A large family of algorithms - supervised or unsupervised; stemming from statistics or geometry theory - has been designed to provide different solutions to the problem of dimensionality reduction. ...Despite the different motivations of these algorithms, we present in this paper a general formulation known as graph embedding to unify them within a common framework. In graph embedding, each algorithm can be considered as the direct graph embedding or its linear/kernel/tensor extension of a specific intrinsic graph that describes certain desired statistical or geometric properties of a data set, with constraints from scale normalization or a penalty graph that characterizes a statistical or geometric property that should be avoided. Furthermore, the graph embedding framework can be used as a general platform for developing new dimensionality reduction algorithms. By utilizing this framework as a tool, we propose a new supervised dimensionality reduction algorithm called marginal Fisher analysis in which the intrinsic graph characterizes the intraclass compactness and connects each data point with its neighboring points of the same class, while the penalty graph connects the marginal points and characterizes the interclass separability. We show that MFA effectively overcomes the limitations of the traditional linear discriminant analysis algorithm due to data distribution assumptions and available projection directions. Real face recognition experiments show the superiority of our proposed MFA in comparison to LDA, also for corresponding kernel and tensor extensions
Artificial synapses are the key building blocks for low‐power neuromorphic computing that can go beyond the constraints of von Neumann architecture. In comparison with two‐terminal memristors and ...three‐terminal transistors with filament‐formation and charge‐trapping mechanisms, emerging electrolyte‐gated transistors (EGTs) have been demonstrated as a promising candidate for neuromorphic applications due to their prominent analog switching performance. Here, a novel graphdiyne (GDY)/MoS2‐based EGT is proposed, where an ion‐storage layer (GDY) is adopted to EGTs for the first time. Benefitting from this Li‐ion‐storage layer, the GDY/MoS2‐based EGT features a robust stability (variation < 1% for over 2000 cycles), an ultralow energy consumption (50 aJ µm−2), and long retention characteristics (>104 s). In addition, a quasi‐linear conductance update with low noise (1.3%), an ultrahigh Gmax/Gmin ratio (103), and an ultralow readout conductance (<10 nS) have been demonstrated by this device, enabling the implementation of the neuromorphic computing with near‐ideal accuracies. Moreover, the non‐volatile characteristics of the GDY/MoS2‐based EGT enable it to demonstrate logic‐in‐memory functions, which can execute logic processing and store logic results in a single device. These results highlight the potential of the GDY/MoS2‐based EGT for next‐generation low‐power electronics beyond von Neumann architecture.
A novel graphdiyne (GDY)/MoS2‐based electrolyte‐gated transistor using GDY as a Li‐ion‐storage layer is proposed, which features robust stability and flexibility, an ultralow energy consumption, a long retention time, a quasi‐linear weight update with low noise, an ultrahigh Gmax/Gmin ratio, and an ultralow readout conductance. This GDY/MoS2‐based EGT has demonstrated its potential in applications of neuromorphic computing and in‐memory computing.
As one of the most promising cathode candidates for room‐temperature sodium‐ion batteries (SIBs), P2‐type layered oxides face the challenge of simultaneously realizing high‐rate performance while ...achieving long cycle life. Here, a stable Na2/3Ni1/6Mn2/3Cu1/9Mg1/18O2 cathode material is proposed that consists of multiple‐layer oriented stacking nanoflakes, in which the nickel sites are partially substituted by copper and magnesium, a characteristic of the material that is confirmed by multiscale scanning transmission electron microscopy and electron energy loss spectroscopy techniques. Owing to the optimal morphology structure modulation and chemical element substitution strategy, the electrode displays remarkable rate performance (73% capacity retention at 30C compared to 0.5C) and outstanding cycling stability in Na half‐cell system couple with unprecedented full battery performance. The underlying thermal stability, phase stability, and Na+ storage mechanisms are clearly elucidated through the systematical characterizations of electrochemical behaviors, in situ X‐ray diffraction at different temperatures, and operando X‐ray diffraction upon Na+ deintercalation/intercalation. Surprisingly, a quasi‐solid‐solution reaction is switched to an absolute solid‐solution reaction and a capacitive Na+ storage mechanism is demonstrated via quantitative electrochemical kinetics calculation during charge/discharge process. Such a simple and effective strategy might reveal a new avenue into the rational design of excellent rate capability and long cycle stability cathode materials for practical SIBs.
A stable copper and magnesium cosubstituted Na2/3Ni1/6Mn2/3Cu1/9Mg1/18O2 cathode material consisting of multiple‐layer oriented stacking nanoflakes is reported. An optimal structure design and a chemical element substitution strategy are demonstrated to greatly improve Na+ transport kinetics and structural stability of P2‐type cathode material, resulting in high‐rate and long cycle life for a sodium‐ion battery.
Ge–Sb–Te (“GST”) alloys are leading phase‐change materials for digital memories and neuro‐inspired computing. Upon fast crystallization, these materials form rocksalt‐like phases with large ...structural and vacancy disorder, leading to an insulating phase at low temperature. Here, a comprehensive description of crystallization, structural disorder, and electronic properties of GeSb2Te4 based on realistic, quantum‐mechanically based (“ab initio”) computer simulations with system sizes of more than 1000 atoms is provided. It is shown how an analysis of the crystallization mechanism based on the smooth overlap of atomic positions kernel reveals the evolution of both geometrical and chemical order. The connection between structural and electronic properties of the disordered, as‐crystallized models, which are relevant to the transport properties of GST, is then studied. Furthermore, it is shown how antisite defects and extended Sb‐rich motifs can lead to Anderson localization in the conduction band. Beyond memory applications, these findings are therefore more generally relevant to disordered rocksalt‐like chalcogenides that exhibit self‐doping, since they can explain the origin of Anderson insulating behavior in both p‐ and n‐doped chalcogenide materials.
The crystallization mechanism of GeSb2Te4 is described via ab initio computer simulations with system sizes of more than 1000 atoms. The smooth overlap of atomic positions kernel is utilized to reveal the evolution of both geometrical and chemical order during crystallization. The connection between structural and electronic properties of the recrystallized models is studied.
Particle swarm optimization (PSO) has exhibited well-known feasibility in problem optimization. However, its optimization performance still encounters challenges when confronted with complicated ...optimization problems with many local areas. In PSO, the interaction among particles and utilization of the communication information play crucial roles in improving the learning effectiveness and learning diversity of particles. To promote the communication effectiveness among particles, this paper proposes a stochastic triad topology to allow each particle to communicate with two random ones in the swarm via their personal best positions. Then, unlike existing studies that employ the personal best positions of the updated particle and the neighboring best position of the topology to direct its update, this paper adopts the best one and the mean position of the three personal best positions in the associated triad topology as the two guiding exemplars to direct the update of each particle. To further promote the interaction diversity among particles, an archive is maintained to store the obsolete personal best positions of particles and is then used to interact with particles in the triad topology. To enhance the chance of escaping from local regions, a random restart strategy is probabilistically triggered to introduce initialized solutions to the archive. To alleviate sensitivity to parameters, dynamic adjustment strategies are designed to dynamically adjust the associated parameter settings during the evolution. Integrating the above mechanism, a stochastic triad topology-based PSO (STTPSO) is developed to effectively search complex solution space. With the above techniques, the learning diversity and learning effectiveness of particles are largely promoted and thus the developed STTPSO is expected to explore and exploit the solution space appropriately to find high-quality solutions. Extensive experiments conducted on the commonly used CEC 2017 benchmark problem set with different dimension sizes substantiate that the proposed STTPSO achieves highly competitive or even much better performance than state-of-the-art and representative PSO variants.
High-dimensional optimization problems are more and more common in the era of big data and the Internet of things (IoT), which seriously challenge the optimization performance of existing optimizers. ...To solve these kinds of problems effectively, this paper devises a dimension group-based comprehensive elite learning swarm optimizer (DGCELSO) by integrating valuable evolutionary information in different elite particles in the swarm to guide the updating of inferior ones. Specifically, the swarm is first separated into two exclusive sets, namely the elite set (ES) containing the top best individuals, and the non-elite set (NES), consisting of the remaining individuals. Then, the dimensions of each particle in NES are randomly divided into several groups with equal sizes. Subsequently, each dimension group of each non-elite particle is guided by two different elites randomly selected from ES. In this way, each non-elite particle in NES is comprehensively guided by multiple elite particles in ES. Therefore, not only could high diversity be maintained, but fast convergence is also likely guaranteed. To alleviate the sensitivity of DGCELSO to the associated parameters, we further devise dynamic adjustment strategies to change the parameter settings during the evolution. With the above mechanisms, DGCELSO is expected to explore and exploit the solution space properly to find the optimum solutions for optimization problems. Extensive experiments conducted on two commonly used large-scale benchmark problem sets demonstrate that DGCELSO achieves highly competitive or even much better performance than several state-of-the-art large-scale optimizers.
The dysregulation of circular RNA (circRNA) expression is involved in the progression of several cancers, including non‐small cell lung cancer (NSCLC). However, the role and underlying molecular ...mechanisms of circRNA FGFR3 (circFGFR3) in NSCLC progression remains unknown. Here, we used quantitative real‐time polymerase chain reaction to validate that circFGFR3 expression was higher in NSCLC tissues than in the paratumor tissues. Furthermore, our study indicated that the forced circFGFR3 expression promoted NSCLC cell invasion and proliferation. Mechanistically, we found that circFGFR3 promoted NSCLC cell invasion and proliferation via competitively combining with miR‐22‐3p to facilitate the galectin‐1 (Gal‐1), p‐AKT, and p‐ERK1/2 expressions. Clinically, we revealed that the high circFGFR3 expression correlates with the poor clinical outcomes in patients with NSCLC. Together, these data provide mechanistic insights into the circFGFR3‐mediated regulation of both the AKT and ERK1/2 signaling pathways by sponging miR‐22‐3p and increasing Gal‐1 expression.
The dysregulation of the circular RNA (circRNA) expression is involved in the progression of several cancers, including non‐small cell lung cancer (NSCLC). Together, these data provide mechanistic insights into circRNA FGFR3 (circFGFR3)‐mediated regulation of both the AKT and ERK1/2 signaling pathways by sponging miR‐22‐3p and increasing Gal‐1 expression.
Nanostructural modification and chemical composition tuning are paramount to developing effective non-noble hydrogen evolution reaction (HER) catalysts for water splitting. Herein, we report a novel ...excellent porous molybdenum tungsten phosphide (Mo-W-P) hybrid nanosheet catalyst for hydrogen evolution, which is synthesized via in situ phosphidation of molybdenum tungsten oxide (Mo-W-O) hybrid nanowires grown on carbon cloth. The three-dimensional (3D) hierarchical hybrid electrocatalyst exhibits impressively high electrocatalytic activity with a low overpotential of 138 mV required to achieve a high current density of 100 mA cm-2 and a small Tafel slope of 52 mV dec-1 in 0.5 M H2SO4, which are significantly higher than those of single MoP nanosheets and WP2 nanorods. Such an outstanding performance of the Mo-W-P hybrid electrocatalyst is attributed to the 3D conductive scaffolds, porous nanosheet structure, and strong synergistic effect of W and Mo atoms in Mo-W-P, making it a very promising catalyst for hydrogen production. Our findings demonstrate that careful control over the morphology and composition of the electrocatalyst can achieve highly efficient hybrid electrocatalysts.
The ADJUVANT study reported the comparative superiority of adjuvant gefitinib over chemotherapy in disease-free survival of resected EGFR-mutant stage II-IIIA non-small cell lung cancer (NSCLC). ...However, not all patients experienced favorable clinical outcomes with tyrosine kinase inhibitors (TKI), raising the necessity for further biomarker assessment. In this work, by comprehensive genomic profiling of 171 tumor tissues from the ADJUVANT trial, five predictive biomarkers are identified (TP53 exon4/5 mutations, RB1 alterations, and copy number gains of NKX2-1, CDK4, and MYC). Then we integrate them into the Multiple-gene INdex to Evaluate the Relative benefit of Various Adjuvant therapies (MINERVA) score, which categorizes patients into three subgroups with relative disease-free survival and overall survival benefits from either adjuvant gefitinib or chemotherapy (Highly TKI-Preferable, TKI-Preferable, and Chemotherapy-Preferable groups). This study demonstrates that predictive genomic signatures could potentially stratify resected EGFR-mutant NSCLC patients and provide precise guidance towards future personalized adjuvant therapy.
Overproduction of oxidants (reactive oxygen species and reactive nitrogen species) in the human body is responsible for the pathogenesis of some diseases. The scavenging of these oxidants is thought ...to be an effective measure to depress the level of oxidative stress of organisms. It has been reported that intake of vegetables and fruits is inversely associated with the risk of many chronic diseases, and antioxidant phytochemicals in vegetables and fruits are considered to be responsible for these health benefits. Antioxidant phytochemicals can be found in many foods and medicinal plants, and play an important role in the prevention and treatment of chronic diseases caused by oxidative stress. They often possess strong antioxidant and free radical scavenging abilities, as well as anti-inflammatory action, which are also the basis of other bioactivities and health benefits, such as anticancer, anti-aging, and protective action for cardiovascular diseases, diabetes mellitus, obesity and neurodegenerative diseases. This review summarizes recent progress on the health benefits of antioxidant phytochemicals, and discusses their potential mechanisms in the prevention and treatment of chronic diseases.
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