Search

Chirality pervades multiple scientific domains-physics, chemistry, biology, and astronomy-and profoundly influences their foundational principles. Recently, the chirality-induced spin selectivity (CISS) phenomenon has captured significant attention in physical chemistry due to its potential applications and intriguing underlying physics. Despite its prominence, the microscopic mechanisms of CISS remain hotly debated, hindering practical applications and further theoretical advancements. Here we challenge the established view that attributes CISS-related phenomena to current-induced spin polarization and electron transport across interfaces. We propose that molecular vibrations in chiral molecules primarily drive spin polarization, thereby governing CISS. Employing an electrochemical cell paired with a precisely engineered magnetic multilayer, we demonstrate that the magnetic interactions akin to interlayer exchange coupling are crucial for CISS. Our theoretical study suggests that molecular vibrations facilitate chirality-dependent spin polarization, which plays a pivotal role in CISS-related phenomena such as magnetoresistance and enantiomer separation using ferromagnets. These findings necessitate a paradigm shift in the design and analysis of systems in various scientific fields, extending the role of spin dynamics from traditional areas such as solid-state physics to chemical reactions, molecular biology, and even drug discovery.

Spin chirality in antiferromagnets offers new opportunities for spintronics. The kagome antiferromagnet Mn3Sn is a paradigmatic material in which the antiferromagnetic order parameter can be detected and controlled by electrical means. However, direct investigation of the magnetic texture of Mn3Sn has been challenging because of the tiny moment hosted in its magnetic octupole, hindering further clarification of this unique material. Here, we address this issue by observing the stray magnetic field from Mn3Sn using a diamond quantum scanning magnetometer. The spatially-resolved intrinsic domains and domain walls in a high-quality single-crystalline Mn3Sn film quantitatively reveal the polarization angle of the magnetic octupole in the kagome plane, the domain's local magnetization, the domain wall's width and chirality, and the octupole order in domain walls. Our nanoscale investigation of Mn3Sn, a powerful complement to macroscopic measurements, paves the road for developing chiral antiferromagnetism and its potential for spintronic applications., Comment: 17 pages, 4 figures

Among many-body instabilities in correlated quantum systems, electronic nematicity, defined by the spontaneous breaking of rotational symmetry, has emerged as a critical phenomenon, particularly within high-temperature superconductors. Recently, this behavior has been identified in CsTi$_3$Bi$_5$, a member of the AV$_3$Sb$_5$ (A = K, Rb, Cs) kagome family, recognized for its intricate and unconventional quantum phases. Despite accumulating indirect evidence, the fundamental mechanisms driving nematicity in CsTi$_3$Bi$_5$ remain inadequately understood, sparking ongoing debates. In this study, we employ polarization-dependent angle-resolved photoemission spectroscopy to reveal definitive signatures of an orbital-selective nematic deformation in the electronic structure of CsTi$_3$Bi$_5$. This direct experimental evidence underscores the pivotal role of orbital degrees of freedom in symmetry breaking, providing new insights into the complex electronic environment. By applying the functional renormalization group technique to a fully interacting ab initio model, we demonstrate the emergence of a finite angular momentum ($d$-wave) Pomeranchuk instability in CsTi$_3$Bi$_5$, driven by the concomitant action of electronic correlations within specific orbital channels and chemical potential detuning away from Van Hove singularities. By elucidating the connection between orbital correlations and symmetry-breaking instabilities, this work lays a crucial foundation for future investigations into the broader role of orbital selectivity in quantum materials, with far-reaching implications for the design and manipulation of novel electronic phases.

We investigate the electronic structure of high-density layers of Sb dopants in a silicon host, so-called Si:Sb $\delta$-layers. We show that, in spite of the known challenges in producing highly confined Sb $\delta$-layers, sufficient confinement is created such that the lowest conduction band states ($\Gamma$ states, studied in depth in other silicon $\delta$-layers), become occupied and can be observed using angle-resolved photoemission spectroscopy. The electronic structure of the Si:Sb $\delta$-layers closely resembles that of Si:P systems, where the observed conduction band is near-parabolic and slightly anisotropic in the $\mathbf{k}_\parallel$ plane. The observed $\Gamma$ state extends ~ 1 nm in the out-of-plane direction, which is slightly wider than the 1/3 monolayer thick dopant distribution. This is caused by a small segregation of the dopant layer, which is nevertheless minimal when comparing with earlier published attempts. Our results serve to demonstrate that Sb is still a feasible dopant alternative for use in the semiconductor $\delta$-layer platform, providing similar electronic functionality to Si:P systems. Additionally, it has the advantages of being less expensive, more controllable, safer to handle, and more compatible with industrial patterning techniques. Si:Sb is therefore a viable platform for emerging quantum device applications.

We develop an axiomatic framework for persistent homology in any degree. We prove the existence and uniqueness for both a persistent version of the Eilenberg-Steenrod axioms for classical homology and a reduced version of this set of axioms., Comment: 19 pages, 2 appendix sections

SrRuO$_3$ is a prototypical transition metal oxide which hosts rich physical properties including itinerant ferromagnetism, high conductivity, and intrinsic Hall effect originating in the Weyl points. Recently, high-quality SrRuO$_3$ films with residual resistivity ratios of more than 50 have been reported to exhibit quantum oscillations at low temperatures in spite of its strong electron correlation. While the origin of the oscillations has been discussed in relation to Weyl orbits based on the Weyl semimetal band structure, so far experimentally reported results are neither consistent with each other nor with theoretically expected behavior, leaving the origin of the oscillations in SrRuO$_3$ films still elusive. In this report, we have carefully evaluated the quantum oscillations observed in three-dimensional thick SrRuO$_3$ films with a high residual resistivity ratio of RRR = 82. We reveal the coexistence of two oscillation components both derived from two-dimensional electronic states and with slightly different masses, suggesting the involvement of the surface Fermi arc states formed between different Weyl point pairs., Comment: 13 pages, 3 figures

The mini proceedings of the "Fourth International Workshop on the Extension Project for the J-PARC Hadron Experimental Facility (HEF-ex 2024) [https://kds.kek.jp/event/46965]" held at J-PARC, February 19-21, 2024, are presented. The workshop was devoted to discussing the physics case that connects both the present and the future Hadron Experimental Facility at J-PARC, covering a wide range of topics in flavor, hadron, and nuclear physics related to both experimental and theoretical activities being conducted at the facility.

Autonomous driving, particularly navigating complex and unanticipated scenarios, demands sophisticated reasoning and planning capabilities. While Multi-modal Large Language Models (MLLMs) offer a promising avenue for this, their use has been largely confined to understanding complex environmental contexts or generating high-level driving commands, with few studies extending their application to end-to-end path planning. A major research bottleneck is the lack of large-scale annotated datasets encompassing vision, language, and action. To address this issue, we propose CoVLA (Comprehensive Vision-Language-Action) Dataset, an extensive dataset comprising real-world driving videos spanning more than 80 hours. This dataset leverages a novel, scalable approach based on automated data processing and a caption generation pipeline to generate accurate driving trajectories paired with detailed natural language descriptions of driving environments and maneuvers. This approach utilizes raw in-vehicle sensor data, allowing it to surpass existing datasets in scale and annotation richness. Using CoVLA, we investigate the driving capabilities of MLLMs that can handle vision, language, and action in a variety of driving scenarios. Our results illustrate the strong proficiency of our model in generating coherent language and action outputs, emphasizing the potential of Vision-Language-Action (VLA) models in the field of autonomous driving. This dataset establishes a framework for robust, interpretable, and data-driven autonomous driving systems by providing a comprehensive platform for training and evaluating VLA models, contributing to safer and more reliable self-driving vehicles. The dataset is released for academic purpose., Comment: WACV 2025, Project Page: https://turingmotors.github.io/covla-ad/

Most two-dimensional (2D) materials experimentally studied so far have hexagons as their building blocks. Only a few exceptions, such as PdSe2, are lower in energy in pentagonal phases and exhibit pentagons as building blocks. While theory has predicted a large number of pentagonal 2D materials, many of them are metastable and their experimental realization is difficult. Here we report the successful synthesis of a metastable pentagonal 2D material, the monolayer pentagonal PdTe2, by symmetry-driven epitaxy. Scanning tunneling microscopy and complementary spectroscopy measurements are used to characterize the monolayer pentagonal PdTe2, which demonstrates well-ordered low-symmetry atomic arrangements and is stabilized by lattice matching with the underlying Pd(100) substrate. Theoretical calculations, along with angle-resolved photoemission spectroscopy, reveal monolayer pentagonal PdTe2 is a semiconductor with an indirect bandgap of 1.05 eV. Our work opens an avenue for the synthesis of pentagon-based 2D materials and gives opportunities to explore their applications such as multifunctional nanoelectronics.

Industry is rapidly moving towards fully autonomous and interconnected systems that can detect and adapt to changing conditions, including machine hardware faults. Traditional methods for adding hardware fault tolerance to machines involve duplicating components and algorithmically reconfiguring a machine's processes when a fault occurs. However, the growing interest in reinforcement learning-based robotic control offers a new perspective on achieving hardware fault tolerance. However, limited research has explored the potential of these approaches for hardware fault tolerance in machines. This paper investigates the potential of two state-of-the-art reinforcement learning algorithms, Proximal Policy Optimization (PPO) and Soft Actor-Critic (SAC), to enhance hardware fault tolerance into machines. We assess the performance of these algorithms in two OpenAI Gym simulated environments, Ant-v2 and FetchReach-v1. Robot models in these environments are subjected to six simulated hardware faults. Additionally, we conduct an ablation study to determine the optimal method for transferring an agent's knowledge, acquired through learning in a normal (pre-fault) environment, to a (post-)fault environment in a continual learning setting. Our results demonstrate that reinforcement learning-based approaches can enhance hardware fault tolerance in simulated machines, with adaptation occurring within minutes. Specifically, PPO exhibits the fastest adaptation when retaining the knowledge within its models, while SAC performs best when discarding all acquired knowledge. Overall, this study highlights the potential of reinforcement learning-based approaches, such as PPO and SAC, for hardware fault tolerance in machines. These findings pave the way for the development of robust and adaptive machines capable of effectively operating in real-world scenarios.

This paper addresses a crucial challenge in retrieval-augmented generation-based relation extractors; the end-to-end training is not applicable to conventional retrieval-augmented generation due to the non-differentiable nature of instance retrieval. This problem prevents the instance retrievers from being optimized for the relation extraction task, and conventionally it must be trained with an objective different from that for relation extraction. To address this issue, we propose a novel End-to-end Trainable Retrieval-Augmented Generation (ETRAG), which allows end-to-end optimization of the entire model, including the retriever, for the relation extraction objective by utilizing a differentiable selection of the $k$ nearest instances. We evaluate the relation extraction performance of ETRAG on the TACRED dataset, which is a standard benchmark for relation extraction. ETRAG demonstrates consistent improvements against the baseline model as retrieved instances are added. Furthermore, the analysis of instances retrieved by the end-to-end trained retriever confirms that the retrieved instances contain common relation labels or entities with the query and are specialized for the target task. Our findings provide a promising foundation for future research on retrieval-augmented generation and the broader applications of text generation in Natural Language Processing., Comment: preprint

Superlattices from twisted graphene mono- and bi-layer systems give rise to on-demand many-body states such as Mott insulators and unconventional superconductors. These phenomena are ascribed to a combination of flat bands and strong Coulomb interactions. However, a comprehensive understanding is lacking because the low-energy band structure strongly changes when the electron filling is varied. Here, we gain direct access to the filling-dependent low energy bands of twisted bilayer graphene (TBG) and twisted double bilayer graphene (TDBG) by applying micro-focused angle-resolved photoemission spectroscopy to in situ gated devices. Our findings for the two systems are in stark contrast: The doping dependent dispersion for TBG can be described in a simple model, combining a filling-dependent rigid band shift with a many-body related bandwidth change. In TDBG, on the other hand, we find a complex behaviour of the low-energy bands, combining non-monotonous bandwidth changes and tuneable gap openings. Our work establishes the extent of electric field tunability of the low energy electronic states in twisted graphene superlattices and can serve to underpin the theoretical understanding of the resulting phenomena., Comment: 26 pages, 4 main figures and 7 supplementary figures

We theoretically investigate collective excitations in the Fulde-Ferrell-Larkin-Ovchinnikov (FFLO) states of Pauli-limited superconducting films. When the long-range Coulomb interaction is absent, excitation spectra consist of two gapless and three gapped modes. The gapless modes are the Nambu-Goldstone modes associated with the spontaneous breaking of the ${\rm U}(1)$ symmetry and the translational symmetry. The gapped modes include the Higgs mode and the twofold degenerate modes that cause the oscillation of the domain width and grayness of FFLO nodal planes. We find that the long-range Coulomb interaction only gaps out the gapless phase mode through the Anderson-Higgs mechanism, while the other modes remain unaffected. Furthermore, the field evolution of the dispersion of the gapless elastic mode, the Nambu-Goldstone mode associated with the translational symmetry breaking, is associated with that of the bandwidth of the mid-gap Andreev bound states. We demonstrate that the signature of the elastic mode can be detected by measuring the density-density response function., Comment: 12 pages, 6 figures

We report the formations of fcc and distorted hcp iron-helium compounds with x in FeHex up to 0.13 and 0.48, respectively, based on experiments at 5-54 GPa and ~1000-2820 K. Upon releasing pressure under room temperature, these fcc and distorted hcp FeHex were still observed by XRD and SIMS measurements. Our first-principles calculations indicate that fcc and hcp FeHex, with helium atoms occupying the tetrahedral and trigonal-planar interstitial sites (instead of the octahedral sites), are dynamically stable throughout 0-50 GPa. These results support that the Earth's core can be a large reservoir of primordial 3He., Comment: 44 pages, 23 figures, 5 tables

Evolving consumer demands and market trends have led to businesses increasingly embracing a production approach that prioritizes flexibility and customization. Consequently, factory workers must engage in tasks that are more complex than before. Thus, productivity depends on each worker's skills in assembling products. Therefore, analyzing the behavior of a worker is crucial for work improvement. However, manual analysis is time consuming and does not provide quick and accurate feedback. Machine learning have been attempted to automate the analyses; however, most of these methods need several labels for training. To this end, we extend the Gaussian process hidden semi-Markov model (GP-HSMM), to enable the rapid and automated analysis of worker behavior without pre-training. The model does not require labeled data and can automatically and accurately segment continuous motions into motion classes. The proposed model is a probabilistic model that hierarchically connects GP-HSMM and HSMM, enabling the extraction of behavioral patterns with different granularities. Furthermore, it mutually infers the parameters between the GP-HSMM and HSMM, resulting in accurate motion pattern extraction. We applied the proposed method to motion data in which workers assembled products at an actual production site. The accuracy of behavior pattern extraction was evaluated using normalized Levenshtein distance (NLD). The smaller the value of NLD, the more accurate is the pattern extraction. The NLD of motion patterns captured by GP-HSMM and HSMM layers in our proposed method was 0.50 and 0.33, respectively, which are the smallest compared to that of the baseline methods.

This work aims to tackle the intent recognition problem in Human-Robot Collaborative assembly scenarios. Precisely, we consider an interactive assembly of a wooden stool where the robot fetches the pieces in the correct order and the human builds the parts following the instruction manual. The intent recognition is limited to the idle state estimation and it is needed to ensure a better synchronization between the two agents. We carried out a comparison between two distinct solutions involving wearable sensors and eye tracking integrated into the perception pipeline of a flexible planning architecture based on Hierarchical Task Networks. At runtime, the wearable sensing module exploits the raw measurements from four 9-axis Inertial Measurement Units positioned on the wrists and hands of the user as an input for a Long Short-Term Memory Network. On the other hand, the eye tracking relies on a Head Mounted Display and Unreal Engine. We tested the effectiveness of the two approaches with 10 participants, each of whom explored both options in alternate order. We collected explicit metrics about the attractiveness and efficiency of the two techniques through User Experience Questionnaires as well as implicit criteria regarding the classification time and the overall assembly time. The results of our work show that the two methods can reach comparable performances both in terms of effectiveness and user preference. Future development could aim at joining the two approaches two allow the recognition of more complex activities and to anticipate the user actions., Comment: 5 pages, 4 figures, AVI2024 conference

The discovery of the quantum spin Hall effect led to the exploration of the electronic transport for spintronic devices. Here, we theoretically investigated the electronic conductance in large-gap realistic quantum spin Hall system, Pt$_2$HgSe$_3$ nanoribbons. By an ab initio approach, we found that the edge states present a penetration depth of about $0.9$\,{nm}, which is much smaller than those predicted in other 2D topological systems. Thus, suggesting that Pt$_2$HgSe$_3$ allows the exploitation of topological transport properties in narrow ribbons. Using non-equilibrium Green's functions calculations, we have examined the electron conductivity upon the presence of Se\,$\leftrightarrow$\,Hg antistructure defects randomly distributed in the Pt$_2$HgSe$_3$ scattering region. By considering scattering lengths up to $109$\,nm, we found localization lengths that can surpass $\mu$m sizes for narrow nanoribbons ($<9$\,nm). These findings can contribute to further understanding the behavior of topological insulators under realistic conditions and their integration within electronic, spintronic devices.

Lattice softness has a significant impact on charge carrier dynamics in condensed matter systems, contributing to the emergence of various properties and functions. Examples include the remarkable carrier lifetimes and defect tolerances of hybrid organic-inorganic perovskites. Recent studies suggest the contribution of quartic anharmonicity of the lattice vibrations. The quartic anharmonicity can be discussed with a double-well potential, and the transition between the two minima can be coarse-grained as a two-state jump stochastic process. Such a stochastic approach is typically employed to describe dynamic fluctuations introduced into a system by two-state transitions in the surroundings. To investigate charge transport in materials, however, it is crucial to describe not only the fluctuations but also the dynamic lattice distortion associated with charge transport. Therefore, there is a need for a theory to describe the charge carrier dynamics proceeding alongside the lattice distortion dynamics. In this study, we present a theory that describes quantum dynamics under the influence of an environment with two stable states, termed a bistable environment. The theory describes the effects of fluctuations and dissipation induced from the bistable environment in a reasonable manner, and the effects exhibit a different temperature dependence than the widely employed Gaussian environment. The physical implication of this temperature dependence is provided in terms of the environmental dynamics. The results of this study are expected to provide a step forward in describing charge carrier dynamics in materials with lattice softness and pronounced lattice anharmonicity, e.g., hybrid organic-inorganic perovskites. Moreover, these findings represent an advancement in our understanding of and capacity to predict and control the physical properties and functions of these materials.

Advancements in AI image generation, particularly diffusion models, have progressed rapidly. However, the absence of an established framework for quantifying the reliability of AI-generated images hinders their use in critical decision-making tasks, such as medical image diagnosis. In this study, we address the task of detecting anomalous regions in medical images using diffusion models and propose a statistical method to quantify the reliability of the detected anomalies. The core concept of our method involves a selective inference framework, wherein statistical tests are conducted under the condition that the images are produced by a diffusion model. With our approach, the statistical significance of anomaly detection results can be quantified in the form of a $p$-value, enabling decision-making with controlled error rates, as is standard in medical practice. We demonstrate the theoretical soundness and practical effectiveness of our statistical test through numerical experiments on both synthetic and brain image datasets., Comment: 30 pages, 7 figures

Angle-resolved photoemission spectroscopy (ARPES) is a technique used to map the occupied electronic structure of solids. Recent progress in X-ray focusing optics has led to the development of ARPES into a microscopic tool, permitting the electronic structure to be spatially mapped across the surface of a sample. This comes at the expense of a time-consuming scanning process to cover not only a three-dimensional energy-momentum ($E, k_z, k_y$) space but also the two-dimensional surface area. Here, we implement a protocol to autonomously search both $\mathbf{k}$- and real space in order to find positions of particular interest, either because of their high photoemission intensity or because of sharp spectral features. The search is based on the use of Gaussian process regression and can easily be expanded to include additional parameters or optimisation criteria. This autonomous experimental control is implemented on the SGM4 micro-focus beamline of the synchrotron radiation source ASTRID2.