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We propose a machine learning approach to the optimal control of fluid restless multi-armed bandits (FRMABs) with state equations that are either affine or quadratic in the state variables. By deriving fundamental properties of FRMAB problems, we design an efficient machine learning based algorithm. Using this algorithm, we solve multiple instances with varying initial states to generate a comprehensive training set. We then learn a state feedback policy using Optimal Classification Trees with hyperplane splits (OCT-H). We test our approach on machine maintenance, epidemic control and fisheries control problems. Our method yields high-quality state feedback policies and achieves a speed-up of up to 26 million times compared to a direct numerical algorithm for fluid problems.

Mechanical strain presents an effective control over symmetry-breaking phase transitions. In quantum paralelectric SrTiO3, strain can induce the ferroelectric transition via modification of local Ti potential landscape. However, brittle bulk materials can only withstand limited strain range (~0.1%). Taking advantage of nanoscopically-thin freestanding membranes, we demonstrated in-situ strain-induced reversible ferroelectric transition in a single freestanding SrTiO3 membranes. We measure the ferroelectric order by detecting the local anisotropy of the Ti 3d orbital using X-ray linear dichroism at the Ti-K pre-edge, while the strain is determined by X-ray diffraction. With reduced thickness, the SrTiO3 membranes remain elastic with >1% tensile strain cycles. A robust displacive ferroelectricity appears beyond a temperature-dependent critical strain. Interestingly, we discover a crossover from a classical ferroelectric transition to a quantum regime at low temperatures, which enhances strain-induced ferroelectricity. Our results offer a new opportunities to strain engineer functional properties in low dimensional quantum materials and provide new insights into the role of the ferroelectric fluctuations in quantum paraelectric SrTiO3., Comment: 19 pages, 4 figures

As one of the most promising methods in self-supervised learning, contrastive learning has achieved a series of breakthroughs across numerous fields. A predominant approach to implementing contrastive learning is applying InfoNCE loss: By capturing the similarities between pairs, InfoNCE loss enables learning the representation of data. Albeit its success, adopting InfoNCE loss requires tuning a temperature, which is a core hyperparameter for calibrating similarity scores. Despite its significance and sensitivity to performance being emphasized by several studies, searching for a valid temperature requires extensive trial-and-error-based experiments, which increases the difficulty of adopting InfoNCE loss. To address this difficulty, we propose a novel method to deploy InfoNCE loss without temperature. Specifically, we replace temperature scaling with the inverse hyperbolic tangent function, resulting in a modified InfoNCE loss. In addition to hyperparameter-free deployment, we observed that the proposed method even yielded a performance gain in contrastive learning. Our detailed theoretical analysis discovers that the current practice of temperature scaling in InfoNCE loss causes serious problems in gradient descent, whereas our method provides desirable gradient properties. The proposed method was validated on five benchmarks on contrastive learning, yielding satisfactory results without temperature tuning., Comment: 10 pages, 5 figures

High Mountain Asia holds the largest concentration of frozen water outside the polar regions, serving as a crucial water source for more than 1.9 billion people. In the face of climate change, precipitation represents the largest source of uncertainty for hydrological modelling in this area. Future precipitation predictions remain challenging due to complex orography, lack of in situ hydrological observations, and limitations in climate model resolution and parametrisation for this region. To address the uncertainty posed by these challenges, climate models are often aggregated into multi-model ensembles. While multi-model ensembles are known to improve the predictive accuracy and analysis of future climate projections, consensus regarding how models are aggregated is lacking. In this study, we propose a probabilistic machine learning framework to systematically combine 13 regional climate models from the Coordinated Regional Downscaling Experiment (CORDEX) over High Mountain Asia. Our approach accounts for seasonal and spatial biases within the models, enabling the prediction of more faithful precipitation distributions. The framework is validated against gridded historical precipitation data and is used to generate projections for the near-future (2036-2065) and far-future (2066-2095) under RCP4.5 and RCP8.5 scenarios., Comment: 16 pages 8 figures (main text), 32 pages 14 figures (total)

Developing efficient hydrogen storage and conversion technologies is essential for sustainable energy. This study investigates the catalytic potential of a dicalcium nitride (Ca2N) monolayer for hydrogen dissociation using density functional theory (DFT) and ab initio molecular dynamics (AIMD) simulations. We find that atomic hydrogen preferentially adsorbs at Ca-centered hollow sites (labeled A sites), while molecular hydrogen adsorption is limited to bridge sites (labeled B sites). AIMD simulations reveal that H2 dissociation at B sites inhibits further adsorption, suggesting a mechanism of controlled H2 dissociation. The current findings emphasize the potential of pristine Ca2N as a catalyst for H2 dissociation-related processes and motivate future investigations of its activity in hydrogen evolution reactions., Comment: 5 figures

Keyword spotting is often implemented by keyword classifier to the encoder in acoustic models, enabling the classification of predefined or open vocabulary keywords. Although keyword spotting is a crucial task in various applications and can be extended to call-for-help detection in emergencies, however, the previous method often suffers from scalability limitations due to retraining required to introduce new keywords or adapt to changing contexts. We explore a simple yet effective approach that leverages off-the-shelf pretrained ASR models to address these challenges, especially in call-for-help detection scenarios. Furthermore, we observed a substantial increase in false alarms when deploying call-for-help detection system in real-world scenarios due to noise introduced by microphones or different environments. To address this, we propose a novel noise-agnostic multitask learning approach that integrates a noise classification head into the ASR encoder. Our method enhances the model's robustness to noisy environments, leading to a significant reduction in false alarms and improved overall call-for-help performance. Despite the added complexity of multitask learning, our approach is computationally efficient and provides a promising solution for call-for-help detection in real-world scenarios., Comment: Accepted to ICASSP 2025

We propose GOTLoc, a robust localization method capable of operating even in outdoor environments where GPS signals are unavailable. The method achieves this robust localization by leveraging comparisons between scene graphs generated from text descriptions and maps. Existing text-based localization studies typically represent maps as point clouds and identify the most similar scenes by comparing embeddings of text and point cloud data. However, point cloud maps have limited scalability as it is impractical to pre-generate maps for all outdoor spaces. Furthermore, their large data size makes it challenging to store and utilize them directly on actual robots. To address these issues, GOTLoc leverages compact data structures, such as scene graphs, to store spatial information, enabling individual robots to carry and utilize large amounts of map data. Additionally, by utilizing publicly available map data, such as OpenStreetMap, which provides global information on outdoor spaces, we eliminate the need for additional effort to create custom map data. For performance evaluation, we utilized the KITTI360Pose dataset in conjunction with corresponding OpenStreetMap data to compare the proposed method with existing approaches. Our results demonstrate that the proposed method achieves accuracy comparable to algorithms relying on point cloud maps. Moreover, in city-scale tests, GOTLoc required significantly less storage compared to point cloud-based methods and completed overall processing within a few seconds, validating its applicability to real-world robotics. Our code is available at https://github.com/donghwijung/GOTLoc.

Graph Neural Networks (GNNs) have been widely adopted for their ability to compute expressive node representations in graph datasets. However, serving GNNs on large graphs is challenging due to the high communication, computation, and memory overheads of constructing and executing computation graphs, which represent information flow across large neighborhoods. Existing approximation techniques in training can mitigate the overheads but, in serving, still lead to high latency and/or accuracy loss. To this end, we propose OMEGA, a system that enables low-latency GNN serving for large graphs with minimal accuracy loss through two key ideas. First, OMEGA employs selective recomputation of precomputed embeddings, which allows for reusing precomputed computation subgraphs while selectively recomputing a small fraction to minimize accuracy loss. Second, we develop computation graph parallelism, which reduces communication overhead by parallelizing the creation and execution of computation graphs across machines. Our evaluation with large graph datasets and GNN models shows that OMEGA significantly outperforms state-of-the-art techniques.

Photonic temporal crystals host a variety of intriguing phenomena, from wave amplification and mixing to exotic band structures, all stemming from the time-periodic modulation of optical properties. While these features have been well described classically, their quantum manifestation has remained elusive. Here, we introduce a quantum electrodynamical model of PTCs that reveals a deeper connection between classical and quantum pictures: the classical momentum gap arises from a localization-delocalization quantum phase transition in a Floquet-photonic synthetic lattice. Leveraging an effective Hamiltonian perspective, we pinpoint the critical momenta and highlight how classical exponential field growth manifests itself as wave-packet acceleration in the quantum synthetic space. Remarkably, when a two-level atom is embedded in such a cavity, its Rabi oscillations undergo irreversible decay to a half-and-half mixed state-a previously unobserved phenomenon driven by photonic delocalization within the momentum gap, even with just a single frequency mode. Our findings establish photonic temporal crystals as versatile platforms for studying nonequilibrium quantum photonics and suggest new avenues for controlling light matter interactions through time domain engineering., Comment: 22 pages, 14 figures

We explore, through a combination of x-ray diffraction and elastoresistivity measurements, the effect of disorder on the strain-tuned charge density wave and associated multicriticality in Pd$_x$ErTe$_3$ (x = 0, 0.01, 0.02 and 0.026). We focus particularly on the behavior near the strain-tuned bicritical point that occurs in pristine ErTe$_3$ (x=0). Our study reveals that while Pd intercalation somewhat broadens the signatures of the CDW phase transitions, the line of first-order transitions at which the CDW reorients as a function of applied strain persists in the presence of disorder and still seemingly terminates at a critical point. The critical point occurs at a lower temperature and a lower strain compared to pristine ErTe$_3$. Similarly, the nematic elastoresistance of Pd$_x$ErTe$_3$, though suppressed in magnitude and broadened relative to that of ErTe$_3$, has a markedly more symmetric response around the critical point. These observations point to disorder driving a reduction in the system's electronic orthorhombicity even while the material remains irrevocably orthorhombic due to the presence of a glide plane in the crystal structure. Disorder, it would appear, reinforces the emergence of a "pseudo-tetragonal" electronic response in this fundamentally orthorhombic material.

Recent lightweight image captioning models using retrieved data mainly focus on text prompts. However, previous works only utilize the retrieved text as text prompts, and the visual information relies only on the CLIP visual embedding. Because of this issue, there is a limitation that the image descriptions inherent in the prompt are not sufficiently reflected in the visual embedding space. To tackle this issue, we propose ViPCap, a novel retrieval text-based visual prompt for lightweight image captioning. ViPCap leverages the retrieved text with image information as visual prompts to enhance the ability of the model to capture relevant visual information. By mapping text prompts into the CLIP space and generating multiple randomized Gaussian distributions, our method leverages sampling to explore randomly augmented distributions and effectively retrieves the semantic features that contain image information. These retrieved features are integrated into the image and designated as the visual prompt, leading to performance improvements on the datasets such as COCO, Flickr30k, and NoCaps. Experimental results demonstrate that ViPCap significantly outperforms prior lightweight captioning models in efficiency and effectiveness, demonstrating the potential for a plug-and-play solution. The source code is available at https://github.com/taewhankim/VIPCAP., Comment: Accepted to AAAI 2025

Extraction of hot carriers (HCs) over the band-edge is a key to harvest solar energy beyond Shockley-Queisser limit1. Graphene is known as a HC-layered material due to phonon bottleneck effect near Dirac point, but limited by low photocarrier density2. Graphene/transition metal dichalcogenide (TMD) heterostructures circumvent this issue by ultrafast carrier transfer from TMD to graphene2,3. Nevertheless, efficient extraction of photocurrent by means of HCs together with carrier multiplication (CM) is still missing. Here, we introduce an ultrathin broadband resonant tunneling (BRT) barrier, TiOX to efficiently extract photocurrent with simultaneous CM and HC measurements in MoS2/graphene/TiOX heterostructure. The BRT layer gives rise to boosting open circuit voltage which is linearly proportional to incident photon energy. Meanwhile, short circuit current rises rapidly over 2Eg with obvious CM feature. This was explained by defining the joint density of states between graphene and TiOX layer over positive and negative voltage. The broadband resonant tunneling states inherently constructed from oxidation states varying from Ti3+ to Ti4+ allow the ultrafast HCs to efficiently transfer from graphene to TiOX layer. We find that the number of available tunneling states is directly proportional to short circuit current, which is well corroborated with TiOX and MoS2 thickness variance. We obtained an optimum thickness of BRT layer of ~2.8 nm, yielding cascade open circuit voltage as high as ~0.7 V, two orders of magnitude higher than that without BRT layer to reach a record efficiency of 5.3% with improved fill factor owing to synergistic HC and CM conversion under 1-SUN with long-term stability.

Verifying the violation of Bell's inequality is one of the most representative methods to demonstrate that entangled photon pairs prepared in a quantum optics-based system exhibit quantum properties. While experiments on Bell inequality violations have been theoretically well-established and extensively conducted to implement various quantum information technologies in laboratory settings, mathematical modeling for accurately predicting the distribution of high-intensity entangled photon pairs in high-loss environments remains an issue that requires further research. As the brightness of the entangled photon pairs increases, the influence of multi-photon effects becomes more significant, leading to a decrease in the CHSH value $S$ and also a reduction in the standard deviation of the CHSH value $\Delta S$. Therefore, a new analysis of the $(S-2)/\Delta S$ value is required to more precisely confirm the degree of CHSH inequality violation including the reliability of $S$. In this paper, we propose a mathematical model to predict the $(S-2)/\Delta S$ value as a function of the brightness of the entangled photon pair source, and we also suggest the need to optimize the brightness of this source. Additionally, we provide experimental evidence supporting this model. The experiment confirms that when the mean photon number is $\mu=0.026$ in an entanglement distribution setup with a total loss of $-19.03$ dB, the CHSH value drops to 2.69, while the $(S-2)/\Delta S$ value increases to 60.95., Comment: 11 pages, 1 table, and 4 figures

In vision-based robot localization and SLAM, Visual Place Recognition (VPR) is essential. This paper addresses the problem of VPR, which involves accurately recognizing the location corresponding to a given query image. A popular approach to vision-based place recognition relies on low-level visual features. Despite significant progress in recent years, place recognition based on low-level visual features is challenging when there are changes in scene appearance. To address this, end-to-end training approaches have been proposed to overcome the limitations of hand-crafted features. However, these approaches still fail under drastic changes and require large amounts of labeled data to train models, presenting a significant limitation. Methods that leverage high-level semantic information, such as objects or categories, have been proposed to handle variations in appearance. In this paper, we introduce a novel VPR approach that remains robust to scene changes and does not require additional training. Our method constructs semantic image descriptors by extracting pixel-level embeddings using a zero-shot, language-driven semantic segmentation model. We validate our approach in challenging place recognition scenarios using real-world public dataset. The experiments demonstrate that our method outperforms non-learned image representation techniques and off-the-shelf convolutional neural network (CNN) descriptors. Our code is available at https: //github.com/woo-soojin/context-based-vlpr.

The proliferation of large language models (LLMs) has led to the adoption of Mixture-of-Experts (MoE) architectures that dynamically leverage specialized subnetworks for improved efficiency and performance. Despite their benefits, MoE models face significant challenges during inference, including inefficient memory management and suboptimal batching, due to misaligned design choices between the model architecture and the system policies. Furthermore, the conventional approach of training MoEs from scratch is increasingly prohibitive in terms of cost. In this paper, we propose a novel framework Read-ME that transforms pre-trained dense LLMs into smaller MoE models (in contrast to "upcycling" generalist MoEs), avoiding the high costs of ground-up training. Our approach employs activation sparsity to extract experts. To compose experts, we examine the widely-adopted layer-wise router design and show its redundancy, and thus we introduce the pre-gating router decoupled from the MoE backbone that facilitates system-friendly pre-computing and lookahead scheduling, enhancing expert-aware batching and caching. Our codesign therefore addresses critical gaps on both the algorithmic and system fronts, establishing a scalable and efficient alternative for LLM inference in resource-constrained settings. Read-ME outperforms other popular open-source dense models of similar scales, achieving improvements of up to 10.1% on MMLU, and improving mean end-to-end latency up to 6.1%. Codes are available at: https://github.com/VITA-Group/READ-ME., Comment: 38th Conference on Neural Information Processing Systems (NeurIPS 2024)

The most massive early-type galaxies (ETGs) are known to form through numerous galaxy mergers. Thus, it is intriguing to study whether their formation in low-density environments, where nearby companions are almost absent, is associated with mergers, which are directly traced by tidal features. Using the 436 most massive ETGs with $M_\mathrm{star}>10^{11.2}\,M_{\odot}$ at $z<0.04$, we determine the variation in the fraction of massive ETGs with tidal features ($f_T$) across different environments and verify whether the most massive ETGs commonly have tidal features in very low density environments. Our main discovery is that the most massive ETGs exhibit tidal features more frequently in lower-density environments. In the highest-density environments, like galaxy clusters, $f_T$ is $0.21\pm0.06$, while in the lowest-density environments it triples to $0.62\pm0.06$. This trend is stronger for more extremely massive ETGs, with $f_T$ reaching $0.92\pm0.08$ in the lowest-density environments. One explanation for our finding is that the most massive ETGs in lower-density environments have genuinely experienced recent mergers more frequently than their counterparts in higher-density environments, suggesting that they possess extended formation histories that continue into the present. Another possibility is that tidal features last shorter in denser environments owing to external factors inherent in these environments. Our additional findings that massive ETGs with bluer $u-r$ colors are a more dominant driver of our main discovery and that dust lanes are more commonly observed in massive ETGs in low-density environments imply that gas-abundant mergers primarily contribute to the increased rate of recent mergers in low-density environments., Comment: 16 pages, 10 figures, published on October 18 in ApJ

Solar vehicles, which simultaneously produce and consume energy, require meticulous energy management. However, potential users often feel uncertain about their operation compared to conventional vehicles. This study presents a simulator designed to help users understand long-distance travel in solar vehicles and recognize the importance of proper energy management. By utilizing Google Maps data and weather information, the simulator replicates real-world driving conditions and provides a dashboard displaying vehicle status, updated hourly based on user-inputted speed. Users can explore various speed policy scenarios and receive recommendations for optimal driving strategies. The simulator's effectiveness was validated using the route of the World Solar Challenge (WSC). This research enables users to monitor energy dynamics before a journey, enhancing their understanding of energy management and informing appropriate speed decisions.

Large language models (LLMs) offer personalized responses based on user interactions, but this use case raises serious privacy concerns. Homomorphic encryption (HE) is a cryptographic protocol supporting arithmetic computations in encrypted states and provides a potential solution for privacy-preserving machine learning (PPML). However, the computational intensity of transformers poses challenges for applying HE to LLMs. In this work, we propose a modified HE-friendly transformer architecture with an emphasis on inference following personalized (private) fine-tuning. Utilizing LoRA fine-tuning and Gaussian kernels, we achieve significant computational speedups -- 6.94x for fine-tuning and 2.3x for inference -- while maintaining performance comparable to plaintext models. Our findings provide a viable proof of concept for offering privacy-preserving LLM services in areas where data protection is crucial., Comment: 27 pages

Recent advancements in image captioning have explored text-only training methods to overcome the limitations of paired image-text data. However, existing text-only training methods often overlook the modality gap between using text data during training and employing images during inference. To address this issue, we propose a novel approach called Image-like Retrieval, which aligns text features with visually relevant features to mitigate the modality gap. Our method further enhances the accuracy of generated captions by designing a Fusion Module that integrates retrieved captions with input features. Additionally, we introduce a Frequency-based Entity Filtering technique that significantly improves caption quality. We integrate these methods into a unified framework, which we refer to as IFCap ($\textbf{I}$mage-like Retrieval and $\textbf{F}$requency-based Entity Filtering for Zero-shot $\textbf{Cap}$tioning). Through extensive experimentation, our straightforward yet powerful approach has demonstrated its efficacy, outperforming the state-of-the-art methods by a significant margin in both image captioning and video captioning compared to zero-shot captioning based on text-only training., Comment: Accepted to EMNLP 2024

Regularization of deep neural networks has been an important issue to achieve higher generalization performance without overfitting problems. Although the popular method of Dropout provides a regularization effect, it causes inconsistent properties in the output, which may degrade the performance of deep neural networks. In this study, we propose a new module called stochastic average pooling, which incorporates Dropout-like stochasticity in pooling. We describe the properties of stochastic subsampling and average pooling and leverage them to design a module without any inconsistency problem. The stochastic average pooling achieves a regularization effect without any potential performance degradation due to the inconsistency issue and can easily be plugged into existing architectures of deep neural networks. Experiments demonstrate that replacing existing average pooling with stochastic average pooling yields consistent improvements across a variety of tasks, datasets, and models., Comment: 17 pages, 8 figures

In order to enable autonomous navigation in underwater environments, a map needs to be created in advance using a Simultaneous Localization and Mapping (SLAM) algorithm that utilizes sensors like a sonar. At this time, loop closure is employed to reduce the pose error accumulated during the SLAM process. In the case of loop detection using a sonar, some previous studies have used a method of projecting the 3D point cloud into 2D, then extracting keypoints and matching them. However, during the 2D projection process, data loss occurs due to image resolution, and in monotonous underwater environments such as rivers or lakes, it is difficult to extract keypoints. Additionally, methods that use neural networks or are based on Bag of Words (BoW) have the disadvantage of requiring additional preprocessing tasks, such as training the model in advance or pre-creating a vocabulary. To address these issues, in this paper, we utilize the point cloud obtained from sonar data without any projection to prevent performance degradation due to data loss. Additionally, by calculating the point-wise structural feature map of the point cloud using mathematical formulas and comparing the similarity between point clouds, we eliminate the need for keypoint extraction and ensure that the algorithm can operate in new environments without additional learning or tasks. To evaluate the method, we validated the performance of the proposed algorithm using the Antarctica dataset obtained from deep underwater and the Seaward dataset collected from rivers and lakes. Experimental results show that our proposed method achieves the best loop detection performance in both datasets. Our code is available at https://github.com/donghwijung/point_cloud_structural_similarity_based_underwater_sonar_loop_detection.

Large language models (LLMs) have shown significant potential in guiding embodied agents to execute language instructions across a range of tasks, including robotic manipulation and navigation. However, existing methods are primarily designed for static environments and do not leverage the agent's own experiences to refine its initial plans. Given that real-world environments are inherently stochastic, initial plans based solely on LLMs' general knowledge may fail to achieve their objectives, unlike in static scenarios. To address this limitation, this study introduces the Experience-and-Emotion Map (E2Map), which integrates not only LLM knowledge but also the agent's real-world experiences, drawing inspiration from human emotional responses. The proposed methodology enables one-shot behavior adjustments by updating the E2Map based on the agent's experiences. Our evaluation in stochastic navigation environments, including both simulations and real-world scenarios, demonstrates that the proposed method significantly enhances performance in stochastic environments compared to existing LLM-based approaches. Code and supplementary materials are available at https://e2map.github.io/., Comment: 19 pages, 28 figures. Project page: https://e2map.github.io. Accepted to ICRA 2025

In recent years, quadruped robots have attracted significant attention due to their practical advantages in maneuverability, particularly when navigating rough terrain and climbing stairs. As these robots become more integrated into various industries, including construction and healthcare, researchers have increasingly focused on developing intuitive interaction methods such as speech and gestures that do not require separate devices such as keyboards or joysticks. This paper aims at investigating a comfortable and efficient interaction method with quadruped robots that possess a familiar form factor. To this end, we conducted two preliminary studies to observe how individuals naturally interact with a quadruped robot in natural and controlled settings, followed by a prototype experiment to examine human preferences for body-based and hand-based gesture controls using a Unitree Go1 Pro quadruped robot. We assessed the user experience of 13 participants using the User Experience Questionnaire and measured the time taken to complete specific tasks. The findings of our preliminary results indicate that humans have a natural preference for communicating with robots through hand and body gestures rather than speech. In addition, participants reported higher satisfaction and completed tasks more quickly when using body gestures to interact with the robot. This contradicts the fact that most gesture-based control technologies for quadruped robots are hand-based. The video is available at https://youtu.be/rysv1p1zvp4., Comment: 16 pages

A high-brightness entangled photon pair (HBEPP) source is essential for conducting entanglement-based quantum key distribution (QKD) between a satellite and a ground station. While an ultrabright source can overcome significant losses in satellite-based QKD (SQKD) and increase the sifted key rate, it also induces the multi-photon effect, raising the system's error rate. To accurately estimate system performance, we first present an analytical model for calculating the measurement probabilities of HBEPP distribution through an asymmetric loss channel. Based on this model, we propose the use of a passive-intensity HBEPP source for SQKD systems, assuming a polarization-independent channel and threshold detectors for measurement. We confirm that fixing the mean photon number at $\bar{\mu}=0.1$ achieves a performance of $99.7\%$ compared to the ideal one-way communication entanglement-based SQKD protocol, which is effectively optimizing the HBEPP source brightness in accordance with system losses., Comment: 9 pages, 5 figures, 3 tables

Vanadium-based kagome systems AV$_3$Sb$_5$ (A = K, Rb, Cs) have emerged as paradigmatic examples exhibiting unconventional charge density waves (CDWs) and superconductivity linked to van Hove singularities (VHSs). Despite extensive studies, the three-dimensional (3D) nature of CDW states in these systems remains elusive. This study employs first-principles density functional theory and a tight-binding model to investigate the stacking-dependent electronic structures of 3D CDWs in AV$_3$Sb$_5$, emphasizing the significant role of interlayer coupling in behaviors of the VHSs associated with diverse 3D CDW orders. We develop a minimal 3D tight-binding model and present a detailed analysis of band structures and density of states for various 3D CDW stacking configurations, including those with and without a $\pi$-phase shift stacking of the inverse star of David, as well as alternating stacking of the inverse star of David and the star of David. We find that VHSs exist below the Fermi level even in 3D CDWs without $\pi$-phase shift stackings, and that these VHSs shift downward in the $\pi$-phase shift stacking CDW structure, stabilizing the $2\times2\times2$ $\pi$-shifted inverse star of David distortions in alternating vanadium layers as the ground state 3D CDW order of AV$_3$Sb$_5$. Our work provides the electronic origin of 3D CDW orders, paving the way for a deeper understanding of CDWs and superconductivity in AV$_3$Sb$_5$ kagome metals., Comment: 4 figures + Supplementary information

The electroweak monopole, when coupled to gravity, turns to the Reissner-Nordstrom type primordial magnetic blackhole whose mass is bounded below, with the lower bound $M_P \sqrt \alpha$. This changes the overall picture of the monopole production mechanism in the early universe drastically and has deep implications in cosmolpgy. In particular, this enhances the possibility that the electroweak monopoles turned to the primordial magnetic blackholes could become the seed of stellar objects and galaxies, and account for the dark matter of the universe. Moreover, this tells that we have a new type of primordial blackhole different from the popular primordial blackhole in cosmology, the electroweak primordial magnetic blackhole based on a totally different production mechanism. We discuss the physical implications of the electroweak primordial magnetic blackhole.

Copper-doped lead apatite, called LK-99, was initially claimed to be a room temperature superconductor driven by flat electron bands, but was later found to be a wide gap insulator. Despite the lack of room temperature superconductivity, there is growing evidence that LK-99 and related compounds host various strong electron correlation phenomena arising from their flat electron bands. Depending on the copper doping site and crystal structure, LK-99 can exhibit two distinct flat bands crossing the Fermi level in the non-interacting limit: either a single or two entangled flat bands. We explore potential correlated metallic and insulating phases in the flat bands of LK-99 compounds by constructing their correlation phase diagrams, and find both non-Fermi liquid and Mott insulating states. We demonstrate that LK-99 is a charge-transfer Mott insulator driven by strong electron correlations, regardless of the flat band type. We also find that the non-Fermi liquid state in the multi-flat band system exhibits strange metal behaviour, while the corresponding state in the single flat band system exhibits pseudogap behaviour. Our findings align with available experimental observations and provide crucial insights into the correlation phenomenology of LK-99 and related compounds that could arise independently of superconductivity. Overall, our research highlights that LK-99 and related compounds offer a compelling platform for investigating correlation physics in flat band systems., Comment: 5 figures + Supplementary information

The geometric characteristics of Bloch wave functions play a crucial role in electronic transport properties. We show that the thermoelectric performance of materials is governed by the geometric structure of Bloch wave functions within the framework of the Boltzmann equation. The essential geometric notion is the Hilbert-Schmidt quantum distance, measuring the resemblance between two quantum states. We establish a geometric characterization of the scattering rate by extending the concept of quantum distance between two states in momentum space at a distance.Employing isotropic quadratic band touching semimetals, where one can concentrate on the role of quantum geometric effects other than the Berry curvature, we find that the response functions for electrical quantum transport and, therefore, the thermoelectric power factor can be succinctly expressed in terms of the maximum quantum distance, $d_\mathrm{max}$. Specifically, when $d_\mathrm{max}$ reaches one, the power factor doubles compared to the case with trivial geometry ($d_\mathrm{max}=0$). Our finding highlights the significance of quantum geometry in improving the performance of thermoelectric devices.

Quantum conductors attached to metallic reservoirs have been demonstrated to overcome the thermodynamic uncertainty relation (TUR), a trade-off relation between the amount of dissipation and the absence of charge and heat current fluctuations. Here, we report large TUR violations when superconducting reservoirs replace metallic ones. The coexistence of different transport processes, namely (multiple) Andreev reflection, where electrons and their retro-reflected holes create Cooper pairs, in addition to the normal quasiparticle transport is identified as the source for such TUR breakdowns. The large TUR violation is a remarkable advantage for building low dissipative and highly stable quantum thermal machines., Comment: 6 pages, 2 figures

Competing interactions in frustrated magnets can give rise to highly degenerate ground states from which correlated liquid-like states of matter often emerge. The scaling of this degeneracy influences the ultimate ground state, with extensive degeneracies potentially yielding quantum spin liquids, while sub-extensive or smaller degeneracies yield static orders. A longstanding problem is to understand how ordered states precipitate from this degenerate manifold and what echoes of the degeneracy survive ordering. Here, we use neutron scattering to experimentally demonstrate a new "nodal line" spin liquid, where spins collectively fluctuate within a sub-extensive manifold spanning one-dimensional lines in reciprocal space. Realized in the spin-orbit coupled, face-centered cubic iridate K$_2$IrCl$_6$, we show that the sub-extensive degeneracy is robust, but remains susceptible to fluctuations or longer range interactions which cooperate to select a magnetic order at low temperatures. Proximity to the nodal line spin liquid influences the ordered state, enhancing the effects of quantum fluctuations and stabilizing it through the opening of a large spin-wave gap. Our results demonstrate quantum fluctuations can act counter-intuitively in frustrated materials: instead of destabilizing ordering, at the brink of the nodal spin liquid they can act to stabilize it and dictate its low-energy physics., Comment: Main article: 9 pages, 3 figures. Supplemental material: 22 pages, 15 figures

The investigation of emerging non-toxic perovskite materials has been undertaken to advance the fabrication of environmentally sustainable lead-free perovskite solar cells. This study introduces a machine learning methodology aimed at predicting innovative halide perovskite materials that hold promise for use in photovoltaic applications. The seven newly predicted materials are as follows: CsMnCl$_4$, Rb$_3$Mn$_2$Cl$_9$, Rb$_4$MnCl$_6$, Rb$_3$MnCl$_5$, RbMn$_2$Cl$_7$, RbMn$_4$Cl$_9$, and CsIn$_2$Cl$_7$. The predicted compounds are first screened using a machine learning approach, and their validity is subsequently verified through density functional theory calculations. CsMnCl$_4$ is notable among them, displaying a bandgap of 1.37 eV, falling within the Shockley-Queisser limit, making it suitable for photovoltaic applications. Through the integration of machine learning and density functional theory, this study presents a methodology that is more effective and thorough for the discovery and design of materials.

In the pursuit of understanding the population of stellar remnants within the Milky Way, we analyze the sample of $\sim 950$ microlensing events observed by the Spitzer Space Telescope between 2014 and 2019. In this study we focus on a sub-sample of nine microlensing events, selected based on their long timescales, small microlensing parallaxes and joint observations by the Gaia mission, to increase the probability that the chosen lenses are massive and the mass is measurable. Among the selected events we identify lensing black holes and neutron star candidates, with potential confirmation through forthcoming release of the Gaia time-series astrometry in 2026. Utilizing Bayesian analysis and Galactic models, along with the Gaia Data Release 3 proper motion data, four good candidates for dark remnants were identified: OGLE-2016-BLG-0293, OGLE-2018-BLG-0483, OGLE-2018-BLG-0662, and OGLE-2015-BLG-0149, with lens masses of $2.98^{+1.75}_{-1.28}~M_{\odot}$, $4.65^{+3.12}_{-2.08}~M_{\odot}$, $3.15^{+0.66}_{-0.64}~M_{\odot}$ and $1.4^{+0.75}_{-0.55}~M_{\odot}$, respectively. Notably, the first two candidates are expected to exhibit astrometric microlensing signals detectable by Gaia, offering the prospect of validating the lens masses. The methodologies developed in this work will be applied to the full Spitzer microlensing sample, populating and analyzing the time-scale ($t_{\rm E}$) vs. parallax ($\pi_{\rm E}$) diagram to derive constraints on the population of lenses in general and massive remnants in particular., Comment: submitted to ApJ

In this paper, we propose an algorithm to generate a static point cloud map based on LiDAR point cloud data. Our proposed pipeline detects dynamic objects using 3D object detectors and projects points of dynamic objects onto the ground. Typically, point cloud data acquired in real-time serves as a snapshot of the surrounding areas containing both static objects and dynamic objects. The static objects include buildings and trees, otherwise, the dynamic objects contain objects such as parked cars that change their position over time. Removing dynamic objects from the point cloud map is crucial as they can degrade the quality and localization accuracy of the map. To address this issue, in this paper, we propose an algorithm that creates a map only consisting of static objects. We apply a 3D object detection algorithm to the point cloud data which are obtained from LiDAR to implement our pipeline. We then stack the points to create the map after performing ground segmentation and projection. As a result, not only we can eliminate currently dynamic objects at the time of map generation but also potentially dynamic objects such as parked vehicles. We validate the performance of our method using two kinds of datasets collected on real roads: KITTI and our dataset. The result demonstrates the capability of our proposal to create an accurate static map excluding dynamic objects from input point clouds. Also, we verified the improved performance of localization using a generated map based on our method.

In this paper, we propose a control algorithm based on reinforcement learning, employing independent rewards for each joint to control excavators in a 3D space. The aim of this research is to address the challenges associated with achieving precise control of excavators, which are extensively utilized in construction sites but prove challenging to control with precision due to their hydraulic structures. Traditional methods relied on operator expertise for precise excavator operation, occasionally resulting in safety accidents. Therefore, there have been endeavors to attain precise excavator control through equation-based control algorithms. However, these methods had the limitation of necessitating prior information related to physical values of the excavator, rendering them unsuitable for the diverse range of excavators used in the field. To overcome these limitations, we have explored reinforcement learning-based control methods that do not demand prior knowledge of specific equipment but instead utilize data to train models. Nevertheless, existing reinforcement learning-based methods overlooked cabin swing rotation and confined the bucket's workspace to a 2D plane. Control confined within such a limited area diminishes the applicability of the algorithm in construction sites. We address this issue by expanding the previous 2D plane workspace of the bucket operation into a 3D space, incorporating cabin swing rotation. By expanding the workspace into 3D, excavators can execute continuous operations without requiring human intervention. To accomplish this objective, distinct targets were established for each joint, facilitating the training of action values for each joint independently, regardless of the progress of other joint learning.

We explore the evolution of the ~107 degree hot gas in normal galaxies out to redshift = 0.5 (lookback time = 5 Gyr), using X-ray luminosity functions (XLF) built from a sample of 575 normal galaxies with z < 0.6 detected in five high galactic latitude Chandra wide-field surveys. After estimating the emission due to the hot gas component (reducing the sample to ~400 galaxies), we compared the XLF in three redshift bins (z = 0.1, 0.3, and 0.5), finding increases in the number of galaxies per unit co-moving volume from z = 0.1 to 0.3 and then from z = 0.3 to 0.5. These XLF changes suggest a significant (~5s) X-ray luminosity evolution of the hot gas, with LX,GAS decreasing by a factor of 6-10 in the last 5 Gyr (from z = 0.5 to 0.1). The relative abundance of LX,GAS~1041 erg s-1 galaxies at higher z, suggests that high z, moderate LX,GAS galaxies may be the optimal target to solve the missing baryon problem. In early-type galaxies, this observational trend is qualitatively consistent with (but larger than) the expected time-dependent mass-loss rate in cooling flow models without AGN feedback. In late-type galaxies, the observational trend is also qualitatively consistent with (but larger than) the effect of the z-dependent SFR., Comment: 29 pages, 14 figures, new Appendix added, accepted for publication in ApJ

We introduce a pipeline that leverages Large Language Models (LLMs) to transform single-turn psychotherapy counseling sessions into multi-turn interactions. While AI-supported online counseling services for individuals with mental disorders exist, they are often constrained by the limited availability of multi-turn training datasets and frequently fail to fully utilize therapists' expertise. Our proposed pipeline effectively addresses these limitations. The pipeline comprises two main steps: 1) Information Extraction and 2) Multi-turn Counseling Generation. Each step is meticulously designed to extract and generate comprehensive multi-turn counseling conversations from the available datasets. Experimental results from both zero-shot and few-shot generation scenarios demonstrate that our approach significantly enhances the ability of LLMs to produce higher quality multi-turn dialogues in the context of mental health counseling. Our pipeline and dataset are publicly available https://github.com/jwkim-chat/A-Data-Augmentation-Pipeline-Leveraging-Large-Language-Models-for-Counseling-Conversations., Comment: IJCAI 2024 AI4Research workshop

Respiratory sound classification (RSC) is challenging due to varied acoustic signatures, primarily influenced by patient demographics and recording environments. To address this issue, we introduce a text-audio multimodal model that utilizes metadata of respiratory sounds, which provides useful complementary information for RSC. Specifically, we fine-tune a pretrained text-audio multimodal model using free-text descriptions derived from the sound samples' metadata which includes the gender and age of patients, type of recording devices, and recording location on the patient's body. Our method achieves state-of-the-art performance on the ICBHI dataset, surpassing the previous best result by a notable margin of 1.17%. This result validates the effectiveness of leveraging metadata and respiratory sound samples in enhancing RSC performance. Additionally, we investigate the model performance in the case where metadata is partially unavailable, which may occur in real-world clinical setting., Comment: Accepted INTERSPEECH 2024

Accurate prediction of the likelihood of recurrence is important in the selection of postoperative treatment for patients with early-stage breast cancer. In this study, we investigated whether deep learning algorithms can predict patients' risk of recurrence by analyzing the pathology images of their cancer histology. A total of 125 hematoxylin and eosin stained breast cancer whole slide images labeled with the risk prediction via genomics assays were used, and we obtained sensitivity of 0.857, 0.746, and 0.529 for predicting low, intermediate, and high risk, and specificity of 0.816, 0.803, and 0.972. When compared to the expert pathologist's regional histology grade information, a Pearson's correlation coefficient of 0.61 was obtained. When we checked the model learned through these studies through the class activation map, we found that it actually considered tubule formation and mitotic rate when predicting different risk groups., Comment: 12 pages, 7 figures