Programmable Memory Research Articles

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%.

Design of thermally programmable 3D shape memory polymer-based devices tailored for endovascular treatment of intracranial aneurysms

Despite recent technological advancements in endovascular embolization devices for treating intracranial aneurysms (ICAs), incomplete occlusion and aneurysm recanalization remain critical challenges. Shape memory polymer (SMP)-based devices, which can be manufactured and tailored to patient-specific aneurysm geometries, possess the potential to overcome the suboptimal treatment outcome of the gold standard: endovascular coiling. In this work, we propose a highly porous patient-specific SMP embolic device fabricated via 3D printing to optimize aneurysm occlusion, and thus, improve the long-term efficacy of endovascular treatment. To facilitate device deployment at the aneurysm via Joule-heating, we introduce a stable, homogeneous coating of poly-pyrrole (PPy) to enhance the electrical conductivity in the SMP material. Using an in-house pulse width modulation circuit, we induced Joule-heating and characterized the shape recovery of the PPy-coated SMP embolic devices. We found that the employed PPy coating enables enhanced electrical and thermal conductivity while only slightly altering the glass transition temperature of the SMP material. Additionally, from a series of parametric studies, we identified the combination of catalyst concentration and pyrrole polymerization time that yielded the shape recovery properties ideal for ICA endovascular therapy. Collectively, these findings highlight a promising material coating for a future coil-free, personalized shape memory polymer (SMP) embolic device, designed to achieve long-lasting, complete occlusion of aneurysms.

Security primitives for memoryless IoT devices based on Physical Unclonable Functions and True Random Number Generators

The article describes various security primitives for significantly resource-constrained devices, such as sensors or sensor networks, IoT devices, wearables, etc. — i.e., devices without programmable memory. It is dedicated to parts which cannot handle complex algorithms of modern secure cryptography, cannot be equipped with programmable memories, or their circuits or data in permanent memories can be easily reverse-engineered. Instead, all security techniques (e.g., identification, authentication, and encryption) are based on modern hardware cryptography, mainly: physical unclonable functions (PUFs) and true random number generators (TRNGs). The paper addresses numerous issues from untraceable identification to mutual authentication to one-time pad encryption. The communication security is considered to be a trade-off between the device’s resources (processing ability, energy consumption, implementation size, response time), preparation complicity (initialization time, size of a server data storage) and the security capabilities and protection levels. Primitives can be included into the communication protocol based on particular needs and available hardware resources.

Programmable Truncated Cuboctahedral Origami Metastructures Actuated by Shape Memory Polymer Hinges

AbstractOver the past few decades, origami‐inspired structures have attracted great attention across various engineering fields due to their unique geometric and mechanical characteristics. Additionally, combining origami structures with active materials is employed to achieve programmable mechanical properties and self‐reconfigurability under external stimuli. In this work, a novel family of truncated cuboctahedral origami metastructures is proposed. These designs integrate shape memory polymers (SMPs) to actively achieve programmable mechanical properties and shape memory behavior. By utilizing SMPs for the creases and stiff materials for the panels, this approach enables deformation along the creases while enhancing the overall structural robustness. The mechanical properties and shape memory processes of these structures are investigated in detail. The proposed origami metastructures exhibit a negative Poisson's ratio and demonstrate excellent energy storage capabilities. Notably, their mechanical properties can be programmed by controlling both temperature and geometric parameters. More particularly, their Poisson's ratio can be tuned within a range of zero to −1. As a result, these truncated cuboctahedral origami metastructures hold significant potential for applications across various engineering domains, particularly in composite structures and active metamaterials.

Exploring vowel errors produced in nonword repetition in children with speech and language disorders.

Accurate nonword repetition (NWR) is contingent on many underlying skills, including encoding, memory and motor planning and programming. Though vowel errors are frequently associated with childhood apraxia of speech (CAS), several recent studies have found that children with developmental language disorder (DLD) produce high rates of vowel errors in NWR tasks. This retrospective analysis explored whether the overall frequency and types of vowel errors produced in NWR distinguish children with DLD, children with CAS, children with speech sound disorder (SSD) and children with typical development (TD). We present data for 24 children (six per DLD, CAS, SSD and TD groups), ranging in age from 50-92 months. Children with DLD, CAS and SSD showed similar articulation scores and children with DLD and children with CAS showed similar expressive language scores. Total vowel errors, total monophthong errors, monophthong substitutions, diphthongization errors, total diphthong errors, diphthong substitutions and diphthong reduction errors were calculated by syllable length and group. Repeated measures analyses of variance were used to examine group differences. Children with DLD and children with CAS produced a higher frequency of total vowel errors compared to children with TD. Children with DLD produced more total monophthong errors than children with TD. Children with DLD and children with CAS produced more total diphthong errors than children with TD. For children with DLD, these were characterised by diphthong substitutions. For children with CAS, these were characterised by diphthong substitutions and diphthong reduction errors. For all measures, error rates in children with SSD did not significantly differ from any of the other three groups. Preliminary evidence indicates that children with DLD and children with CAS both show high rates of vowel errors in NWR tasks and weaknesses in encoding and memory. For children with CAS, additional motor planning difficulties are associated with an increased likelihood to reduce diphthongs. Children with SSD show more mild processing difficulties than children with DLD and children with CAS, though they do not perform as well as TD peers. Future work should replicate and further specify the processing weaknesses that affect vowel accuracy in NWR tasks in a larger sample. What is already known on the subject Nonword repetition (NWR) tasks are often included in diagnostic batteries to identify children with developmental language disorder (DLD). Poor performance on these tasks have historically been attributed to phonological working memory deficits in children with DLD. However, repeating nonwords relies on a number of underlying processing skills and many of these skills are affected to varying degrees in children with speech and language disorders. An in-depth analysis of vowel errors has the potential to reveal the shared as well as specific underlying processing weaknesses in children with DLD, children with childhood apraxia of speech (CAS) and children with speech sound disorder (SSD). What this paper adds to existing knowledge We found that children with DLD and children with CAS show low vowel competence compared to children with typical development. A nuanced examination of vowel error types further revealed that children with DLD and children with CAS show weaknesses in encoding and memory. Motor planning and programming weaknesses were unique to CAS. Children with SSD show more mild processing deficits and their performance did not significantly differ from any of the other three groups. What are the clinical implications of this work? Examining the types of vowel errors produced by children with DLD, children with CAS and children with SSD in NWR allows us to further specify the underlying processing weaknesses that differentiate these three groups. This research informs theoretical accounts of language processing in children with different types of speech and language disorders and has the potential to improve the diagnostic utility of NWR tasks.

Symbolic testing of floating-point bugs and exceptions

Numerical software are susceptible to floating-point bugs and exceptions, which may lead to severe threats like denial of service attacks. Static analysis techniques such as symbolic execution are effective in detecting general bugs which often cause memory error or program crash. Unfortunately, these methods do not deal well with numerical code as they do not support floating-point constraints and math functions symbolically. In this paper, we propose a new analysis framework YUSE, which can detect floating-point bugs by constructing constraints and exploring paths which contain floating-point expressions. Specifically, we introduce interval computation and interval constraint propagation in non-relational numerical abstract domains, and symbolically model math functions, to accurately detect floating-point bugs and exceptions. Moreover, we leverage two-phase constraint solving to enhance YUSE’s performance. Experimental results show that YUSE outperforms two state-of-the-art tools, Frama-c and Fpse-study, in terms of effectiveness and efficiency, with 1.4× and 7.1× faster than Frama-c and Fpse-study, respectively. Moreover, YUSE found 20 new bugs in real-world software, 12 of which were assigned CVE IDs and 8 of which were confirmed by developers.

Don't Forget About Your Memory...Exploring Need And Impact Of A Cognitive Strategies Group On Older Adults Following Comprehensive Geriatric Assessment

Abstract Background The increasing prevalence of mild cognitive impairment (MCI) and dementia in older adults in Ireland, with projections of up to 20% of over 80-year-olds affected, is widely documented (TILDA 2023). Due attention is required in service planning to provide tailored non-pharmacological and lifestyle interventions to this growing cohort. This review aims to analyse the need for and impact of a cognitive strategies and brain health intervention group on older adults attending the Integrated Day Hospital (IDH) as part of an ICPOP service. Methods Retrospective analysis of the health care record was complete on older adults over 70 years with recent deterioration in cognitive or functional status. Cognitive screening scores and diagnoses received at the IDH were gathered to inform presence of MCI or dementia. Permission was sought from the authors of a cognitive strategies and brain health intervention group which was delivered to a sample of older adults and evaluated as part of the Jog Your Memory Program (HSE 2020). Results Preliminary results indicate a cohort of 355 older adults. Analysis of cognitive screening scores reflect prevalence of MCI at 15% and dementia at 35%. The Jog Your Memory program is implemented over 4 sessions. 2 programs were complete in October 2023 and April 2024 with 16 participants. Evaluation was complete with 100% of participants reporting benefits including acquisition of memory strategies, improved confidence and increased knowledge of MCI. Implementing this program optimises therapist time indicating cost saving. Final findings will be available for the purpose of this poster presentation. Conclusion The prevalence of cognitive impairment is broadly in keeping with previous audit (2023). The intervention group implementation and evaluation are ongoing. Participant feedback suggests the intervention is meeting the needs of this cohort. Future work will focus on scaling up the intervention group and integrating with other local services.

Development of an enhanced educational IDE for 8051/8052 microcontroller

This paper presents and explains the development of an IDE for microcontroller 8051/8052 with new features and functionalities. These features include a built-in editor; a keypad editor and auto enabling/disabling array of buttons for eliminating syntax errors. The implementation of the interrupts is entirely based on the 8052 datasheets by taking into consideration the “Interrupt Priorities” and “Interrupt Enables”. Multiple view windows for internal ram, external ram, program memory and special function register enhance the developed IDE. One-pass cross technique was adopted for the assembling phase. The assembler plays the role of assigning addresses, translating mnemonics, and recognizing errors by considering all the 255 instructions and directives Step-by-step, skip-line debugging, breakpoints to be inserted at any part of the source code have been included to ameliorate the students’ interaction with the system. An option is provided to users to manually create external incoming transmission for triggering purposes. The IDE passes the test and verification processes successfully.

Vanadium Dioxide by Atomic Layer Deposition: A Promising Material for Next-Generation Memory Devices.

The synthesis of a vanadium dioxide (VO2) film using atomic layer deposition (ALD) with vanadium tetrachloride (VCl4) as a precursor for the realization of programmable memory devices is reported. X-ray diffraction analysis revealed the epitaxial growth of VO2 on c-Al2O3. The phase transition was monitored using resistivity measurements across varying temperatures, demonstrating a decrease of >4 orders of magnitude at the transition temperature, thereby confirming the high quality of the material. From this material, memristive devices are fabricated as resistive random-access memory (RRAM). On the basis of spiking voltage inputs, these RRAM exhibited cycle stability over 512 cycles and state retention stability for >450 s, showing <2% drift. With respect to synaptic-like applications, the RRAM devices were piloted through step patterns to enable multilevel memory states. These ALD-grown VO2-based devices demonstrate potential for use as synaptic connections with multiweight synapses, advancing scalability in neuromorphic applications.

Constructing a supercapacitor-memristor through non-linear ion transport in MOF nanochannels.

The coexistence and coupling of capacitive and memristive effects have been an important subject of scientific interest. While the capacitive effect in memristors has been extensively studied, the reciprocal scenario of the memristive effect in capacitors remains unexplored. In this study, we introduce a supercapacitor-memristor (CAPistor) concept, which is constructed by leveraging non-linear ion transport within the pores of a metal-organic framework zeolitic-imidazolate framework (ZIF-7). Within the nanochannels of the ZIF-7 electrode in an aqueous pseudocapacitor, the anionic species (OH-) of the electrolyte can be enriched and dissipated in different voltage regimes. This difference leads to a hysteresis effect in ion conductivity, constituting a memristive behavior in the pseudocapacitor. Thus, the pseudocapacitor-converted CAPistor seamlessly integrates the programmable resistance and memory functions of an ionic memristor into a supercapacitor, demonstrating enormous potential to extend the traditional energy storage applications of supercapacitors into emerging fields, including biomimetic nanofluidic ionics and neuromorphic computing.

Mild Fabrication of Polymeric Porous/Nonporous Micro‐Composite Structures via Enhancing the Photothermal Conversion of Ultrafast Laser

AbstractLocalized foaming of polymers is promising to fabricate polymeric composite structures for applications in biomedical, aerospace, automotive, etc. However, the severe pyrolysis and carbonization of polymer chains during localized foaming may degrade the bulk strength. Herein, a mild localized foaming strategy is proposed to fabricate poly(lactic acid) (PLA) micro‐foams within dense PLA matrices using ultrafast lasers. The PLA substrates doped with graphene (Gr) and azodicarbonamide (AC) are developed for mild foaming. A picosecond laser is applied to pattern PLA micro‐foams on the PLA/Gr/AC substrates to fabricate porous/nonporous micro‐composite structures. The thermal behavior of the PLA/Gr/AC substrates is characterized to evaluate their capability in photothermal conversion and mild foaming during ultrafast laser pulses. To optimize the laser foaming process, the microstructure of the laser‐foamed PLA processed with different laser parameters is studied. It demonstrates that the oxidation of the functional groups dominates the side reactions on the PLA chains with slight variation in molecular weight during laser foaming, indicating that the PLA chains keep almost intact and mild localized foaming is realized. Furthermore, the developed technology is demonstrated for micro‐patterning and texturing, material toughness programming, and shape memory programming, showing promising applications for smart materials, metamaterials, micro‐robotics, and biological scaffolds.

Activity-dependent transcriptional programs in memory regulate motor recovery after stroke

Stroke causes death of brain tissue leading to long-term deficits. Behavioral evidence from neurorehabilitative therapies suggest learning-induced neuroplasticity can lead to beneficial outcomes. However, molecular and cellular mechanisms that link learning and stroke recovery are unknown. We show that in a mouse model of stroke, which exhibits enhanced recovery of function due to genetic perturbations of learning and memory genes, animals display activity-dependent transcriptional programs that are normally active during formation or storage of new memories. The expression of neuronal activity-dependent genes are predictive of recovery and occupy a molecular latent space unique to motor recovery. With motor recovery, networks of activity-dependent genes are co-expressed with their transcription factor targets forming gene regulatory networks that support activity-dependent transcription, that are normally diminished after stroke. Neuronal activity-dependent changes at the circuit level are influenced by interactions with microglia. At the molecular level, we show that enrichment of activity-dependent programs in neurons lead to transcriptional changes in microglia where they differentially interact to support intercellular signaling pathways for axon guidance, growth and synaptogenesis. Together, these studies identify activity-dependent transcriptional programs as a fundamental mechanism for neural repair post-stroke.

4D printing bio-inspired chiral metamaterials for flexible sensors

Chiral metamaterials were a typical metamaterial configuration, which had broad application prospects in vibration isolation, energy absorption and other fields. Although 4D-printed chiral metamaterials have been investigated by introducing stimulus-responsive materials to achieve programmable properties of metamaterials, chiral metamaterials are mostly fabricated by single materials, which limits the adjustable domains of the mechanical properties and the design freedom of metamaterials. In this work, inspired by the configuration of collagen fibers of biological tissues, the bio-inspired chiral metamaterials with wave ligaments were developed. The bi-phase (TPE@PLA-SMP) composite chiral metamaterials were fabricated by combining active phase (shape memory polylactic acid, PLA-SMP) with passive phase (thermoplastic elastomer, TPE). The influences of geometric parameters, ligament gradient forms, and TPE distribution modes on the mechanical properties of the developed metamaterials were characterized. When the TPE was distributed at 0° and 45°, the deformation of the mechanical metamaterials occurred mainly in the TPE cells, resulting in the J-shaped displacement-force curve. The programmable and reconfigurable properties of the metamaterials (including configuration, and energy absorption performance) under thermal stimulation were demonstrated. The specific energy absorption (SEA) of the metamaterial 2θ = π/2 was realized to transform between 0.92 kJ/kg and 0.38 kJ/kg by shape memory programming. Flexible sensors based on TPE@PLA-SMP composite metamaterials were developed by filling CNC-CNT@PVA composite hydrogels (minimum resistivity 8.1 Ω m), which enabled stable output of electrical signals under repeated loads. A motion state monitor was developed and realized to monitor the wearer's movement such as running, demonstrating its potential application prospects in fields such as flexible electronics.

The effects of working memory training on working memory, self-regulation, and analogical reasoning of preschool children.

This study examined the effects of working memory training (WMT) on WM and fluid intelligence. A novel four-pronged model of mediated learning, cognitive functions, task characteristics and metacognition is presented as a conceptual basis for the Modifiability of a Working Memory Program (MWMP). Our basic assumption is that increasing WM depends on a synchronized combination of the four components. A group of typically developed preschool children (n = 62) participated in the experimental group, receiving the MWMP. They were compared with a control group (n = 56) of preschool children who engaged in a substitute program. This comparison allowed us to discern the specific effects of the MWMP. All participants received tests of WM, self-regulation and analogical reasoning before and after the intervention. The MWMP was administered to children in the experimental group for 10 weekly sessions, each lasting 40 minutes, in small groups of two children. The children in the control group engaged in didactic activities that were part of a school curriculum routine for kindergartners for the same length of time. We used ANCOVA analysis to compare the Treatment x Time with age and socioeconomic status as covariates. The findings revealed a higher improvement in some WM and self-regulation tests among children in the experimental group compared to those in the control group. However, no significant transfer effects were observed in analogical thinking. The findings confirm the effectiveness of a non-computerized WMT among kindergartners and support our four-pronged theoretical model. We also discuss earlier findings on far-transfer effects and educational implications. We suggest that future WM studies adopt the following: (1) the development of training methods that are theoretically anchored; (2) training procedures should not rely heavily on computerized exercises but can be adapted to group characteristics, educational settings and cost-effectiveness aspects; (3) varying the task characteristics and training strategies to stimulate task-intrinsic motivation; (4) identifying training strategies to produce cognitive improvements underlying WM; (5) intervention should target individuals in early development as much as possible; (6) development of training procedures that facilitate motivation; and (7) providing empirical evidence of far-transfer effects for WM training. The empirical evidence should link gains in WM capacity and achievements in academic and other life domains.

Digital Light Processing Resins with Programmable Shape Memory for Biomedical Applications.

The creation of biodegradable and biocompatible shape memory polymers amenable to biofabrication techniques remains a challenge. The ability to create shape-changing biodegradable objects that are triggered at body temperature opens up possibilities in tissue engineering, minimally invasive surgery, and actuating bioimplants. Merging Digital Light Processing (DLP) printing with shape memory polymers brings us closer to new smart biomedical outcomes. Previously, we developed a poly(caprolactone-co-trimethylenecarbonate) urethane acrylate resin for the DLP fabrication of biodegradable 3D objects. In further studies, we observed that some of these resins possessed shape memory properties, triggered by body temperature (37 °C). In this subsequent study, we explored the shape memory properties and tunability of this resin family via changes in copolymer composition, molecular weight, and identity of the acrylate end-capping unit. We found that we could create a library of shape memory resins, amenable to DLP printing, which allowed the creation of shape-actuating structures with some tunability over the speed of shape memory and mechanical properties. We observed that increased mole fraction of caprolactone in the copolymer and increased molecular weight of the polymer led to a decrease in speed of the shape memory switch. Furthermore, we observed a trade-off between the composition and the end-capping moiety on the mechanical properties of the polymers. These polymeric resins were able to be processed into shapes that were able to perform work, including the release of cargo and grabbing/lifting of an object. This platform now provides a way to tune the speed and mechanical properties of a shape memory DLP object created from common and scalable polymerization techniques. This work ultimately provides a new platform to develop customizable and biodegradable devices capable of actuating and delivery devices for numerous biomedical applications.

Stair‐Stepping Mechanical Metamaterials with Programmable Load Plateaus

AbstractMaterials with target load plateaus offer the potential for developing innovative vibration suppression and isolation systems for applications such as satellite platforms, submarines, and electric vehicles. However, implementing these materials can pose significant challenges. In this study, stair‐stepping mechanical metamaterials with programmable load plateaus are presented, which are created via a three‐level (unit, module, and 3D object) construction strategy. The strategy inspired by the inverse design concept achieves tunability in the number and properties of load plateaus within the force–displacement profiles of the metamaterials. This approach even yields appealing stair‐stepping response patterns, as validated by experiments and finite element simulations. Promisingly, programming the unit from its initial configuration to a “zero stiffness” configuration enables these metamaterials excellent vibration isolation performance. Furthermore, two reversible methods are proposed for switching among various unit configurations, namely shape memory programming and supporting payload. This innovative design strategy for programmable load plateaus opens up new possibilities for creating metamaterials with customized force–displacement responses. It also provides opportunities to incorporate multimodal vibration isolation capabilities into precision devices.

SURE: A Visualized Failure Indexing Approach using Program Memory Spectrum

Failure indexing is a longstanding crux in software debugging, the goal of which is to automatically divide failures (e.g., failed test cases) into distinct groups according to the culprit root causes, as such multiple faults residing in a faulty program can be handled independently and simultaneously. The community of failure indexing has long been plagued by two challenges: 1) The effectiveness of division is still far from promising. Specifically, existing failure indexing techniques only employ a limited source of software run-time data, for example, code coverage, to be failure proximity and further divide them, which typically delivers unsatisfactory results. 2) The outcome can be hardly comprehensible. Specifically, a developer who receives the division result is just aware of how all failures are divided, without knowing why they should be divided the way they are. This leads to difficulties for developers to be convinced by the division result, which in turn affects the adoption of the results. To tackle these two problems, in this paper, we propose SURE, a vi SU alized failu R e ind E xing approach using the program memory spectrum. We first collect the run-time memory information (i.e., variables’ names and values, as well as the depth of the stack frame) at several preset breakpoints during the execution of a failed test case, and transform the gathered memory information into a human-friendly image (called program memory spectrum, PMS). Then, any pair of PMS images that serve as proxies for two failures is fed to a trained Siamese convolutional neural network, to predict the likelihood of them being triggered by the same fault. Last, a clustering algorithm is adopted to divide all failures based on the mentioned likelihood. In the experiments, we use 30% of the simulated faults to train the neural network, and use 70% of the simulated faults as well as real-world faults to test. Results demonstrate the effectiveness of SURE: It achieves 101.20% and 41.38% improvements in faults number estimation, as well as 105.20% and 35.53% improvements in clustering, compared with the state-of-the-art technique in this field, in simulated and real-world environments, respectively. Moreover, we carry out a human study to quantitatively evaluate the comprehensibility of PMS, revealing that this novel type of representation can help developers better comprehend failure indexing results.

Clinical application of plasma P-tau217 to assess eligibility for amyloid-lowering immunotherapy in memory clinic patients with early Alzheimer’s disease

BackgroundWith the approval of disease-modifying treatments (DMTs) for early Alzheimer’s disease (AD), there is an increased need for efficient and non-invasive detection methods for cerebral amyloid-β (Aβ) pathology. Current methods, including positron emission tomography (PET) and cerebrospinal fluid (CSF) analysis, are costly and invasive methods that may limit access to new treatments. Plasma tau phosphorylated at threonine-217 (P-tau217) presents a promising alternative, yet optimal cutoffs for treatment eligibility with DMTs like aducanumab require further investigation. This study evaluates the efficacy of one- and two-cutoff strategies for determining DMT eligibility at the Butler Hospital Memory & Aging Program (MAP).MethodsIn this retrospective, cross-sectional diagnostic cohort study, we first developed P-tau217 cutoffs using site-specific and BioFINDER-2 training data, which were then tested in potential DMT candidates from Butler MAP (total n = 150). ROC analysis was used to calculate the area under the curve (AUC) and accuracy of P-tau217 interpretation strategies, using Aβ-PET/CSF testing as the standard of truth.ResultsPotential DMT candidates at Butler MAP (n = 50), primarily diagnosed with mild cognitive impairment (n = 29 [58%]) or mild dementia (21 [42%]), were predominantly Aβ-positive (38 [76%]), and half (25 [50%]) were subsequently treated with aducanumab. Elevated P-tau217 predicted cerebral Aβ positivity in potential DMT candidates (AUC = 0.97 [0.92–1]), with diagnostic accuracy ranging from 0.88 (0.76–0.95, p = 0.028) to 0.96 (0.86–1, p < .001). When using site-specific cutoffs, a subset of DMT candidates (10%) exhibited borderline P-tau217 (between 0.273 and 0.399 pg/mL) that would have potentially required confirmatory testing.ConclusionsThis study, which included participants treated with aducanumab, confirms the utility of one- and two-cutoff strategies for interpreting plasma P-tau217 in assessing DMT eligibility. Using P-tau217 could potentially replace more invasive diagnostic methods, and all aducanumab-treated participants would have been deemed eligible based on P-tau217. However, false positives remain a concern, particularly when applying externally derived cutoffs that exhibited lower specificity which could have led to inappropriate treatment of Aβ-negative participants. Future research should focus on prospective validation of P-tau217 cutoffs to enhance their generalizability and inform standardized treatment decision-making across diverse populations.

Hyperspectral in-memory computing with optical frequency combs and programmable optical memories

The rapid rise of machine learning drives demand for extensive matrix-vector multiplication operations, thereby challenging the capacities of traditional von Neumann computing systems. Researchers explore alternatives, such as in-memory computing architecture, to find energy-efficient solutions. In particular, there is renewed interest in optical computing systems, which could potentially handle matrix-vector multiplication in a more energy-efficient way. Despite promising initial results, developing high-throughput optical computing systems to rival electronic hardware remains a challenge. Here, we propose and demonstrate a hyperspectral in-memory computing architecture, which simultaneously utilizes space and frequency multiplexing, using optical frequency combs and programmable optical memories. Our carefully designed three-dimensional opto-electronic computing system offers remarkable parallelism, programmability, and scalability, overcoming typical limitations of optical computing. We have experimentally demonstrated highly parallel, single-shot multiply-accumulate operations with precision exceeding 4 bits in both matrix-vector and matrix-matrix multiplications, suggesting the system’s potential for a wide variety of deep learning and optimization tasks. Our approach presents a realistic pathway to scale beyond peta operations per second, a major stride towards high-throughput, energy-efficient optical computing.

3D printed auxetic metamaterials with tunable mechanical properties and morphological fitting abilities

Research on creating special structures with a negative Poisson’s ratio, known as auxetic metamaterials, is widely conducted for potential applications in flexible electronics, aerospace, biomedicine, and other fields. Auxiticity is the phenomenon of transverse expansion that occurs when stretched longitudinally. However, conventional metamaterial tunable properties have certain limitations, mainly in the form of limited Poisson’s ratio tuning range and transverse deformation. In this work, a series of symmetric antichiral auxetic structures with significant auxetic effect and low Poisson’s ratio of −5 are designed and fabricated by 3D printing. Based on the manipulating of unit parameters and the use of shape memory polymers, the auxetic metamaterials exhibit tunable mechanical properties and programmable shape memory behaviors. Owing to these features, the auxetic metamaterials are demonstrated to achieve excellent shape-fitting and size-fitting capabilities, which are followed by expanding in a full range to contact any objects (due to auxiticity) and then contract to conform their shapes (due to shape memory). By integrating flexible electromyography(EMG) electrodes, the shape memory auxetic metamaterials can be used as self-adaptive wearable devices to fit with human limbs, which can ensure the good attachment, high signal to Noise Ratio (SNR), and breathability when collect EMG signals.