Electrical Probing of Field-Driven Cascading Quantized Transitions of Skyrmion Cluster States in MnSi Nanowires

Magnetic skyrmions are topologically stable whirlpool-like spin textures that offer great promise as information carriers for future ultra-dense memory and logic devices1-4. To enable such applications, particular attention has been focused on the skyrmions properties in highly confined geometry such as one dimensional nanowires5-8. Hitherto it is still experimentally unclear what happens when the width of the nanowire is comparable to that of a single skyrmion. Here we report the experimental demonstration of such scheme, where magnetic field-driven skyrmion cluster (SC) states with small numbers of skyrmions were demonstrated to exist on the cross-sections of ultra-narrow single-crystal MnSi nanowires (NWs) with diameters, comparable to the skyrmion lattice constant (18 nm). In contrast to the skyrmion lattice in bulk MnSi samples, the skyrmion clusters lead to anomalous magnetoresistance (MR) behavior measured under magnetic field parallel to the NW long axis, where quantized jumps in MR are observed and directly associated with the change of the skyrmion number in the cluster, which is supported by Monte Carlo simulations. These jumps show the key difference between the clustering and crystalline states of skyrmions, and lay a solid foundation to realize skyrmion-based memory devices that the number of skyrmions can be counted via conventional electrical measurements

DeepSeek LLM: Scaling Open-Source Language Models with Longtermism

The rapid development of open-source large language models (LLMs) has been truly remarkable. However, the scaling law described in previous literature presents varying conclusions, which casts a dark cloud over scaling LLMs. We delve into the study of scaling laws and present our distinctive findings that facilitate scaling of large scale models in two commonly used open-source configurations, 7B and 67B. Guided by the scaling laws, we introduce DeepSeek LLM, a project dedicated to advancing open-source language models with a long-term perspective. To support the pre-training phase, we have developed a dataset that currently consists of 2 trillion tokens and is continuously expanding. We further conduct supervised fine-tuning (SFT) and Direct Preference Optimization (DPO) on DeepSeek LLM Base models, resulting in the creation of DeepSeek Chat models. Our evaluation results demonstrate that DeepSeek LLM 67B surpasses LLaMA-2 70B on various benchmarks, particularly in the domains of code, mathematics, and reasoning. Furthermore, open-ended evaluations reveal that DeepSeek LLM 67B Chat exhibits superior performance compared to GPT-3.5

Risk prediction and diagnosis of urban gas pipeline accidents based on polymorphic fuzzy Bayesian network

In order to evaluate the risk level of the urban gas pipeline system, and provide the reference for follow-up prevention efforts, a quantitative analysis method of gas pipeline accident risk was proposed based on polymorphic fuzzy Bayesian network. Firstly, risk factors were sorted out from 86 accident investigation reports, so that the city gas pipeline risk element system was established. Subsequently, the fault tree model was built to seek the match between risk hazards and accidents, which can convert into the Bayesian network structure. After that, fuzzy set theory and probability distribution method were introduced to calculate the prior probability of the root node and the conditional probability of the intermediate nodes, evidence-based inference of Bayesian network was used to predict the probability of accidents, analyze the importance of risk elements, and reverse diagnose key causal factors. Finally, this method was applied to the risk analysis of the “10·21” large pipeline gas leakage accident in Shenyang. The results of the case validation show the a priori probability of the accident is 688%, which verifies the effectiveness of the risk system. Besides, important risk elements derived from prediction and backward diagnosis are consistent with the direct causes analyzed in the accident investigation report. The polymorphic fuzzy Bayesian network approach for gas pipeline system risk can evaluate gas pipeline accident risk accurately and identify key risk-causing factors, which provides some reference for decision making in the safety management of city gas pipelines

Congestion strategy of weekday peak-hour private vehicle usage in Singapore’s central business district (CBD) : a regression analysis

Traffic congestion in the city centre is a prevalent issue in many developed countries including Singapore, where detrimental effects to GDP include decreasing productivities due to time lost and business activity disruptions. This paper analyses how the congregation of business activities, or “Lure” factor of the Central Business District (CBD), contributes to the peak-hour CBD traffic congestion. Our results found that the “Lure” proxies used, namely, the formation of business entities in the finance and insurance sector and the rental value of office spaces, are significant in explaining the variations in the private cars and vehicles transactions counts into the CBD, especially during the morning peak-hour periods. Despite the effectiveness of congestion pricing as a urban traffic strategy, it might not be a sustainable solution in the long run as it is at best a stop-gap measure which does not address the root cause of CBD peak-hour congestion. Hence, our findings are in support of the government’s move to decentralise the current CBD so as to spread its congregation of business activities and reduce its employment density to other regional centres. Other viable options are to increase flexibility in working hours and work schedules.Bachelor of Art

Batch Active Learning with Graph Neural Networks via Multi-Agent Deep Reinforcement Learning

Graph neural networks (GNNs) have achieved tremendous success in many graph learning tasks such as node classification, graph classification and link prediction. For the classification task, GNNs' performance often highly depends on the number of labeled nodes and thus could be significantly hampered due to the expensive annotation cost. The sparse literature on active learning for GNNs has primarily focused on selecting only one sample each iteration, which becomes inefficient for large scale datasets. In this paper, we study the batch active learning setting for GNNs where the learning agent can acquire labels of multiple samples at each time. We formulate batch active learning as a cooperative multi-agent reinforcement learning problem and present a novel reinforced batch-mode active learning framework BiGeNe. To avoid the combinatorial explosion of the joint action space, we introduce a value decomposition method that factorizes the total Q-value into the average of individual Q-values. Moreover, we propose a novel multi-agent Q-network consisting of a graph convolutional network (GCN) component and a gated recurrent unit (GRU) component. The GCN component takes both the informativeness and inter-dependences between nodes into account and the GRU component enables the agent to consider interactions between selected nodes in the same batch. Experimental results on multiple public datasets demonstrate the effectiveness and efficiency of our proposed method

Identification of RP11‐770J1.4 as immune‐related lncRNA regulating the CTXN1–cGAS–STING axis in histologically lower‐grade glioma

Abstract Human gliomas are lethal brain cancers. Emerging evidence revealed the regulatory role of long noncoding RNAs (lncRNAs) in tumors. Here, we performed a comprehensive analysis of the expression profiles of RNAs in histologically lower‐grade glioma (LGG). Enrichment analysis revealed that glioma is influenced by immune‐related signatures. Survival analysis further established the close correlation between network features and glioma prognosis. Subsequent experiments showed lncRNA RP11‐770J1.4 regulates CTXN1 expression through hsa‐miR‐124‐3p. Correlation analysis identified lncRNA RP11‐770J1.4 was immune related, specifically involved in the cytosolic DNA sensing pathway. Downregulated lncRNA RP11‐770J1.4 resulted in increased spontaneous gene expression of the cGAS–STING pathway. Single‐cell RNA sequencing analysis, along with investigations in a glioblastoma stem cell model and patient sample analysis, demonstrated the predominant localization of CTXN1 within tumor cores rather than peripheral regions. Immunohistochemistry staining established a negative correlation between CTXN1 expression and infiltration of CD8+ T cells. In vivo, Ctxn1 knockdown in GL261 cells led to decreased tumor burden and improved survival while increasing infiltration of CD8+ T cells. These findings unveil novel insights into the lncRNA RP11‐770J1.4–CTXN1 as a potential immune regulatory axis, highlighting its therapeutic implications for histologically LGGs

Charge ordering phase submerged in ferromagnetic ordering phase in the Nd

In this letter, Nd0.5Sr0.5Mn$_{1-x}$GaxO3 (0.0 $\leqslant$ x $\leqslant$ 0.075) manganites have been investigated. Diffuse satellite spots and streaks can be seen in transmission electron microscopy and the antiferromagnetic signal in electron spin resonance also occur early at 235 K which is much higher than the usually defined $T_{{\rm CO}}$ of the Nd0.5Sr0.5Mn$_{1-x}$GaxO3 sample, indicating that the charge ordering phase already appears in the ferromagnetic regions ($T_{{\rm C}}$ = 250 K). This charge ordering phase is submerged by the ferromagnetic phase so that it is invisible in routine measurements. As the $T_{{\rm C}}$ moves to lower temperature with Ga doping, the charge ordering phase exhibits itself gradually around 240 K

A multiphysics model of the compactly-assembled industrial alkaline water electrolysis cell

Electrolysis occupies a dominant position in the long-term application of hydrogen energy, as it can use the power surplus directly from renewable energies to produce hydrogen. Alkaline water electrolysis (AWE) is a mature and reliable technology standing out from other types of electrolysis because of its simplicity and low cost. Several multiphysics processes inside the AWE cell, such as the electrochemical, thermal, and fluidic processes. Developing the multiphysics model to quantify the relationship between these physics fields is essential for cell design. This paper establishes a three-dimensional numerical model to consider the quantitative relationship between the electrochemical process and fluidic process inside the cell of industrial AWE. The model considers the structural design of industrial AWE equipment, revealing that the shunting current effect introduced by the structure design cannot be ignored in the model. The simulation results present that the multiphysics model considering the bubble effect can estimate the current–voltage (I-V) characteristic curve more accurately with a relative error smaller than 5%, especially at a current density higher than 2500 A/m2. The model established is supposed to advance the development of water electrolysis models and guide the electrolyzer design of industrial AWE cell.</p