Dynamic deformability of individual PbSe nanocrystals during superlattice phase transitions

The behavior of individual nanocrystals during superlattice phase transitions can profoundly affect the structural perfection and electronic properties of the resulting superlattices. However, details of nanocrystal morphological changes during superlattice phase transitions are largely unknown due to the lack of direct observation. Here, we report the dynamic deformability of PbSe semiconductor nanocrystals during superlattice phase transitions that are driven by ligand displacement. Real-time high-resolution imaging with liquid-phase transmission electron microscopy reveals that following ligand removal, the individual PbSe nanocrystals experience drastic directional shape deformation when the spacing between nanocrystals reaches 2 to 4 nm. The deformation can be completely recovered when two nanocrystals move apart or it can be retained when they attach. The large deformation, which is responsible for the structural defects in the epitaxially fused nanocrystal superlattice, may arise from internanocrystal dipole-dipole interactions

The interplay between thermodynamics and kinetics in the solid-state synthesis of layered oxides.

In the synthesis of inorganic materials, reactions often yield non-equilibrium kinetic byproducts instead of the thermodynamic equilibrium phase. Understanding the competition between thermodynamics and kinetics is a fundamental step towards the rational synthesis of target materials. Here, we use in situ synchrotron X-ray diffraction to investigate the multistage crystallization pathways of the important two-layer (P2) sodium oxides Na0.67MO2 (M = Co, Mn). We observe a series of fast non-equilibrium phase transformations through metastable three-layer O3, O3' and P3 phases before formation of the equilibrium two-layer P2 polymorph. We present a theoretical framework to rationalize the observed phase progression, demonstrating that even though P2 is the equilibrium phase, compositionally unconstrained reactions between powder precursors favour the formation of non-equilibrium three-layered intermediates. These insights can guide the choice of precursors and parameters employed in the solid-state synthesis of ceramic materials, and constitutes a step forward in unravelling the complex interplay between thermodynamics and kinetics during materials synthesis

Spot Dynamics of a Reaction-Diffusion System on the Surface of a Torus

Quasi-stationary states consisting of localized spots in a reaction-diffusion system are considered on the surface of a torus with major radius $R$ and minor radius $r$. Under the assumption that these localized spots persist stably, the evolution equation of the spot cores is derived analytically based on the higher-order matched asymptotic expansion with the analytic expression of the Green's function of the Laplace--Beltrami operator on the toroidal surface. Owing to the analytic representation, one can investigate the existence of equilibria with a single spot, two spots, and the ring configuration where $N$ localized spots are equally spaced along a latitudinal line with mathematical rigor. We show that localized spots at the innermost/outermost locations of the torus are equilibria for any aspect ratio $alpha=frac{R}{r}$. In addition, we find that there exists a range of the aspect ratio in which localized spots stay at a special location of the torus. The theoretical results and the linear stability of these spot equilibria are confirmed by solving the nonlinear evolution of the Brusselator reaction-diffusion model by numerical means. We also compare the spot dynamics with the point vortex dynamics, which is another model of spot structures

All too human?

Review of three books: 'Music and humanism: an essay in the aesthetics of music' by R A Sharpe; 'The spheres of music: a gathering of essays' by Leonard B Meyer; Critical entertainments: music old and new' by Charles Rosen, which appeared in Musical Times Autumn 2001

Automated Lane Change Behavior Prediction and Environmental Perception Based on SLAM Technology

In addition to environmental perception sensors such as cameras, radars, etc. in the automatic driving system, the external environment of the vehicle is perceived, in fact, there is also a perception sensor that has been silently dedicated in the system, that is, the positioning module. This paper explores the application of SLAM (Simultaneous Localization and Mapping) technology in the context of automatic lane change behavior prediction and environment perception for autonomous vehicles. It discusses the limitations of traditional positioning methods, introduces SLAM technology, and compares LIDAR SLAM with visual SLAM. Real-world examples from companies like Tesla, Waymo, and Mobileye showcase the integration of AI-driven technologies, sensor fusion, and SLAM in autonomous driving systems. The paper then delves into the specifics of SLAM algorithms, sensor technologies, and the importance of automatic lane changes in driving safety and efficiency. It highlights Tesla's recent update to its Autopilot system, which incorporates automatic lane change functionality using SLAM technology. The paper concludes by emphasizing the crucial role of SLAM in enabling accurate environment perception, positioning, and decision-making for autonomous vehicles, ultimately enhancing safety and driving experience

Emerging Synergies Between Large Language Models and Machine Learning in Ecommerce Recommendations

With the boom of e-commerce and web applications, recommender systems have become an important part of our daily lives, providing personalized recommendations based on the user's preferences. Although deep neural networks (DNNs) have made significant progress in improving recommendation systems by simulating the interaction between users and items and incorporating their textual information, these DNN-based approaches still have some limitations, such as the difficulty of effectively understanding users' interests and capturing textual information. It is not possible to generalize to different seen/unseen recommendation scenarios and reason about their predictions. At the same time, the emergence of large language models (LLMs), represented by ChatGPT and GPT-4, has revolutionized the fields of natural language processing (NLP) and artificial intelligence (AI) due to their superior capabilities in the basic tasks of language understanding and generation, and their impressive generalization and reasoning capabilities. As a result, recent research has sought to harness the power of LLM to improve recommendation systems. Given the rapid development of this research direction in the field of recommendation systems, there is an urgent need for a systematic review of existing LLM-driven recommendation systems for researchers and practitioners in related fields to gain insight into. More specifically, we first introduced a representative approach to learning user and item representations using LLM as a feature encoder. We then reviewed the latest advances in LLMs techniques for collaborative filtering enhanced recommendation systems from the three paradigms of pre-training, fine-tuning, and prompting. Finally, we had a comprehensive discussion on the future direction of this emerging field

Emergent Majorana metal from a chiral spin liquid

We propose a novel mechanism to explain the emergence of an intermediate gapless spin liquid phase (IGP) in the antiferromagnetic Kitaev model in an externally applied magnetic field, sandwiched between the well-known gapped chiral spin liquid (CSL) and the gapped partially polarized (PP) phase. We propose in moderate fields $\pi$-fluxes nucleate in the ground state and can trap Majorana zero modes. As these fluxes proliferate with increasing field, the Majorana zero modes overlap creating an emergent Majorana metallic state with a `Fermi surface' at zero energy. We further show that the Majorana spectral function captures the dynamical spin and dimer correlations obtained by the infinite Projected Entangled Pair States (iPEPS) ansatz. We discuss the implications of our results for candidate Kitaev materials.Comment: 6+13 pages, 4+7 figure

Stencil Computation with Vector Outer Product

Matrix computation units have been equipped in current architectures to accelerate AI and high performance computing applications. The matrix multiplication and vector outer product are two basic instruction types. The latter one is lighter since the inputs are vectors. Thus it provides more opportunities to develop flexible algorithms for problems other than dense linear algebra computing and more possibilities to optimize the implementation. Stencil computations represent a common class of nested loops in scientific and engineering applications. This paper proposes a novel stencil algorithm using vector outer products. Unlike previous work, the new algorithm arises from the stencil definition in the scatter mode and is initially expressed with formulas of vector outer products. The implementation incorporates a set of optimizations to improve the memory reference pattern, execution pipeline and data reuse by considering various algorithmic options and the data sharing between input vectors. Evaluation on a simulator shows that our design achieves a substantial speedup compared with vectorized stencil algorithm

A Multi-tasking Model of Speaker-Keyword Classification for Keeping Human in the Loop of Drone-assisted Inspection

Audio commands are a preferred communication medium to keep inspectors in the loop of civil infrastructure inspection performed by a semi-autonomous drone. To understand job-specific commands from a group of heterogeneous and dynamic inspectors, a model must be developed cost-effectively for the group and easily adapted when the group changes. This paper is motivated to build a multi-tasking deep learning model that possesses a Share-Split-Collaborate architecture. This architecture allows the two classification tasks to share the feature extractor and then split subject-specific and keyword-specific features intertwined in the extracted features through feature projection and collaborative training. A base model for a group of five authorized subjects is trained and tested on the inspection keyword dataset collected by this study. The model achieved a 95.3% or higher mean accuracy in classifying the keywords of any authorized inspectors. Its mean accuracy in speaker classification is 99.2%. Due to the richer keyword representations that the model learns from the pooled training data, adapting the base model to a new inspector requires only a little training data from that inspector, like five utterances per keyword. Using the speaker classification scores for inspector verification can achieve a success rate of at least 93.9% in verifying authorized inspectors and 76.1% in detecting unauthorized ones. Further, the paper demonstrates the applicability of the proposed model to larger-size groups on a public dataset. This paper provides a solution to addressing challenges facing AI-assisted human-robot interaction, including worker heterogeneity, worker dynamics, and job heterogeneity.Comment: Accepted by Engineering Applications of Artificial Intelligence journal on Oct 31th. Upload the accepted clean versio

A Review of Software Reliability Testing Techniques

In the era of intelligent systems, the safety and reliability of software have received more attention. Software reliability testing is a significant method to ensure reliability, safety and quality of software. The intelligent software technology has not only offered new opportunities but also posed challenges to software reliability technology. The focus of this paper is to explore the software reliability testing technology under the impact of intelligent software technology. In this study, the basic theories of traditional software and intelligent software reliability testing were investigated via related previous works, and a general software reliability testing framework was established. Then, the technologies of software reliability testing were analyzed, including reliability modeling, test case generation, reliability evaluation, testing criteria and testing methods. Finally, the challenges and opportunities of software reliability testing technology were discussed at the end of this paper