Weak Memory Research Articles

Flexible TiO2-WO3−x hybrid memristor with enhanced linearity and synaptic plasticity for precise weight tuning in neuromorphic computing

Tungsten oxide (WO3)-based memristors show promising applications in neuromorphic computing. However, single-layer WO3 memristors suffer from issues such as weak memory performance and nonlinear conductance variations. In this work, a functional layer based on the hybrids of WO3−x and TiO2 is proposed for constructing flexible memristors featuring outstanding synaptic characteristics. Applying diverse electrical stimulations to the memristor enables a range of synaptic functions, elucidating its conduction mechanism through the conductive filament model. The incorporation of TiO2 not only enhances the memristor’s memory characteristics but makes its conductance more linear, symmetrical and uniform during the long-term changes. Furthermore, in view of the enhanced device performance by TiO2 doping, the potential of this device for simple behavioral simulation and processing of complex computing problems is explored. The “learning-forgetting-relearning” characteristics and device integrability are visually demonstrated. Applying the device to a convolutional neural network, the recognition accuracy of MNIST handwritten digits reaches 98.7%.

View-based axiomatic reasoning for the weak memory models PSO and SRA

Weak memory models describe the semantics of concurrent programs in modern multicore architectures. As these semantics deviate from the commonly assumed model of sequential consistency, reasoning techniques like Owicki-Gries-style proof calculi need to be adapted to specific memory models. To avoid having to design a new proof calculus for every new memory model, a uniform approach for axiomatic reasoning has recently been proposed. This approach bases reasoning on memory-model independent axioms about thread views and how they are changed by program actions like reads and writes. It allows to prove program correctness based on axioms only. Such proofs are valid for all memory models instantiating the axioms.In this paper, we study instantiations of the axioms for two memory models, the Partial Store Order (PSO) and the Strong Release Acquire (SRA) model. We see that both models fulfil all but one axiom, a different one though. For PSO, the missing axiom refers to message-passing abilities of memory models; for SRA, the missing axiom refers to the independence of actions on executing threads. We discuss the consequences of these missing axioms and illustrate the reasoning technique on a specific litmus test.

Barrier Polarity Reversal Based on Interfacial Modification of Au Nanoparticles for Nonvolatile Multilevel Memory and Optoelectronic Synapses.

Optoelectronic synaptic devices, integrating light sensing and information processing capabilities, have emerged as advantageous tools for the implementation of visual neuromorphic computing. However, the transient light-triggered response characteristic typically results in unstable memory retention times and restricted current response ranges, posing significant challenges to the development and practical application of neural network systems. In response to these limitations, this study developed a nonvolatile optoelectronic memory based on the indium tin oxide (ITO)/Au nanoparticles (NPs)/amorphous Ga2O3 (a-Ga2O3)/Pt structure. Unlike conventional optoelectronic memories, this device features a modification with Au NPs that markedly enhances the Schottky barrier height at the interface. Au NPs function as a charge-trapping layer for sensitive and large-scale modulation of the barrier by the light field, thereby enabling the nonvolatile reversal of the device's barrier polarity. This innovative approach enables controllable multilevel data storage with an ultra large on/off ratio (∼104) and excellent retention capability exceeding 12,000 s. Additionally, the device emulates essential synaptic functions and demonstrates potential application values in visual weak signal perception and image memory. This study introduces a mechanism for Schottky barrier polarity control and presents a promising strategy for the development of future high-performance integrated devices and optoelectronic synaptic elements.

The motivation of reading of vocational-technical (VT) schoolchildren

The investigation of reading motivation is important in studying, regulating, and prognosing the development of personalities. The article deals with the investigation of one thousand vocational-technical schoolchildren and 300 teachers and tutors from seven schools. The data was collected through questionnaires, interviews, and observation. The computer of Vilnius University was used to amend the collected material. The reading motivation of regular and irregular readers was different. Some differences in the motivation of girls and boys were found. The reading motivation of VT schoolchildren is complicated, unstable, and not a strong construct, reflecting the main socio-psychological features of VT schoolchildren's socialization period. One-third of the tested young people indicated that reading is a matter of great importance. Another part of the schoolchildren expressed their positive but passive standpoint. The main obstacles in reading activity are time deficit, difficulties in finding interesting literature to read, challenges in buying books, and problems related to weak eyesight, memory, and attention. It is important to investigate the situational motivation for reading among VT schoolchildren in their school library. The main tasks of educators and librarians are to evaluate schoolchildren's reading practices and to uphold the prestige of reading in VT schools.

<p>A Catalogue of Historical and Instrumentally-Recorded M<sub>s</sub> ≥ 7 Earthquakes in Taiwan</p>

A catalogue of forty-nine instrumentally-recorded earthquakes with magnitudes ≥ 7 in Taiwan from 1906 to date is compiled from regional and international catalogues and literature. Included also are seven historical earthquakes that occurred from 1792 to 1867. The earthquake magnitude is the surface-wave magnitude, Ms. The moment magnitude, Mw, is also evaluated either from Ms or obtained from related documents. Except for one event, Mw is smaller than or equal to Ms. There are the ‘doublets’, ‘triplets’, and ‘quadruplets’ in these earthquakes. The spatial distribution of epicenters shows that most of the events occurred in offshore eastern Taiwan and only a few inland events happened in western Taiwan. The shortest inter-occurrence time between two consecutive events is less than 1 day; while the longest one is 5742 days between No. 47 event and No. 48 event. The inter-occurrence time somewhat increases with time. The time series of earthquakes shows irregular recurrence behavior with aperiodicity, fractality, and a weak memory effect. The damage produced by earthquakes is much higher from inland events (mainly in western Taiwan) than from offshore ones.

Upaya Membentuk Motivasi Belajar Peserta Didik melalui Model Flipped Classroom dalam Pembelajaran PAI

The purpose of this study was to find out and analyze students' learning motivation through the flipped classroom model in PAI learning. This study uses a qualitative approach by uncovering and studying the application of the flipped classroom learning model in PAI learning. Data collection techniques were obtained through interviews and observations in order to gather information related to the implementation of the flipped classroom model. The research results explain that in the application of this learning model it can be carried out in different times and places between educators and students. This model prioritizes students to be more independent in learning. There are obstacles in the application of this learning, namely the difficulty of educators in monitoring students, students' weak memory, lack of support from parents, school facilities and infrastructure, and students' difficulties in understanding the lessons given. The efforts that can be made are to motivate students in overcoming obstacles from the flipped classroom learning model, namely refreshing, parenting counseling, innovation, and skill improvisation.

Differences in the consolidation by spontaneous and evoked ripples in the presence of active dendrites.

Ripples are a typical form of neural activity in hippocampal neural networks associated with the replay of episodic memories during sleep as well as sleep-related plasticity and memory consolidation. The emergence of ripples has been observed both dependent as well as independent of input from other brain areas and often coincides with dendritic spikes. Yet, it is unclear how input-evoked and spontaneous ripples as well as dendritic excitability affect plasticity and consolidation. Here, we use mathematical modeling to compare these cases. We find that consolidation as well as the emergence of spontaneous ripples depends on a reliable propagation of activity in feed-forward structures which constitute memory representations. This propagation is facilitated by excitable dendrites, which entail that a few strong synapses are sufficient to trigger neuronal firing. In this situation, stimulation-evoked ripples lead to the potentiation of weak synapses within the feed-forward structure and, thus, to a consolidation of a more general sequence memory. However, spontaneous ripples that occur without stimulation, only consolidate a sparse backbone of the existing strong feed-forward structure. Based on this, we test a recently hypothesized scenario in which the excitability of dendrites is transiently enhanced after learning, and show that such a transient increase can strengthen, restructure and consolidate even weak hippocampal memories, which would be forgotten otherwise. Hence, a transient increase in dendritic excitability would indeed provide a mechanism for stabilizing memories.

Effect of antimetabolite regimen on cellular and humoral immune response to SARS-COV-2 vaccination in solid organ transplant recipients

ObjectiveNovel mRNA-based vaccines have been proven to be powerful tools in combating the global pandemic caused by SARS-CoV-2 protecting individuals, especially the immunocompromised, from COVID-19. Still, it remains largely unknown how solid organ transplant and different immunosuppressive medications affect development of vaccine-induced immunity. MethodsIn this work, we monitored humoral and cellular memory responses after mRNA SARS-CoV-2 two-doses and booster doses vaccination in cystic fibrosis lung transplanted patients (CFT) and compared them with both cystic fibrosis patients without lung transplant (CF) and with kidney transplant recipients (KT). In particular, we investigated the effects of immunosuppressive regimens on immune memory to SARS-CoV-2 after mRNA SARS-CoV-2 vaccine in transplanted patients. ResultsOur results showed that immunocompromised transplanted patients displayed a weak cellular and humoral memory to SARS-CoV-2 mRNA vaccination. In addition, obtained data clearly demonstrate that immunosuppressive therapy regimen including antimetabolites, further reduces patients’ ability to respond to vaccination at both humoral and cell-mediated level. Notably, patient treated with antimetabolites showed a lower humoral and cellular response also after a booster dose vaccination. ConclusionThese results, even if obtained on a small patient's cohort, question whether immunocompromised patients need interventions to improve vaccine SARS-CoV-2 mRNA vaccine response such as additional jab or modulation of immunosuppressive therapy.

Exposure memory and susceptibility to ambient PM2.5: A perspective from hepatic cholesterol and bile acid metabolism

Both epidemiological and experimental studies increasingly show that exposure to ambient fine particulate matter (PM2.5) is related to the occurrence and development of chronic diseases, such as metabolic diseases. However, whether PM2.5 has "exposure memory" and how these memories affect chronic disease development like hepatic metabolic homeostasis are unknown. Therefore, we aimed to explore the effects of exposure transition on liver cholesterol and bile acids (BAs) metabolism in mice. In this study, C57BL/6 mice were exposed to concentrated ambient PM2.5 or filtered air (FA) in a whole-body exposure facility for an initial period of 10 weeks, followed by another 8 weeks of exposure switch (PM2.5 to FA and FA to PM2.5) comparing to non-switch groups (FA to FA and PM2.5 to PM2.5), which were finally divided into four groups (FF of FA to FA, PP of PM2.5 to PM2.5, PF of PM2.5 to FA, and FP of FA to PM2.5). Our results showed no significant difference in food intake, body composition, glucose homeostasis, and lipid metabolism between FA and PM2.5 groups after the initial exposure before the exposure switch. At the end of the exposure switch, the mice switched from FA to PM2.5 exposure exhibited a high sensitivity to late-onset PM2.5 exposure, as indicated by significantly elevated hepatic cholesterol levels and disturbed BAs metabolism. However, the mice switched from PM2.5 to FA exposure retained a certain memorial effects of previous PM2.5 exposure in hepatic cholesterol levels, cholesterol metabolism, and BAs metabolism. Furthermore, 18-week PM2.5 exposure significantly increased hepatic free BAs levels, which were completely reversed by the FA exposure switch. Finally, the changes in small heterodimeric partner (SHP) and nuclear receptor subfamily 5 group A member 2 (LRH1) in response to exposure switch mechanistically explained the above alterations. Therefore, mice switching from PM2.5 exposure to FA showed only a weak memory of prior PM2.5 exposure. In contrast, the early FA caused mice to be more susceptible to subsequent PM2.5 exposure.

Analisis Hakikat Keterampilan Menulis Lanjutan Pada Kelas Tinggi

The aim of this research is to find the problems of advanced writing skills of upper elementary school students, and provide solutions. The research method used is a literature review. Based on the research results, it can be concluded that there are two factors that cause students' difficulties in writing at a high level, namely internal factors and external factors. Internal factors include students' weak motor skills, weak visual memory, low students' interest and motivation to learn, and finally students' study habits in class and at home. Meanwhile, external factors include lack of attention from parents towards students, an atmosphere at home that is less supportive, environmental conditions and finally the influence of social media. The lack of writing skills in elementary school students may also be caused by the less than optimal use of innovative learning methods. One way to optimize the writing learning process and increase students' writing motivation is to increase students' interest in writing through interesting learning models and learning media.

Trace Semantics for C++11 Memory Model

The C and C++ languages introduced the relaxed-memory concurrency into the language specification for efficiency purposes in 2011. Trace semantics can provide the mathematical foundation for the proposed C++11 memory model, and there is a lack of investigation of trace semantics for C++11. The Promising Semantics (PS) of Kang et al. provides the standard SC-style operational semantics for the C++11 concurrency model, where “SC” refers to “Sequential Consistency”. Inspired by PS, in this paper we first investigate the trace semantics for the relaxed read and write accesses under C++11, acting in the denotational semantics style. In our semantic model, a trace is in the form of a sequence of snapshots, and the snapshots record the modification in the relevant global or local variables, and the thread view. Moreover, the trace semantics for the release/acquire accesses under C++11 is also explored, based on the separated thread views and newly added message views. When considering this trace model, different accesses bring in their unique snapshots, and make distinguished effects on the production of the sequences. For any given program, the proposed trace semantics in this paper produces all the valid traces directly. Further, our trace semantics, together with that for TSO and MCA ARMv8, has the possibility to be the foundation of the meta model of the trace semantics for weak memory models.

The epistemic role of vividness

Abstract The vividness of mental imagery is epistemically relevant. Intuitively, vivid and intense memories are epistemically better than weak and hazy memories, and using a clear and precise mental image in the service of spatial reasoning is epistemically better than using a blurry and imprecise mental image. But how is vividness epistemically relevant? I argue that vividness is higher-order evidence about one’s epistemic state, rather than first-order evidence about the world. More specifically, the vividness of a mental image is higher-order evidence about the amount of first-order information one has about its subject matter. When vividness is sufficiently low, it can give one reason to doubt the epistemic basis of the mental image and thereby act as a defeater. This account has important implications for theorizing about the epistemic roles of memory, imagination, mental imagery and phenomenal consciousness.

Modeling and Analysis of Dekker-Based Mutual Exclusion Algorithms

Mutual exclusion is a fundamental problem in concurrent/parallel/distributed systems. The first pure-software solution to this problem for two processes, which is not based on hardware instructions like test-and-set, was proposed in 1965 by Th.J. Dekker and communicated by E.W. Dijkstra. The correctness of this algorithm has generally been studied under the strong memory model, where the read and write operations on a memory cell are atomic or indivisible. In recent years, some variants of the algorithm have been proposed to make it RW-safe when using the weak memory model, which makes it possible, e.g., for multiple read operations to occur simultaneously to a write operation on the same variable, with the read operations returning (flickering) a non-deterministic value. This paper proposes a novel approach to formal modeling and reasoning on a mutual exclusion algorithm using Timed Automata and the Uppaal tool, and it applies this approach through exhaustive model checking to conduct a thorough analysis of the Dekker’s algorithm and some of its variants proposed in the literature. This paper aims to demonstrate that model checking, although necessarily limited in the scalability of the number N of the processes due to the state explosions problem, is effective yet powerful for reasoning on concurrency and process action interleaving, and it can provide significant results about the correctness and robustness of the basic version and variants of the Dekker’s algorithm under both the strong and weak memory models. In addition, the properties of these algorithms are also carefully studied in the context of a tournament-based binary tree for N≥2 processes.

Paradoxical boosting of weak and strong spatial memories by hippocampal dopamine uncaging.

The ability to remember changes in the surroundings is fundamental for daily life. It has been proposed that novel events producing dopamine release in the hippocampal CA1 region could modulate spatial memory formation. However, the role of hippocampal dopamine increase on weak or strong spatial memories remains unclear. We show that male mice exploring two objects located in a familiar environment for 5 minutes created a short-term memory (weak) that cannot be retrieved one day later, whereas 10 minutes exploration created a long-term memory (strong) that can be retrieved one day later. Remarkably, hippocampal dopamine elevation during the encoding of weak object location memories (OLMs) allowed their retrieval one day later but dopamine elevation during the encoding of strong OLMs promoted the preference for a familiar object location over a novel object location after 24 hours. Moreover, dopamine uncaging after the encoding of OLMs did not have effect on weak memories whereas on strong memories diminished the exploration of the novel object location. Additionally, hippocampal dopamine elevation during the retrieval of OLMs did not allow the recovery of weak memories and did not affect the retrieval of strong memory traces. Finally, dopamine elevation increased hippocampal theta oscillations, indicating that dopamine promotes the recurrent activation of specific groups of neurons. Our experiments demonstrate that hippocampal dopaminergic modulation during the encoding of OLMs depends on memory strength indicating that hyperdopaminergic levels that enhance weak experiences could compromise the normal storage of strong memories.Significance statement Increased levels of dopamine have been related to cognitive enhancement. Hippocampal dopamine elevation caused by novelty exposure has been proposed as a strategy to enhance memory based on the observation that surprising events create flashbulb memories that are remembered for long time. However, hyperdopaminergic levels could also underlie maladaptive memories, such as non-desired preservation of traumatic experiences. Our experiments show that dopamine elevation in the dorsal hippocampus during the encoding of spatial memories has paradoxical effects, while the enhancement of weak memories allows their retrieval, the dopaminergic modulation of strong memories limits the ability to modify preexisting spatial memories by changes in the environment. We conclude that cognitive enhancement through dopamine boosting must consider diverse aspects of memory formation.

Examining the role of the visuospatial sketchpad in children's math calculation skills using Baddeley and Hitch's model of working memory

Math difficulties (MDs) occur in about 3–7 % of children and have been associated with academic, health, and occupational challenges. To date, findings about the role of working memory in MDs have been conflicting. The Automated Working Memory Assessment Battery (AWMA), which assesses all components of Baddeley and Hitch's model of working memory, was used to investigate which component of the model was most related to math calculation skills in elementary-school children. Participants were 94 (52 male) children (M age = 9 years 1 month; Range = 6 years 0 months to 11 years 8 months). As hypothesized, math calculation scores were correlated with all four working memory components (phonological loop, visuospatial sketchpad, verbal and visuospatial central executive). After accounting for age, phonological processing, and attention, the visuospatial sketchpad was the only memory component that contributed to the prediction of math calculation scores, explaining an additional 10.2 % of unique variance. Short-term visuospatial memory should be assessed in children having difficulty with math and children could benefit from interventions that include attention to the development of both visuospatial memory and math calculation skills. This study did not use a longitudinal design and so we cannot conclude that weak visuospatial memory impedes the development of math calculation skills. Future research should use longitudinal designs and investigate other types of math skills.

Turbulence spreading by resonant wave-wave interactions: A fractional kinetics approach.

This paper is concerned with the processes of spatial propagation and penetration of turbulence from the regions where it is locally excited into initially laminar regions. The phenomenon has come to be known as "turbulence spreading" and witnessed a renewed attention in the literature recently. Here, we propose a comprehensive theory of turbulence spreading based on fractional kinetics. We argue that the use of fractional-derivative equationspermits a general approach focusing on fundamentals of the spreading process regardless of a specific turbulence model and/or specific instability type. The starting point is the Hamiltonian of resonant wave-wave interactions, from which a family of scaling laws for the asymptotic spreading is derived. Both three- and four-wave interactions are considered. The results span from a subdiffusive spreading in the parameter range of weak chaos to avalanche propagation in regimes with population inversion. Attention is paid to how nonergodicity introduces weak mixing, memory and intermittency into spreading dynamics, and how the properties of non-Markovianity and nonlocality emerge from the presence of islands of regular dynamics in phase space. Also we resolve an existing question concerning turbulence spillover into gap regions, where the instability growth is locally suppressed, and show that the spillover occurs through exponential (Anderson-like) localization in case of four-wave interactions and through an algebraic (weak) localization in case of triad interactions. In the latter case an inverse-cubic behavior of the spillover function is found. Wherever relevant, we contrast our findings against the available observational and numerical evidence, and we also commit ourselves to establish connections with the models of turbulence spreading proposed previously.

Is the expression of different discrete emotions related to time? Evidence from online Chinese reviews using sentiment analysis and human behavior dynamics.

The online behavior of online users has taken on complex and diverse characteristics, and posting product reviews on e-commerce platforms is no exception. In fact, reviews contain rich and multi-dimensional discrete emotional information, and whether there is a relationship between the expression of these different discrete emotions and the time interval between product purchase and review posting as well as their related characteristics are the issues that this study needs to analyze and solve in depth. Based on the OCC model (named after three proposers) of psychological emotional cognitive evaluation theory as the basis for emotion classification, the study used the massive amounts of Chinese reviews of mobile phones on the Chinese e-commerce platform Jingdong Mall as the research object, applied supervised machine learning methods to classify discrete emotions, and constructed a large corpus containing satisfaction, disappointment, admiration, reproach, love, and hate; then the study delved into the distribution and behavioral dynamics characteristics of consumers' comments containing the different discrete emotions at different "purchase-comment" time intervals. The results showed that the first peak of the distribution curves of the six discrete emotions at different "purchase-comment" time intervals occurs on the first day after purchase and then decreases gradually but at different rates. The three curves for satisfaction, love, and hate emotions also show a second peak on the eleventh day, which is more similar to the bimodal distribution, implying that the corresponding product reviews are more objective. In addition, the distribution of reviews containing the six discrete emotions at different "purchase-comment" time intervals follows a power-law distribution and has the temporal characteristics of human behavioral dynamics, that is, "strong paroxysms and weak memory". However, the reviews containing the admiration and reproach emotions were most intensively written by consumers after the purchase, indicating that the service provided by the seller, logistics, and e-commerce platform stimulates more consumers to give quick responses and detailed reviews. This study is not only of great significance for exploring the internal mechanisms of consumer discrete emotional expression but also provides important decision-making references for potential consumer purchasing decisions, product updates for developers, marketing strategy formulation for marketing teams, and service improvement for sellers, logistics companies, and e-commerce platforms.

Optimal Re-Materialization Strategies for Heterogeneous Chains: How to Train Deep Neural Networks with Limited Memory

Training in Feed Forward Deep Neural Networks is a memory-intensive operation which is usually performed on GPUs with limited memory capacities. This may force data scientists to limit the depth of the models or the resolution of the input data if data does not fit in the GPU memory. The re-materialization technique, whose idea comes from the checkpointing strategies developed in the Automatic Differentiation literature, allows data scientists to limit the memory requirements related to the storage of intermediate data (activations), at the cost of an increase in the computational cost. This paper introduces a new strategy of re-materialization of activations that significantly reduces memory usage. It consists in selecting which activations are saved and which activations are deleted during the forward phase, and then recomputing the deleted activations when they are needed during the backward phase. We propose an original computation model that combines two types of activation savings: either only storing the layer inputs, or recording the complete history of operations that produced the outputs. This paper focuses on the fully heterogeneous case, where the computation time and the memory requirement of each layer is different. We prove that finding the optimal solution is NP-hard and that classical techniques from Automatic Differentiation literature do not apply. Moreover, the classical assumption of memory persistence of materialized activations, used to simplify the search of optimal solutions, does not hold anymore. Thus, we propose a weak memory persistence property and provide a Dynamic Program to compute the optimal sequence of computations. This algorithm is made available through the Rotor software, a PyTorch plug-in dealing with any network consisting of a sequence of layers, each of them having an arbitrarily complex structure. Through extensive experiments, we show that our implementation consistently outperforms existing re-materialization approaches for a large class of networks, image sizes and batch sizes.

Durable protective efficiency provide by mRNA vaccines require robust immune memory to antigens and weak immune memory to lipid nanoparticles

The Pegylated lipids in lipid nanoparticle (LNPs) vaccines have been found to cause acute hypersensitivity reactions in recipients, and generate anti-LNPs immunity after repeated administration, thereby reducing vaccine effectiveness. To overcome these challenges, we developed a new type of LNPs vaccine (SAPC-LNPs) which was co-modified with sialic acid (SA) - lipid derivative and cleavable PEG - lipid derivative. This kind of mRNA vaccine can target dendritic cells (DCs) and rapidly escape from early endosomes (EE) and lysosomes with a total endosomal escape rate up to 98 %. Additionally, the PEG component in SAPC-LNPs was designed to detach from the LNPs under the catalysis of carboxylesterase in vivo, which reduced the probability of PEG being attached to LNPs entering antigen-presenting cells. Compared with commercially formulated vaccines (1.5PD-LNPs), mice treated with SAPC-LNPs generated a more robust immune memory to tumor antigens and a weaker immune memory response to LNPs, and showed lower side effects and long-lasting protective efficiency. We also discovered that the anti-tumor immune memory formed by SAPC-LNPs mRNA vaccine was directly involved in the immune cycle to rattack tumor. This immune memory continued to strengthen with multiple cycles, supporting that the immune memory should be incorporated into the theory of tumor immune cycle.

How Hard Is Weak-Memory Testing?

Weak-memory models are standard formal specifications of concurrency across hardware, programming languages, and distributed systems. A fundamental computational problem is consistency testing : is the observed execution of a concurrent program in alignment with the specification of the underlying system? The problem has been studied extensively across Sequential Consistency (SC) and weak memory, and proven to be NP-complete when some aspect of the input (e.g., number of threads/memory locations) is unbounded. This unboundedness has left a natural question open: are there efficient parameterized algorithms for testing? The main contribution of this paper is a deep hardness result for consistency testing under many popular weak-memory models: the problem remains NP-complete even in its bounded setting, where candidate executions contain a bounded number of threads, memory locations, and values. This hardness spreads across several Release-Acquire variants of C11, a popular variant of its Relaxed fragment, popular Causal Consistency models, and the POWER architecture. To our knowledge, this is the first result that fully exposes the hardness of weak-memory testing and proves that the problem admits no parameterization under standard input parameters. It also yields a computational separation of these models from SC, x86-TSO, PSO, and Relaxed, for which bounded consistency testing is either known (for SC), or shown here (for the rest), to be in polynomial time.