult to imagine that in the 1980s and early-1990s, the disease did not feature prominently on any public health report. Yet, it was already then that epidemiologists recognized the growing societal burden of dementia and rationalized that dementia is not necessarily part of aging. Indeed, the conviction that dementia is pathologically distinct from aging led to various efforts in search of unravelling its risk factors and understanding its pre-clinical phase. Among the early pioneers, the population-based Rotterdam Study was initiated in 1990 clearly aiming on chronic diseases including dementia, and among this Alzheimer's disease, as one of its focus points. Ever since, the Rotterdam Study has been an important cornerstone in increasing our knowledge about dementia from an epidemiological perspective. Here, we summarize the main findings originating from this study, and put these into perspective with previous and current work in the field. With an expanding scope of the Rotterdam Study over the years, we discuss findings on occurrence, modifiable risk factors, imaging, and its genetic underpinnings. Importantly, we conclude with recommendations—or, perhaps better stated, a wish list—for future research which may help us reach our finish line: finding an effective preventive or therapeutic intervention against dementia.
Multimodal Neuroimaging in Alzheimer's Disease: Early Diagnosis, Physiopathological Mechanisms, and Impact of Lifestyle
Authors: Chételat Gaël
Published: February 2025

Over the last ten years, we have conducted research in Alzheimer's disease (AD) using multimodal neuroimaging techniques to improve diagnosis, further our understanding of the pathological mechanisms underlying the disease, and support the development of innovative non-pharmacological preventive strategies. Our works emphasized the interest of hippocampal subfield volumetry in early diagnosis and the need for further development in this field including optimization, standardization, and automatization of the techniques. Also, we conducted several studies in cognitively intact at-risk elderly (e.g., subjective cognitive decline patients and APOE4 carriers) to better identify biomarkers associated with increased risk of developing AD. Regarding the physiopathological mechanisms, specific multimodal neuroimaging techniques allowed us to highlight the relevance of diaschisis, the mismatch between neurodegeneration and local Aβ deposition and the regional variation in the mechanisms underlying structural or functional alterations. Further works integrating other biomarkers known to play a role in the physiopathology of AD (tau, TDP-43, inflammation, etc.) in a longitudinal design would be useful to get a comprehensive understanding of their relative role, sequence, and causal relationships. Our works also highlighted the relevance of functional connectivity in further understanding the specificity of cognitive deficits in AD and how connectivity differentially influences the propagation of the different AD biomarkers. Finally, we conducted several studies on the links between lifestyle factors and neuroimaging biomarkers to unravel mechanisms of reserve. Further efforts are needed to better understand which lifestyle factor, or combination of factors, impact on AD pathology, and when, to help translating our knowledge to training programs that might prevent or delay brain and cognitive changes leading to AD dementia.
Preclinical Alzheimer's Disease: Implications for Refinement of the Concept
Authors: Vos Stephanie J.B.  , Jelle Visser Pieter
Published: February 2025

Increasing interest in clinical trials and clinical research settings to identify Alzheimer's disease (AD) in the earliest stages of the disease has led to the concept of preclinical AD. Individuals with preclinical AD have AD pathology without clinical symptoms yet. Accumulating evidence has shown that biomarkers can identify preclinical AD and that preclinical AD is associated with a poor clinical outcome. Little is known yet about the role of vascular and lifestyle risk factors in the development of preclinical AD. In order to better understand preclinical AD pathology and clinical progression rates, there is a need to refine the concept of preclinical AD. This will be of great value for advancements in future research, clinical trials, and eventually clinical practice.
Cerebrospinal Fluid Biomarkers in Alzheimer's Disease: An Invaluable Tool for Clinical Diagnosis and Trial Enrichment
Authors: Parnetti Lucilla  , Eusebi Paolo
Published: February 2025

Alzheimer's disease (AD) is the most common neurodegenerative disorder, affecting around 35 million people worldwide. Cerebrospinal fluid (CSF) biomarkers entered the diagnostic criteria as support for early diagnosis. The classical biochemical signature of AD includes total tau (T-tau), phosphorylated tau (P-tau), and the 42 amino acid peptide (Aβ42) of amyloid-β. Recent observations suggest that the use of CSF Aβ42:Aβ40ratio rather than CSF Aβ42alone could contribute to reduce inter-laboratory variation in Aβ values and increasing diagnostic performance of the CSF AD biomarkers in routine practice. However, research efforts aimed at enriching the CSF biomarker panel are ongoing. The CSF AD signature is also crucial for the design of clinical trials for AD, since it best guarantees AD pathology as the cause of cognitive impairment. Accordingly, CSF biomarkers have been now reported in the inclusion criteria of Phase I, Phase II, and Phase III clinical trials as enrichment strategy. So far, one of the most important reasons for the failure of AD clinical trials was the inclusion of participants with unlikely AD pathology. In order to implement the use of CSF biomarkers in AD routine diagnostic work-up and as accepted strategy for enriching trial populations, inter-laboratory variability should be minimized. Increasing efforts should also be devoted to promote data sharing practices, encouraging individual participant data meta-analyses.
Lost in Translation? Finding Our Way To Effective Alzheimer's Disease Therapies
Authors: Quinn Joseph F.
Published: February 2025

Efforts over the past two decades to develop effective disease-modifying treatments for Alzheimer's disease have been disappointing, while parallel efforts in another chronic neurologic disease, multiple sclerosis, have been remarkably productive. In an effort to advance development of therapeutics for Alzheimer's disease, these two fields are contrasted in terms of the utility of animal models, definition of study populations, and utility of biomarkers. Possible solutions are suggested, and the review concludes with description of some active peer-reviewed, publicly funded clinical studies which address some of the identified weaknesses in past clinical trials for age-related dementia.
Toward a New Concept of Alzheimer's Disease Models: A Perspective from Neuroinflammation
Authors: Gutierrez Antonia  , Vitorica Javier
Published: February 2025

The continuing failure to develop an effective treatment for Alzheimer's disease urges a better understanding of the pathogenic mechanisms and the improvement of current animal models to facilitate success for clinical interventions. The transgenic models have been so far designed to recapitulate one, or both, protein lesions found in the brain of patients, the extracellular amyloid plaques and the intraneuronal neurofibrillary tangles. However, in recent years, a third pathogenic component is gaining strength in the onset and progression of this disease, the neuroinflammatory response mediated primarily by the brain's resident immune cells, microglia. This has been highlighted by the identification of genes involved in innate immunity as risk factors to develop this neurodegenerative disease. Our current concept, mostly derived from amyloid-β producing models which show a robust microglial activation, supports an initial beneficial role of these glial cells followed by a pro-inflammatory cytotoxic function later on. This view is now challenged by emerging data in human postmortem samples. We have recently demonstrated that in the hippocampus of Braak V-VI individuals there is a prominent degenerative process of the microglial population, driven by phospho-tau, that might compromise neuronal homeostasis. This scenario of microglial dysfunction/degeneration should be taken into account for developing more reliable animal models of this disease and improve their predictive value for human drug efficacy testing. Finally, correcting dysregulated brain inflammatory responses might be a promising avenue to restore cognitive function.
Iron and Alzheimer's Disease: An Update on Emerging Mechanisms
Authors: Lane Darius J.R.  , Ayton Scott  , Bush Ashley I.
Published: February 2025

Iron is a crucial transition metal for life and is the most abundant transition metal in the brain. However, iron's biological utility as an effective redox cycling metal also endows it with the potential to catalyze production of noxious free radicals. This “Janus-faced” nature of iron demands a tight regulation of cellular its metabolism. This regulation is crucial in the CNS, where iron plays myriad keystone roles in CNS processes, including mitochondrial energy transduction, enzyme catalysis, mitochondrial function, myelination, neurotransmitter anabolism and catabolism. Aberrations in brain iron homeostasis can elevate levels of this redox-active metal, leading to mislocalization of the metal and catastrophic oxidative damage to sensitive cellular and subcellular structures. Iron dyshomeostasis has been strongly linked to the pathogenesis of Alzheimer's disease (AD), as well as other major neurodegenerative diseases. Despite the growing societal burden of AD, no disease-modifying therapy exists, necessitating continued investment into both drug-development and the fundamental science investigating the disease-causing mechanisms. Targeting iron dyshomeostasis in the brain represents a rational approach to treat the underlying disease. Here we provide an update on known and emerging iron-associated mechanisms involved in AD. We conclude with an overview of evidence suggesting that, in addition to apoptosis, neuronal loss in AD involves “ferroptosis”, a newly discovered iron- and lipid-peroxidation-dependent form of regulated necrosis. The ferroptosis field is rapidly progressing and may provide key insights for future drug-development with disease-modifying potential in AD.
Metabolic Dysfunction in Alzheimer's Disease: From Basic Neurobiology to Clinical Approaches
Authors: Clarke Julia R.  , Ribeiro Felipe C.  , Frozza Rudimar L.  , De Felice Fernanda G.  , Lourenco Mychael V.
Published: February 2025

Clinical trials have extensively failed to find effective treatments for Alzheimer's disease (AD) so far. Even after decades of AD research, there are still limited options for treating dementia. Mounting evidence has indicated that AD patients develop central and peripheral metabolic dysfunction, and the underpinnings of such events have recently begun to emerge. Basic and preclinical studies have unveiled key pathophysiological mechanisms that include aberrant brain stress signaling, inflammation, and impaired insulin sensitivity. These findings are in accordance with clinical and neuropathological data suggesting that AD patients undergo central and peripheral metabolic deregulation. Here, we review recent basic and clinical findings indicating that metabolic defects are central to AD pathophysiology. We further propose a view for future therapeutics that incorporates metabolic defects as a core feature of AD pathogenesis. This approach could improve disease understanding and therapy development through drug repurposing and/or identification of novel metabolic targets.
New Beginnings in Alzheimer's Disease: The Most Prevalent Tauopathy
Authors: Hernández Félix  , Llorens-Martín María  , Bolós Marta  , Pérez Mar  , Cuadros Raquel  , Pallas-Bazarra Noemí  , Zabala Juan C.  , Avila Jesús
Published: February 2025

Alzheimer's disease (AD) is characterized by the presence of two aberrant structures: namely senile plaques, composed of amyloid-β peptide (Aβ), and neurofibrillary tangles, composed of tau protein. In this regard, Aβ and tau protein have been widely studied in research efforts aiming to find a therapy for AD. Aβ and tau pathologies do not always overlap. The precursor of Aβ is expressed in peripheral tissues and in the central nervous system (CNS), whereas tau is mainly a neuronal protein. Since AD is a disease of the CNS, it has been proposed that Aβ may initiate the disease process, with tau being the executor. In this review, we will focus on future studies of tau pathology, although we will comment on new beginnings for AD, as other molecules other than Aβ and tau may be involved in the onset of dementia.
Tau Conformation as a Target for Disease-Modifying Therapy: The Role of Truncation
Authors: Novak Petr  , Cehlar Ondrej  , Skrabana Rostislav  , Novak Michal
Published: February 2025

Tau protein plays a major role in the pathogenesis of Alzheimer's disease. Despite many decades of intensive research, the cause of the conformational switch that leads to the remodeling of the highly flexible conformational ensemble of intrinsically disordered protein tau into insoluble filaments is still elusive. We show here that truncation of tau may play a causative role in this conformational change, as evidenced by results obtained from in vitro experiments and from transgenic animal models. This conformational change is a common denominator of pathological tau protein assemblies, and a salient drug target. The long-running research of truncated tau has led to the generation of the first active tau vaccine that has entered clinical trials.
The Conundrum of GSK3 Inhibitors: Is it the Dawn of a New Beginning?
Authors: Bhat Ratan V.  , Andersson Ulf  , Andersson Shalini  , Knerr Laurent  , Bauer Udo  , Sundgren-Andersson Anna K.
Published: February 2025

Spanning over three decades of extensive drug discovery research, the efforts to develop a potent and selective GSK3 inhibitor as a therapeutic for the treatment of type 2 diabetes, Alzheimer's disease (AD), bipolar disorders and cancer have been futile. Since its initial discovery in 1980 and subsequent decades of research, one cannot underscore the importance of the target and the promise of a game changing disease modifier. Several pharmaceutical companies, biotech companies, and academic institutions raged in a quest to unravel the biology and discover potent and selective GSK3 inhibitors, some of which went through clinical trials.However, the conundrum of what happened to the fate of the AstraZeneca's GSK3 inhibitors and the undertaking to find a therapeutic that could control glycogen metabolism and aberrant tau hyperphosphorylation in the brain, and rescue synaptic dysfunction has largely been untold. AstraZeneca was in the forefront of GSK3 drug discovery research with six GSK3 drug candidates, one of which progressed up to Phase II clinical trials in the quest to untangle the tau hypothesis for AD. Analysis of key toxicity issues, serendipitous findings and efficacy, and biomarker considerations in relation to safety margins have limited the potential of small molecule therapeutics as a way forward. To guide future innovation of this important target, we reveal the roller coaster journey comprising of two decades of preclinical and clinical GSK3 drug discovery at AstraZeneca; the understanding of which could lead to improved GSK3 therapies for disease. These learnings in combination with advances in achieving kinase selectivity, different modes of action as well as the recent discovery of novel conjugated peptide technology targeting specific tissues have potentially provided a venue for scientific innovation and a new beginning for GSK3 drug discovery.
The Amyloid-β Oligomer Hypothesis: Beginning of the Third Decade
Authors: Cline Erika N.  , Assunção Bicca Maíra  , Viola Kirsten L.  , Klein William L.
Published: February 2025

The amyloid-β oligomer (AβO) hypothesis was introduced in 1998. It proposed that the brain damage leading to Alzheimer's disease (AD) was instigated by soluble, ligand-like AβOs. This hypothesis was based on the discovery that fibril-free synthetic preparations of AβOs were potent CNS neurotoxins that rapidly inhibited long-term potentiation and, with time, caused selective nerve cell death (Lambert et al., 1998). The mechanism was attributed to disrupted signaling involving the tyrosine-protein kinase Fyn, mediated by an unknown toxin receptor. Over 4,000 articles concerning AβOs have been published since then, including more than 400 reviews. AβOs have been shown to accumulate in an AD-dependent manner in human and animal model brain tissue and, experimentally, to impair learning and memory and instigate major facets of AD neuropathology, including tau pathology, synapse deterioration and loss, inflammation, and oxidative damage. As reviewed by Hayden and Teplow in 2013, the AβO hypothesis “has all but supplanted the amyloid cascade.” Despite the emerging understanding of the role played by AβOs in AD pathogenesis, AβOs have not yet received the clinical attention given to amyloid plaques, which have been at the core of major attempts at therapeutics and diagnostics but are no longer regarded as the most pathogenic form of Aβ. However, if the momentum of AβO research continues, particularly efforts to elucidate key aspects of structure, a clear path to a successful disease modifying therapy can be envisioned. Ensuring that lessons learned from recent, late-stage clinical failures are applied appropriately throughout therapeutic development will further enable the likelihood of a successful therapy in the near-term.
Amyloid Accumulation and Cognitive Decline in Clinically Normal Older Individuals: Implications for Aging and Early Alzheimer's Disease
Authors: Mormino Elizabeth C.  , Papp Kathryn V.
Published: February 2025

The aberrant accumulation of the amyloid protein is a critical and early event in the Alzheimer's disease (AD) cascade. Given the early involvement of this pathological process, it is not surprising that many clinically normal (CN) older individuals demonstrate evidence of abnormal Aβ at postmortem examination and in vivo using either CSF or PET imaging. Converging evidence across multiple research groups suggests that the presence of abnormal Aβ among CN individuals is associated with elevated risk of future clinical impairment and cognitive decline. Amyloid positivity in conjunction with biomarkers of neuronal injury offers further insight into which CN are most at risk for short-term decline. Although in its infancy, tau PET has demonstrated early increases among Aβ+ that will likely be an important indicator of risk among CN. Overall, the detection of early Aβ among CN individuals has provided an important opportunity to understand the contributions of this pathology to age-related cognitive decline and to explore early intervention with disease modifying strategies.
Extracting Distributed Architecture from Source Code Using an Evolutionary Approach
Authors: Soltanpour Mina  , Izadkhah Habib  , Lotfi Shahriar
Published: February 2025

As a principle, creating a distributed program is usually much more difficult than creating a sequential program with the same functionality from a source code. Therefore, automatic distribution of a program into a distributed computing environment has always been an open research problem. The purpose of the automatic distribution is to achieve the highest speed up by creating most concurrency in the source code. Therefore, the aim of this paper is to propose a new quality function by considering synchronous and asynchronous invocations. Also, it presents an evolutionary approach that uses genetic algorithm and hill climbing for minimizing the proposed quality function, aiming to extract the distributed architecture from the source code. The results show that the proposed approach adequately extract the distribution architecture of a source code and improves the speed up.
Challenges in HPC Evaluation: Towards a Methodology for Scientific Application Requirements
Authors: Ferro Mariza  , Silva Gabrieli  , Klôh Vinícius  , Yokoyama André  , Mury Antônio  , Schulze Bruno
Published: February 2025

Achieving exaflop capabilities imposes some challenging tasks, such as the performance evaluation in HPC, the understanding of the computational requirements of scientific applications, its relation with power consumption, and ways to monitor a wide range of parameters in heterogeneous environments. Considering these challenges, this work proposes a methodology for evaluating the performance of scientific applications in HPC and the analyses approaches and the tools to monitor the performance and the power consumption. Additionally, we present how to use this methodology to assist in the evaluation process of the energy industry applications while our own performance and monitoring tool is proposed.
Scaling Big Data Neuroscience: From Interactive Analytics to HPC Platforms
Authors: Petruzza Steve  , Venkat Aniketh  , Gyulassy Attila  , Scorzelli Giorgio  , Federer Frederick  , Angelucci Alessandra  , Pascucci Valerio  , Bremer Peer-Timo
Published: February 2025

High-throughput microscopy techniques generate an ever growing amount of data that are fundamental to gather scientific, biologically and medically relevant insights. This growing amount of data dramatically affects the scientific workflow at every step. Visualization and analysis tasks are performed with limited interactivity and the implementations often require HPC skills and lack of portability, usability and maintainability.
APPGRIT: A Parallel Pipeline for Graph Representation in Text Mining
Authors: Zamanian Alireza  , Bakhshi Germi Saeed  , Mahmoudpour Ehsan
Published: February 2025

Graph-based approaches have been shown to be efficient in information extraction, especially in the case of text mining. Compared to methods like vector space models, a graph representation of a document has less information loss caused by feature extraction. However, constructing graph models are more CPU and memory intensive, thus utilizing HPC solutions seems inevitable in this case. This paper suggests a pipeline method of constructing a graph model that lets for an arbitrary level of parallel processing and distributed computing. This method also enables a wide range of data visualization opportunity. It is shown that big data hardware and software infrastructures could be used without any algorithmic limit. Results show a significant decrease in runtime.
NATO-EU Cooperation on Counter-Terrorism
Authors: Vistisen Anders  , Wiseman Jason
Published: February 2025

Resulting from the Joint Declaration of 2016, NATO-EU cooperation continues to advance in the political and military domains, having attained multiple areas of substantive partnership in cyber defense, strategic communications and maritime cooperation but with much more remaining to be accomplished in the countering of hybrid threats and bolstering of resilience. Through a combination of military and civilian CSDP missions, the EU's efforts to address the threat of terrorism and hostile information operations are currently underway in the form of 6 on-going military operations and 10 ongoing civilian operations with more than 4 000 personnel currently in deployment.
Countering Hybrid Threats in Megacities and Densely Populated Urban Areas
Authors: Pani Massimo
Published: February 2025

In the next fifteen years, we will assist to a political and world powers' transfer from traditional hegemonic states, to a network of diverse kind of actors, including non-state actors: megacities. These overcrowded urban areas will offer many benefits to dwellers but also result in many complex, diverse and potentially contrasted challenges, which will require innovative solutions in scale and scope, from policy makers, military and police forces
Industry 4.0 Intelligence Under Attack: From Cognitive Hack to Data Poisoning
Authors: Terziyan Vagan  , Golovianko Mariia  , Gryshko Svitlana
Published: February 2025

Artificial intelligence is an unavoidable asset of Industry 4.0. Artificial actors participate in real-time decision-making and problem solving in various industrial processes, including planning, production, and management. Their efficiency, as well as intelligent and autonomous behavior is highly dependent on the ability to learn from examples, which creates new vulnerabilities exploited by security threats. Today's disruptive attacks of hackers go beyond system's infrastructures targeting not only hard-coded software or hardware, but foremost data and trained decision models, in order to approach system's intelligence and compromise its work. This paper intends to reveal security threats which are new in the industrial context by observing the latest discoveries in the AI domain. Our focus is data poisoning attacks caused by adversarial training samples and subsequent corruption of machine learning process.
Network Layer Attacks Against Cyber-Physical Systems and Mitigations
Authors: Unal Devrim
Published: February 2025

A “Cyber-Physical System (CPS)” is an intelligent system interacting with other systems through networks and having physical and information technology components. CPSs are already in use in critical technologies such as Industry 4.0, advanced defense technologies and advanced cyber defense. The end devices within CPSs are generally constrained devices and most solutions in use in classical networks are therefore not applicable for CPS. In this article we investigate the network layer attacks against CPSs and possible mitigations.
Risks and Advantages in Using Artificial Intelligence on Cyber Defence and Cyber Attack
Authors: Destré Eric
Published: February 2025

Despite Artificial Intelligence can still be considered as a fashion term mostly used by marketing, technologies approaching Artificial Intelligence have been used in both military and civil areas for a long time. Today, the development of these technologies is exponential and their need is various. We may find them in areas such as data filtering and analysis, modeling and simulation, automation. They have been used to develop and advance numerous fields and industries, including finance, healthcare, education, transportation, military armament, cyber security, and more. In this paper we be presented the risks and advantages in using AI on cyber security and more precisely on cyber defence and cyber-attack, especially in a more and more world-wide interconnected context.
What Does Data Analytics Offer for Extracting Knowledge from Middle-of-Life Product Data?
Authors: Abou Eddahab Fatima-Zahra  , Horváth Imre
Published: February 2025

Companies are getting increasingly interested in learning how different customers use their products. Collecting data about the use of products provides useful insights and facilitates design enhancements. Effective data analytics needs dedicated tools. In this paper, we summarize the results of our literature research done with special attention to existing tools. We observed that everything is changing rapidly and getting more complex in terms of data and processing methods and tools. While remarkable attention has been paid to processing big data, much less is being devoted to effective semantic progressing of middle-of-life (MoL) data. One of our findings is that commercialized data analytics tools have not addressed extraction, aggregation, and handling genuine MoL data adequately. Another one is that the currently available tools are in the lack of the capability to adapt themselves to designers needs and to produce results that could be reused in multiple design tasks. Nowadays products are equipped with smart capabilities and this offers new opportunities for exploiting middle-of-life data. The knowledge aggregated in this study will be used in the development of a sophisticated toolbox. This will: (i) integrate various tools under a unified interface, (ii) implement various semantics orientated and smart reasoning-based functions, and (iii) facilitate data transformations by practicing designers in contexts.
Indoor Object Reconstruction Based on Acquisition by Low-Cost Devices
Authors: Bondar Sergej  , Salem Borhan  , Stjepandić Josip
Published: February 2025

Reverse engineering (RE) is used in the manufacturing industry as a powerful approach to generate the digital twin of a physical object. Hereby, a data acquisition and model reconstruction methodology has been combined to resolve these issues. Such a data set often has to replace the missing original digital model. Subsequently, a model reconstruction plan has to be derived so that an editable CAD model which fulfils process requirements can be generated using standard geometry creation tools. Such a reversed engineered CAD model preferably contains form-feature based design intent and can be easily modified due to new design and manufacturing constraints. In some cases the scanned data are not complete due to the gaps in the acquisition procedure. To repair such meshes mobile low-cost scanner can be taken into account. In this paper the Microsoft HoloLens is used, a mixed reality headset. It scans the environment around and enables the user to place holograms on the real world and interact with it. To permit this experience, the HoloLens scans our environment and reconstructs a spatial mapping 3D mesh of it. So this device is used as in-door scenes 3D scanner to reconstruct a piping system. This approach is proven in reconstruction of piping systems.
Ballistocardiogram-Based Heart Rate Variation Monitoring Using Unsupervised Learning
Authors: Shen Gang  , Yang Mingqi  , Zhang Biyong
Published: February 2025

Continuous heart rate variation monitoring is an important measure to diagnose and prevent the fatal cardiovascular diseases. Due to the recent development of the wearable sensory devices, ballistocardiographic (BCG) records can be collected over a long period of time without the restrictions to the patient's normal activities. However, the physical activities of the patient may severely interfere with the BCG sampling and thus leading to the degrading of the signal quality. In this paper, we introduce a novel approach to recovering the beat-to-beat heart interval estimates by applying an unsupervised machine learning framework consisting of the bag of words model and the n-gram model. First, we adopt L1 and L2 norm minimization based linear filters to reveal the global signal patterns. Then we learn the cluster centers of the signal segments containing local peaks with a Gaussian mixture approach. N-gram model is applied to detect the repetitive pattern of the cluster centers within the signals, the learned heartbeat model acts as a further filter by the convolution with the time-varying signals. Finally, auto-correlation based interval prediction is used with the help of a Gaussian prior. The proposed approach is tested using the data collected from a number of subjects with different ages and genders. We compare the resulting estimates with the blood pressure oximeter references, and the experiments show that the proposed algorithm is able to provide reliable and accurate estimates with the standard error for the interval estimate of 12.2ms.
A Strategy to Reinforce the Textile and Clothing Sector Cluster in Portugal
Authors: Ramesh Bharath  , Dieguez Teresa  , Pinto Ferreira Luís  , Silva F.J.G.
Published: February 2025

The need for open innovation emerged from worldwide organizational competitiveness, as well as from the demand for constant improvement and innovation. Companies today must possess a competitive edge and be sustainable. Organizational assets within the house are often insufficient; yet, they constitute a precious key in the joining of efforts with external players and in achieving better performance. The present paper was conducted in the Clothing and Textile industry in Portugal, one of the most relevant activity sectors in the country's performance. The study aims to contribute to the reflexion of conceiving and maintaining a harmonious dynamic balance between the different features of open innovation. By interviewing some of the top authority players from the textile sector in Portugal, the paper's findings suggest that open innovation constitutes a combined model which involves Government, Universities, Research Centres, Associations and Enterprises. This paper concludes that open innovation requires action and will produce a greater impact if all the players adopt a cluster approach, even if this is undertaken at different levels and by means of varied tactics.
Model-Based Design and Optimization of Electric Vehicles
Authors: Skarka Wojciech
Published: February 2025

Model-Based Design (MBD) is a process that enables fast and cost-effective development of dynamic and complex systems, including control systems, signal processing, communications systems on the one hand and mechanical systems on the other hand. In Model-Based Design, a system model is at the center of the development process, from development requirements through design, implementation, and testing. At each stage of design, the model is a reflection of the state of knowledge about the designed system. The model is an executable specification that you continually refine throughout the development process. This is especially important for complex systems whose operation is difficult to predict. through simulations performed on the model, we can therefore determine whether the development of the system is going well. Simulation shows whether the model works correctly. The General Model-based Design method has been applied to the design of an electric car. In addition, this method has been used to optimize the vehicle towards minimizing energy consumption and, consequently, increasing the range of the vehicle, which is a critical functional parameter for currently developed electric vehicles. The application of optimization methods in a combination with the Model-Based Design creating additional possibilities of verification of other calculation methods (eg FEM) used in the design of the vehicle.
Inventory Management Method with Demand Forecast in e-Commerce
Authors: Tanaka Kenji  , Sagawa Daishi
Published: February 2025

In the last decade, while the whole retail market has been sluggish, e-commerce (EC) has steadily expanded its market share. However, EC has a higher logistics cost than other retail formats, in particular, inventory management is a problem. In EC, the demand for goods tends to fluctuate greatly depending on its price because the price comparison in EC is much easier than that of other retail formats, and it makes the inventory management more difficult. Since the demand for goods fluctuates greatly due to price setting, EC companies suffer losses from the disposal of goods due to excessive inventory or from the loss of sales opportunities due to under stocking their inventory. Conventional methods, such as periodic ordering and quantitative ordering, are not appropriate to cope with the fluctuation of demand in EC. Thus, an inventory management method which can correspond to the fluctuation of demand due to price change is required. This paper proposes a demand forecast inventory management method which predicts demand by using planned pricing data and decides order timing and order quantity based on the prediction. This paper shows that the demand forecast inventory management method we propose can reduce inventory costs by 10% compared to periodic ordering by applying the method to the actual sales data and inventory data of an EC company.