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In this paper, we introduce Dynamic Layer Operations (DLO), a novel approach for vertically scaling transformer-based Large Language Models (LLMs) by dynamically expanding, activating, or skipping layers using a sophisticated routing policy based on layerwise feature similarity. Unlike traditional Mixture-of-Experts (MoE) methods that focus on extending the model width, our approach targets model depth, addressing the redundancy observed across layer representations for various input samples. Our framework is integrated with the Supervised Fine-Tuning (SFT) stage, eliminating the need for resource-intensive Continual Pre-Training (CPT). Experimental results demonstrate that DLO not only outperforms the original unscaled models but also achieves comparable results to densely expanded models with significantly improved efficiency. Our work offers a promising direction for building efficient yet powerful LLMs. We will release our implementation and model weights upon acceptance.

The conditional diffusion model has been demonstrated as an efficient tool for learning robot policies, owing to its advancement to accurately model the conditional distribution of policies. The intricate nature of real-world scenarios, characterized by dynamic obstacles and maze-like structures, underscores the complexity of robot local navigation decision-making as a conditional distribution problem. Nevertheless, leveraging the diffusion model for robot local navigation is not trivial and encounters several under-explored challenges: (1) Data Urgency. The complex conditional distribution in local navigation needs training data to include diverse policy in diverse real-world scenarios; (2) Myopic Observation. Due to the diversity of the perception scenarios, diffusion decisions based on the local perspective of robots may prove suboptimal for completing the entire task, as they often lack foresight. In certain scenarios requiring detours, the robot may become trapped. To address these issues, our approach begins with an exploration of a diverse data generation mechanism that encompasses multiple agents exhibiting distinct preferences through target selection informed by integrated global-local insights. Then, based on this diverse training data, a diffusion agent is obtained, capable of excellent collision avoidance in diverse scenarios. Subsequently, we augment our Local Diffusion Planner, also known as LDP by incorporating global observations in a lightweight manner. This enhancement broadens the observational scope of LDP, effectively mitigating the risk of becoming ensnared in local optima and promoting more robust navigational decisions., Comment: 8 pages, 6 figures, accepted by IROS 2024

The conceptualization and categorization of natural objects in the human mind have long intrigued cognitive scientists and neuroscientists, offering crucial insights into human perception and cognition. Recently, the rapid development of Large Language Models (LLMs) has raised the attractive question of whether these models can also develop human-like object representations through exposure to vast amounts of linguistic and multimodal data. In this study, we combined behavioral and neuroimaging analysis methods to uncover how the object concept representations in LLMs correlate with those of humans. By collecting large-scale datasets of 4.7 million triplet judgments from LLM and Multimodal LLM (MLLM), we were able to derive low-dimensional embeddings that capture the underlying similarity structure of 1,854 natural objects. The resulting 66-dimensional embeddings were found to be highly stable and predictive, and exhibited semantic clustering akin to human mental representations. Interestingly, the interpretability of the dimensions underlying these embeddings suggests that LLM and MLLM have developed human-like conceptual representations of natural objects. Further analysis demonstrated strong alignment between the identified model embeddings and neural activity patterns in many functionally defined brain ROIs (e.g., EBA, PPA, RSC and FFA). This provides compelling evidence that the object representations in LLMs, while not identical to those in the human, share fundamental commonalities that reflect key schemas of human conceptual knowledge. This study advances our understanding of machine intelligence and informs the development of more human-like artificial cognitive systems.

Magnetism and topology are two major areas of condensed matter physics. The combination of magnetism and topology gives rise to more novel physical effects, which have attracted strongly theoretical and experimental attention. Recently, the concept of altermagnetism has been introduced, characterized by a dual nature: real-space antiferromagnetism and reciprocal-space anisotropic spin polarization. The amalgamation of altermagnetism with topology may lead to the emergence of previously unobserved topological phases and the associated physical effects. In this study, utilizing a four-band lattice model that incorporates altermagnetism and spin group symmetry, we demonstrate that type-I, type-II, and type-III bipolarized Weyl semimetals can exist in altermagnetic systems. Through the first-principles electronic structure calculations, we predict four ideal two-dimensional type-I altermagnetic bipolarized Weyl semimetals Fe$_2$WTe$_4$ and Fe$_2$MoZ$_4$ (Z=S,Se,Te). More significantly, we introduce the quantum crystal valley Hall effect, a phenomenon achievable in three of these materials namely Fe$_2$WTe$_4$, Fe$_2$MoS$_4$, and Fe$_2$MoTe$_4$, when spin-orbit coupling is considered. Furthermore, these materials have the potential to transition from a quantum crystal valley Hall phase to a Chern insulator phase under strain. In contrast, Fe$_2$MoSe$_4$ remains to be a Weyl semimetal under spin-orbit coupling but is distinguished by possessing only a single pair of Weyl points. Additionally, the position, polarization, and number of Weyl points in Fe$_2$WTe$_4$ and Fe$_2$MoZ$_4$ can be manipulated by adjusting the direction of the N\'eel vector. Consequently, Fe$_2$WTe$_4$ and Fe$_2$MoZ$_4$ emerge as promising experimental platforms for investigating the distinctive physical attributes of various altermagnetic topological phases., Comment: 7 pages, 5 figures

We studied the two-qubit quantum Rabi model and found its dark state solutions with at most N photons. One peculiar case presents when $N=3$, which has constant eigenenergy in the whole coupling regime and leads to level crossings within the same parity subspace. We also discovered asymptotic solutions with at most $N=2i+3$ $(i=1,2,3,\dots)$ photons, and constant eigenenergy $N\hbar \omega$ when coupling $g$ becomes much larger than photon frequency $\omega$. Although generally all photon number states are involved in the two-qubit quantum Rabi model, such $N$-photon solutions exist and may have applications in quantum information processing with ultrastrong couplings., Comment: 11 pages, 6 figures

Emotion decoding plays an important role in affective human-computer interaction. However, previous studies ignored the dynamic real-world scenario, where human experience a blend of multiple emotions which are incrementally integrated into the model, leading to the multi-label class incremental learning (MLCIL) problem. Existing methods have difficulty in solving MLCIL issue due to notorious catastrophic forgetting caused by partial label problem and inadequate label semantics mining. In this paper, we propose an augmented emotional semantics learning framework for multi-label class incremental emotion decoding. Specifically, we design an augmented emotional relation graph module with label disambiguation to handle the past-missing partial label problem. Then, we leverage domain knowledge from affective dimension space to alleviate future-missing partial label problem by knowledge distillation. Besides, an emotional semantics learning module is constructed with a graph autoencoder to obtain emotion embeddings in order to guide the semantic-specific feature decoupling for better multi-label learning. Extensive experiments on three datasets show the superiority of our method for improving emotion decoding performance and mitigating forgetting on MLCIL problem.

Autonomous systems often employ multiple LiDARs to leverage the integrated advantages, enhancing perception and robustness. The most critical prerequisite under this setting is the estimating the extrinsic between each LiDAR, i.e., calibration. Despite the exciting progress in multi-LiDAR calibration efforts, a universal, sensor-agnostic calibration method remains elusive. According to the coarse-to-fine framework, we first design a spherical descriptor TERRA for 3-DoF rotation initialization with no prior knowledge. To further optimize, we present JEEP for the joint estimation of extrinsic and pose, integrating geometric and motion information to overcome factors affecting the point cloud registration. Finally, the LiDAR poses optimized by the hierarchical optimization module are input to time synchronization module to produce the ultimate calibration results, including the time offset. To verify the effectiveness, we conduct extensive experiments on eight datasets, where 16 diverse types of LiDARs in total and dozens of calibration tasks are tested. In the challenging tasks, the calibration errors can still be controlled within 5cm and 1{\deg} with a high success rate., Comment: 19 pages, 19 figures

In the task of Knowledge Graph Completion (KGC), the existing datasets and their inherent subtasks carry a wealth of shared knowledge that can be utilized to enhance the representation of knowledge triplets and overall performance. However, no current studies specifically address the shared knowledge within KGC. To bridge this gap, we introduce a multi-level Shared Knowledge Guided learning method (SKG) that operates at both the dataset and task levels. On the dataset level, SKG-KGC broadens the original dataset by identifying shared features within entity sets via text summarization. On the task level, for the three typical KGC subtasks - head entity prediction, relation prediction, and tail entity prediction - we present an innovative multi-task learning architecture with dynamically adjusted loss weights. This approach allows the model to focus on more challenging and underperforming tasks, effectively mitigating the imbalance of knowledge sharing among subtasks. Experimental results demonstrate that SKG-KGC outperforms existing text-based methods significantly on three well-known datasets, with the most notable improvement on WN18RR., Comment: The paper has been accepted for publication at TACL. And the arXiv version is a pre-MIT Press publication version

Robustness to malicious attacks is of paramount importance for distributed learning. Existing works often consider the classical Byzantine attacks model, which assumes that some workers can send arbitrarily malicious messages to the server and disturb the aggregation steps of the distributed learning process. To defend against such worst-case Byzantine attacks, various robust aggregators have been proven effective and much superior to the often-used mean aggregator. In this paper, we show that robust aggregators are too conservative for a class of weak but practical malicious attacks, as known as label poisoning attacks, where the sample labels of some workers are poisoned. Surprisingly, we are able to show that the mean aggregator is more robust than the state-of-the-art robust aggregators in theory, given that the distributed data are sufficiently heterogeneous. In fact, the learning error of the mean aggregator is proven to be optimal in order. Experimental results corroborate our theoretical findings, demonstrating the superiority of the mean aggregator under label poisoning attacks., Comment: Accepted by IJCAI 2024

We study the joint distribution of central $L$-values and orders of Tate-Shafarevich groups of quadratic twists of elliptic curves. In particular, adapting Radziwill and Soundararajan's principles of establishing upper and lower bounds for the distribution of central values in families of $L$-functions, we obtain conditional upper and lower bounds for such a joint distribution for rank zero twists. These lead us to a conjecture on the full asymptotic for the joint distribution., Comment: 24 pages, comments welcome

The study on minimal linear codes has received great attention due to their significant applications in secret sharing schemes and secure two-party computation. Until now, numerous minimal linear codes have been discovered. However, to the best of our knowledge, no infinite family of minimal ternary linear codes was found from vectorial functions. In this paper, we present a necessary and sufficient condition for a large class of ternary linear codes from vectorial functions such that those codes are minimal. Based on that, we construct several minimal ternary linear codes with three-weight from vectorial regular plateaued functions, and determine their weight distributions. Moreover, we also give a necessary and sufficient condition for a large family of ternary linear codes from vectorial functions such that the codes are minimal and violate the AB condition simultaneously. According to this characterization, we find several minimal ternary linear codes violating the AB condition. Notably, our results show that our method can be applied to solve a problem on minimal linear codes proposed by Li et al.

Large Language Models (LLMs) have catalyzed transformative advances across a spectrum of natural language processing tasks through few-shot or zero-shot prompting, bypassing the need for parameter tuning. While convenient, this modus operandi aggravates ``hallucination'' concerns, particularly given the enigmatic ``black-box'' nature behind their gigantic model sizes. Such concerns are exacerbated in high-stakes applications (e.g., healthcare), where unaccountable decision errors can lead to devastating consequences. In contrast, human decision-making relies on nuanced cognitive processes, such as the ability to sense and adaptively correct misjudgments through conceptual understanding. Drawing inspiration from human cognition, we propose an innovative \textit{metacognitive} approach, dubbed \textbf{CLEAR}, to equip LLMs with capabilities for self-aware error identification and correction. Our framework facilitates the construction of concept-specific sparse subnetworks that illuminate transparent decision pathways. This provides a novel interface for model \textit{intervention} after deployment. Our intervention offers compelling advantages: (\textit{i})~at deployment or inference time, our metacognitive LLMs can self-consciously identify potential mispredictions with minimum human involvement, (\textit{ii})~the model has the capability to self-correct its errors efficiently, obviating the need for additional tuning, and (\textit{iii})~the rectification procedure is not only self-explanatory but also user-friendly, enhancing the interpretability and accessibility of the model. By integrating these metacognitive features, our approach pioneers a new path toward engendering greater trustworthiness and accountability in the deployment of LLMs.

In this work, the effect of electron-phonon (e-ph) coupling on both electron and phonon transport of metals is investigated via first principles calculations. A Monte-Carlo (MC) approach for solving the coupled electron-phonon Boltzmann transport equations is developed to investigate thermal conductivity of metals. In this approach, the anisotropic electron band structure, phonon dispersion in the full Brillouin zone, and mode-dependent thermal relaxation time of electrons and phonons are calculated from first principles. Using this approach, MC simulations of coupled e-ph thermal transport at different temperatures in {\alpha}-U and Ag are performed. These two materials were selected as a way to demonstrate the applicability of the method in different fields. The results indicate that the electron relaxation time due to phonon scattering is orders of magnitude smaller than the phonon relaxation time due to electron scattering. The results also show that, in phonon thermal transport, the impact of ph-e scattering is almost negligible and the ph-ph scattering dominates phonon transport. At high temperature, the electrons dominate thermal transport in both {\alpha}-U and Ag. However, at low temperature, the phonon contribution to the total thermal conductivity of {\alpha}-U is significant. Moreover, the Lorenz ratio deviates from the Sommerfeld value at low to intermediate temperatures, where the Wiedemann-Franz law is not applicable. Finally, we show that the Ag electronic thermal conductivity shows a stronger size effect than its phonon thermal conductivity., Comment: 36 Pages, 14 Figures

Spin-orbit coupling is a key to realize many novel physical effects in condensed matter physics, but the mechanism to achieve strong spin-orbit coupling effect in light element antiferromagnetic compounds has not been explored. In this work, based on symmetry analysis and the first-principles electronic structure calculations, we demonstrate that strong spin-orbit coupling effect can be realized in light element altermagnetic materials, and propose a mechanism for realizing the corresponding effective spin-orbit coupling. This mechanism reveals the cooperative effect of crystal symmetry, electron occupation, electronegativity, electron correlation, and intrinsic spin-orbit coupling. Our work not only promotes the understanding of light element compounds with strong spin-orbit coupling effect, but also provides an alternative for realizing light element compounds with an effective strong spin-orbit coupling., Comment: 5 pages, 4 figures

Altermagnetism is a new magnetic phase with k-dependent spin polarization and may exist in an insulating state with a high N\'eel temperature. This provides a new opportunity to obtain both spin and electric polarization in one material. Here, based on symmetry analysis and the first-principles electronic structures calculations, we predict that LiFe2F6 is a d-wave altermagnetic and charge-ordering-mediated ferroelectric material. Moreover, the LiFe2F6 transforms into a ferrimagnetic and ferroelectric phase with strong magnetoelectric coupling under biaxial compressive strain. Interestingly, the spins of the valence band and the conduction band are opposite in ferrimagnetic LiFe2F6, which facilitates a simultaneous spin-triplet excitonic insulator phase. More importantly, the spin-triplet excitons with spin 1 and -1 can be switched by electric fields in ferrimagnetic LiFe2F6 due to strong magnetoelectric coupling. Due to the abundance of novel physical properties, LiFe2F6 will certainly attract a wide range of theoretical and experimental interest., Comment: 6 pages, 4 figures

Altermagnetism, a new magnetic phase, has been theoretically proposed and experimentally verified to be distinct from ferromagnetism and antiferromagnetism. Although altermagnets have been found to possess many exotic physical properties, the very limited availability of known altermagnetic materials (e.g., 14 confirmed materials) hinders the study of such properties. Hence, discovering more types of altermagnetic materials is crucial for a comprehensive understanding of altermagnetism and thus facilitating new applications in the next-generation information technologies, e.g., storage devices and high-sensitivity sensors. Here, we report 25 new altermagnetic materials that cover metals, semiconductors, and insulators, discovered by an AI search engine unifying symmetry analysis, graph neural network pre-training, optimal transport theory, and first-principles electronic structure calculation. The wide range of electronic structural characteristics reveals that various novel physical properties manifest in these newly discovered altermagnetic materials, e.g., anomalous Hall effect, anomalous Kerr effect, and topological property. Noteworthy, we discovered 8 i-wave altermagnetic materials for the first time. Overall, the AI search engine performs much better than human experts and suggests a set of new altermagnetic materials with unique properties, outlining its potential for accelerated discovery of the materials with targeting properties., Comment: 38 pages; 22 figures; 3 tables

We study the two-qubit asymmetric quantum Rabi model (AQRM) and find its dark-state solution. Such solutions have at most one photon and constant eigenenergy in the whole coupling regime, causing level crossings in the spectrum, although there is no explicit conserved quantity except energy. We find an operator in the eigenenergy basis to label all the degeneracies with its eigenvalues, and compare it with the well-known hidden symmetry which exists when bias parameter $\epsilon$ is a multiple of half of the resonator frequency $\omega$. Extended to the multimode case, we find symmetries related with conserved bosonic number operators, which also cause level crossings. This provides a perspective for symmetry studies on generalized Rabi models., Comment: 7 pages, 3 figures

Robot navigation using deep reinforcement learning (DRL) has shown great potential in improving the performance of mobile robots. Nevertheless, most existing DRL-based navigation methods primarily focus on training a policy that directly commands the robot with low-level controls, like linear and angular velocities, which leads to unstable speeds and unsmooth trajectories of the robot during the long-term execution. An alternative method is to train a DRL policy that outputs the navigation path directly. However, two roadblocks arise for training a DRL policy that outputs paths: (1) The action space for potential paths often involves higher dimensions comparing to low-level commands, which increases the difficulties of training; (2) It takes multiple time steps to track a path instead of a single time step, which requires the path to predicate the interactions of the robot w.r.t. the dynamic environment in multiple time steps. This, in turn, amplifies the challenges associated with training. In response to these challenges, we propose PathRL, a novel DRL method that trains the policy to generate the navigation path for the robot. Specifically, we employ specific action space discretization techniques and tailored state space representation methods to address the associated challenges. In our experiments, PathRL achieves better success rates and reduces angular rotation variability compared to other DRL navigation methods, facilitating stable and smooth robot movement. We demonstrate the competitive edge of PathRL in both real-world scenarios and multiple challenging simulation environments.

Anomalous Hall effect (AHE), one of the most important electronic transport phenomena, generally appears in ferromagnetic materials but is rare in materials without magnetic elements. Here, we present a study of La$_3$MgBi$_5$, whose band structure carries multitype Dirac fermions. Although magnetic elements are absent in La$_3$MgBi$_5$, clear signals of AHE can be observed. In particular, the anomalous Hall conductivity is extremely large, reaching 42,356 $\Omega^{-1}$ cm$^{-1}$ with an anomalous Hall angle of 8.8 %, the largest one that has been observed in the current AHE systems. The AHE is suggested to originate from the combination of skew scattering and Berry curvature. Another unique property discovered in La$_3$MgBi$_5$ is the axial diamagnetism. The diamagnetism is significantly enhanced and dominates the magnetization in the axial directions, which is the result of restricted motion of the Dirac fermion at Fermi level. Our findings not only establish La$_3$MgBi$_5$ as a suitable platform to study AHE and quantum transport, but also indicate the great potential of 315-type Bi-based materials for exploring novel physical properties.

This paper studies Byzantine-robust stochastic optimization over a decentralized network, where every agent periodically communicates with its neighbors to exchange local models, and then updates its own local model by stochastic gradient descent (SGD). The performance of such a method is affected by an unknown number of Byzantine agents, which conduct adversarially during the optimization process. To the best of our knowledge, there is no existing work that simultaneously achieves a linear convergence speed and a small learning error. We observe that the learning error is largely dependent on the intrinsic stochastic gradient noise. Motivated by this observation, we introduce two variance reduction methods, stochastic average gradient algorithm (SAGA) and loopless stochastic variance-reduced gradient (LSVRG), to Byzantine-robust decentralized stochastic optimization for eliminating the negative effect of the stochastic gradient noise. The two resulting methods, BRAVO-SAGA and BRAVO-LSVRG, enjoy both linear convergence speeds and stochastic gradient noise-independent learning errors. Such learning errors are optimal for a class of methods based on total variation (TV)-norm regularization and stochastic subgradient update. We conduct extensive numerical experiments to demonstrate their effectiveness under various Byzantine attacks.

We have found the special dark state solutions of the anisotropic two-qubit quantum Rabi model (QRM), which has at most one photon, and constant eigenenergy in the whole coupling regime. Accordingly, we propose a scheme to deterministically generate two kinds of the two-qubit Bell states through adiabatic evolution along the dark states. With the assistance of the Stark shift, the generation time can be reduced to subnanosecond scales, proportional to the reverse of the resonator frequency, with fidelity reaching 99%. Furthermore, the other two kinds of Bell states can also be ultrafast generated.

In this article, we study the cyclicity problem of elliptic curves $E/\Bbb{Q}$ modulo primes in a given arithmetic progression. We extend the recent work of Akbal and G\"ulo\u{g}lu by proving an unconditional asymptotic for such a cyclicity problem over arithmetic progressions for CM elliptic curves $E$, which also presents a generalisation of the previous works of Akbary, Cojocaru, M.R. Murty, V.K. Murty, and Serre. In addition, we refine the conditional estimates of Akbal and G\"ulo\u{g}lu, which gives log-power savings (for small moduli) and consequently improves the work of Cojocaru and M.R. Murty. Moreover, we study the average exponent of $E$ modulo primes in a given arithmetic progression and obtain several conditional and unconditional estimates, extending the previous works of Freiberg, Kim, Kurlberg, and Wu., Comment: Accepted by Q. J. Math

Optical chirality plays an essential role in chiral light-matter interactions with broad applications in sensing and spectroscopy. Conventional methods of generating optical chirality usually employ chiral structures or chiral excitations. Here, we propose to use an achiral metasurface consisting of gold disk array excited by a linearly polarized light to generate optical chirality. Using full-wave numerical simulations, we show that the metasurface can give rise to large-area optical chirality of the same sign for the wavelength ranging from 1.2 um to 1.5 um. The magnitude of the chirality is comparable to that of circularly polarized plane waves. The emergence of the optical chirality can be attributed to the asymmetric polarization singularity lines (C lines) in the near fields of the metasurface. We further explore the application of the proposed metasurface in chiral discriminations by simulating the absorption of chiral helix particles immersed in the near fields, and demonstrate that the left-handed and right-handed helix particles give rise to different absorptions. The phenomenon can be understood using an analytical theory based on the dipole approximation, which predicts differential absorption quantitatively agrees with the numerical simulation results. Our study uncovers the subtle relationship between near-field optical chirality, polarization singularities, and symmetry. The results can find applications in optical sensing, chiral quantum optics, and optical manipulations of small particles., Comment: 6 pages, 5 figures

It is important for deep reinforcement learning (DRL) algorithms to transfer their learned policies to new environments that have different visual inputs. In this paper, we introduce Prompt based Proximal Policy Optimization ($P^{3}O$), a three-stage DRL algorithm that transfers visual representations from a target to a source environment by applying prompting. The process of $P^{3}O$ consists of three stages: pre-training, prompting, and predicting. In particular, we specify a prompt-transformer for representation conversion and propose a two-step training process to train the prompt-transformer for the target environment, while the rest of the DRL pipeline remains unchanged. We implement $P^{3}O$ and evaluate it on the OpenAI CarRacing video game. The experimental results show that $P^{3}O$ outperforms the state-of-the-art visual transferring schemes. In particular, $P^{3}O$ allows the learned policies to perform well in environments with different visual inputs, which is much more effective than retraining the policies in these environments., Comment: This paper has been accepted to be presented at the upcoming IEEE International Conference on Multimedia & Expo (ICME) in 2023

Reliable localization is crucial for autonomous robots to navigate efficiently and safely. Some navigation methods can plan paths with high localizability (which describes the capability of acquiring reliable localization). By following these paths, the robot can access the sensor streams that facilitate more accurate location estimation results by the localization algorithms. However, most of these methods require prior knowledge and struggle to adapt to unseen scenarios or dynamic changes. To overcome these limitations, we propose a novel approach for localizability-enhanced navigation via deep reinforcement learning in dynamic human environments. Our proposed planner automatically extracts geometric features from 2D laser data that are helpful for localization. The planner learns to assign different importance to the geometric features and encourages the robot to navigate through areas that are helpful for laser localization. To facilitate the learning of the planner, we suggest two techniques: (1) an augmented state representation that considers the dynamic changes and the confidence of the localization results, which provides more information and allows the robot to make better decisions, (2) a reward metric that is capable to offer both sparse and dense feedback on behaviors that affect localization accuracy. Our method exhibits significant improvements in lost rate and arrival rate when tested in previously unseen environments.

It is rarely reported that collinear antiferromagnetic (AFM) metals can have anomalous Hall effect (AHE). In this letter, based on symmetry analysis and the first-principles electronic structure calculations, we predict that two existing collinear antiferromagnets Nb$_{2}$FeB$_{2}$ and Ta$_{2}$FeB$_{2}$, whose N\'eel temperatures are above room temperature, have very large AHE with anomalous Hall conductance (AHC) -100 $\Omega^{-1}$ cm$^{-1}$ and $-54\Omega^{-1}$ cm$^{-1}$, respectively. We further complete the symmetry resquirements for realizing the AHE in collinear antiferromagnetism.

Applying pressure around megabar is indispensable in the synthesis of high-temperature superconducting hydrides, such as SH$_3$ and LaH$_{10}$. Stabilizing the high-pressure phase of hydride around ambient condition is a severe challenge. Based on the density-functional theory calculations, we give the first example that the structure of hydride CaBH$_5$ predicted above 280 GPa, can maintain its dynamical stability with pressure down to 1 GPa, by modulating the charge transfer from metal atoms to hydrogen atoms via the replacement of Ca with alkali metal atoms e.g. Cs, in which the [BH$_5$]$^{2-}$ anion shrinks along $c$ axis and expands in the $ab$ plane, experiencing an anisotropic virtual high pressure. This mechanism, namely charge transfer modulated virtual high pressure effect, plays a vital role in enhancing the structural stability and leading to the reemergence of ambient-pressure-forbidden [BH$_5$]$^{2-}$ anion around 1 GPa in CsBH$_5$. Moreover, we find that CsBH$_5$ is a strongly coupled superconductor, with transition temperature as high as 98 K, well above the liquid-nitrogen temperature. Our findings provide a novel mechanism to reduce the critical pressure required by hydrogen-rich compound without changing its crystal structure, and also shed light on searching ambient-pressure high-temperature superconductivity in metal borohydrides., Comment: accepted for publication as a Letter in Phys. Rev. B

In this letter, we give a characterization for a generic construction of bent functions. This characterization enables us to obtain another efficient construction of bent functions and to give a positive answer on a problem of bent functions.

Deterministic single-photon sources are important and ubiquitous in quantum information protocols. However, to the best of our knowledge, none of them work in the ultrastrong light-matter coupling regime, and each excitation process can only emit one photon. We propose a deterministic single-photon source in circuit QED which can work in the ultrastrong coupling regime. Here, two qubits are excited simultaneously in one process and two deterministic single photons can be sequentially emitted with an arbitrary time separation. This happens through two consecutive adiabatic transfers along the one-photon solutions of the two-qubit Rabi and Jaynes-Cummings model, which has constant eigenenergy in the whole coupling regime. Unlike the stimulated Raman adiabatic passage, the system goes back to the initial state of another period automatically after photon emission. Our scheme can approach unity single-photon efficiency, indistinguishability, and purity simultaneously. With the assistance of the Stark shift, a deterministic single photon can be generated within a time proportional to the inverse of the resonator frequency., Comment: 7 +4 pages, 5 figures

Tensor program tuning is a non-convex objective optimization problem, to which search-based approaches have proven to be effective. At the core of the search-based approaches lies the design of the cost model. Though deep learning-based cost models perform significantly better than other methods, they still fall short and suffer from the following problems. First, their feature extraction heavily relies on expert-level domain knowledge in hardware architectures. Even so, the extracted features are often unsatisfactory and require separate considerations for CPUs and GPUs. Second, a cost model trained on one hardware platform usually performs poorly on another, a problem we call cross-hardware unavailability. In order to address these problems, we propose TLP and MTLTLP. TLP is a deep learning-based cost model that facilitates tensor program tuning. Instead of extracting features from the tensor program itself, TLP extracts features from the schedule primitives. We treat schedule primitives as tensor languages. TLP is thus a Tensor Language Processing task. In this way, the task of predicting the tensor program latency through the cost model is transformed into a natural language processing (NLP) regression task. MTL-TLP combines Multi-Task Learning and TLP to cope with the cross-hardware unavailability problem. We incorporate these techniques into the Ansor framework and conduct detailed experiments. Results show that TLP can speed up the average search time by 9.1X and 3.0X on CPU and GPU workloads, respectively, compared to the state-of-the-art implementation. MTL-TLP can achieve a speed-up of 4.7X and 2.9X on CPU and GPU workloads, respectively, using only 7% of the target hardware data.

Simultaneous localization and mapping (SLAM) based on laser sensors has been widely adopted by mobile robots and autonomous vehicles. These SLAM systems are required to support accurate localization with limited computational resources. In particular, point cloud registration, i.e., the process of matching and aligning multiple LiDAR scans collected at multiple locations in a global coordinate framework, has been deemed as the bottleneck step in SLAM. In this paper, we propose a feature filtering algorithm, PFilter, that can filter out invalid features and can thus greatly alleviate this bottleneck. Meanwhile, the overall registration accuracy is also improved due to the carefully curated feature points. We integrate PFilter into the well-established scan-to-map LiDAR odometry framework, F-LOAM, and evaluate its performance on the KITTI dataset. The experimental results show that PFilter can remove about 48.4% of the points in the local feature map and reduce feature points in scan by 19.3% on average, which save 20.9% processing time per frame. In the mean time, we improve the accuracy by 9.4%., Comment: 7 pages, 5 figures. Accepted for IEEE IROS 2022