Can We Utilize Pre-trained Language Models within Causal Discovery Algorithms?

Scaling laws have allowed Pre-trained Language Models (PLMs) into the field of causal reasoning. Causal reasoning of PLM relies solely on text-based descriptions, in contrast to causal discovery which aims to determine the causal relationships between variables utilizing data. Recently, there has been current research regarding a method that mimics causal discovery by aggregating the outcomes of repetitive causal reasoning, achieved through specifically designed prompts. It highlights the usefulness of PLMs in discovering cause and effect, which is often limited by a lack of data, especially when dealing with multiple variables. Conversely, the characteristics of PLMs which are that PLMs do not analyze data and they are highly dependent on prompt design leads to a crucial limitation for directly using PLMs in causal discovery. Accordingly, PLM-based causal reasoning deeply depends on the prompt design and carries out the risk of overconfidence and false predictions in determining causal relationships. In this paper, we empirically demonstrate the aforementioned limitations of PLM-based causal reasoning through experiments on physics-inspired synthetic data. Then, we propose a new framework that integrates prior knowledge obtained from PLM with a causal discovery algorithm. This is accomplished by initializing an adjacency matrix for causal discovery and incorporating regularization using prior knowledge. Our proposed framework not only demonstrates improved performance through the integration of PLM and causal discovery but also suggests how to leverage PLM-extracted prior knowledge with existing causal discovery algorithms

Ninety‐Day Stroke Recurrence in Minor Stroke: Systematic Review and Meta‐Analysis of Trials and Observational Studies

Background Risk of recurrence after minor ischemic stroke is usually reported with transient ischemic attack. No previous meta‐analysis has focused on minor ischemic stroke alone. The objective was to evaluate the pooled proportion of 90‐day stroke recurrence for minor ischemic stroke, defined as a National Institutes of Health Stroke Scale severity score of ≤5. Methods and Results Published papers found on PubMed from 2000 to January 12, 2021, reference lists of relevant articles, and experts in the field were involved in identifying relevant studies. Randomized controlled trials and observational studies describing minor stroke cohort with reported 90‐day stroke recurrence were selected by 2 independent reviewers. Altogether 14 of 432 (3.2%) studies met inclusion criteria. Multilevel random‐effects meta‐analysis was performed. A total of 6 randomized controlled trials and 8 observational studies totaling 45 462 patients were included. The pooled 90‐day stroke recurrence was 8.6% (95% CI, 6.5–10.7), reducing by 0.60% (95% CI, 0.09–1.1; P =0.02) with each subsequent year of publication. Recurrence was lowest in dual antiplatelet trial arms (6.3%, 95% CI, 4.5–8.0) when compared with non‐dual antiplatelet trial arms (7.2%, 95% CI, 4.7–9.6) and observational studies 10.6% (95% CI, 7.0–14.2). Age, hypertension, diabetes, ischemic heart disease, or known atrial fibrillation had no significant association with outcome. Defining minor stroke with a lower National Institutes of Health Stroke Scale threshold made no difference – score ≤3: 8.6% (95% CI, 6.0–11.1), score ≤4: 8.4% (95% CI, 6.1–10.6), as did excluding studies with n<500%–7.3% (95% CI, 5.5–9.0). Conclusions The risk of recurrence after minor ischemic stroke is declining over time but remains important

Chemical Transformations of Anisotropic Platelets and Spherical Nanocrystals

CONSPECTUS: Inorganic nanocrystal design has been continuously evolving with a better understanding of the chemical reaction mechanisms between chemical stimuli and nanocrystals. Under certain conditions, molecular compounds can be effective as chemical stimuli to induce transformative reactions of nanocrystals toward new materials that would differ in geometric shape, composition, and crystallographic structure. To explore such evolutionary processes, two-dimensional (2D) layered transition-metal chalcogenide (TMC) nanostructures are an interesting structural platform because they not only exhibit unique transformation pathways due to their structural anisotropy but also present new opportunities for improved material properties for potential applications such as catalysis and energy conversion and storage. The high surface area/volume ratio, interlayer van der Waals (vdW) spacing, and different coordination states between the unsaturated edges and the fully saturated basal planes of the chalcogens are characteristic of 2D layered TMC nanostructures, which subsequently lead to anisotropic chemical processes during chemical transformations, such as regioselective reactions at the interfacial boundaries in the pathways for either porous or solid heteronanostructures. In this Account, we first discuss the chemical reactivity of 2D layered TMC nanostructures. By categorizing the external stimuli in terms of chemical principles, such as Lewis acid-base chemistry, a desirable regioselective chemical reaction can occur with controlled reactivity. In association with the knowledge obtained from the nanoscale chemical reactivity of 2D layered nanocrystals, similar efforts in other important morphologies such as 1D and isotropic 0D nanocrystals are introduced. For instance, for 1D and 0D metal oxide nanocrystals, the effects of molecular stimuli on the atomic-level changes in the crystal lattice are demonstrated, eventually leading to a variety of shape transformations.11Nsciescopu

Active User Detection of Machine-type Communications via Dimension Spreading Neural Network

Massive machine-type communication (mMTC), key component for internet of things (IoT), concerns the access of massive machine-type communication devices to the basestation. To support the massive connectivity, grant-free access and non-orthogonal multiple access (NOMA) have been recently introduced. In the grant-free transmission, each device transmits information without the granting process so that the basestation needs to identify the active devices among all potential devices. This process, called an active user detection (AUD), is a challenging problem in the NOMA-based systems since it is difficult to find out the active devices from the superimposed received signal. An aim of this paper is to propose a new type of AUD scheme suitable for the highly overloaded mMTC, referred to as dimension spreading deep neural network-based AUD (DSDNNAUD). The key feature of DSDNN-AUD is to set the dimension of hidden layers being larger than the size of a transmit vector to improve the representation quality of the support. In doing so, the proposed scheme can better discriminate the supports generated from correlated structured environment. Numerical results demonstrate that the proposed AUD scheme outperforms the conventional approaches in both AUD success probability and throughput performance.N

Comparison of amoxicillin photodegradation in the UV/H₂O₂ and UV/persulfate systems : reaction kinetics, degradation pathways, and antibacterial activity

The extensive use of non-metabolized amoxicillin (AMX) has led to the contamination of the aquatic environment, which requires effective treatment methods. This study compares the reaction kinetics, degradation pathways, and antibacterial activity of AMX in the UV/H₂O₂ and UV/persulfate (S₂O₈²⁻, PS) systems. UV irradiation alone shows a negligible effect on AMX degradation, while the addition of H₂O₂ or PS increases the degradation efficiency of AMX significantly due to the generation of HO∙ and SO₄∙⁻. The second-order rate constants of AMX with HO∙ and SO₄∙⁻ are 3.9 × 10⁹ M⁻¹ s⁻¹ and 3.5 × 10⁹ M⁻¹ s⁻¹, respectively. In the UV/PS system at neutral pH, the contributions of UV, HO∙, and SO₄∙⁻ for AMX degradation are 7.3%, 22.8%, and 69.9%, respectively. The degradation efficiency of AMX decreases with the presence of natural organic matter and inorganic anions in the water matrices. Based on the experimental evidence substantiated with theoretical calculations, the degradation pathways of AMX in the UV/H₂O₂ and UV/PS systems were proposed, including hydroxylation (+16 Da), hydrolysis (+18 Da), and decarboxylation (−44 Da). The frontier electron density of AMX was calculated to predict the susceptible regions to HO∙ and SO₄∙⁻ attack. The antibacterial activity of AMX solution decreases significantly after applying UV/H₂O₂ or UV/PS processes. UV/H₂O₂ is more cost-effective than UV/PS process in degrading AMX

Morphology-Conserving Non-Kirkendall Anion Exchange of Metal Oxide Nanocrystals

© 2020 American Chemical Society.Nanoscale dynamic processes such as the diffusion of ions within solid-state structures are critical for understanding and tuning material properties in a wide range of areas, such as energy storage and conversion, catalysis, and optoelectronics. In the generation of new types of nanocrystals (NCs), diffusion-mediated ion exchange reactions have also been proposed as one of the most effective transformational strategies. However, retaining the original morphology and crystal structure of metal oxide NCs has been challenging because of Kirkendall void formation, and there has been no success, especially for anion exchange. Here we show that with the aid of an oxygen extracting reagent (OER), anion diffusion is dramatically accelerated and morphology-conserving anion exchange without Kirkendall void formation is possible. In the case of the conversion of Fe3O4 to Fe3S4, oxygen extraction and subsequent formation of the amorphous phase facilitate the migration of incoming sulfur anions by approximately 100-fold, which is close to the level of the outgoing cation diffusivity. We also demonstrate that the working principle of the morphology-conserving non-Kirkendall anion exchange is operative for metal oxide NCs with different shapes and crystal structures11Nsciescopu

Resource-Aware Device Allocation of Data-Parallel Applications on Heterogeneous Systems

As recent heterogeneous systems comprise multi-core CPUs and multiple GPUs, efficient allocation of multiple data-parallel applications has become a primary goal to achieve both maximum total performance and efficiency. However, the efficient orchestration of multiple applications is highly challenging because a detailed runtime status such as expected remaining time and available memory size of each computing device is hidden. To solve these problems, we propose a dynamic data-parallel application allocation framework called ADAMS. Evaluations show that our framework improves the average total execution device time by 1.85× over the round-robin policy in the non-shared-memory system with small data set