Pressurizing Field-Effect Transistors of Few-Layer MoS2 in a Diamond Anvil Cell

Hydrostatic pressure applied using diamond anvil cells (DAC) has been widely explored to modulate physical properties of materials by tuning their lattice degree of freedom. Independently, electrical field is able to tune the electronic degree of freedom of functional materials via, for example, the field-effect transistor (FET) configuration. Combining these two orthogonal approaches would allow discovery of new physical properties and phases going beyond the known phase space. Such experiments are, however, technically challenging and have not been demonstrated. Herein, we report a feasible strategy to prepare and measure FETs in a DAC by lithographically patterning the nanodevices onto the diamond culet. Multiple-terminal FETs were fabricated in the DAC using few-layer MoS2 and BN as the channel semiconductor and dielectric layer, respectively. It is found that the mobility, conductance, carrier concentration, and contact conductance of MoS2 can all be significantly enhanced with pressure. We expect that the approach could enable unprecedented ways to explore new phases and properties of materials under coupled mechano-electrostatic modulation.Comment: 15 pages, 5 figure

Combination therapy of vitamin C and thiamine for septic shock in a multicentre, double-blind, randomized, controlled study (ATESS): study protocol for a randomized controlled trial

Background Septic shock is a life-threatening condition with underlying circulatory and cellular/metabolic abnormalities. Vitamin C and thiamine are potential candidates for adjunctive therapy; they are expected to improve outcomes based on recent experimental and clinical research. The aim of the Ascorbic Acid and Thiamine Effect in Septic Shock (ATESS) trial is to evaluate the effects of early combination therapy with intravenous vitamin C and thiamine on recovery from organ failure in patients with septic shock. Methods This study is a randomized, double-blind, placebo-controlled, multicentre trial in adult patients with septic shock recruited from six emergency departments in South Korea. Patients will be randomly allocated into the treatment or control group (1:1 ratio), and we will recruit 116 septic shock patients (58 per group). For the treatment group, vitamin C (50 mg/kg) and thiamine (200 mg) will be mixed in 50 ml of 0.9% saline and administered intravenously every 12 h for a total of 48 h. For the placebo group, an identical volume of 0.9% saline will be administered in the same manner. The primary outcome is the delta Sequential Organ Failure Assessment (SOFA) score (ΔSOFA = initial SOFA at enrolment – follow-up SOFA after 72 h). Discussion This trial will provide valuable evidence about the effectiveness of vitamin C and thiamine therapy for septic shock. If effective, this therapy might improve survival and become one of the main therapeutic adjuncts for patients with septic shock. Trial registration ClinicalTrials.gov, NCT03756220. Registered on 5 December 2018.This work was supported by a National Research Foundation of Korea grant funded by the Korean government (No. 2018R1C1B6006821). The government did not have any role in the study design; collection, management, analysis, and interpretation of data; writing of the report; and the decision to submit the report for publication

Learning to Detect Incongruence in News Headline and Body Text via a Graph Neural Network

This paper tackles the problem of detecting incongruities between headlines and body text, where a news headline is irrelevant or even in opposition to the information in its body. Our model, called the graph-based hierarchical dual encoder (GHDE), utilizes a graph neural network to efficiently learn the content similarity between news headlines and long body paragraphs. This paper also releases a million-item-scale dataset of incongruity labels that can be used for training. The experimental results show that the proposed graph-based neural network model outperforms previous state-of-the-art models by a substantial margin (5.3%) on the area under the receiver operating characteristic (AUROC) curve. Real-world experiments on recent news articles confirm that the trained model successfully detects headline incongruities. We discuss the implications of these findings for combating infodemics and news fatigue.11Nsciescopu

Effect of emergency physician-operated emergency short-stay ward on emergency department stay length and clinical outcomes: a case-control study

Abstract Background We hypothesized that an emergency short-stay ward (ESSW) mainly operated by emergency medicine physicians may reduce the length of patient stay in emergency department without expense of clinical outcomes. Methods We retrospectively analysed adult patients who visited the emergency department of the study hospital and were subsequently admitted to wards from 2017 to 2019. We divided study participants into three groups: patients admitted to ESSW and treated by the department of emergency medicine (ESSW-EM), patients admitted to ESSW and treated by other departments (ESSW-Other) and patients admitted to general wards (GW). The co-primary outcomes were ED length of stay and 28-day hospital mortality. Results In total, 29,596 patients were included in the study, and 8,328 (31.3%), 2,356 (8.9%), and 15,912 (59.8%) of them were classified as ESSW-EM, ESSW-Other and GW groups, respectively. The ED length of stay of the ESSW-EM (7.1 h ± 5.4) was shorter than those of the ESSW-Other (8.0 ± 6.2, P < 0.001) and the GW (10.2 ± 9.8, P < 0.001 for both). Hospital mortality of ESSW-EM (1.9%) was lower than that of GW (4.1%, P < 0.001). In the multivariable linear regression analysis, the ESSW-EM was independently associated with shorter ED length of stay compared with the both ESSW-Other (coefficient, 1.08; 95% confidence interval, 0.70–1.46; P < 0.001) and GW (coefficient, 3.35; 95% confidence interval, 3.12–3.57; P < 0.001). In the multivariable logistic regression analyses, the ESSW-EM was independently associated with lower hospital mortality compared with both the ESSW-Other group (adjusted P = 0.030) and the GW group (adjusted P < 0.001). Conclusions In conclusion, the ESSW-EM was independently associated with shorter ED length of stay compared with both the ESSW-Other and the GW in the adult ED patients. Independent association was found between the ESSW-EM and lower hospital mortality compared with the GW

Reliable Domain-Specific Exclusive Logic Gates Using Reconfigurable Sequential Logic Based on Antiparallel Bipolar Memristors

The development of memristor-based stateful logic circuits can minimize data movement during the computing process to achieve in-memory computing, mitigating the von Neumann bottleneck in the current computing architecture. Herein, a method to combine resistance-resistance (R-R) and voltage-resistance (V-R) logic gates to implement exclusive logic gates composed of APMR-two-2(XOR, IMP, RIMP) and APMR-three-4XOR, where APMR means antiparallel memristors with a series resistor, is suggested. The proposed gates can accelerate XOR logic operation in a single cycle and expand for the n-bit input. The performance of the proposed logic gate is then demonstrated with a 1-bit full adder-subtractor along with the comparison of an n-bit ripple carry adder. It shows that the implementation for the n-bit adder takes 4n+1 memristors within 2n+1 steps, which significantly improves the optimization in terms of space- and time-related costs compared with other memristive logic gates. Subsequently, the improved adder circuit can be further utilized to implement an n-bit multiplier. In addition, the evaluation of the device stress on the various logic gates confirms that the proposed logic gates are reliable.N

DSQNet: A Deformable Model-Based Supervised Learning Algorithm for Grasping Unknown Occluded Objects

Grasping previously unseen objects for the first time, in which only partially occluded views of the object are available, remains a difficult challenge. Despite their recent successes, deep learning-based end-to-end methods remain impractical when training data and resources are limited and multiple grippers are used. Two-step methods that first identify the object shape and structure using deformable shape templates, then plan and execute the grasp, are free from those limitations, but also have difficulty with partially occluded objects. In this paper, we propose a two-step method that merges a richer set of shape primitives, the deformable superquadrics, with a deep learning network, DSQNet, that is trained to identify complete object shapes from partial point cloud data. Grasps are then generated that take into account the kinematic and structural properties of the gripper while exploiting the closed-form equations available for deformable superquadrics. A seven-dof robotic arm equipped with a parallel jaw gripper is used to conduct experiments involving a collection of household objects, achieving average grasp success rates of 93% (compared to 86% for existing methods), with object recognition times that are ten times faster. Code is available at https://github.com/seungyeon-k/DSQNet-public Note to Practitioners-This paper provides a comprehensive two-step method for grasping previously unseen objects, in which only partially occluded views of the object may be available. End-to-end deep learning-based methods typically require large amounts of training data, in the form of images of the objects taken from different angles and with different levels of occlusion, and grasping experiments that record the success and failure of each attempt; if a new gripper is used, more often than not the training data must be recollected and a new set of experiments performed. Two-step methods that first identify the object structure and shape using deformable shape templates, then plan the grasp based on knowledge of the object shape, are currently a more practical solution, but also have difficulty when only occluded views of the object are available. Our newly proposed two-step method takes advantage of a more flexible set of shape primitives, and also uses a supervised deep learning network to identify the object from occluded views. Our experimental results indicate improved grasp success rates against the state-of-the-art, with recognition rates that are up to ten times faster. Our method shows high recognition and grasping performance so is well applicable on most of the general household objects, but it cannot be directly applied to more diverse public 3D datasets since it requires some human-annotated segmentation labels. In future research, we will develop our deep learning network to automatically learn segmentation without human-annotated labels, allowing it to recognize more complex and diverse object shapes.N