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Scene coordinate regression (SCR) methods have emerged as a promising area of research due to their potential for accurate visual localization. However, many existing SCR approaches train on samples from all image regions, including dynamic objects and texture-less areas. Utilizing these areas for optimization during training can potentially hamper the overall performance and efficiency of the model. In this study, we first perform an in-depth analysis to validate the adverse impacts of these areas. Drawing inspiration from our analysis, we then introduce an error-guided feature selection (EGFS) mechanism, in tandem with the use of the Segment Anything Model (SAM). This mechanism seeds low reprojection areas as prompts and expands them into error-guided masks, and then utilizes these masks to sample points and filter out problematic areas in an iterative manner. The experiments demonstrate that our method outperforms existing SCR approaches that do not rely on 3D information on the Cambridge Landmarks and Indoor6 datasets., Comment: ECCV2024

Neural networks, despite their remarkable performance in widespread applications, including image classification, are also known to be vulnerable to subtle adversarial noise. Although some diffusion-based purification methods have been proposed, for example, DiffPure, those methods are time-consuming. In this paper, we propose One Step Control Purification (OSCP), a diffusion-based purification model that can purify the adversarial image in one Neural Function Evaluation (NFE) in diffusion models. We use Latent Consistency Model (LCM) and ControlNet for our one-step purification. OSCP is computationally friendly and time efficient compared to other diffusion-based purification methods; we achieve defense success rate of 74.19\% on ImageNet, only requiring 0.1s for each purification. Moreover, there is a fundamental incongruence between consistency distillation and adversarial perturbation. To address this ontological dissonance, we propose Gaussian Adversarial Noise Distillation (GAND), a novel consistency distillation framework that facilitates a more nuanced reconciliation of the latent space dynamics, effectively bridging the natural and adversarial manifolds. Our experiments show that the GAND does not need a Full Fine Tune (FFT); PEFT, e.g., LoRA is sufficient.

Kerr-Newman black holes in a de Sitter space have the limit of rotating Nariai black holes with the near-horizon geometry of a warped ${\rm dS}_3 \times {\rm S}^1/Z_2$ when the black hole horizon and the cosmological horizon coincide or approach close to each other. We study the effect of rotation on the emission of charges in the near-extremal rotating charged Nariai black hole and compare it to those from the near-extremal Nariai black hole and near-extremal Kerr-Newman black hole in de Sitter space. The emission has an exponential amplification for charges with high energy and becomes catastrophic when the two horizons are very close to each together. The angular momentum of black holes decreases the mean number of charges by a factor not by an order. We observe a catastrophic emission of boson condensation for charges with the effective energy equal to the chemical potential in the spacelike outer region of the cosmological horizon. Further, the rotating charged Nariai black holes can evolve into singular spacetimes with a naked singularity by the Schwinger pair production., Comment: 11 pages, 6 figures. arXiv admin note: text overlap with arXiv:2309.00218

Deep learning technology has brought convenience and advanced developments but has become untrustworthy because of its sensitivity to inconspicuous perturbations (i.e., adversarial attacks). Attackers utilize this sensitivity to slightly manipulate transmitted messages. To defend against such attacks, we have devised a strategy for "attacking" the message before it is attacked. This strategy, dubbed Fast Preemption, provides an efficient transferable preemptive defense by using different models for labeling inputs and learning crucial features. A forward-backward cascade learning algorithm is used to compute protective perturbations, starting with forward propagation optimization to achieve rapid convergence, followed by iterative backward propagation learning to alleviate overfitting. This strategy offers state-of-the-art transferability and protection across various systems. With the running of only three steps, our Fast Preemption framework outperforms benchmark training-time, test-time, and preemptive adversarial defenses. We have also devised the first to our knowledge effective white-box adaptive reversion attack and demonstrate that the protection added by our defense strategy is irreversible unless the backbone model, algorithm, and settings are fully compromised. This work provides a new direction to developing active defenses against adversarial attacks., Comment: Under Review

Deep learning techniques have shown promising results in the automatic classification of respiratory sounds. However, accurately distinguishing these sounds in real-world noisy conditions poses challenges for clinical deployment. Additionally, predicting signals with only background noise could undermine user trust in the system. In this study, we propose an audio enhancement (AE) pipeline as a pre-processing step before respiratory sound classification, aiming to improve performance in noisy environments. Multiple experiments were conducted using different audio enhancement model structures, demonstrating improved classification performance compared to the baseline method of noise injection data augmentation. Specifically, the integration of the AE pipeline resulted in a 2.59% increase in the ICBHI classification score on the ICBHI respiratory sound dataset and a 2.51% improvement on our recently collected Formosa Archive of Breath Sounds (FABS) in multi-class noisy scenarios. Furthermore, a physician validation study assessed the clinical utility of our system. Quantitative analysis revealed enhancements in efficiency, diagnostic confidence, and trust during model-assisted diagnosis with our system compared to raw noisy recordings. Workflows integrating enhanced audio led to an 11.61% increase in diagnostic sensitivity and facilitated high-confidence diagnoses. Our findings demonstrate that incorporating an audio enhancement algorithm significantly enhances robustness and clinical utility., Comment: The following article has been submitted to The Journal of the Acoustical Society of America (JASA). After it is published, it will be found at https://pubs.aip.org/asa/jasa

Interrogation of biological pathways is an integral part of omics data analysis. Large language models (LLMs) enable the generation of custom pathways and gene sets tailored to specific scientific questions. These targeted sets are significantly smaller than traditional pathway enrichment analysis libraries, reducing multiple hypothesis testing and potentially enhancing statistical power. Lomics (Large Language Models for Omics Studies) v1.0 is a python-based bioinformatics toolkit that streamlines the generation of pathways and gene sets for transcriptomic analysis. It operates in three steps: 1) deriving relevant pathways based on the researcher's scientific question, 2) generating valid gene sets for each pathway, and 3) outputting the results as .GMX files. Lomics also provides explanations for pathway selections. Consistency and accuracy are ensured through iterative processes, JSON format validation, and HUGO Gene Nomenclature Committee (HGNC) gene symbol verification. Lomics serves as a foundation for integrating LLMs into omics research, potentially improving the specificity and efficiency of pathway analysis.

Emotional Voice Conversion (EVC) modifies speech emotion to enhance communication by amplifying positive cues and reducing negative ones. This complex task involves entangled factors like voice quality, speaker traits, and content. Traditional deep learning models like GANs and autoencoders have achieved some success in EVC by learning mappings or disentangling features but face challenges like instability and voice quality degradation. Diffusion models offer stable training and high-quality generation. We propose a diffusion-based EVC framework that disentangles emotion and speaker identity using mutual information loss and auxiliary models. An expressive guidance mechanism is introduced to improve emotion conversion while maintaining speaker traits. Experimental results demonstrate our approach's effectiveness for unseen speakers and emotions, achieving state-of-the-art performance in EVC tasks.

One develops a fast computational methodology for principal component analysis on manifolds. Instead of estimating intrinsic principal components on an object space with a Riemannian structure, one embeds the object space in a numerical space, and the resulting chord distance is used. This method helps us analyzing high, theoretically even infinite dimensional data, from a new perspective. We define the extrinsic principal sub-manifolds of a random object on a Hilbert manifold embedded in a Hilbert space, and the sample counterparts. The resulting extrinsic principal components are useful for dimension data reduction. For application, one retains a very small number of such extrinsic principal components for a shape of contour data sample, extracted from imaging data.

In this work, we report a pronounced light upconversion in few-layer transition metal dichalcogenides. Our joint theory-experiment study attributes the upconversion photoluminescence to a resonant exciton-exciton annihilation involving a pair of dark excitons with opposite momenta, followed by the spontaneous emission of upconverted bright excitons, which can have a high upconversion efficiency. Additionally, the upconversion photoluminescence is generic in MoS2, MoSe2, WS2, and WSe2, showing a high tuneability from green to ultraviolet light.

Automatic chart understanding is crucial for content comprehension and document parsing. Multimodal large language models (MLLMs) have demonstrated remarkable capabilities in chart understanding through domain-specific alignment and fine-tuning. However, the application of alignment training within the chart domain is still underexplored. To address this, we propose ChartMoE, which employs the mixture of expert (MoE) architecture to replace the traditional linear projector to bridge the modality gap. Specifically, we train multiple linear connectors through distinct alignment tasks, which are utilized as the foundational initialization parameters for different experts. Additionally, we introduce ChartMoE-Align, a dataset with over 900K chart-table-JSON-code quadruples to conduct three alignment tasks (chart-table/JSON/code). Combined with the vanilla connector, we initialize different experts in four distinct ways and adopt high-quality knowledge learning to further refine the MoE connector and LLM parameters. Extensive experiments demonstrate the effectiveness of the MoE connector and our initialization strategy, e.g., ChartMoE improves the accuracy of the previous state-of-the-art from 80.48% to 84.64% on the ChartQA benchmark.

Hydrogen embrittlement can result in a sudden failure in metallic materials, which is particularly harmful in industrially relevant alloys, such as steels. A more comprehensive understanding of hydrogen interactions with microstructural features is critical for preventing hydrogen-induced damage and promoting a hydrogen-based environment-benign economy. We use the Kelvin probe-based potentiometric hydrogen electrode method and thermal desorption spectroscopy to investigate hydrogen interactions with different hydrogen traps in ferritic FeCr alloys with different chromium contents, dislocation densities, and grain sizes. In addition, we confirm the validity of a novel nanohardness-based diffusion coefficient approach by performing in situ nanoindentation testing. Simultaneous acquisition of the dynamic time-resolved mechanical response of FeCr alloys to hydrogen and the hydrogen diffusivities in these alloys is possible during continuous hydrogen supply. Dislocations, grain boundaries and Cr atoms induce reversible hydrogen trapping sites in these ferritic alloys, leading to the reduction of the hydrogen diffusion coefficients and the increase of the absorbed hydrogen., Comment: 39 pages, 11 figures

This tutorial provides an introduction to CPAchecker for users. CPAchecker is a flexible and configurable framework for software verification and testing. The framework provides many abstract domains, such as BDDs, explicit values, intervals, memory graphs, and predicates, and many program-analysis and model-checking algorithms, such as abstract interpretation, bounded model checking, Impact, interpolation-based model checking, k -induction, PDR, predicate abstraction, and symbolic execution. This tutorial presents basic use cases for CPAchecker in formal software verification, focusing on its main verification techniques with their strengths and weaknesses. It also shows further use cases of CPAchecker for test-case generation and witness-based result validation. The envisioned readers are assumed to possess a background in automatic formal verification and program analysis, but prior knowledge of CPAchecker is not required. This tutorial and user guide is based on CPAchecker in version 3.0. This user guide's latest version and other documentation are available at https://cpachecker.sosy-lab.org/doc.php., Comment: 39 pages, 17 figures, 6 tables

During Large Hadron Collider (LHC) Long Shutdown 3 (LS3) (2026-28), the ALICE experiment is replacing its inner-most three tracking layers by a new detector, Inner Tracking System 3. It will be based on newly developed wafer-scale monolithic active pixel sensors, which are bent into truly cylindrical layers and held in place by light mechanics made from carbon foam. Unprecedented low values of material budget (per layer) and closeness to interaction point (19 mm) lead to a factor two improvement in pointing resolutions from very low $p_\text{T}$ (O(100MeV/$c$)), achieving, for example, 20 ${\mu}$m and 15 ${\mu}$m in the transversal and longitudinal directions, respectively, for 1 GeV/c primary charged pions. After a successful R\&D phase 2019-2023, which demonstrated the feasibility of this innovational detector, the final sensor and mechanics are being developed right now. This contribution will briefly review the conceptual design and the main R&D achievements, as well as the current activities and road to completion and installation. It concludes with a projection of the improved physics performance, in particular for heavy-flavour hadrons, as well as for thermal dielectrons, that will come into reach with this new detector installed., Comment: Proceeding for the SQM2024

Discrete flavor symmetries have been an appealing approach for explaining the observed flavor structure, which is not justified in the Standard Model (SM). Typically, these models require a so-called flavon field in order to give rise to the flavor structure upon the breaking of the flavor symmetry by the vacuum expectation value (VEV) of the flavon. Generally, in order to obtain the desired vacuum alignment, a flavon potential that includes additional so-called driving fields is required. On the other hand, allowing the flavor symmetry to be modular leads to a structure where the couplings are all holomorphic functions that depend only on a complex modulus, thus greatly reducing the number of parameters in the model. We show that these elements can be combined to simultaneously explain the flavor structure and dark matter (DM) relic abundance. We present a modular model with flavon vacuum alignment that allows for realistic flavor predictions while providing a successful fermionic DM candidate., Comment: 24 pages, 7 figures

The switching and control of optical fields based on nonlinear optical effects are often limited to relatively weak nonlinear susceptibility and strong optical pump fields. Here, an optical medium with programmable susceptibility tensor based on polarizable atoms is proposed. Under a structured optical pump, the ground state population of atoms could be efficiently controlled by tuning the chirality and intensity of optical fields, and thus the optical response of the medium is programmable in both space and time. We demonstrate the potential of this approach by engineering the spatial distribution of the complex susceptibility tensor of the medium in photonic structures to realize nonreciprocal optical effects. Specifically, we investigate the advantages of chiral interaction between atoms and photons in an atom-cladded waveguide, theoretically showing that reconfigurable, strong, and fastly switchable isolation of optical signals in a selected optical mode is possible. The susceptibility-programmable medium provides a promising way to efficiently control the optical field, opening up a wide range of applications for integrated photonic devices and structured optics., Comment: 7 pages, 4 figures

This paper presents the development of a Physics-realistic and Photo-\underline{r}ealistic humanoid robot testbed, PR2, to facilitate collaborative research between Embodied Artificial Intelligence (Embodied AI) and robotics. PR2 offers high-quality scene rendering and robot dynamic simulation, enabling (i) the creation of diverse scenes using various digital assets, (ii) the integration of advanced perception or foundation models, and (iii) the implementation of planning and control algorithms for dynamic humanoid robot behaviors based on environmental feedback. The beta version of PR2 has been deployed for the simulation track of a nationwide full-size humanoid robot competition for college students, attracting 137 teams and over 400 participants within four months. This competition covered traditional tasks in bipedal walking, as well as novel challenges in loco-manipulation and language-instruction-based object search, marking a first for public college robotics competitions. A retrospective analysis of the competition suggests that future events should emphasize the integration of locomotion with manipulation and perception. By making the PR2 testbed publicly available at https://github.com/pr2-humanoid/PR2-Platform, we aim to further advance education and training in humanoid robotics.

Cross-domain speech enhancement (SE) is often faced with severe challenges due to the scarcity of noise and background information in an unseen target domain, leading to a mismatch between training and test conditions. This study puts forward a novel data simulation method to address this issue, leveraging noise-extractive techniques and generative adversarial networks (GANs) with only limited target noisy speech data. Notably, our method employs a noise encoder to extract noise embeddings from target-domain data. These embeddings aptly guide the generator to synthesize utterances acoustically fitted to the target domain while authentically preserving the phonetic content of the input clean speech. Furthermore, we introduce the notion of dynamic stochastic perturbation, which can inject controlled perturbations into the noise embeddings during inference, thereby enabling the model to generalize well to unseen noise conditions. Experiments on the VoiceBank-DEMAND benchmark dataset demonstrate that our domain-adaptive SE method outperforms an existing strong baseline based on data simulation., Comment: Accepted to IEEE SLT 2024

With the proliferation of AI agents in various domains, protecting the ownership of AI models has become crucial due to the significant investment in their development. Unauthorized use and illegal distribution of these models pose serious threats to intellectual property, necessitating effective copyright protection measures. Model watermarking has emerged as a key technique to address this issue, embedding ownership information within models to assert rightful ownership during copyright disputes. This paper presents several contributions to model watermarking: a self-authenticating black-box watermarking protocol using hash techniques, a study on evidence forgery attacks using adversarial perturbations, a proposed defense involving a purification step to counter adversarial attacks, and a purification-agnostic proxy learning method to enhance watermark reliability and model performance. Experimental results demonstrate the effectiveness of these approaches in improving the security, reliability, and performance of watermarked models.

Deploying large language models (LLMs) on edge devices presents significant challenges due to the substantial computational overhead and memory requirements. Activation sparsification can mitigate these challenges by reducing the number of activated neurons during inference. Existing methods typically employ thresholding-based sparsification based on the statistics of activation tensors. However, these methods do not explicitly model the impact of activation sparsification on performance, leading to suboptimal performance degradation. To address this issue, this paper reformulates the activation sparsification problem by introducing a new objective that optimizes the sparsification decisions. Building on this reformulation, we propose CHESS, a general activation sparsification approach via CHannel-wise thrEsholding and Selective Sparsification. First, channel-wise thresholding assigns a unique threshold to each activation channel in the feed-forward network (FFN) layers. Then, selective sparsification involves applying thresholding-based activation sparsification to specific layers within the attention modules. Finally, we detail the implementation of sparse kernels to accelerate LLM inference. Experimental results demonstrate that the proposed CHESS achieves lower performance degradation over 8 downstream tasks while activating fewer parameters compared to existing methods, thus speeding up the LLM inference by up to 1.27x.

Pair density wave (PDW) is a distinct superconducting state characterized by a periodic modulation of its order parameter in real space. Its intricate interplay with the charge density wave (CDW) state is a continuing topic of interest in condensed matter physics. While PDW states have been discovered in cuprates and other unconventional superconductors, the understanding of diverse PDWs and their interactions with different types of CDWs remains limited. Here, utilizing scanning tunneling microscopy, we unveil the subtle correlations between PDW ground states and two distinct CDW phases -- namely, anion-centered-CDW (AC-CDW) and hollow-centered-CDW (HC-CDW) -- in 2H-NbSe$_2$. In both CDW regions, we observe coexisting PDWs with a commensurate structure that aligns with the underlying CDW phase. The superconducting gap size, $\Delta(r)$, related to the pairing order parameter is in phase with the charge density in both CDW regions. Meanwhile, the coherence peak height, $H(r)$, qualitatively reflecting the electron-pair density, exhibits a phase difference of approximately $2\pi/3$ relative to the CDW. The three-fold rotational symmetry is preserved in the HC-CDW region but is spontaneously broken in the AC-CDW region due to the PDW state, leading to the emergence of nematic superconductivity., Comment: 21 pages, 5 figures

We present an advanced \textit{ab initio} quantum Monte Carlo (QMC) calculation of the ground state of undoped CaCuO\textsubscript{2}. We extend the traditional single-determinant Slater-Jastrow approach to include multi-determinant wave functions, inhomogeneous Jastrow factors, and orbital optimization. Our results demonstrate not only an improvement in the variational bound of the ground state energy -- 2.3 eV per formula unit lower than previous state of the art techniques -- but also confirm the presence of spin fluctuations in multi-determinant wave functions in a strongly correlated cuprate system, which is integral to understanding high-$T_c$ superconductivity. This is the first demonstration of capturing spin fluctuations in QMC wave functions on a cuprate, establishing the groundwork for new studies on doped cuprates in the superconducting state, where spin fluctuations require more accurate characterization., Comment: 9 pages, 6 figures

This paper focuses on remainder estimates of the magnetic $L^p$-Hardy inequalities for $1

In dynamic discrete choice models, some parameters, such as the discount factor, are being fixed instead of being estimated. This paper proposes two sensitivity analysis procedures for dynamic discrete choice models with respect to the fixed parameters. First, I develop a local sensitivity measure that estimates the change in the target parameter for a unit change in the fixed parameter. This measure is fast to compute as it does not require model re-estimation. Second, I propose a global sensitivity analysis procedure that uses model primitives to study the relationship between target parameters and fixed parameters. I show how to apply the sensitivity analysis procedures of this paper through two empirical applications.

The periodic coronal rain and in-phase radiative intensity pulsations have been observed in multiple wavelengths in recent years. However, due to the lack of three-dimensional coronal magnetic fields and thermodynamic data in observations, it remains challenging to quantify the coronal heating rate that drives the mass cycles. In this work, based on the MURaM code, we conduct a three-dimensional radiative magnetohydrodynamic simulation spanning from the convective zone to the corona, where the solar atmosphere is heated self-consistently through dissipation resulting from magneto-convection. For the first time, we model the periodic coronal rain in an active region. With a high spatial resolution, the simulation well resembles the observational features across different extreme ultraviolet wavelengths. These include the realistic interweaving coronal loops, periodic coronal rain and periodic intensity pulsations, with two periods of 3.0~h and 3.7~h identified within one loop system. Moreover, the simulation allows for a detailed three-dimensional depiction of coronal rain on small scales, revealing adjacent shower-like rain clumps $\sim500$~km in width and showcasing their multi-thermal internal structures. We further reveal that these periodic variations essentially reflect the cyclic energy evolution of the coronal loop under thermal non-equilibrium state. Importantly, as the driver of the mass circulation, the self-consistent coronal heating rate is considerably complex in time and space, with hour-level variations in one order of magnitude, minute-level bursts, and varying asymmetry reaching ten times between footpoints. This provides an instructive template for the ad hoc heating function, and further enhances our understanding of the coronal heating process., Comment: 14 Pages, 7 figures, accepted for publication in ApJL

As large language models (LLMs) continue to advance, aligning these models with human preferences has emerged as a critical challenge. Traditional alignment methods, relying on human or LLM annotated datasets, are limited by their resource-intensive nature, inherent subjectivity, and the risk of feedback loops that amplify model biases. To overcome these limitations, we introduce WildFeedback, a novel framework that leverages real-time, in-situ user interactions to create preference datasets that more accurately reflect authentic human values. WildFeedback operates through a three-step process: feedback signal identification, preference data construction, and user-guided evaluation. We applied this framework to a large corpus of user-LLM conversations, resulting in a rich preference dataset that reflects genuine user preferences. This dataset captures the nuances of user preferences by identifying and classifying feedback signals within natural conversations, thereby enabling the construction of more representative and context-sensitive alignment data. Our extensive experiments demonstrate that LLMs fine-tuned on WildFeedback exhibit significantly improved alignment with user preferences, as evidenced by both traditional benchmarks and our proposed user-guided evaluation. By incorporating real-time feedback from actual users, WildFeedback addresses the scalability, subjectivity, and bias challenges that plague existing approaches, marking a significant step toward developing LLMs that are more responsive to the diverse and evolving needs of their users. In summary, WildFeedback offers a robust, scalable solution for aligning LLMs with true human values, setting a new standard for the development and evaluation of user-centric language models., Comment: 24 pages

The exotic neutral gauge boson is a powerful candidate for the new physics beyond the standard model. As a promising model, the left-right symmetric model has been proposed to explain the neutrino mass, dark matter, and matter-antimatter asymmetry, etc., in which exotic gauge bosons $Z^\prime, W^{\prime \pm}$ have been put forward as well as other new right-handed particles. We investigate the $\mu^+ \mu^- \to q\bar{q} $ and $ \mu^+ \mu^- \to l^+ l^- $ processes involving the $Z^\prime$ boson as an intermediate particle. The coupling strength, decay width and mass are the key parameters on the production and decay processes of the $Z^\prime$ boson. The results indicate that the angular distributions of final particles are sensitive to the couplings of $Z^\prime$ to the other fermions. Asymmetries defined from the angular distributions are ideal quantities to demonstrate the discrepancy between the standard model process and the processes with $Z^\prime$ participated and they are also appropriate observables to discriminate the couplings of $Z^\prime$ to other particles. Compared with the current results at the Large Hadron Collider (LHC), the future muon collider has a great potential to explore the new parameter space with $Z^\prime$ boson., Comment: 22 pages, 10 figures

The rapid advancement of single-cell ATAC sequencing (scATAC-seq) technologies holds great promise for investigating the heterogeneity of epigenetic landscapes at the cellular level. The amplification process in scATAC-seq experiments often introduces noise due to dropout events, which results in extreme sparsity that hinders accurate analysis. Consequently, there is a significant demand for the generation of high-quality scATAC-seq data in silico. Furthermore, current methodologies are typically task-specific, lacking a versatile framework capable of handling multiple tasks within a single model. In this work, we propose ATAC-Diff, a versatile framework, which is based on a latent diffusion model conditioned on the latent auxiliary variables to adapt for various tasks. ATAC-Diff is the first diffusion model for the scATAC-seq data generation and analysis, composed of auxiliary modules encoding the latent high-level variables to enable the model to learn the semantic information to sample high-quality data. Gaussian Mixture Model (GMM) as the latent prior and auxiliary decoder, the yield variables reserve the refined genomic information beneficial for downstream analyses. Another innovation is the incorporation of mutual information between observed and hidden variables as a regularization term to prevent the model from decoupling from latent variables. Through extensive experiments, we demonstrate that ATAC-Diff achieves high performance in both generation and analysis tasks, outperforming state-of-the-art models.

We present the IAMReX, an adaptive and parallel solver for particle-resolved simulations on the multi-level grid. The fluid equations are solved using a finite-volume scheme on the block-structured semi-staggered grids with both subcycling and non-subcycling methods. The particle-fluid interaction is resolved using the multidirect forcing immersed boundary method. The associated Lagrangian markers used to resolve fluid-particle interface only exist on the finest-level grid, which greatly reduces memory usage. The volume integrals are numerically calculated to capture the free motion of particles accurately, and the repulsive potential model is also included to account for the particle-particle collision. We demonstrate the versatility, accuracy, and efficiency of the present multi-level framework by simulating fluid-particle interaction problems with various types of kinematic constraints. The cluster of monodisperse particles case is presented at the end to show the capability of the current solver in handing with multiple particles. The source code and testing cases used in this work can be accessed at https://github.com/ruohai0925/IAMR/tree/development. Input scripts and raw postprocessing data are also available for reproducing all results.

One of the primary challenges in short packet ultra-reliable and low-latency communications (URLLC) is to achieve reliable channel estimation and data detection while minimizing the impact on latency performance. Given the small packet size in mini-slot-assisted URLLC, relying solely on pilot-based coherent detection is almost impossible to meet the seemingly contradictory requirements of high channel estimation accuracy, high reliability, low training overhead, and low latency. In this paper, we explore differential modulation both in the frequency domain and in the time domain, and propose adopting an adaptive approach that integrates both differential and coherent detection to achieve mini-slot-assisted short packet URLLC, striking a balance among training overhead, system performance, and computational complexity. Specifically, differential (especially in the frequency domain) and coherent detection schemes can be dynamically activated based on application scenarios, channel statistics, information payloads, mini-slot deployment options, and service requirements. Furthermore, we derive the block error rate (BLER) for pilot-based, frequency domain, and time domain differential OFDM using non-asymptotic information-theoretic bounds. Simulation results validate the feasibility and effectiveness of adaptive differential and coherent detection., Comment: 14 pages, 8 figures, journal

AI-assisted lesion detection models play a crucial role in the early screening of cancer. However, previous image-based models ignore the inter-frame contextual information present in videos. On the other hand, video-based models capture the inter-frame context but are computationally expensive. To mitigate this contradiction, we delve into Video-to-Image knowledge distillation leveraging DEtection TRansformer (V2I-DETR) for the task of medical video lesion detection. V2I-DETR adopts a teacher-student network paradigm. The teacher network aims at extracting temporal contexts from multiple frames and transferring them to the student network, and the student network is an image-based model dedicated to fast prediction in inference. By distilling multi-frame contexts into a single frame, the proposed V2I-DETR combines the advantages of utilizing temporal contexts from video-based models and the inference speed of image-based models. Through extensive experiments, V2I-DETR outperforms previous state-of-the-art methods by a large margin while achieving the real-time inference speed (30 FPS) as the image-based model., Comment: BIBM2024

In this work, we investigate compact K\"ahler manifolds with non-negative or quasi-positive mixed curvature coming from a linear combination of the Ricci and holomorphic sectional curvature, which covers various notions of curvature considered in the literature. Specifically, we prove a splitting theorem, analogous to the Cheeger-Gromoll splitting theorem, for complete K\"ahler manifolds with non-negative mixed curvature containing a line, and then establish a structure theorem for compact K\"ahler manifolds with non-negative mixed curvature. We also show that the Hodge numbers of compact K\"ahler manifolds with quasi-positive mixed curvature must vanish. Both results are based on the conformal perturbation method., Comment: 29 pages

This paper introduces SayRea, an interactive system that facilitates the extraction of contextual rules for personalized context-aware service recommendations in mobile scenarios. The system monitors a user's execution of registered services on their smartphones (via accessibility service) and proactively requests a single-sentence reason from the user. By utilizing a Large Language Model (LLM), SayRea parses the reason and predicts contextual relationships between the observed service and potential contexts (such as setting the alarm clock deep in the evening). In this way, SayRea can significantly reduce the cognitive load on users in anticipating future needs and selecting contextual attributes. A 10-day field study involving 20 participants showed that SayRea accumulated an average of 62.4 rules per user and successfully recommended 45% of service usage. The participants provided positive feedback on the system's usability, interpretability, and controllability. The findings highlight SayRea's effectiveness in personalized service recommendations and its potential to enhance user experience in mobile scenarios.

The bubble nuclei are important components of exotic nuclear structures characterized by special depletions of central densities. Focusing on bubble structures of $^{36}$Ar, the characterizations of bubble nuclei were explored with the framework of the extended quantum molecular dynamics model. Three density distribution modes were uncovered for the first time, i.e. micro-bubble, bubble, and cluster resonances, which show unique spectral signature compared to the monopole resonance spectrum as the excitation intensity was increased in bubbles. Of pivotal importance is the revelation that the bubble mode's oscillation frequency closely resembles macroscopic bubble dynamics, building a connection between classical macroscopic phenomena and the quantum complexity of the nuclear structure. The discovery marks a crucial step forward in deciphering the relationship between classical and quantum domains within the enigmatic world of atomic nuclei.

Spatial perception, particularly at small and medium scales, is an essential human sense but poses a significant challenge for the blind and visually impaired (BVI). Traditional learning methods for BVI individuals are often constrained by the limited availability of suitable learning environments and high associated costs. To tackle these barriers, we conducted comprehensive studies to delve into the real-world challenges faced by the BVI community. We have identified several key factors hindering their spatial perception, including the high social cost of seeking assistance, inefficient methods of information intake, cognitive and behavioral disconnects, and a lack of opportunities for hands-on exploration. As a result, we developed AngleSizer, an innovative teaching assistant that leverages smartphone technology. AngleSizer is designed to enable BVI individuals to use natural interaction gestures to try, feel, understand, and learn about sizes and angles effectively. This tool incorporates dual vibration-audio feedback, carefully crafted teaching processes, and specialized learning modules to enhance the learning experience. Extensive user experiments validated its efficacy and applicability with diverse abilities and visual conditions. Ultimately, our research not only expands the understanding of BVI behavioral patterns but also greatly improves their spatial perception capabilities, in a way that is both cost-effective and allows for independent learning., Comment: The paper was accepted by IMWUT/Ubicomp 2024

On-chip micro-ring resonators (MRRs) have been proposed for constructing delay reservoir computing (RC) systems, offering a highly scalable, high-density computational architecture that is easy to manufacture. However, most proposed RC schemes have utilized passive integrated optical components based on silicon-on-insulator (SOI), and RC systems based on lithium niobate on insulator (LNOI) have not yet been reported. The nonlinear optical effects exhibited by lithium niobate microphotonic devices introduce new possibilities for RC design. In this work, we design an RC scheme based on a series-coupled MRR array, leveraging the unique interplay between thermo-optic nonlinearity and photorefractive effects in lithium niobate. We first demonstrate the existence of three regions defined by wavelength detuning between the primary LNOI micro-ring resonator and the coupled micro-ring array, where one region achieves an optimal balance between nonlinearity and high memory capacity at extremely low input energy, leading to superior computational performance. We then discuss in detail the impact of each ring's nonlinearity and the system's symbol duration on performance. Finally, we design a wavelength-division multiplexing (WDM) based multi-task parallel computing scheme, showing that the computational performance for multiple tasks matches that of single-task computations., Comment: 17 pages, 6 figures

We study diffractive vector meson production at small-$x$ in the collision of electrons and polarized deuterons $e+d^{\uparrow}$. We consider the polarization dependence of the nuclear wave function of the deuteron, which results in an azimuthal angular dependence of the produced vector meson when the deuteron is transversely polarized. The Fourier coefficients extracted from the azimuthal angular dependence of the vector meson differential cross-section exhibit notable differences between longitudinally and transversely polarized deuterons. The angular dependence of the extracted effective deuteron radius provides direct insight into the structure of the polarized deuteron wave function. Furthermore, we observe slightly increased gluon saturation effects when the deuteron is longitudinally polarized compared to the transversely polarized case. The small-$x$ observables studied in this work will be accessible at the future Electron-Ion Collider.

The third quantization (3rd Q) for bosons provides the exact steady-state solution of the Lindblad equation with quadratic Hamiltonians. By decomposing the interaction of the Bose Hubbard model (BHM) according to Hartree approximation, we present a self-consistent formalism for solving the open-system bosonic Lindblad equation with weak interactions in the steady state. The 3rd Q with Hartree approximation takes into account the infinite Fock space of bosons while its demand of resource scales polynomially with the system size. We examine the method by analyzing three examples of the BHM, including the uniform chain, interaction induced diode effect, and Su-Schrieffer-Heeger (SSH) Hubbard model. When compared with the simulations with capped boson numbers for small systems, the 3rd Q with Hartree approximation captures the qualitative behavior and suggests an upper bound of the steady-state value. Thus, the manageable 3rd Q with Hartree approximation allows us to characterize and predict large-system behavior of quantum transport in interacting bosonic systems relevant to cold-atom experiments., Comment: 11 pages, 8 figures

We introduce Contested Logistics Games, a variant of logistics problems that account for the presence of an adversary that can disrupt the movement of goods in selected areas. We model this as a large two-player zero-sum one-shot game played on a graph representation of the physical world, with the optimal logistics plans described by the (possibly randomized) Nash equilibria of this game. Our logistics model is fairly sophisticated, and is able to handle multiple modes of transport and goods, accounting for possible storage of goods in warehouses, as well as Leontief utilities based on demand satisfied. We prove computational hardness results related to equilibrium finding and propose a practical double-oracle solver based on solving a series of best-response mixed-integer linear programs. We experiment on both synthetic and real-world maps, demonstrating that our proposed method scales to reasonably large games. We also demonstrate the importance of explicitly modeling the capabilities of the adversary via ablation studies and comparisons with a naive logistics plan based on heuristics., Comment: GameSec '24

Although fast radio bursts (FRBs) were discovered more than a decade ago, and they have been one of the active fields in astronomy and cosmology, their origins are still unknown. An interesting topic closely related to the origins of FRBs is their classifications. On the other hand, FRBs are actually a promising probe to study cosmology. In the literature, some new classifications of FRBs different from repeaters and non-repeaters were suggested, and some tight empirical relations have been found for them. In particular, Guo and Wei suggested to classify FRBs into the ones associated with old or young populations, which have also some new empirical relations. They also proposed to use one of the empirical relations without dispersion measure (DM) to calibrate FRBs as standard candles for cosmology. This shows the potential of the new classification and the empirical relations for FRBs. Nowadays, more than 50 FRBs have been well localized, and hence their redshifts $z$ are observationally known. Thus, it is time to check the empirical relations with the current localized FRBs. We find that many empirical relations still hold, and in particular the one used to calibrate FRBs as standard candles for cosmology stands firm., Comment: 15 pages, 2 tables, 7 figures, revtex4

Quantum computing has tremendous potential to overcome some of the fundamental limitations present in classical information processing. Yet, today's technological limitations in the quality and scaling prevent exploiting its full potential. Quantum computing based on superconducting quantum processing units (QPUs) is among the most promising approaches towards practical quantum advantage. In this article the basic technological approach of IQM Quantum Computers is described covering both the QPU and the rest of the full-stack quantum computer. In particular, the focus is on a 20-qubit quantum computer featuring the Garnet QPU and its architecture, which we will scale up to 150 qubits. We also present QPU and system-level benchmarks, including a median 2-qubit gate fidelity of 99.5% and genuinely entangling all 20 qubits in a Greenberger-Horne-Zeilinger (GHZ) state.