Rare-earth Engineering of the Magnetocaloric Effect in RMn6Sn6

We present a comprehensive study of the magnetocaloric effect (MCE) in a family of kagome magnets with formula RMn6Sn6 (R=Tb, Ho, Er, and Lu). These materials have a small rare-earth content and tunable magnetic ordering, hence they provide a venue to study the fundamentals of the MCE. We examine the effect of different types of order (ferromagnetic, ferrimagnetic, and antiferromagnetic) and the presence of a metamagnetic transition on the MCE. We extend the study to a high-entropy rare-earth alloys of the family, and conclude with several guidelines for enhancing the MCE in tunable magnetic materials with a small rare-earth content.Comment: Main Text: 14 pages, 6 figures Supplemental: 3 pages, 2 figures, 1 tabl

Gravitational anomaly in the ferrimagnetic topological Weyl semimetal NdAlSi

Quantum anomalies are the breakdowns of classical conservation laws that occur in quantum-field theory description of a physical system. They appear in relativistic field theories of chiral fermions and are expected to lead to anomalous transport properties in Weyl semimetals. This includes a chiral anomaly, which is a violation of the chiral current conservation that takes place when a Weyl semimetal is subjected to parallel electric and magnetic fields. A charge pumping between Weyl points of opposite chirality causes the chiral magnetic effect that has been extensively studied with electrical transport. On the other hand, if the thermal gradient, instead of the electrical field, is applied along the magnetic field, then as a consequence of the gravitational (also called the thermal chiral) anomaly an energy pumping occurs within a pair of Weyl cones. As a result, this is expected to generate anomalous heat current contributing to the thermal conductivity. We report an increase of both the magneto-electric and magneto-thermal conductivities in quasi-classical regime of the magnetic Weyl semimetal NdAlSi. Our work also shows that the anomalous electric and heat currents, which occur due to the chiral magnetic effect and gravitational anomalies respectively, are still linked by a 170 years old relation called the Wiedemann-Franz law.Comment: 26 pages, 8 figure

Beyond Object Recognition: A New Benchmark towards Object Concept Learning

Understanding objects is a central building block of artificial intelligence, especially for embodied AI. Even though object recognition excels with deep learning, current machines still struggle to learn higher-level knowledge, e.g., what attributes an object has, and what can we do with an object. In this work, we propose a challenging Object Concept Learning (OCL) task to push the envelope of object understanding. It requires machines to reason out object affordances and simultaneously give the reason: what attributes make an object possesses these affordances. To support OCL, we build a densely annotated knowledge base including extensive labels for three levels of object concept (category, attribute, affordance), and the causal relations of three levels. By analyzing the causal structure of OCL, we present a baseline, Object Concept Reasoning Network (OCRN). It leverages causal intervention and concept instantiation to infer the three levels following their causal relations. In experiments, OCRN effectively infers the object knowledge while following the causalities well. Our data and code are available at https://mvig-rhos.com/ocl.Comment: ICCV 2023. Webpage: https://mvig-rhos.com/oc

The effect of argatroban on early neurological deterioration and outcomes in minor ischemic stroke: preliminary findings

BackgroundMinor ischemic stroke (MIS) is associated with early neurological deterioration (END) and poor prognosis. Here, we investigated whether argatroban administration can mitigate MIS-associated END and improve functional outcomes by monitoring activated partial thrombin time (APTT).MethodsData were collected for patients with MIS admitted to our hospital from January 2019 to December 2022. Patients were divided into a dual antiplatelet therapy (DAPT) group (aspirin + clopidogrel) and an argatroban group (aspirin + argatroban). Those in the latter group who achieved a target APTT of 1.5–3-fold that of baseline and <100 s at 2 h after argatroban infusion were included in the argatroban subgroup. The primary outcome was the END rate of the DAPT group versus that of the argatroban group or the argatroban subgroup. Secondary outcomes included the proportion of patients with modified Rankin Scale (mRS) 0–2 at 7 and 90 days. In addition, baseline date were compared between patients with and without END in the argatroban group.Results363 patients were included in the DAPT group and 270 in the argatroban group. There were no significant differences in any above outcome between them. 207 pairs were included in the DAPT group and the argatroban subgroup after 1:1 propensity score matching (PSM). Significant differences were observed in the proportion of END (OR, 2.337; 95% CI, 1.200–4.550, p = 0.011) and mRS 0–2 at 7 days (OR, 0.624; 95% CI, 0.415–0.939, p = 0.023), but not in mRS 0–2 at 90 days or the hemorrhagic events between the two groups. In the argatroban group, univariate analysis showed that the rate of diabetes (OR, 2.316; 95% CI, 1.107–4.482, p = 0.023), initial random blood glucose (OR, 1.235; 95% CI, 1.070–1.425, p = 0.004), drinking history (OR, 0.445; 95% CI, 0.210–0.940, p = 0.031) or those reaching the target APTT (OR, 0.418; 95% CI, 0.184–0.949, p = 0.033) was significantly different among patients with and without END. However, there were no statistical differences in these parameters between them following multivariate analysis.ConclusionIn patients with MIS, argatroban administration and reaching the target APTT can reduce the incidence of END and improve short-term functional prognosis

Preoperative Prediction of Lymph Node Metastasis in Colorectal Cancer with Deep Learning

Objective. To develop an artificial intelligence method predicting lymph node metastasis (LNM) for patients with colorectal cancer (CRC). Impact Statement. A novel interpretable multimodal AI-based method to predict LNM for CRC patients by integrating information of pathological images and serum tumor-specific biomarkers. Introduction. Preoperative diagnosis of LNM is essential in treatment planning for CRC patients. Existing radiology imaging and genomic tests approaches are either unreliable or too costly. Methods. A total of 1338 patients were recruited, where 1128 patients from one centre were included as the discovery cohort and 210 patients from other two centres were involved as the external validation cohort. We developed a Multimodal Multiple Instance Learning (MMIL) model to learn latent features from pathological images and then jointly integrated the clinical biomarker features for predicting LNM status. The heatmaps of the obtained MMIL model were generated for model interpretation. Results. The MMIL model outperformed preoperative radiology-imaging diagnosis and yielded high area under the curve (AUCs) of 0.926, 0.878, 0.809, and 0.857 for patients with stage T1, T2, T3, and T4 CRC, on the discovery cohort. On the external cohort, it obtained AUCs of 0.855, 0.832, 0.691, and 0.792, respectively (T1-T4), which indicates its prediction accuracy and potential adaptability among multiple centres. Conclusion. The MMIL model showed the potential in the early diagnosis of LNM by referring to pathological images and tumor-specific biomarkers, which is easily accessed in different institutes. We revealed the histomorphologic features determining the LNM prediction indicating the model ability to learn informative latent features

Review of Recently Progress on Neural Electronics and Memcomputing Applications in Intrinsic SiOx-Based Resistive Switching Memory

In this chapter, we focus on the recent process on memcomputing (memristor + computing) in intrinsic SiOx-based resistive switching memory (ReRAM or called memristor). In the first section of the chapter, we investigate neuromorphic computing by mimicking the synaptic behaviors in integrating one-diode and one-resistive switching element (1D-1R) architecture. The power consumption can be minimized further in synaptic functions because sneak-path current has been suppressed and the capability for spike-induced synaptic behaviors has been demonstrated, representing critical milestones and achievements for the application of conventional SiOx-based materials in future advanced neuromorphic computing. In the next section of chapter, we will discuss an implementation technique of implication operations for logic-in-memory computation by using a SiOx-based memristor. The implication function and its truth table have been implemented with the unipolar or nonpolar operation scheme. Furthermore, a circuit with 1D-1R architecture with a 4 × 4 crossbar array has been demonstrated, which realizes the functionality of a one-bit full adder as same as CMOS logic circuits with lower design area requirement. This chapter suggests that a simple, robust approach to realize memcomputing chips is quite compatible with large-scale CMOS manufacturing technology by using an intrinsic SiOx-based memristor

Stripe Helical Magnetism and Two Regimes of Anomalous Hall Effect in NdAlGe

We report the magnetic and electronic transport properties of the inversion and time-reversal symmetry breaking Weyl semimetal NdAlGe. This material is analogous to NdAlSi, whose helical magnetism presents a rare example of a Weyl-mediated collective phenomenon, but with a larger spin-orbit coupling. Our neutron diffraction experiments revealed that NdAlGe, similar to NdAlSi, supports an incommensurate Ising spin density wave ($T_{\text{inc}}=6.8$ K) with a small helical spin canting of 3$^\circ$ and a long-wavelength of $\sim$ 35 nm, which transitions to a commensurate ferrimagnetic state below $T_{\text{com}}=5.1$ K. Using small-angle neutron scattering, we showed that the zero-field cooled ferrimagnetic domains form stripes in real space with characteristic length scales of 18 nm and 72 nm parallel and perpendicular to the [110] direction, respectively. Interestingly, for the transport properties, NdAlSi does not exhibit an anomalous Hall effect (AHE) that is commonly observed in magnetic Weyl semimetals. In contrast to NdAlSi, we identify two different AHE regimes in NdAlGe that are respectively governed by intrinsic Berry curvature and extrinsic disorders/spin fluctuations. Our study suggests that Weyl-mediated magnetism prevails in this group of noncentrosymmetric magnetic Weyl semimetals NdAl$X$, but transport properties including AHE are affected by material-specific extrinsic effects such as disorders, despite the presence of prominent Berry curvature.Comment: Preprint, 16 pages, 6 main figures, 6 supplementary figure