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Embodied Question Answering (EQA) is an essential yet challenging task for robotic home assistants. Recent studies have shown that large vision-language models (VLMs) can be effectively utilized for EQA, but existing works either focus on video-based question answering without embodied exploration or rely on closed-form choice sets. In real-world scenarios, a robotic agent must efficiently explore and accurately answer questions in open-vocabulary settings. To address these challenges, we propose a novel framework called EfficientEQA for open-vocabulary EQA, which enables efficient exploration and accurate answering. In EfficientEQA, the robot actively explores unknown environments using Semantic-Value-Weighted Frontier Exploration, a strategy that prioritizes exploration based on semantic importance provided by calibrated confidence from black-box VLMs to quickly gather relevant information. To generate accurate answers, we employ Retrieval-Augmented Generation (RAG), which utilizes BLIP to retrieve useful images from accumulated observations and VLM reasoning to produce responses without relying on predefined answer choices. Additionally, we detect observations that are highly relevant to the question as outliers, allowing the robot to determine when it has sufficient information to stop exploring and provide an answer. Experimental results demonstrate the effectiveness of our approach, showing an improvement in answering accuracy by over 15% and efficiency, measured in running steps, by over 20% compared to state-of-the-art methods.

Reconstructing objects from posed images is a crucial and complex task in computer graphics and computer vision. While NeRF-based neural reconstruction methods have exhibited impressive reconstruction ability, they tend to be time-comsuming. Recent strategies have adopted 3D Gaussian Splatting (3D-GS) for inverse rendering, which have led to quick and effective outcomes. However, these techniques generally have difficulty in producing believable geometries and materials for glossy objects, a challenge that stems from the inherent ambiguities of inverse rendering. To address this, we introduce GlossyGS, an innovative 3D-GS-based inverse rendering framework that aims to precisely reconstruct the geometry and materials of glossy objects by integrating material priors. The key idea is the use of micro-facet geometry segmentation prior, which helps to reduce the intrinsic ambiguities and improve the decomposition of geometries and materials. Additionally, we introduce a normal map prefiltering strategy to more accurately simulate the normal distribution of reflective surfaces. These strategies are integrated into a hybrid geometry and material representation that employs both explicit and implicit methods to depict glossy objects. We demonstrate through quantitative analysis and qualitative visualization that the proposed method is effective to reconstruct high-fidelity geometries and materials of glossy objects, and performs favorably against state-of-the-arts.

In the realm of recommendation systems, users exhibit a diverse array of behaviors when interacting with items. This phenomenon has spurred research into learning the implicit semantic relationships between these behaviors to enhance recommendation performance. However, these methods often entail high computational complexity. To address concerns regarding efficiency, pre-training presents a viable solution. Its objective is to extract knowledge from extensive pre-training data and fine-tune the model for downstream tasks. Nevertheless, previous pre-training methods have primarily focused on single-behavior data, while multi-behavior data contains significant noise. Additionally, the fully fine-tuning strategy adopted by these methods still imposes a considerable computational burden. In response to this challenge, we propose DPCPL, the first pre-training and prompt-tuning paradigm tailored for Multi-Behavior Sequential Recommendation. Specifically, in the pre-training stage, we commence by proposing a novel Efficient Behavior Miner (EBM) to filter out the noise at multiple time scales, thereby facilitating the comprehension of the contextual semantics of multi-behavior sequences. Subsequently, we propose to tune the pre-trained model in a highly efficient manner with the proposed Customized Prompt Learning (CPL) module, which generates personalized, progressive, and diverse prompts to fully exploit the potential of the pre-trained model effectively. Extensive experiments on three real-world datasets have unequivocally demonstrated that DPCPL not only exhibits high efficiency and effectiveness, requiring minimal parameter adjustments but also surpasses the state-of-the-art performance across a diverse range of downstream tasks.

Single nitrogen vacancy (NV) centers in diamond have been used extensively for high-sensitivity nanoscale sensing, but conventional approaches use confocal microscopy to measure individual centers sequentially, limiting throughput and access to non-local physical properties. Here we design and implement a multiplexed NV sensing platform that allows us to read out many single NV centers simultaneously using a low-noise camera. Using this platform, we coherently manipulate and read out the spin states of hundreds of individual NV centers in parallel, achieving comparable magnetic field sensitivity to confocal measurements. We also implement a parallelized version of spin-to-charge-conversion readout for low NV center spin state readout noise and use it to demonstrate multiplexed covariance magnetometry, in which we measure six two-point magnetic field correlators from four NV centers simultaneously. The number of correlators we can measure is limited only by the available laser power, opening the door to massively multiplexed covariance magnetometry. Our platform significantly increases the throughput and broadens the applications of nanoscale sensing using diamond quantum sensors.

Recently, 3D Gaussian Splatting(3DGS) has revolutionized neural rendering with its high-quality rendering and real-time speed. However, when it comes to indoor scenes with a significant number of textureless areas, 3DGS yields incomplete and noisy reconstruction results due to the poor initialization of the point cloud and under-constrained optimization. Inspired by the continuity of signed distance field (SDF), which naturally has advantages in modeling surfaces, we present a unified optimizing framework integrating neural SDF with 3DGS. This framework incorporates a learnable neural SDF field to guide the densification and pruning of Gaussians, enabling Gaussians to accurately model scenes even with poor initialized point clouds. At the same time, the geometry represented by Gaussians improves the efficiency of the SDF field by piloting its point sampling. Additionally, we regularize the optimization with normal and edge priors to eliminate geometry ambiguity in textureless areas and improve the details. Extensive experiments in ScanNet and ScanNet++ show that our method achieves state-of-the-art performance in both surface reconstruction and novel view synthesis.

We present DC-Gaussian, a new method for generating novel views from in-vehicle dash cam videos. While neural rendering techniques have made significant strides in driving scenarios, existing methods are primarily designed for videos collected by autonomous vehicles. However, these videos are limited in both quantity and diversity compared to dash cam videos, which are more widely used across various types of vehicles and capture a broader range of scenarios. Dash cam videos often suffer from severe obstructions such as reflections and occlusions on the windshields, which significantly impede the application of neural rendering techniques. To address this challenge, we develop DC-Gaussian based on the recent real-time neural rendering technique 3D Gaussian Splatting (3DGS). Our approach includes an adaptive image decomposition module to model reflections and occlusions in a unified manner. Additionally, we introduce illumination-aware obstruction modeling to manage reflections and occlusions under varying lighting conditions. Lastly, we employ a geometry-guided Gaussian enhancement strategy to improve rendering details by incorporating additional geometry priors. Experiments on self-captured and public dash cam videos show that our method not only achieves state-of-the-art performance in novel view synthesis, but also accurately reconstructing captured scenes getting rid of obstructions. See the project page for code, data: https://linhanwang.github.io/dcgaussian/., Comment: 10 pages,7 figures;project page: https://linhanwang.github.io/dcgaussian/; Accepted to NeurIPS 2024

Explicit Congestion Notification (ECN)-based congestion control schemes have been widely adopted in high-speed data center networks (DCNs), where the ECN marking threshold plays a determinant role in guaranteeing a packet lossless DCN. However, existing approaches either employ static settings with immutable thresholds that cannot be dynamically self-adjusted to adapt to network dynamics, or fail to take into account many-to-one traffic patterns and different requirements of different types of traffic, resulting in relatively poor performance. To address these problems, this paper proposes a novel learning-based automatic ECN tuning scheme, named PET, based on the multi-agent Independent Proximal Policy Optimization (IPPO) algorithm. PET dynamically adjusts ECN thresholds by fully considering pivotal congestion-contributing factors, including queue length, output data rate, output rate of ECN-marked packets, current ECN threshold, the extent of incast, and the ratio of mice and elephant flows. PET adopts the Decentralized Training and Decentralized Execution (DTDE) paradigm and combines offline and online training to accommodate network dynamics. PET is also fair and readily deployable with commodity hardware. Comprehensive experimental results demonstrate that, compared with state-of-the-art static schemes and the learning-based automatic scheme, our PET achieves better performance in terms of flow completion time, convergence rate, queue length variance, and system robustness.

Language-guided scene-aware human motion generation has great significance for entertainment and robotics. In response to the limitations of existing datasets, we introduce LaserHuman, a pioneering dataset engineered to revolutionize Scene-Text-to-Motion research. LaserHuman stands out with its inclusion of genuine human motions within 3D environments, unbounded free-form natural language descriptions, a blend of indoor and outdoor scenarios, and dynamic, ever-changing scenes. Diverse modalities of capture data and rich annotations present great opportunities for the research of conditional motion generation, and can also facilitate the development of real-life applications. Moreover, to generate semantically consistent and physically plausible human motions, we propose a multi-conditional diffusion model, which is simple but effective, achieving state-of-the-art performance on existing datasets.

The advent of 3D Gaussian Splatting (3DGS) has recently brought about a revolution in the field of neural rendering, facilitating high-quality renderings at real-time speed. However, 3DGS heavily depends on the initialized point cloud produced by Structure-from-Motion (SfM) techniques. When tackling with large-scale scenes that unavoidably contain texture-less surfaces, the SfM techniques always fail to produce enough points in these surfaces and cannot provide good initialization for 3DGS. As a result, 3DGS suffers from difficult optimization and low-quality renderings. In this paper, inspired by classical multi-view stereo (MVS) techniques, we propose GaussianPro, a novel method that applies a progressive propagation strategy to guide the densification of the 3D Gaussians. Compared to the simple split and clone strategies used in 3DGS, our method leverages the priors of the existing reconstructed geometries of the scene and patch matching techniques to produce new Gaussians with accurate positions and orientations. Experiments on both large-scale and small-scale scenes validate the effectiveness of our method, where our method significantly surpasses 3DGS on the Waymo dataset, exhibiting an improvement of 1.15dB in terms of PSNR., Comment: See the project page for code, data: https://kcheng1021.github.io/gaussianpro.github.io

Monocular depth estimation from RGB images plays a pivotal role in 3D vision. However, its accuracy can deteriorate in challenging environments such as nighttime or adverse weather conditions. While long-wave infrared cameras offer stable imaging in such challenging conditions, they are inherently low-resolution, lacking rich texture and semantics as delivered by the RGB image. Current methods focus solely on a single modality due to the difficulties to identify and integrate faithful depth cues from both sources. To address these issues, this paper presents a novel approach that identifies and integrates dominant cross-modality depth features with a learning-based framework. Concretely, we independently compute the coarse depth maps with separate networks by fully utilizing the individual depth cues from each modality. As the advantageous depth spreads across both modalities, we propose a novel confidence loss steering a confidence predictor network to yield a confidence map specifying latent potential depth areas. With the resulting confidence map, we propose a multi-modal fusion network that fuses the final depth in an end-to-end manner. Harnessing the proposed pipeline, our method demonstrates the ability of robust depth estimation in a variety of difficult scenarios. Experimental results on the challenging MS$^2$ and ViViD++ datasets demonstrate the effectiveness and robustness of our method.

The development of virtual try-on has revolutionized online shopping by allowing customers to visualize themselves in various fashion items, thus extending the in-store try-on experience to the cyber space. Although virtual try-on has attracted considerable research initiatives, existing systems only focus on the quality of image generation, overlooking whether the fashion item is a good match to the given person and clothes. Recognizing this gap, we propose to design a one-stop Smart Fitting Room, with the novel formulation of matching-aware virtual try-on. Following this formulation, we design a Hybrid Matching-aware Virtual Try-On Framework (HMaVTON), which combines retrieval-based and generative methods to foster a more personalized virtual try-on experience. This framework integrates a hybrid mix-and-match module and an enhanced virtual try-on module. The former can recommend fashion items available on the platform to boost sales and generate clothes that meets the diverse tastes of consumers. The latter provides high-quality try-on effects, delivering a one-stop shopping service. To validate the effectiveness of our approach, we enlist the expertise of fashion designers for a professional evaluation, assessing the rationality and diversity of the clothes combinations and conducting an evaluation matrix analysis. Our method significantly enhances the practicality of virtual try-on. The code is available at https://github.com/Yzcreator/HMaVTON.

Multi-camera setups find widespread use across various applications, such as autonomous driving, as they greatly expand sensing capabilities. Despite the fast development of Neural radiance field (NeRF) techniques and their wide applications in both indoor and outdoor scenes, applying NeRF to multi-camera systems remains very challenging. This is primarily due to the inherent under-calibration issues in multi-camera setup, including inconsistent imaging effects stemming from separately calibrated image signal processing units in diverse cameras, and system errors arising from mechanical vibrations during driving that affect relative camera poses. In this paper, we present UC-NeRF, a novel method tailored for novel view synthesis in under-calibrated multi-view camera systems. Firstly, we propose a layer-based color correction to rectify the color inconsistency in different image regions. Second, we propose virtual warping to generate more viewpoint-diverse but color-consistent virtual views for color correction and 3D recovery. Finally, a spatiotemporally constrained pose refinement is designed for more robust and accurate pose calibration in multi-camera systems. Our method not only achieves state-of-the-art performance of novel view synthesis in multi-camera setups, but also effectively facilitates depth estimation in large-scale outdoor scenes with the synthesized novel views., Comment: See the project page for code, data: https://kcheng1021.github.io/ucnerf.github.io

This report provides a concise overview of the proposed North system, which aims to achieve automatic word/syllable recognition for Taiwanese Hakka (Sixian). The report outlines three key components of the system: the acquisition, composition, and utilization of the training data; the architecture of the model; and the hardware specifications and operational statistics. The demonstration of the system has been made public at https://asrvm.iis.sinica.edu.tw/hakka_sixian.

Perceiving and manipulating 3D articulated objects in diverse environments is essential for home-assistant robots. Recent studies have shown that point-level affordance provides actionable priors for downstream manipulation tasks. However, existing works primarily focus on single-object scenarios with homogeneous agents, overlooking the realistic constraints imposed by the environment and the agent's morphology, e.g., occlusions and physical limitations. In this paper, we propose an environment-aware affordance framework that incorporates both object-level actionable priors and environment constraints. Unlike object-centric affordance approaches, learning environment-aware affordance faces the challenge of combinatorial explosion due to the complexity of various occlusions, characterized by their quantities, geometries, positions and poses. To address this and enhance data efficiency, we introduce a novel contrastive affordance learning framework capable of training on scenes containing a single occluder and generalizing to scenes with complex occluder combinations. Experiments demonstrate the effectiveness of our proposed approach in learning affordance considering environment constraints. Project page at https://chengkaiacademycity.github.io/EnvAwareAfford/, Comment: In 37th Conference on Neural Information Processing Systems (NeurIPS 2023). Website at https://chengkaiacademycity.github.io/EnvAwareAfford/

3D scene reconstruction is a long-standing vision task. Existing approaches can be categorized into geometry-based and learning-based methods. The former leverages multi-view geometry but can face catastrophic failures due to the reliance on accurate pixel correspondence across views. The latter was proffered to mitigate these issues by learning 2D or 3D representation directly. However, without a large-scale video or 3D training data, it can hardly generalize to diverse real-world scenarios due to the presence of tens of millions or even billions of optimization parameters in the deep network. Recently, robust monocular depth estimation models trained with large-scale datasets have been proven to possess weak 3D geometry prior, but they are insufficient for reconstruction due to the unknown camera parameters, the affine-invariant property, and inter-frame inconsistency. Here, we propose a novel test-time optimization approach that can transfer the robustness of affine-invariant depth models such as LeReS to challenging diverse scenes while ensuring inter-frame consistency, with only dozens of parameters to optimize per video frame. Specifically, our approach involves freezing the pre-trained affine-invariant depth model's depth predictions, rectifying them by optimizing the unknown scale-shift values with a geometric consistency alignment module, and employing the resulting scale-consistent depth maps to robustly obtain camera poses and achieve dense scene reconstruction, even in low-texture regions. Experiments show that our method achieves state-of-the-art cross-dataset reconstruction on five zero-shot testing datasets., Comment: Accepted to ICCV 2023. Project webpage is at: https://aim-uofa.github.io/FrozenRecon/

The altermagnetism can achieve spin-split bands in collinear symmetry-compensated antiferromagnets. Here, we predict altermagnetic order in Janus monolayer $\mathrm{Cr_2SO}$ with eliminated inversion symmetry, which can realize the combination of piezoelectricity and altermagnetism in a two-dimensional material, namely 2D piezoelectric altermagnetism. It is found that $\mathrm{Cr_2SO}$ is an altermagnetic semiconductor, and the spin-split bands of both valence and conduction bands are near the Fermi level. The $\mathrm{Cr_2SO}$ has large out-of-plane piezoelectricity ($|d_{31}|$$=$0.97 pm/V), which is highly desirable for ultrathin piezoelectric device application. Due to spin-valley locking, both spin and valley can be polarized by simply breaking the corresponding crystal symmetry with uniaxial strain. Our findings provide a platform to integrate spin, piezoelectricity and valley in a single material, which is useful for multi-functional device applications., Comment: 6 pages, 5 figures

Video anomaly detection (VAD) is a vital task with great practical applications in industrial surveillance, security system, and traffic control. Unlike previous unsupervised VAD methods that adopt a fixed structure to learn normality without considering different detection demands, we design a spatial-temporal hierarchical architecture (STHA) as a configurable architecture to flexibly detect different degrees of anomaly. The comprehensive structure of the STHA is delineated into a tripartite hierarchy, encompassing the following tiers: the stream level, the stack level, and the block level. Specifically, we design several auto-encoder-based blocks that possess varying capacities for extracting normal patterns. Then, we stack blocks according to the complexity degrees with both intra-stack and inter-stack residual links to learn hierarchical normality gradually. Considering the multisource knowledge of videos, we also model the spatial normality of video frames and temporal normality of RGB difference by designing two parallel streams consisting of stacks. Thus, STHA can provide various representation learning abilities by expanding or contracting hierarchically to detect anomalies of different degrees. Since the anomaly set is complicated and unbounded, our STHA can adjust its detection ability to adapt to the human detection demands and the complexity degree of anomaly that happened in the history of a scene. We conduct experiments on three benchmarks and perform extensive analysis, and the results demonstrate that our method performs comparablely to the state-of-the-art methods. In addition, we design a toy dataset to prove that our model can better balance the learning ability to adapt to different detection demands., Comment: submitted to IEEE TCSVT, under peer review

This paper discusses the results for the second edition of the Monocular Depth Estimation Challenge (MDEC). This edition was open to methods using any form of supervision, including fully-supervised, self-supervised, multi-task or proxy depth. The challenge was based around the SYNS-Patches dataset, which features a wide diversity of environments with high-quality dense ground-truth. This includes complex natural environments, e.g. forests or fields, which are greatly underrepresented in current benchmarks. The challenge received eight unique submissions that outperformed the provided SotA baseline on any of the pointcloud- or image-based metrics. The top supervised submission improved relative F-Score by 27.62%, while the top self-supervised improved it by 16.61%. Supervised submissions generally leveraged large collections of datasets to improve data diversity. Self-supervised submissions instead updated the network architecture and pretrained backbones. These results represent a significant progress in the field, while highlighting avenues for future research, such as reducing interpolation artifacts at depth boundaries, improving self-supervised indoor performance and overall natural image accuracy., Comment: Published at CVPRW2023

Coexistence of ferromagnetism, piezoelectricity and valley in two-dimensional (2D) materials is crucial to advance multifunctional electronic technologies. Here, Janus ScXY (X$\neq$Y=Cl, Br and I) monolayers are predicted to be in-plane piezoelectric ferromagnetic (FM) semiconductors with dynamical, mechanical and thermal stabilities. The predicted piezoelectric strain coefficients $d_{11}$ and $d_{31}$ (absolute values) are higher than ones of most 2D materials. Moreover, the $d_{31}$ (absolute value) of ScClI reaches up to 1.14 pm/V, which is highly desirable for ultrathin piezoelectric device application. To obtain spontaneous valley polarization, charge doping are explored to tune the direction of magnetization of ScXY. By appropriate hole doping, their easy magnetization axis can change from in-plane to out-of-plane, resulting in spontaneous valley polarization. Taking ScBrI with 0.20 holes per f.u. as a example, under the action of an in-plane electric field, the hole carriers of K valley turn towards one edge of the sample, which will produce anomalous valley Hall effect (AVHE), and the hole carriers of $\Gamma$ valley move in a straight line. These findings could pave the way for designing piezoelectric and valleytronic devices., Comment: 7 pages,7 figures

With the growing popularity of intelligent assistants (IAs), evaluating IA quality becomes an increasingly active field of research. This paper identifies and quantifies the feedback effect, a novel component in IA-user interactions: how the capabilities and limitations of the IA influence user behavior over time. First, we demonstrate that unhelpful responses from the IA cause users to delay or reduce subsequent interactions in the short term via an observational study. Next, we expand the time horizon to examine behavior changes and show that as users discover the limitations of the IA's understanding and functional capabilities, they learn to adjust the scope and wording of their requests to increase the likelihood of receiving a helpful response from the IA. Our findings highlight the impact of the feedback effect at both the micro and meso levels. We further discuss its macro-level consequences: unsatisfactory interactions continuously reduce the likelihood and diversity of future user engagements in a feedback loop., Comment: PAKDD 2023

We propose a novel and flexible DNA-storage architecture, which divides the storage space into fixed-size units (blocks) that can be independently and efficiently accessed at random for both read and write operations, and further allows efficient sequential access to consecutive data blocks. In contrast to prior work, in our architecture a pair of random-access PCR primers of length 20 does not define a single object, but an independent storage partition, which is internally blocked and managed independently of other partitions. We expose the flexibility and constraints with which the internal address space of each partition can be managed, and incorporate them into our design to provide rich and functional storage semantics, such as block-storage organization, efficient implementation of data updates, and sequential access. To leverage the full power of the prefix-based nature of PCR addressing, we define a methodology for transforming the internal addressing scheme of a partition into an equivalent that is PCR-compatible. This allows us to run PCR with primers that can be variably elongated to include a desired part of the internal address, and thus narrow down the scope of the reaction to retrieve a specific block or a range of blocks within the partition with sufficiently high accuracy. Our wetlab evaluation demonstrates the practicality of the proposed ideas and a 140x reduction in sequencing cost and latency for retrieval of individual blocks within the partition.

This paper examines the problem of optimizing the charging pattern of electric vehicles (EV) by taking real-time electricity grid carbon intensity into consideration. The objective of the proposed charging scheme is to minimize the carbon emissions contributed by EV charging events, while simultaneously satisfying constraints posed by EV user's charging schedules, charging station transformer limits, and battery physical constraints. Using real-world EV charging data and California electricity generation records, this paper shows that our carbon-aware real-time charging scheme saves an average of 3.81% of carbon emission while delivering satisfactory amount of energy. Furthermore, by using an adaptive balanced factor, we can reduce 26.00% of carbon emission on average while compromising 12.61% of total energy delivered., Comment: Accepted at SmartGridComm 2022; First two authors contribute equally

Valleytronics has been widely investigated for providing new degrees of freedom to future information coding and processing. Here, it is proposed that valley polarization can be achieved by electric field induced magnetic anisotropy (MA) transformation. Through the first-principle calculations, our idea is illustrated by a concrete example of $\mathrm{VSi_2P_4}$ monolayer. The increasing electric field can induce a transition of MA from in-plane to out-of-plane by changing magnetic anisotropy energy (MAE) from negative to positive value, which is mainly due to increasing magnetocrystalline anisotropy (MCA) energy. The out-of-plane magnetization is in favour of spontaneous valley polarization in $\mathrm{VSi_2P_4}$. Within considered electric field range, $\mathrm{VSi_2P_4}$ is always ferromagnetic (FM) ground state. In a certain range of electric field, the coexistence of semiconductor and out-of-plane magnetization makes $\mathrm{VSi_2P_4}$ become a true ferrovalley (FV) material. The anomalous valley Hall effect (AVHE) can be observed under in-plane and out-of-plane electrical field in $\mathrm{VSi_2P_4}$. Our works pave the way to design the ferrovalley material by electric field., Comment: 6 pages, 6 figures. arXiv admin note: text overlap with arXiv:2207.13420

Two-dimensional (2D) ferromagnets have been a fascinating subject of research, and magnetic anisotropy (MA) is indispensable for stabilizing the 2D magnetic order. Here, we investigate magnetic anisotropy energy (MAE), magnetic and electronic properties of $\mathrm{VSi_2P_4}$ by using the generalized gradient approximation plus $U$ (GGA+$U$) approach. For large $U$, the magnetic shape anisotropy (MSA) energy has a more pronounced contribution to the MAE, which can overcome the magnetocrystalline anisotropy (MCA) energy to evince an easy-plane. For fixed out-of-plane MA, monolayer $\mathrm{VSi_2P_4}$ undergoes ferrovalley (FV), half-valley-metal (HVM), valley-polarized quantum anomalous Hall insulator (VQAHI), HVM and FV states with increasing $U$. However, for assumptive in-plane MA, there is no special quantum anomalous Hall (QAH) state and spontaneous valley polarization within considered $U$ range. According to the MAE and electronic structure with fixed out-of-plane or in-plane MA, the intrinsic phase diagram shows common magnetic semiconductor (CMS), FV and VQAHI in monolayer $\mathrm{VSi_2P_4}$. At representative $U$$=$3 eV widely used in references, $\mathrm{VSi_2P_4}$ can be regarded as a 2D-$XY$ magnet, not Ising-like 2D long-range order magnets predicted in previous works with only considering MCA energy. Our findings shed light on importance of MSA in determining magnetic and electronic properties of monolayer $\mathrm{VSi_2P_4}$., Comment: 7 pages,7 figures. arXiv admin note: text overlap with arXiv:2205.07012

Multi-view depth estimation plays a critical role in reconstructing and understanding the 3D world. Recent learning-based methods have made significant progress in it. However, multi-view depth estimation is fundamentally a correspondence-based optimization problem, but previous learning-based methods mainly rely on predefined depth hypotheses to build correspondence as the cost volume and implicitly regularize it to fit depth prediction, deviating from the essence of iterative optimization based on stereo correspondence. Thus, they suffer unsatisfactory precision and generalization capability. In this paper, we are the first to explore more general image correlations to establish correspondences dynamically for depth estimation. We design a novel iterative multi-view depth estimation framework mimicking the optimization process, which consists of 1) a correlation volume construction module that models the pixel similarity between a reference image and source images as all-to-all correlations; 2) a flow-based depth initialization module that estimates the depth from the 2D optical flow; 3) a novel correlation-guided depth refinement module that reprojects points in different views to effectively fetch relevant correlations for further fusion and integrate the fused correlation for iterative depth update. Without predefined depth hypotheses, the fused correlations establish multi-view correspondence in an efficient way and guide the depth refinement heuristically. We conduct sufficient experiments on ScanNet, DeMoN, ETH3D, and 7Scenes to demonstrate the superiority of our method on multi-view depth estimation and its best generalization ability., Comment: 10 pages, 9 figures

Gradient-enhanced Kriging (GE-Kriging) is a well-established surrogate modelling technique for approximating expensive computational models. However, it tends to get impractical for high-dimensional problems due to the size of the inherent correlation matrix and the associated high-dimensional hyper-parameter tuning problem. To address these issues, a new method, called sliced GE-Kriging (SGE-Kriging), is developed in this paper for reducing both the size of the correlation matrix and the number of hyper-parameters. We first split the training sample set into multiple slices, and invoke Bayes' theorem to approximate the full likelihood function via a sliced likelihood function, in which multiple small correlation matrices are utilized to describe the correlation of the sample set rather than one large one. Then, we replace the original high-dimensional hyper-parameter tuning problem with a low-dimensional counterpart by learning the relationship between the hyper-parameters and the derivative-based global sensitivity indices. The performance of SGE-Kriging is finally validated by means of numerical experiments with several benchmarks and a high-dimensional aerodynamic modeling problem. The results show that the SGE-Kriging model features an accuracy and robustness that is comparable to the standard one but comes at much less training costs. The benefits are most evident for high-dimensional problems with tens of variables.

Buoyancy effect on low-flow condition convective heat transfer of non-conventional coolants, such as liquid metal and molten salts, is a crucial safety factor to advanced reactors under transient or accidental scenarios. The distinct heat transfer characteristics of non-unitary Prandtl fluids and the inherent complexity of the low-flow mixed convection phenomena requires the development of novel turbulent and heat transfer models that are adaptive to different spatiotemporal scales involved in the mixed convection heat transfer. In this work, direct numerical simulation of low-flow mixed convection is carried out at low-to-unitary Prandtl numbers that are of industrial interest. Time-averaged statistics, turbulent Prandtl number, as well as time signals are analyzed to investigate mixed convection phenomenon. From the time-averaged statistics, buoyant plume altered velocity boundary layer as well as the intensity of the fluctuation near both walls and channel centerline. Buoyancy effect also rendered different degree of convective heat transfer enhancement and impairment depends on Prandtl and Richardson number. Analysis of time series was conducted on the sodium mixed convection case to emphasize on the low-Pr mixed convection behavior at transition region. Resulting power spectra density and wavelet spectrogram suggests possible large convective structure in transition region. Future work will focus on providing broader data coverage on Pr-Re-Ri parameter space to facilitate more comprehensive analysis of mixed convection.

The passive safety is a crucial feature of advanced nuclear reactor (Gen IV) design. During loss of power scenarios, the downcomer plays a crucial role. The fluid-flow behavior in the downcomer can involve forced to mixed to natural convection and characterizing the heat transfer for these changing regimes is a daunting challenge. High-resolution heat transfer numerical database can potentially support the development of precise and affordable reduced resolution heat transfer models. These models can be designed based on a multiscale hierarchy developed as part of the recently DOE-funded center of excellence for thermal-fluids applications in nuclear energy. In this paper, the downcomer is simplified to heated parallel plates and High Prandtl number fluid (FLiBe) is considered for all simulations. The calculations are performed for a wide range of Richardson number from 0 to 400 at 2 different FLiBe Prandtl numbers (12 and 24) which result in 40 simulated Cases in total. Time averaged and time series statistics as well as Nusselt number correlations are investigated to illuminate the mixed convection behaviors. The calculated database will be instrumental in understanding the flow behaviors in the downcomer. Ultimately, we aimed to evaluate existing heat transfer correlations and some modifications will be proposed for Cases where no satisfactory choice is available., Comment: arXiv admin note: text overlap with arXiv:2203.06670

Existing monocular depth estimation methods have achieved excellent robustness in diverse scenes, but they can only retrieve affine-invariant depth, up to an unknown scale and shift. However, in some video-based scenarios such as video depth estimation and 3D scene reconstruction from a video, the unknown scale and shift residing in per-frame prediction may cause the depth inconsistency. To solve this problem, we propose a locally weighted linear regression method to recover the scale and shift with very sparse anchor points, which ensures the scale consistency along consecutive frames. Extensive experiments show that our method can boost the performance of existing state-of-the-art approaches by 50% at most over several zero-shot benchmarks. Besides, we merge over 6.3 million RGBD images to train strong and robust depth models. Our produced ResNet50-backbone model even outperforms the state-of-the-art DPT ViT-Large model. Combining with geometry-based reconstruction methods, we formulate a new dense 3D scene reconstruction pipeline, which benefits from both the scale consistency of sparse points and the robustness of monocular methods. By performing the simple per-frame prediction over a video, the accurate 3D scene shape can be recovered., Comment: Technical error

In this paper, we propose a sequential directional importance sampling (SDIS) method for rare event estimation. SDIS expresses a small failure probability in terms of a sequence of auxiliary failure probabilities, defined by magnifying the input variability. The first probability in the sequence is estimated with Monte Carlo simulation in Cartesian coordinates, and all the subsequent ones are computed with directional importance sampling in polar coordinates. Samples from the directional importance sampling densities used to estimate the intermediate probabilities are drawn in a sequential manner through a resample-move scheme. The latter is conveniently performed in Cartesian coordinates and directional samples are obtained through a suitable transformation. For the move step, we discuss two Markov Chain Monte Carlo (MCMC) algorithms for application in low and high-dimensional problems. Finally, an adaptive choice of the parameters defining the intermediate failure probabilities is proposed and the resulting coefficient of variation of the failure probability estimate is analyzed. The proposed SDIS method is tested on five examples in various problem settings, which demonstrate that the method outperforms existing sequential sampling reliability methods.

The freeway on-ramp merging section is often identified as a crash-prone spot due to the high frequency of traffic conflicts. Very few traffic conflict analysis studies comprehensively consider different vehicle types at freeway merging section. Thus, the main objective of this study is to analyse conflicts between different vehicle types at freeway merging section. Field data are collected by Unmanned Aerial Vehicle (UAV) at merging areas in Shanghai, China. Vehicle extraction method is utilized to obtain vehicle trajectories. Time-to-collision (TTC) is utilized as the surrogate safety measure. TTC of car-car conflicts are the smallest while TTC of truck-truck conflicts are the largest. Traffic conflicts frequently occur at on-ramp and acceleration lane. Results show the spatial distribution of lane-change conflicts is significantly different between different vehicle types, suggesting that vehicle drivers should maintain safe distance especially car drivers. Besides, in order to decrease lane-change conflict at merging area, traffic management agencies are suggested to change dotted lie to solid lane at the beginning of acceleration lane.

Although the importance of using static analysis to detect taint-style vulnerabilities in Linux-based embedded firmware is widely recognized, existing approaches are plagued by three major limitations. (a) Approaches based on symbolic execution may miss alias information and therefore suffer from a high false-negative rate. (b) Approaches based on VSA (value set analysis) often provide an over-approximate pointer range. As a result, many false positives could be produced. (c) Existing work for detecting taint-style vulnerability does not consider indirect call resolution, whereas indirect calls are frequently used in Internet-facing embedded devices. As a result, many false negatives could be produced. In this work, we propose a precise demand-driven flow-, context- and field-sensitive alias analysis approach. Based on this new approach, we also design a novel indirect call resolution scheme. Combined with sanitization rule checking, our solution discovers taint-style vulnerabilities by static taint analysis. We implemented our idea with a prototype called EmTaint and evaluated it against 35 real-world embedded firmware samples from six popular vendors. EmTaint discovered at least 192 bugs, including 41 n-day bugs and 151 0-day bugs. At least 115 CVE/PSV numbers have been allocated from a subset of the reported vulnerabilities at the time of writing. Compared to state-of-the-art tools such as KARONTE and SaTC, EmTaint found significantly more bugs on the same dataset in less time., Comment: 14 pages, 4 figures

The Bayesian neural network (BNN) method is used to construct a predictive model for fragment prediction of proton induced spallation reactions with the guidance of a simplified EPAX formula. Compared to the experimental data, it is found that the BNN + sEPAX model can reasonably extrapolate with less information compared with BNN method. The BNN + sEPAX method provides a new approach to predict the energy-dependent residual cross sections produced in proton-induced spallation reactions from tens of MeV/u up to several GeV/u., Comment: 9 pages, 1 table, and 7 figures

GaN-based lateral Schottky diodes (SBDs) have attracted great attention for high-power applications due to its combined high electron mobility and large critical breakdown field. However, the breakdown voltage (BV) of the SBDs are far from exploiting the material advantages of GaN at present, limiting the desire to use GaN for ultra-high voltage (UHV) applications. Then, a golden question is whether the excellent properties of GaN-based materials can be practically used in the UHV field? Here we demonstrate UHV AlGaN/GaN SBDs on sapphire with a BV of 10.6 kV, a specific on-resistance of 25.8 m{\Omega}.cm2, yielding a power figure of merit of more than 3.8 GW/cm2. These devices are designed with single channel and 85-{\mu}m anode-to-cathode spacing, without other additional electric field management, demonstrating its great potential for the UHV application in power electronics., Comment: 18 pages, 7 figures. This work has ever been successfully submitted to Nature Electronics for possible publication in July 2021, and the manuscript ID is NATELECTRON-21074250

We present a novel p-GaN gate HEMT structure with reduced hole concentration near the Schottky interface by doping engineering in MOCVD, which aims at lowering the electric field across the gate. By employing an additional unintentionally doped GaN layer, the gate leakage current is suppressed and the gate breakdown voltage is boosted from 10.6 to 14.6 V with negligible influence on the threshold voltage and on-resistance. Time-dependent gate breakdown measurements reveal that the maximum gate drive voltage increases from 6.2 to 10.6 V for a 10-year lifetime with a 1% gate failure rate. This method effectively expands the operating voltage margin of the p-GaN gate HEMTs without any other additional process steps.

Claims from observational studies often fail to replicate. A study was undertaken to assess the reliability of cohort studies used in a highly cited meta-analysis of the association between ambient nitrogen dioxide, NO2, and fine particulate matter, PM2.5, concentrations early in life and development of asthma later in life. The numbers of statistical tests possible were estimated for 19 base papers considered for the meta-analysis. A p-value plot for NO2 and PM2.5 was constructed to evaluate effect heterogeneity of p-values used from the base papers. The numbers of statistical tests possible in the base papers were large - median 13,824, interquartile range 1,536-221,184; range 96-42M, in comparison to statistical test results presented. Statistical test results drawn from the base papers are unlikely to provide unbiased measures for meta-analysis. The p-value plot indicated that heterogeneity of the NO2 results across the base papers is consistent with a two-component mixture. First, it makes no sense to average across a mixture in meta-analysis. Second, the shape of the p-value plot for NO2 appears consistent with the possibility of analysis manipulation to obtain small p-values in several of the cohort studies. As for PM2.5, all corresponding p-values fall on a 45-degree line indicating complete randomness rather than a true association. Our interpretation of the meta-analysis is that the random p-values indicating no cause-effect associations are more plausible and that their meta-analysis will not likely replicate in the absence of bias. We conclude that claims made in the base papers used for meta-analysis are unreliable due to bias induced by multiple testing and multiple modelling, MTMM. We also show there is evidence that the heterogeneity across the base papers used for meta-analysis is more complex than simple sampling from a normal process., Comment: 68 pages including supplemental material. arXiv admin note: text overlap with arXiv:2010.08628

Identifying singers is an important task with many applications. However, the task remains challenging due to many issues. One major issue is related to the confounding factors from the background instrumental music that is mixed with the vocals in music production. A singer identification model may learn to extract non-vocal related features from the instrumental part of the songs, if a singer only sings in certain musical contexts (e.g., genres). The model cannot therefore generalize well when the singer sings in unseen contexts. In this paper, we attempt to address this issue. Specifically, we employ open-unmix, an open source tool with state-of-the-art performance in source separation, to separate the vocal and instrumental tracks of music. We then investigate two means to train a singer identification model: by learning from the separated vocal only, or from an augmented set of data where we "shuffle-and-remix" the separated vocal tracks and instrumental tracks of different songs to artificially make the singers sing in different contexts. We also incorporate melodic features learned from the vocal melody contour for better performance. Evaluation results on a benchmark dataset called the artist20 shows that this data augmentation method greatly improves the accuracy of singer identification.

In recent years, IoT platforms and systems have been rapidly emerging. Although IoT is a new technology, new does not mean simpler (than existing networked systems). Contrarily, the complexity (of IoT platforms and systems) is actually being increased in terms of the interactions between the physical world and cyberspace. The increased complexity indeed results in new vulnerabilities. This paper seeks to provide a review of the recently discovered logic bugs that are specific to IoT platforms and systems. In particular, 17 logic bugs and one weakness falling into seven categories of vulnerabilities are reviewed in this survey.

In a standard computed tomography (CT) image, pixels having the same Hounsfield Units (HU) can correspond to different materials and it is, therefore, challenging to differentiate and quantify materials. Dual-energy CT (DECT) is desirable to differentiate multiple materials, but DECT scanners are not widely available as single-energy CT (SECT) scanners. Here we develop a deep learning approach to perform DECT imaging by using standard SECT data. A deep learning model to map low-energy image to high-energy image using a two-stage convolutional neural network (CNN) is developed. The model was evaluated using patients who received contrast-enhanced abdomen DECT scan with a popular DE application: virtual non-contrast (VNC) imaging and contrast quantification. The HU differences between the predicted and original high-energy CT images are 3.47, 2.95, 2.38 and 2.40 HU for ROIs on the spine, aorta, liver, and stomach, respectively. The HU differences between VNC images obtained from original DECT and deep learning DECT are 4.10, 3.75, 2.33 and 2.92 HU for the 4 ROIs, respectively. The aorta iodine quantification difference between iodine maps obtained from original DECT and deep learning DECT images is 0.9\%, suggesting high consistency between the predicted and the original high-energy CT images. This study demonstrates that highly accurate DECT imaging with single low-energy data is achievable by using a deep learning approach. The proposed method can significantly simplify the DECT system design, reducing the scanning dose and imaging cost., Comment: 7 pages, 3 figures

With the increasing crossover between quantum information and machine learning, quantum simulation of neural networks has drawn unprecedentedly strong attention, especially for the simulation of associative memory in Hopfield neural networks due to their wide applications and relatively simple structures that allow for easier mapping to the quantum regime. Quantum stochastic walk, a strikingly powerful tool to analyze quantum dynamics, has been recently proposed to simulate the firing pattern and associative memory with a dependence on Hamming Distance. We successfully map the theoretical scheme into a three-dimensional photonic quantum chip and realize quantum stochastic walk evolution through well-controlled detunings of the propagation constant. We demonstrate a good match rate of the associative memory between the experimental quantum scheme and the expected result for Hopfield neural networks. The ability of quantum simulation for an important feature of a neural network, combined with the scalability of our approach through low-loss integrated chip and straightforward Hamiltonian engineering, provides a primary but steady step towards photonic artificial intelligence devices for optimization and computation tasks of greatly improved efficiencies., Comment: 7 pages, 4 figures

In this paper, we introduce a novel attentional similarity module for the problem of few-shot sound recognition. Given a few examples of an unseen sound event, a classifier must be quickly adapted to recognize the new sound event without much fine-tuning. The proposed attentional similarity module can be plugged into any metric-based learning method for few-shot learning, allowing the resulting model to especially match related short sound events. Extensive experiments on two datasets shows that the proposed module consistently improves the performance of five different metric-based learning methods for few-shot sound recognition. The relative improvement ranges from +4.1% to +7.7% for 5-shot 5-way accuracy for the ESC-50 dataset, and from +2.1% to +6.5% for noiseESC-50. Qualitative results demonstrate that our method contributes in particular to the recognition of transient sound events., Comment: This is a pre-print version of an ICASSP 2019 paper

We propose to employ a multilevel detection (MLDT) technique to allow multiple users which respectively transmit messages over independent fading channels to share the same resource, e.g., the same signature sequence in the CDMA (code division multiple access) system. The users are separated by the different channel gains including amplitudes and phases resultant from the independent fading channels. In a CDMA system with a fixed amount of available signature sequences, the number of users can be doubled or tripled by using MLDT although there is the cost of some BER performance degradation., Comment: 26 pages, 14 figures. Not published by now. Some results appear in the Master theses of Bei-Hao Chang, I-Hsien Yeh and Ying-Chen Lin respectively at National Taiwan University

A coupling of a dark photon $A'$ from a $U(1)_{A'}$ with the standard model (SM) particles can be generated through kinetic mixing represented by a parameter $\epsilon$. A non-zero $\epsilon$ also induces a mixing between $A'$ and $Z$ if dark photon mass $m_{A'}$ is not zero. This mixing can be large when $m_{A'}$ is close to $m_Z$ even if the parameter $\epsilon$ is small. Many efforts have been made to constrain the parameter $\epsilon$ for a low dark photon mass $m_{A'}$ compared with the $Z$ boson mass $m_Z$. We study the search for dark photon in $e^+e^- \to \gamma A' \to \gamma \mu^+ \mu^-$ for a dark photon mass $m_{A'}$ as large as kinematically allowed at future $e^+e^-$ colliders. For large $m_{A'}$, care should be taken to properly treat possible large mixing between $A'$ and $Z$. We obtain sensitivities to the parameter $\epsilon$ for a wide range of dark photon mass at planed $e^+\;e^-$ colliders, such as Circular Electron Positron Collider (CEPC), International Linear Collider (ILC) and Future Circular Collider (FCC-ee). For the dark photon mass $20~\text{GeV}\lesssim m_{A^{\prime}}\lesssim 330~\text{GeV}$, the $2\sigma$ exclusion limits on the mixing parameter are $\epsilon\lesssim 10^{-3}-10^{-2}$. The CEPC with $\sqrt{s}=240~\text{GeV}$ and FCC-ee with $\sqrt{s}=160~\text{GeV}$ are more sensitive than the constraint from current LHCb measurement once the dark photon mass $m_{A^{\prime}}\gtrsim 50~\text{GeV}$. For $m_{A^{\prime}}\gtrsim 220~\text{GeV}$, the sensitivity at the FCC-ee with $\sqrt{s}=350~\text{GeV}$ and $1.5~\text{ab}^{-1}$ is better than that at the 13~TeV LHC with $300~\text{fb}^{-1}$, while the sensitivity at the CEPC with $\sqrt{s}=240~\text{GeV}$ and $5~\text{ab}^{-1}$ can be even better than that at 13~TeV LHC with $3~\text{ab}^{-1}$ for $m_{A^{\prime}}\gtrsim 180~\text{GeV}$., Comment: 21 pages, 5 figures, 2 tables

Artificial intelligence is making great changes in academy and industry with the fast development of deep learning, which is a branch of machine learning and statistical learning. Fully convolutional network [1] is the standard model for semantic segmentation. Conditional random fields coded as CNN [2] or RNN [3] and connected with FCN has been successfully applied in object detection [4]. In this paper, we introduce a multi-resolution neural network for FCN and apply Gaussian filter to the extended CRF kernel neighborhood and the label image to reduce the oscillating effect of CRF neural network segmentation, thus achieve higher precision and faster training speed., Comment: 11 pages, 9 figures, 2 tables

One of the interesting portals linking a dark sector and the standard model (SM) is the kinetic mixing between the SM $U(1)_Y$ field with a new dark photon $A'$ from a $U(1)_{A'}$ gauge interaction. Stringent limits have been obtained for the kinetic mixing parameter $\epsilon$ through various processes. In this work, we study the possibility of searching for a dark photon interaction at a circular $e^+e^-$ collider through the process $e^+ e^-\to \gamma A^{\prime *} \to \gamma \mu^+\mu^-$. We find that the constraint on $\epsilon^2$ for dark photon mass in the few tens of GeV range, assuming that the $\mu^+\mu^-$ invariant mass can be measured to an accuracy of $0.5\%m_{A'}$, can be better than $3\times 10^{-6}$ for the proposed CEPC with a ten-year running at 3$\sigma$ (statistic) level, and better than $2\times 10^{-6}$ for FCC-ee with even just one-year running at $\sqrt{s} = 240$ GeV, better than the LHC and other facilities can do in a similar dark photon mass range. For FCC-ee, running at $\sqrt{s}=160$ GeV, the constraint can be even better., Comment: RevTex, 9 pages with five figures. Minor modifications added

Compared to imaging in the visible and near-infrared regions below 900 nm, imaging in the second near-infrared window (NIR-II, 1000-1700 nm) is a promising method for deep-tissue high-resolution optical imaging in vivo mainly due to the reduced scattering of photons traversing through biological tissues. Herein, semiconducting single-walled carbon nanotubes with large diameters were used for in vivo fluorescence imaging in the long-wavelength NIR region (1500-1700 nm, NIR-IIb). With this imaging agent, 3-4 um wide capillary blood vessels at a depth of about 3 mm could be resolved. Meanwhile, the blood-flow speeds in multiple individual vessels could be mapped simultaneously. Furthermore, NIR-IIb tumor imaging of a live mouse was explored. NIR-IIb imaging can be generalized to a wide range of fluorophores emitting at up to 1700 nm for high-performance in vivo optical imaging., Comment: 29 pages, 5 main figures and 11 supplementary figures

In vivo fluorescence imaging in the second near-infrared window (1.0-1.7 microns) can afford deep tissue penetration and high spatial resolution, owing to the reduced scattering of long-wavelength photons. Here, we synthesize a series of low-bandgap donor/acceptor copolymers with tunable emission wavelengths of 1050-1350 nm in this window. Non-covalent functionalization with phospholipid-polyethylene glycol results in water-soluble and biocompatible polymeric nanoparticles, allowing for live cell molecular imaging at > 1000 nm with polymer fluorophores for the first time. Importantly, the high quantum yield of the polymer allows for in vivo, deep-tissue and ultrafast imaging of mouse arterial blood flow with an unprecedented frame rate of > 25 frames per second. The high time resolution results in spatially and time resolved imaging of the blood flow pattern in cardiogram waveform over a single cardiac cycle (~ 200 ms) of a mouse, which has not been observed with fluorescence imaging in this window before., Comment: 43 pages, 5 main text figures and 13 supplementary figures, Published in Nature Communications, 2014

Although earlier concentration has addressed the use of corporate e-learning programs (CELP), the dissimilitude between pre and post installation reaction to CELP is less explored. This study adopted a two-phase investigation to survey learner intention to use CELP and actual behavior within an international accounting firm. In the pre-installation phase, a survey was conducted to evaluate learner intention to use CELP, followed one year later by a second investigation to examine learner reactions to CELP and the actual usage frequency/duration. The results of this study identified there is actually a difference between intention and actual usage duration. Further questionnaire surveys were implemented to identify learner reactions and factors that could potentially contribute to the gap between intention and actual usage duration. Results also indicated that scheduling was the critical factor leading to the differences in actual usage. The conclusions clarify the relationships among learner intention to use CELP, actual usage frequency/ duration, and subsequent reactions towards it.

We present a sensor capable of detecting solution-based nanoparticles using an optical fiber tip functionalized with a photonic crystal cavity. When sensor tips are retracted from a nanoparticle solution after being submerged, we find that a combination of convective fluid forces and optically-induced trapping cause an aggregation of nanoparticles to form directly on cavity surfaces. A simple readout of quantum dot photoluminescence coupled to the optical fiber shows that nanoparticle presence and concentration can be detected through modified cavity properties. Our sensor can detect both gold and iron oxide nanoparticles and can be utilized for molecular sensing applications in biomedicine., Comment: 13 pages, 5 figures