We present the numerical results for low temperature behavior of the transverse-field Ising model on a frustrated checkerboard lattice, with focus on the effect of both quantum and thermal fluctuations. Applying the recently-developed continuous-time quantum Monte Carlo algorithm, we compute the magnetization and susceptibility down to extremely low temperatures while changing the magnitude of both transverse and longitudinal magnetic fields. Several characteristic behaviors are observed, which were not inferred from the previously studied quantum order from disorder at zero temperature, such as a horizontal-type stripe ordering at a substantial longitudinal field and a persistent critical behavior down to low temperature in a weak longitudinal field region.

The adaptive immune system of vertebrates can detect, respond to, and memorize diverse pathogens from past experience. While the clonal selection of T helper (Th) cells is the simple and established mechanism to better recognize new pathogens, the question that still remains unexplored is how the Th cells can acquire better ways to bias the responses of immune cells for eliminating pathogens more efficiently by translating the recognized antigen information into regulatory signals. In this work, we address this problem by associating the adaptive immune network organized by the Th cells with reinforcement learning (RL). By employing recent advancements of network-based RL, we show that the Th immune network can acquire the association between antigen patterns of and the effective responses to pathogens. Moreover, the clonal selection as well as other inter-cellular interactions are derived as a learning rule of the network. We also demonstrate that the stationary clone-size distribution after learning shares characteristic features with those observed experimentally. Our theoretical framework may contribute to revising and renewing our understanding of adaptive immunity as a learning system.

Janus monolayer transition metal dichalcogenides (TMDs), created by post-growth substitution of the top chalcogen layer, represent a new direction for engineering 2D crystal properties. However, their rapid ambient degradation and the difficulty of obtaining large-area monolayer samples have limited the available experimental probes, leaving their detailed electronic structure near the Fermi level largely unexplored. In this work, by performing micro-focused angle-resolved photoemission spectroscopy ({\mu}-ARPES) on an identical sample transformed from monolayer WSe2 to Janus WSSe via a H2 plasma-assisted chalcogen-exchange method, we reveal the evolution of its electronic band structure. We observe ARPES signature consistent with the Rashba-type spin splitting due to broken horizontal mirror symmetry, and a significant upward shift of the highest valence band at the {\Gamma}-point by approximately 160 meV. These direct observations clarify the key electronic modifications that govern the material's properties and provide a pathway for band engineering in Janus TMDs.

Deep learning is the backbone of artificial intelligence technologies, and it can be regarded as a kind of multilayer feedforward neural network. An essence of deep learning is information propagation through layers. This suggests that there is a connection between deep neural networks and dynamical systems in the sense that information propagation is explicitly modeled by the time-evolution of dynamical systems. In this study, we perform pattern recognition based on the optimal control of continuous-time dynamical systems, which is suitable for physical hardware implementation. The learning is based on the adjoint method to optimally control dynamical systems, and the deep (virtual) network structures based on the time evolution of the systems are used for processing input information. As a key example, we apply the dynamics-based recognition approach to an optoelectronic delay system and demonstrate that the use of the delay system allows for image recognition and nonlinear classifications using only a few control signals. This is in contrast to conventional multilayer neural networks, which require a large number of weight parameters to be trained. The proposed approach provides insight into the mechanisms of deep network processing in the framework of an optimal control problem and presents a pathway for realizing physical computing hardware.

The external field control of antiferromagnetism is a significant subject both for basic science and technological applications. As a useful macroscopic response to detect magnetic states, the anomalous Hall effect (AHE) is known for ferromagnets, but it has never been observed in antiferromagnets until the recent discovery in Mn3Sn. Here we report another example of the AHE in a related antiferromagnet, namely, in the hexagonal chiral antiferromagnet Mn3Ge. Our single-crystal study reveals that Mn3Ge exhibits a giant anomalous Hall conductivity $|\sigma_{xz}|$ ~ 60 per ohm per cm at room temperature and approximately 380 per ohm per cm at 5 K in zero field, reaching nearly half of the value expected for the quantum Hall effect per atomic layer with Chern number of unity. Our detailed analyses on the anisotropic Hall conductivity indicate that in comparison with the in-plane-field components $|\sigma_{xz}|$ and $|\sigma_{zy}|$, which are very large and nearly comparable in size, we find $|\sigma_{yx}|$ obtained in the field along the c axis is found to be much smaller. The anomalous Hall effect shows a sign reversal with the rotation of a small magnetic field less than 0.1 T. The soft response of the AHE to magnetic field should be useful for applications, for example, to develop switching and memory devices based on antiferromagnets.

A numerical analysis on the computational capability of physical reservoir computing utilizing a spin-torque oscillator with two free layers is reported. Conventional spintronics devices usually consist of two ferromagnets, where the direction of magnetization in one layer, called the free layer, can move while that of the other, the reference layer, is fixed. Recently, however, devices with two free layers, where the reference layer is replaced by another free layer, have been developed for various practical applications. Adding another free layer drastically changes the dynamical response of the device through the couplings via the spin-transfer effect and the dipole magnetic field. A numerical simulation of the Landau-Lifshitz-Gilbert equation and a statistical analyses of the Lyapunov exponent and the synchronization index reveal the appearance of an amplitude-modulated oscillation and chaos in the oscillators with two free layers. Such complex dynamics qualitatively change the computational capability of physical reservoir computing because the computational resource is dynamics of the physical system. An evaluation of the short-term memory capacity clarifies that oscillators with two free layers have a larger capacity than those of conventional oscillators. An enhancement in capacity near the edge of echo state property, i.e., the boundary between zero and finite synchronization index, is also found.

Deep learning is the backbone of artificial intelligence technologies, and it can be regarded as a kind of multilayer feedforward neural network. An essence of deep learning is information propagation through layers. This suggests that there is a connection between deep neural networks and dynamical systems in the sense that information propagation is explicitly modeled by the time-evolution of dynamical systems. In this study, we perform pattern recognition based on the optimal control of continuous-time dynamical systems, which is suitable for physical hardware implementation. The learning is based on the adjoint method to optimally control dynamical systems, and the deep (virtual) network structures based on the time evolution of the systems are used for processing input information. As a key example, we apply the dynamics-based recognition approach to an optoelectronic delay system and demonstrate that the use of the delay system allows for image recognition and nonlinear classifications using only a few control signals. This is in contrast to conventional multilayer neural networks, which require a large number of weight parameters to be trained. The proposed approach provides insight into the mechanisms of deep network processing in the framework of an optimal control problem and presents a pathway for realizing physical computing hardware.

We theoretically show that the magnon wavepacket has a rotational motion in two ways; a self-rotation and a motion along the boundary of the sample (edge current). They are similar to cyclotron motion of electrons, but unlike electrons the magnons have no charge and the rotation is not due to Lorenz force. These rotational motions are caused by the Berry phase in momentum space from magnon band structure. Furthermore, these rotational motions of the magnon give an additional correction term to the magnon Hall effect. We also discuss the Berry curvature effect in the classical limit of long-wavelength magnetostatic spin waves having macroscopic coherence length.

Small angle neutron scattering measurements have been performed to study the thermodynamic stability of skyrmion-lattice phases in Cu$_2$OSeO$_3$. We found that the two distinct skyrmion-lattice phases [SkX(1) and SkX(2) phases] can be stabilized through different thermal histories; by cooling from the paramagnetic phase under finite magnetic field, the SkX(2) phase is selected. On the other hand, the 30$^{\circ}$-rotated SkX(1) phase becomes dominant by heating the sample from the ordered conical phase under finite field. This difference in stabilization is surprisingly similar to the irreversibility observed in spin glasses. The zero-field cooling results in the co-existence of the two phases. It is further found that once one of the skyrmion-lattice phases is formed, it is hardly destabilized. This indicates unusual thermal stability of the two skyrmion-lattice phases originating from an unexpectedly large energy barrier between them.

We present Geometry of Interaction (GoI) models for Multiplicative Polarized Linear Logic, MLLP, which is the multiplicative fragment of Olivier Laurent's Polarized Linear Logic. This is done by uniformly adding multipoints to various categorical models of GoI. Multipoints are shown to play an essential role in semantically characterizing the dynamics of proof networks in polarized proof theory. For example, they permit us to characterize the key feature of polarization, focusing, as well as being fundamental to our construction of concrete polarized GoI models. Our approach to polarized GoI involves two independent studies, based on different categorical perspectives of GoI. (i) Inspired by the work of Abramsky, Haghverdi, and Scott, a polarized GoI situation is defined in which multipoints are added to a traced monoidal category equipped with a reflexive object $U$. Using this framework, categorical versions of Girard's Execution formula are defined, as well as the GoI interpretation of MLLP, proofs. Running the Execution formula is shown to characterize the focusing property (and thus polarities) as well as the dynamics of cut-elimination. (ii) The Int construction of Joyal-Street-Verity is another fundamental categorical structure for modelling GoI. Here, we investigate it in a multipointed setting. Our presentation yields a compact version of Hamano-Scott's polarized categories, and thus denotational models of MLLP. These arise from a contravariant duality between monoidal categories of positive and negative objects, along with an appropriate bimodule structure (representing "non-focused proofs") between them. Finally, as a special case of (ii) above, a compact model of MLLP is also presented based on Rel (the category of sets and relations) equipped with multi-points.

Using the Born-Oppenheimer approximation, we derive an effective Hamiltonian for an optomechanical system that leads to a nonlinear Kerr effect in the system's vacuum. The oscillating mirror at one edge of the optomechanical system induces a squeezing effect in the intensity spectrum of the cavity field. A near-resonant laser field is applied at the other edge to drive the cavity field, in order to enhance the Kerr effect. We also propose a quantum-nondemolition-measurement setup to monitor a system with two cavities separated by a common oscillating mirror, based on our effective Hamiltonian approach.

We report high-resolution photoemission spectroscopy of newly-discovered iron-based layered superconductor La(O$_{0.93}$F$_{0.07}$)FeAs (Tc = 24 K). We found that the superconducting gap shows a marked deviation from the isotropic s-wave symmetry. The estimated gap size at 5 K is 3.6 meV in the s- or axial p-wave case, while it is 4.1 meV in the polar p- or d-wave case. We also found a pseudogap of 15-20 meV above Tc, which is gradually filled-in with increasing temperature and closes at temperature far above Tc similarly to copper-oxide high-temperature superconductors.

The advantage of MoS2, compared with graphene, is the direct growth on various oxide substrates by chemical vapor deposition (CVD) without utilizing catalytic metal substrates, which facilitates practical applications for electronics. The carrier mobility is, however, degraded from the intrinsic limit mainly due to short-range scattering caused by S vacancies formed during CVD growth. If the upper limit for the crystallinity of CVD-MoS2 on oxide substrates is determined by the MoS2/substrate interaction during growth, it will hinder the advantage. In this study, we investigated the interaction between monolayer MoS2 and a SiO2/Si substrate and the difference in crystallinity between the top and bottom S surfaces due to the MoS2/substrate interaction. Raman and photoluminescence spectroscopy indicated that doping and strain were induced in MoS2 from the substrate, but they could be removed by transferring MoS2 to a new substrate using polymers. The newly developed polymer-transfer technique enabled selective transfer of the bottom or top surface of CVD-MoS2 onto a new SiO2/Si substrate. The metal-insulator transition was clearly observed for both the normal and inverse transfers, suggesting that the crystallinity of CVD-MoS2 is high and that the crystallinity of the bottom surface interacting with the substrate was similar to that of the top free surface. These results provide positive prospects for the further improvement of the crystallinity of MoS2 on oxide substrates by reconsidering the growth conditions.

We present a theory for the relaxation time of the octupole order parameter in nanoscale chiral antiferromagnets (AFMs) coupled to thermal baths and spin injection sources. Using stochastic spin dynamics simulations, we demonstrate that the octupole moment relaxes through two distinct mechanisms$-$escape over a barrier and precessional dephasing$-$as the barrier for octupole fluctuations is lowered relative to the thermal energy. Notably, the octupole moment relaxes orders of magnitude faster than the typical dipolar order parameters, reaching picosecond timescales. By combining Langer's theory with an effective low-energy description of octupole dynamics in chiral AFMs, we derive analytical expressions for the relaxation times. We find that relaxation in chiral AFMs parallels dipole relaxation in XY magnets, with exchange fields serving the role of the dipole fields. Further, by drawing on the analogy between order parameter dynamics in XY magnets under spin injection and current-biased Josephson junctions, we propose a new scheme for electrically tuning the octupole relaxation times. Our work offers fundamental insights for the development of next-generation spintronic devices that harness octupole order parameters for information encoding, especially in octupole-based probabilistic computing.

Realization of topological superconductors (TSCs) hosting Majorana fermions is a central challenge in condensed-matter physics. One approach is to use the superconducting proximity effect (SPE) in heterostructures, where a topological insulator contacted with a superconductor hosts an effective p-wave pairing by the penetration of Cooper pairs across the interface. However, this approach suffers a difficulty in accessing the topological interface buried deep beneath the surface. Here, we propose an alternative approach to realize topological superconductivity without SPE. In a Pb(111) thin film grown on TlBiSe2, we discover that the Dirac-cone state of substrate TlBiSe2 migrates to the top surface of Pb film and obtains an energy gap below the superconducting transition temperature of Pb. This suggests that a BCS superconductor is converted into a TSC by the topological proximity effect. Our discovery opens a route to manipulate topological superconducting properties of materials.

Magnetic skyrmions are topologically stable spin swirls with particle-like character and potentially suitable for the design of high-density information bits. While most known skyrmion systems arise in noncentrosymmetric systems with Dzyaloshinskii-Moriya interaction, also centrosymmetric magnets with a triangular lattice can give rise to skyrmion formation, with geometrically-frustrated lattice being considered essential in this case. Until today, it remains an open question if skyrmions can also exist in the absence of both geometrically-frustrated lattice and inversion symmetry breaking. Here, we discover a square skyrmion lattice state with 1.9 nm diameter skyrmions in the centrosymmetric tetragonal magnet GdRu2Si2 without geometrically-frustrated lattice by means of resonant X-ray scattering and Lorentz transmission electron microscopy experiments. A plausible origin of the observed skyrmion formation is four-spin interactions mediated by itinerant electrons in the presence of easy-axis anisotropy. Our results suggest that rare-earth intermetallics with highly-symmetric crystal lattices may ubiquitously host nanometric skyrmions of exotic origins.

High-speed machine vision is increasing its importance in both scientific and technological applications. Neuro-inspired photonic computing is a promising approach to speed-up machine vision processing with ultralow latency. However, the processing rate is fundamentally limited by the low frame rate of image sensors, typically operating at tens of hertz. Here, we propose an image-sensor-free machine vision framework, which optically processes real-world visual information with only a single input channel, based on a random temporal encoding technique. This approach allows for compressive acquisitions of visual information with a single channel at gigahertz rates, outperforming conventional approaches, and enables its direct photonic processing using a photonic reservoir computer in a time domain. We experimentally demonstrate that the proposed approach is capable of high-speed image recognition and anomaly detection, and furthermore, it can be used for high-speed imaging. The proposed approach is multipurpose and can be extended for a wide range of applications, including tracking, controlling, and capturing sub-nanosecond phenomena.

Cu2OSeO3 is known as a unique example of insulating multiferroic compounds with skyrmion spin texture, which is characterized by the chiral cubic crystal structure at ambient pressure. Recently, it has been reported that this compound shows pressure-induced structural transition with large enhancement of magnetic ordering temperature Tc. In the present study, we have investigated the detailed crystal structure in the high pressure phase, by combining the synchrotron X-ray diffraction experiment with the diamond anvil cell and the analysis based on the genetic algorithm. Our results suggest that the original pyrochlore Cu network is sustained even after the structural transition, while the orientation of SeO3 molecule as well as the position of oxygen in the middle of Cu tetrahedra are significantly modified. The latter features may be the key for the reported enhancement of Tc and associated stabilization of skyrmion phase at room temperature.

We theoretically study a cavity filled with atoms, which provides the optical-mechanical interaction between the modified cavity photonic field and a movable mirror at one end. We show that the cavity field ``dresses'' these atoms, producing two types of polaritons, effectively enhancing the radiation pressure of the cavity field upon the end mirror, as well as establishing an additional squeezing mode of the end mirror. This squeezing produces an adiabatic entanglement, which is absent in usual vacuum cavities, between the oscillating mirror and the rest of the system. We analyze the entanglement and quantify it using the Loschmidt echo and fidelity.