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In variational algorithms, quantum circuits are conventionally parametrized with respect to single-qubit gates. In this study, we parameterize a generalized controlled gate and propose an algorithm to estimate the optimal parameters for locally minimizing the cost value, where we extend the free quaternion selection method, an optimization method for a single-qubit gate. To benchmark the performance, we apply the proposed method to various optimization problems, including the Variational Quantum Eigensolver (VQE) for Ising and molecular Hamiltonians, Variational Quantum Algorithms (VQA) for fidelity maximization, and unitary compilation of time evolution operators. In these applications, the proposed method shows efficient optimization and greater expressibility with shallower circuits than other methods. Furthermore, this method is also capable of generalizing and fully optimizing particle-number-conserving gates, which are in demand in chemical systems applications. Taking advantage of this property, we have actually approximated time evolution operators of molecular Hamiltonian and simulated the dynamics with shallower circuits in comparison to the standard implementation by Trotter decomposition., Comment: 21 pages, 15 figures

Appropriate symmetry breaking generates an anomalous Hall (AH) effect, even in antiferromagnetic (AFM) materials. Itinerant magnets with $d$ electrons are typical examples that show a significant response. By contrast, the process by which a response emerges from $f$-electron AFM structures remains unclear. In this study, we show that an AFM material, Ce$_2$CuGe$_6$, yields a large AH conductivity (AHC) of $550$ $\Omega^{-1}$cm$^{-1}$, which exceeds the values previously reported in $d$-electron AFM materials. Observed features, including the scaling relation against electrical conductivity, suggest that this AH transport is induced cooperatively by both intrinsic and extrinsic mechanisms derived from the AFM structure., Comment: 5 pages, 4 figures

Antiferromagnets without parity-time ($\mathcal{PT}$) symmetry offer novel perspectives in the field of functional magnetic materials. Among them, those with ferromagnetic-like responses are promising candidates for future applications such as antiferromagnetic (AF) memory; however, examples showing large effects are extremely limited. In this study, we show that the orthorhombic system TaMnP exhibits a large anomalous Hall conductivity (AHC) $\sim360-370$ $\Omega^{-1}$cm$^{-1}$ in spite of the small net magnetization $\sim10^{-2}$ $\mu_B$/Mn. Our neutron scattering experiment and the observation of the AH effect indicated that a magnetic structure of TaMnP was dominated by an AF component represented by $B_{3g}$ with the propagation vector $q=0$. Furthermore, we confirmed that the obtained AHC is among the largest observed in AF materials at zero fields. Additionally, our first-principles calculations revealed that the spin-orbit interaction originating in the nonmagnetic Ta-$5d$ electrons significantly contributes to enhancing Berry curvatures in the momentum space. We found that the magnetic fields along all the crystal axes triggered the AF domain switching, indicating the possibility of controlling the AF domain using the small net magnetization, which is symmetrically different., Comment: 8 pages, 6 figures

This paper introduces LLM-jp, a cross-organizational project for the research and development of Japanese large language models (LLMs). LLM-jp aims to develop open-source and strong Japanese LLMs, and as of this writing, more than 1,500 participants from academia and industry are working together for this purpose. This paper presents the background of the establishment of LLM-jp, summaries of its activities, and technical reports on the LLMs developed by LLM-jp. For the latest activities, visit https://llm-jp.nii.ac.jp/en/.

Topotactic chemical reaction (TCR) is a chemical process that transforms one crystalline phase to another while maintaining one or more of the original structural frameworks, typically induced by the local insertion, removal, or replacement of atoms in a crystal. The utilization of TCR in atomic-layer materials and surfaces of bulk crystals leads to exotic quantum phases, as highlighted by the control of topological phases, the emergence of two-dimensional (2D) superconductivity, and the realization of 2D ferromagnetism. Advanced surface-sensitive spectroscopies such as angle-resolved photoemission spectroscopy (ARPES) and scanning tunneling microscopy (STM) are leading techniques to visualize the electronic structure of such exotic states and provide us a guide to further functionalize material properties. In this review article, we summarize the recent progress in this field, with particular emphasis on intriguing results obtained by combining spectroscopies and TCR in thin films., Comment: 23 pages, 10 figures

We developed a new aerial push-button with tactile feedback using focused airborne ultrasound. This study has two significant novelties compared to past related studies: 1) ultrasound emitters are equipped behind the user's finger and reflected ultrasound emission that is focused just above the solid plane placed under the finger presents tactile feedback to a finger pad, and 2) tactile feedback is presented at two stages during pressing motion; at the time of pushing the button and withdrawing the finger from it. The former has a significant advantage in apparatus implementation in that the input surface of the device can be composed of a generic thin plane including touch panels, potentially capable of presenting input touch feedback only when the user touches objects on the screen. We experimentally found that the two-stage tactile presentation is much more effective in strengthening perceived tactile stimulation and feeling of input completion when compared with a conventional single-stage method. This study proposes a composition of an aerial push-button in much more practical use than ever. The proposed system composition is expected to be one of the simplest frameworks in the airborne ultrasound tactile interface., Comment: 9 pages, 15 figures, original manuscript edited by Microsoft Word

The aerodynamic performance of an isolated coaxial rotor in forward flight is analyzed by a high-fidelity computational fluid dynamics (CFD) approach. The analysis focuses on the high-speed forward flight with an advance ratio of 0.5 or higher. The effect of the degree of the rolling moment on the rotor thrust, called lift offset, is studied in detail. The coaxial rotor model is a pair of contrarotating rotors, each rotor consisting of two untwisted blades with a radius of 1.016 m. The pitch angle of the blades is controlled by both collective and cyclic as in a conventional single main-rotor helicopter. CFD analysis is performed using a flow solver based on the compressible Navier-Stokes equations with a Reynolds-averaged turbulence model. Laminar/turbulent transition in the boundary layer is taken into account in the calculation. The rotor trim for target forces and moments is achieved using a gradient-based delta-form blade pitch angle adjusting technique in conjunction with CFD analysis. The reliability of the calculations is confirmed by comparison with published wind tunnel experiments and two comprehensive analyses. Applying the lift offset improves the lift-to-effective drag ratio (lift-drag ratio) and reduces thrust fluctuations. However, in the case where the advance ratio exceeds 0.6, the lift-drag ratio drops significantly even if the lift offset is 0.3. The thrust fluctuation also increases with such a high advance ratio. Detailed analysis reveals that the degradation of aerodynamic performance and vibratory aerodynamic loads is closely related to the pitch angle control to compensate for the reduction in thrust on the retreating side due to the increased reverse flow region. It is effective to reduce the collective and longitudinal cyclic pitch angles for the improvement of the aerodynamic performance of coaxial rotors with an appropriate lift offset.

Most existing multi-hop datasets are extractive answer datasets, where the answers to the questions can be extracted directly from the provided context. This often leads models to use heuristics or shortcuts instead of performing true multi-hop reasoning. In this paper, we propose a new multi-hop dataset, MoreHopQA, which shifts from extractive to generative answers. Our dataset is created by utilizing three existing multi-hop datasets: HotpotQA, 2WikiMultihopQA, and MuSiQue. Instead of relying solely on factual reasoning, we enhance the existing multi-hop questions by adding another layer of questioning that involves one, two, or all three of the following types of reasoning: commonsense, arithmetic, and symbolic. Our dataset is created through a semi-automated process, resulting in a dataset with 1,118 samples that have undergone human verification. We then use our dataset to evaluate five different large language models: Mistral 7B, Gemma 7B, Llama 3 (8B and 70B), and GPT-4. We also design various cases to analyze the reasoning steps in the question-answering process. Our results show that models perform well on initial multi-hop questions but struggle with our extended questions, indicating that our dataset is more challenging than previous ones. Our analysis of question decomposition reveals that although models can correctly answer questions, only a portion - 38.7% for GPT-4 and 33.4% for Llama3-70B - achieve perfect reasoning, where all corresponding sub-questions are answered correctly. Evaluation code and data are available at https://github.com/Alab-NII/morehopqa, Comment: 8 pages, 5 figures. First three authors contributed equally

Stochastic gradient descent (SGD) is a frequently used optimization technique in classical machine learning and Variational Quantum Eigensolver (VQE). For the implementation of VQE on quantum hardware, the results are always affected by measurement shot noise. However, there are many unknowns about the structure and properties of the measurement noise in VQE and how it contributes to the optimization. In this work, we analyze the effect of measurement noise to the optimization dynamics. Especially, we focus on escaping from saddle points in the loss landscape, which is crucial in the minimization of the non-convex loss function. We find that the escape time (1) decreases as the measurement noise increases in a power-law fashion and (2) is expressed as a function of $\eta/N_s$ where $\eta$ is the learning rate and $N_s$ is the number of measurements. The latter means that the escape time is approximately constant when we vary $\eta$ and $N_s$ with the ratio $\eta/N_s$ held fixed. This scaling behavior is well explained by the stochastic differential equation (SDE) that is obtained by the continuous-time approximation of the discrete-time SGD. According to the SDE, $\eta/N_s$ is interpreted as the variance of measurement shot noise. This result tells us that we can learn about the optimization dynamics in VQE from the analysis based on the continuous-time SDE, which is theoretically simpler than the original discrete-time SGD., Comment: 10pages, 8figures

Psycholinguistic research suggests that humans may build a representation of linguistic input that is 'good-enough' for the task at hand. This study examines what architectural features make language models learn human-like good-enough language processing. We focus on the number of layers and self-attention heads in Transformers. We create a good-enough language processing (GELP) evaluation dataset (7,680 examples), which is designed to test the effects of two plausibility types, eight construction types, and three degrees of memory cost on language processing. To annotate GELP, we first conduct a crowdsourcing experiment whose design follows prior psycholinguistic studies. Our model evaluation against the annotated GELP then reveals that the full model as well as models with fewer layers and/or self-attention heads exhibit a good-enough performance. This result suggests that models with shallower depth and fewer heads can learn good-enough language processing., Comment: To appear in Findings of ACL2024

In multi-agent reinforcement learning, decentralized execution is a common approach, yet it suffers from the redundant computation problem. This occurs when multiple agents redundantly perform the same or similar computation due to overlapping observations. To address this issue, this study introduces a novel method referred to as locally centralized team transformer (LCTT). LCTT establishes a locally centralized execution framework where selected agents serve as leaders, issuing instructions, while the rest agents, designated as workers, act as these instructions without activating their policy networks. For LCTT, we proposed the team-transformer (T-Trans) architecture that allows leaders to provide specific instructions to each worker, and the leadership shift mechanism that allows agents autonomously decide their roles as leaders or workers. Our experimental results demonstrate that the proposed method effectively reduces redundant computation, does not decrease reward levels, and leads to faster learning convergence., Comment: 6 pages, 5 figures, Under review

Underwater datacenters (UDCs) hold promise as next-generation data storage due to their energy efficiency and environmental sustainability benefits. While the natural cooling properties of water save power, the isolated aquatic environment and long-range sound propagation in water create unique vulnerabilities which differ from those of on-land data centers. Our research discovers the unique vulnerabilities of fault-tolerant storage devices, resource allocation software, and distributed file systems to acoustic injection attacks in UDCs. With a realistic testbed approximating UDC server operations, we empirically characterize the capabilities of acoustic injection underwater and find that an attacker can reduce fault-tolerant RAID 5 storage system throughput by 17% up to 100%. Our closed-water analyses reveal that attackers can (i) cause unresponsiveness and automatic node removal in a distributed filesystem with only 2.4 minutes of sustained acoustic injection, (ii) induce a distributed database's latency to increase by up to 92.7% to reduce system reliability, and (iii) induce load-balance managers to redirect up to 74% of resources to a target server to cause overload or force resource colocation. Furthermore, we perform open-water experiments in a lake and find that an attacker can cause controlled throughput degradation at a maximum allowable distance of 6.35 m using a commercial speaker. We also investigate and discuss the effectiveness of standard defenses against acoustic injection attacks. Finally, we formulate a novel machine learning-based detection system that reaches 0% False Positive Rate and 98.2% True Positive Rate trained on our dataset of profiled hard disk drives under 30-second FIO benchmark execution. With this work, we aim to help manufacturers proactively protect UDCs against acoustic injection attacks and ensure the security of subsea computing infrastructures., Comment: Accepted to IEEE S&P 2024

We study the Milnor fiber boundary for hyperplane arrangements in $\mathbb{C}^3$. This is one of the examples of non-isolated surface singularities, which are studied by N\'emethi--Szil\'ard. In this paper, we compute the first homology group of the Milnor fiber boundary for a generic arrangement and prove it is combinatorially determined. In particular, this gives the affirmative answer to the conjecture of Suciu., Comment: 18 pages

In this paper, we give a characterization of homogeneous quandles with abelian inner automorphism groups. In particular, we show that such a quandle is expressed as an abelian extension of a trivial quandle. Our construction is a generalization of the recent work by Furuki and Tamaru, which gives a construction of disconnected flat quandles.

QRAO (Quantum Random Access Optimization) is a relaxation algorithm that reduces the number of qubits required to solve a problem by encoding multiple variables per qubit using QRAC (Quantum Random Access Code). Reducing the number of qubits is a common way of dealing with the impact of noise on a quantum algorithm. Our interest lies in the impact of noise on the quality of the binary solution of QRAO, which is unknown. We demonstrate that the mean approximation ratio of the (3, 1)-QRAC Hamiltonian, i.e., the Hamiltonian utilizing the encoding of 3 bits into 1 qubit by QRAC, is less affected by noise compared to the Ising Hamiltonian used in quantum annealer and QAOA (Quantum Approximate Optimization Algorithm). Based on this observation, we discuss a plausible mechanism behind the robustness of QRAO under depolarizing noise. Finally, we assess the number of shots required to estimate the values of binary variables correctly under depolarizing noise and show that the (3, 1)-QRAC Hamiltonian requires less shots to achieve the same accuracy compared to the Ising Hamiltonian., Comment: 9 pages, 5 figures

The large anomalous Hall effect (AHE) in antiferromagnetic (AF) materials arises from symmetry breaking equivalent to a ferromagnetic (FM) state. Consequently, this suggests that the observed AHE is induced by the intrinsic mechanism of the band structure effect, which in turn induces dissipationless transverse conductivity. Confirmation of impurity-insensitive anomalous Hall conductivity (AHC) is crucial to conclude this interpretation; however, experimental investigations in AF materials are limited by the lack of high quality systems. In this study, we show that the AF material NbMnP, which exhibits a large AHE, offers a high quality single crystal. Our findings clearly revealed that the large AHC and the tiny net magnetization of $\sim10^{-3} \mu_{\mathrm{B}}$/Mn are inherent in this material, irrespective of disorder. NbMnP is a novel AF material that generates FM responses in the regime where there is less impurity scattering., Comment: 11 pages, 4 figures

A common way of assessing language learners' mastery of vocabulary is via multiple-choice cloze (i.e., fill-in-the-blank) questions. But the creation of test items can be laborious for individual teachers or in large-scale language programs. In this paper, we evaluate a new method for automatically generating these types of questions using large language models (LLM). The VocaTT (vocabulary teaching and training) engine is written in Python and comprises three basic steps: pre-processing target word lists, generating sentences and candidate word options using GPT, and finally selecting suitable word options. To test the efficiency of this system, 60 questions were generated targeting academic words. The generated items were reviewed by expert reviewers who judged the well-formedness of the sentences and word options, adding comments to items judged not well-formed. Results showed a 75% rate of well-formedness for sentences and 66.85% rate for suitable word options. This is a marked improvement over the generator used earlier in our research which did not take advantage of GPT's capabilities. Post-hoc qualitative analysis reveals several points for improvement in future work including cross-referencing part-of-speech tagging, better sentence validation, and improving GPT prompts.

Developing an accurate and fast anomaly detection model is an important task in real-time computer vision applications. There has been much research to develop a single model that detects either structural or logical anomalies, which are inherently distinct. The majority of the existing approaches implicitly assume that the anomaly can be represented by identifying the anomalous location. However, we argue that logical anomalies, such as the wrong number of objects, can not be well-represented by the spatial feature maps and require an alternative approach. In addition, we focused on the possibility of detecting logical anomalies by using an out-of-distribution detection approach on the feature space, which aggregates the spatial information of the feature map. As a demonstration, we propose a method that incorporates a simple out-of-distribution detection method on the feature space against state-of-the-art reconstruction-based approaches. Despite the simplicity of our proposal, our method PUAD (Picturable and Unpicturable Anomaly Detection) achieves state-of-the-art performance on the MVTec LOCO AD dataset., Comment: 8 pages, 4 figures

To detect and track structural changes in atomic nuclei, the systematic study of nuclear levels with firm spin-parity assignments is important. While linear polarization measurements have been applied to determine the electromagnetic character of gamma-ray transitions, the applicable range is strongly limited due to the low efficiency of the detection system. The multi-layer Cadmium-Telluride (CdTe) Compton camera can be a state-of-the-art gamma-ray polarimeter for nuclear spectroscopy with the high position sensitivity and the detection efficiency. We demonstrated the capability to operate this detector as a reliable gamma-ray polarimeter by using polarized 847-keV gamma rays produced by the $^{56}\rm{Fe}({\it p},{\it p'}\gamma)$ reaction. By combining the experimental data and simulated calculations, the modulation curve for the gamma ray was successfully obtained. A remarkably high polarization sensitivity was achieved, compatible with a reasonable detection efficiency. Based on the obtained results, a possible future gamma-ray polarimetery is discussed.

All vehicles must follow the rules that govern traffic behavior, regardless of whether the vehicles are human-driven or Connected Autonomous Vehicles (CAVs). Road signs indicate locally active rules, such as speed limits and requirements to yield or stop. Recent research has demonstrated attacks, such as adding stickers or projected colored patches to signs, that cause CAV misinterpretation, resulting in potential safety issues. Humans can see and potentially defend against these attacks. But humans can not detect what they can not observe. We have developed an effective physical-world attack that leverages the sensitivity of filterless image sensors and the properties of Infrared Laser Reflections (ILRs), which are invisible to humans. The attack is designed to affect CAV cameras and perception, undermining traffic sign recognition by inducing misclassification. In this work, we formulate the threat model and requirements for an ILR-based traffic sign perception attack to succeed. We evaluate the effectiveness of the ILR attack with real-world experiments against two major traffic sign recognition architectures on four IR-sensitive cameras. Our black-box optimization methodology allows the attack to achieve up to a 100% attack success rate in indoor, static scenarios and a >80.5% attack success rate in our outdoor, moving vehicle scenarios. We find the latest state-of-the-art certifiable defense is ineffective against ILR attacks as it mis-certifies >33.5% of cases. To address this, we propose a detection strategy based on the physical properties of IR laser reflections which can detect 96% of ILR attacks., Comment: The first two authors are co-first. Accepted to NDSS '24

We analyse $\textit{XMM-Newton}$ RGS spectra of Wolf-Rayet (WR) 140, an archetype long-period eccentric WR+O colliding wind binary. We evaluate the spectra of O and Fe emission lines and find that the plasmas emitting these lines have the largest approaching velocities with the largest velocity dispersions between phases 0.935 and 0.968 where the inferior conjunction of the O star occurs. This behaviour is the same as that of the Ne line-emission plasma presented in our previous paper. We perform diagnosis of electron number density $n_{\rm e}$ using He-like triplet lines of O and Ne-like Fe-L lines. The former results in a conservative upper limit of $n_{\rm e} \lesssim 10^{10}$-10$^{12}$ cm$^{-3}$ on the O line-emission site, while the latter can not impose any constraint on the Fe line-emission site because of statistical limitations. We calculate the line-of-sight velocity and its dispersion separately along the shock cone. By comparing the observed and calculated line-of-sight velocities, we update the distance of the Ne line-emission site from the stagnation point. By assuming radiative cooling of the Ne line-emission plasma using the observed temperature and the local stellar wind density, we estimate the line-emission site extends along the shock cone by at most $\pm$58 per cent (phase 0.816) of the distance from the stagnation point. In this framework, excess of the observed velocity dispersion over the calculated one is ascribed to turbulence in the hot-shocked plasma at earlier orbital phases of 0.816, 0.912, and 0.935, with the largest velocity dispersion of 340-630 km s$^{-1}$ at phase 0.912., Comment: 16 pages, 9 figures, Accepted for publication in MNRAS

We perform a finite-size scaling analysis of the critical point in the heavy-quark region of QCD at nonzero temperature. Our previous analysis on the Binder cumulant at $N_t=4$ is extended to finer lattices with $N_t=6$ and $8$. The aspect ratio is also extended up to $15$ to suppress the non-singular contribution. High-precision analysis of the Binder cumulant is realized by an efficient Monte-Carlo simulation with the hopping-parameter expansion (HPE). Effects of higher-order terms in the HPE are incorporated by the reweighting method., Comment: 7 pages, 8 figures, proceedings of Lattice 2023

In this review, we detail the commonality of mathematical intuitions that underlie three numerical methods used for the quantitative description of electron swarms propagating in a gas under the effect of externally applied electric and/or magnetic fields. These methods can be linked to the integral transport equation, following a common thread much better known in the theory of neutron transport than in the theory of electron transport. First, we discuss the exact solution of the electron transport problem using Monte Carlo (MC) simulations. In reality we will progress much further, showing the interpretative role that the diagrams used in quantum theory and quantum field theory can play in the development of MC. Then, we present two methods, the Monte Carlo Flux and the Propagator method, which have been developed at this moment. The first one is based on a modified MC method, while the second shows the advantage of explicitly applying the mathematical idea of propagator to the transport problem., Comment: Review paper, 46 pages, 4 figures

What makes a presupposition of an utterance -- information taken for granted by its speaker -- different from other pragmatic inferences such as an entailment is projectivity (e.g., the negative sentence the boy did not stop shedding tears presupposes the boy had shed tears before). The projectivity may vary depending on the combination of presupposition triggers and environments. However, prior natural language understanding studies fail to take it into account as they either use no human baseline or include only negation as an entailment-canceling environment to evaluate models' performance. The current study attempts to reconcile these issues. We introduce a new dataset, projectivity of presupposition (PROPRES, which includes 12k premise-hypothesis pairs crossing six triggers involving some lexical variety with five environments. Our human evaluation reveals that humans exhibit variable projectivity in some cases. However, the model evaluation shows that the best-performed model, DeBERTa, does not fully capture it. Our findings suggest that probing studies on pragmatic inferences should take extra care of the human judgment variability and the combination of linguistic items., Comment: Accepted by the 27th Conference on Computational Natural Language Learning (CoNLL2023)

A Divide with cusps is the image of a proper generic immersion from finite intervals and circles into a $2$-disk which allows to have cusps. A divide with cusps is the generalization of the notion of the divide which is introduced by A'Campo. From a divide with cusps, we can define the associated link in $S^3$. In this paper, we give the characterization of the link in $S^3$ which can be described as the associated link of a divide with cusps. In particular, we prove that every strongly invertible link and $2$-periodic link can be described as the link of a divide with cusps., Comment: 15 pages, 17 figures

Antiferromagnetic (AF) topological materials offer a fertile ground to explore a variety of quantum phenomena such as axion magnetoelectric dynamics and chiral Majorana fermions. To realize such intriguing states, it is essential to establish a direct link between electronic states and topology in the AF phase, whereas this has been challenging because of the lack of a suitable materials platform. Here we report the experimental realization of the AF topological-insulator phase in NdBi. By using micro-focused angle-resolved photoemission spectroscopy, we discovered contrasting surface electronic states for two types of AF domains; the surface having the out-of-plane component in the AF-ordering vector displays Dirac-cone states with a gigantic energy gap, whereas the surface parallel to the AF-ordering vector hosts gapless Dirac states despite the time-reversal-symmetry breaking. The present results establish an essential role of combined symmetry to protect massless Dirac fermions under the presence of AF order and widen opportunities to realize exotic phenomena utilizing AF topological materials., Comment: 28 pages, 12 figures