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Current passive deepfake face-swapping detection methods encounter significance bottlenecks in model generalization capabilities. Meanwhile, proactive detection methods often use fixed watermarks which lack a close relationship with the content they protect and are vulnerable to security risks. Dynamic watermarks based on facial features offer a promising solution, as these features provide unique identifiers. Therefore, this paper proposes a Facial Feature-based Proactive deepfake detection method (FaceProtect), which utilizes changes in facial characteristics during deepfake manipulation as a novel detection mechanism. We introduce a GAN-based One-way Dynamic Watermark Generating Mechanism (GODWGM) that uses 128-dimensional facial feature vectors as inputs. This method creates irreversible mappings from facial features to watermarks, enhancing protection against various reverse inference attacks. Additionally, we propose a Watermark-based Verification Strategy (WVS) that combines steganography with GODWGM, allowing simultaneous transmission of the benchmark watermark representing facial features within the image. Experimental results demonstrate that our proposed method maintains exceptional detection performance and exhibits high practicality on images altered by various deepfake techniques.

Spin masers with optical detection and artificial feedback are widely used in fundamental and practical applications. However, a full picture of the maser dynamics is still absent. By solving the feedback driven Bloch equations, we simulated the dynamics of an ideal spin maser in a broad parameter space. Rich nonlinear dynamics including high order harmonics generation, nonperiodic spin oscillations and frequency comb were revealed when the artificial feedback interaction exceeds the normal spin-field interaction. We also propose a pulse feedback spin maser protocol, which constructs an ultralow field magnetic frequency comb and could be useful in precision atomic magnetometers in searching for spin-dependent exotic interactions., Comment: 6 pages,5 figures

In this paper, we present a wavefunction of the universe, which correspond to an Euclidean charged wineglass (half)-wormholes semiclassically, as a possible creation for our inflationary universe. We calculate the Euclidean action of the charged wormhole, and find that the initial state of universe brought by such an Euclidean charged wormhole can exhibit a high probability weight for a long period of inflation. We compare our result with that of axion wormholes, and evaluate the potential of other corresponding Euclidean configurations as the pre-inflationary initial states., Comment: 27 pages, 4 figures

Night-to-Day translation (Night2Day) aims to achieve day-like vision for nighttime scenes. However, processing night images with complex degradations remains a significant challenge under unpaired conditions. Previous methods that uniformly mitigate these degradations have proven inadequate in simultaneously restoring daytime domain information and preserving underlying semantics. In this paper, we propose \textbf{N2D3} (\textbf{N}ight-to-\textbf{D}ay via \textbf{D}egradation \textbf{D}isentanglement) to identify different degradation patterns in nighttime images. Specifically, our method comprises a degradation disentanglement module and a degradation-aware contrastive learning module. Firstly, we extract physical priors from a photometric model based on Kubelka-Munk theory. Then, guided by these physical priors, we design a disentanglement module to discriminate among different illumination degradation regions. Finally, we introduce the degradation-aware contrastive learning strategy to preserve semantic consistency across distinct degradation regions. Our method is evaluated on two public datasets, demonstrating a significant improvement in visual quality and considerable potential for benefiting downstream tasks., Comment: 8 pages

Accurate brain tumor segmentation remains a challenging task due to structural complexity and great individual differences of gliomas. Leveraging the pre-eminent detail resilience of CRF and spatial feature extraction capacity of V-net, we propose a multimodal 3D Volume Generative Adversarial Network (3D-vGAN) for precise segmentation. The model utilizes Pseudo-3D for V-net improvement, adds conditional random field after generator and use original image as supplemental guidance. Results, using the BraTS-2018 dataset, show that 3D-vGAN outperforms classical segmentation models, including U-net, Gan, FCN and 3D V-net, reaching specificity over 99.8%., Comment: 13 pages, 7 figures, Annual Conference on Medical Image Understanding and Analysis (MIUA) 2024

Large Language Models (LLMs) have demonstrated the potential to address some issues within the semiconductor industry. However, they are often general-purpose models that lack the specialized knowledge needed to tackle the unique challenges of this sector, such as the intricate physics and chemistry of semiconductor devices and processes. SemiKong, the first industry-specific LLM for the semiconductor domain, provides a foundation that can be used to develop tailored proprietary models. With SemiKong 1.0, we aim to develop a foundational model capable of understanding etching problems at an expert level. Our key contributions include (a) curating a comprehensive corpus of semiconductor-related texts, (b) creating a foundational model with in-depth semiconductor knowledge, and (c) introducing a framework for integrating expert knowledge, thereby advancing the evaluation process of domain-specific AI models. Through fine-tuning a pre-trained LLM using our curated dataset, we have shown that SemiKong outperforms larger, general-purpose LLMs in various semiconductor manufacturing and design tasks. Our extensive experiments underscore the importance of developing domain-specific LLMs as a foundation for company- or tool-specific proprietary models, paving the way for further research and applications in the semiconductor domain. Code and dataset will be available at https://github.com/aitomatic/semikong, Comment: On-going work

Evaluating the output of Large Language Models (LLMs) is one of the most critical aspects of building a performant compound AI system. Since the output from LLMs propagate to downstream steps, identifying LLM errors is crucial to system performance. A common task for LLMs in AI systems is tool use. While there are several benchmark environments for evaluating LLMs on this task, they typically only give a success rate without any explanation of the failure cases. To solve this problem, we introduce SpecTool, a new benchmark to identify error patterns in LLM output on tool-use tasks. Our benchmark data set comprises of queries from diverse environments that can be used to test for the presence of seven newly characterized error patterns. Using SPECTOOL , we show that even the most prominent LLMs exhibit these error patterns in their outputs. Researchers can use the analysis and insights from SPECTOOL to guide their error mitigation strategies.

Single object tracking(SOT) relies on precise object bounding box initialization. In this paper, we reconsidered the deficiencies in the current approaches to initializing single object trackers and propose a new paradigm for single object tracking algorithms, ClickTrack, a new paradigm using clicking interaction for real-time scenarios. Moreover, click as an input type inherently lack hierarchical information. To address ambiguity in certain special scenarios, we designed the Guided Click Refiner(GCR), which accepts point and optional textual information as inputs, transforming the point into the bounding box expected by the operator. The bounding box will be used as input of single object trackers. Experiments on LaSOT and GOT-10k benchmarks show that tracker combined with GCR achieves stable performance in real-time interactive scenarios. Furthermore, we explored the integration of GCR into the Segment Anything model(SAM), significantly reducing ambiguity issues when SAM receives point inputs.

Motivated by the vast gap between photometric and spectroscopic data volumes, there is great potential in using 5D kinematic information to identify and study substructures of the Milky Way. We identify substructures in the Galactic halo using 46,575 RR Lyrae stars (RRLs) from Gaia DR3 with the photometric metallicities and distances newly estimated by Li et al. (2023). Assuming a Gaussian prior distribution of radial velocity, we calculate the orbital distribution characterized by the integrals of motion for each RRL based on its 3D positions, proper motions and corresponding errors, and then apply the friends-of-friends algorithm to identify groups moving along similar orbits. We have identified several known substructures, including Sagittarius (Sgr) Stream, Hercules-Aquila Cloud (HAC), Virgo Overdensity (VOD), Gaia-Enceladus-Sausage (GES), Orphan-Chenab stream, Cetus-Palca, Helmi Streams, Sequoia, Wukong and Large Magellanic Cloud (LMC) leading arm, along with 18 unknown groups. Our findings indicate that HAC and VOD have kinematic and chemical properties remarkably similar to GES, with most HAC and VOD members exhibiting eccentricity as high as GES, suggesting that they may share a common origin with GES. The ability to identify the low mass and spatially dispersed substructures further demonstrates the potential of our method, which breaks the limit of spectroscopic survey and is competent to probe the substructures in the whole Galaxy. Finally, we have also identified 18 unknown groups with good spatial clustering and proper motion consistency, suggesting more excavation of Milky Way substructures in the future with only 5D data., Comment: 23 pages, 19 figures, 4 tables, accepted for publication in ApJ, version before language edition

Existing click-through rate (CTR) prediction works have studied the role of feature interaction through a variety of techniques. Each interaction technique exhibits its own strength, and solely using one type could constrain the model's capability to capture the complex feature relationships, especially for industrial large-scale data with enormous users and items. Recent research shows that effective CTR models often combine an MLP network with a dedicated feature interaction network in a two-parallel structure. However, the interplay and cooperative dynamics between different streams or branches remain under-researched. In this work, we introduce a novel Multi-Branch Cooperation Network (MBCnet) which enables multiple branch networks to collaborate with each other for better complex feature interaction modeling. Specifically, MBCnet consists of three branches: the Expert-based Feature Grouping and Crossing (EFGC) branch that promotes the model's memorization ability of specific feature fields, the low rank Cross Net branch and Deep branch to enhance both explicit and implicit feature crossing for improved generalization. Among branches, a novel cooperation scheme is proposed based on two principles: branch co-teaching and moderate differentiation. Branch co-teaching encourages well-learned branches to support poorly-learned ones on specific training samples. Moderate differentiation advocates branches to maintain a reasonable level of difference in their feature representations. The cooperation strategy improves learning through mutual knowledge sharing via co-teaching and boosts the discovery of diverse feature interactions across branches. Extensive experiments on large-scale industrial datasets and online A/B test demonstrate MBCnet's superior performance, delivering a 0.09 point increase in CTR, 1.49% growth in deals, and 1.62% rise in GMV. Core codes will be released soon., Comment: 10 pages

This paper introduces the partial Gini covariance, a novel dependence measure that addresses the challenges of high-dimensional inference with heavy-tailed errors, often encountered in fields like finance, insurance, climate, and biology. Conventional high-dimensional regression inference methods suffer from inaccurate type I errors and reduced power in heavy-tailed contexts, limiting their effectiveness. Our proposed approach leverages the partial Gini covariance to construct a robust statistical inference framework that requires minimal tuning and does not impose restrictive moment conditions on error distributions. Unlike traditional methods, it circumvents the need for estimating the density of random errors and enhances the computational feasibility and robustness. Extensive simulations demonstrate the proposed method's superior power and robustness over standard high-dimensional inference approaches, such as those based on the debiased Lasso. The asymptotic relative efficiency analysis provides additional theoretical insight on the improved efficiency of the new approach in the heavy-tailed setting. Additionally, the partial Gini covariance extends to the multivariate setting, enabling chi-square testing for a group of coefficients. We illustrate the method's practical application with a real-world data example.

Cataract is one of the most common blinding eye diseases and can be treated by surgery. However, because cataract patients may also suffer from other blinding eye diseases, ophthalmologists must diagnose them before surgery. The cloudy lens of cataract patients forms a hazy degeneration in the fundus images, making it challenging to observe the patient's fundus vessels, which brings difficulties to the diagnosis process. To address this issue, this paper establishes a new cataract image restoration method named Catintell. It contains a cataract image synthesizing model, Catintell-Syn, and a restoration model, Catintell-Res. Catintell-Syn uses GAN architecture with fully unsupervised data to generate paired cataract-like images with realistic style and texture rather than the conventional Gaussian degradation algorithm. Meanwhile, Catintell-Res is an image restoration network that can improve the quality of real cataract fundus images using the knowledge learned from synthetic cataract images. Extensive experiments show that Catintell-Res outperforms other cataract image restoration methods in PSNR with 39.03 and SSIM with 0.9476. Furthermore, the universal restoration ability that Catintell-Res gained from unpaired cataract images can process cataract images from various datasets. We hope the models can help ophthalmologists identify other blinding eye diseases of cataract patients and inspire more medical image restoration methods in the future., Comment: 12 pages, 8 figures

The retinal fundus images are utilized extensively in the diagnosis, and their quality can directly affect the diagnosis results. However, due to the insufficient dataset and algorithm application, current fundus image quality assessment (FIQA) methods are not powerful enough to meet ophthalmologists` demands. In this paper, we address the limitations of datasets and algorithms in FIQA. First, we establish a new FIQA dataset, Fundus Quality Score(FQS), which includes 2246 fundus images with two labels: a continuous Mean Opinion Score varying from 0 to 100 and a three-level quality label. Then, we propose a FIQA Transformer-based Hypernetwork (FTHNet) to solve these tasks with regression results rather than classification results in conventional FIQA works. The FTHNet is optimized for the FIQA tasks with extensive experiments. Results on our FQS dataset show that the FTHNet can give quality scores for fundus images with PLCC of 0.9423 and SRCC of 0.9488, significantly outperforming other methods with fewer parameters and less computation complexity.We successfully build a dataset and model addressing the problems of current FIQA methods. Furthermore, the model deployment experiments demonstrate its potential in automatic medical image quality control. All experiments are carried out with 10-fold cross-validation to ensure the significance of the results., Comment: 11 pages, 7 figures

We present cosmological results from the measurement of clustering of galaxy, quasar and Lyman-$\alpha$ forest tracers from the first year of observations with the Dark Energy Spectroscopic Instrument (DESI Data Release 1). We adopt the full-shape (FS) modeling of the power spectrum, including the effects of redshift-space distortions, in an analysis which has been validated in a series of supporting papers. In the flat $\Lambda$CDM cosmological model, DESI (FS+BAO), combined with a baryon density prior from Big Bang Nucleosynthesis and a weak prior on the scalar spectral index, determines matter density to $\Omega_\mathrm{m}=0.2962\pm 0.0095$, and the amplitude of mass fluctuations to $\sigma_8=0.842\pm 0.034$. The addition of the cosmic microwave background (CMB) data tightens these constraints to $\Omega_\mathrm{m}=0.3056\pm 0.0049$ and $\sigma_8=0.8121\pm 0.0053$, while further addition of the the joint clustering and lensing analysis from the Dark Energy Survey Year-3 (DESY3) data leads to a 0.4% determination of the Hubble constant, $H_0 = (68.40\pm 0.27)\,{\rm km\,s^{-1}\,Mpc^{-1}}$. In models with a time-varying dark energy equation of state, combinations of DESI (FS+BAO) with CMB and type Ia supernovae continue to show the preference, previously found in the DESI DR1 BAO analysis, for $w_0>-1$ and $w_a<0$ with similar levels of significance. DESI data, in combination with the CMB, impose the upper limits on the sum of the neutrino masses of $\sum m_\nu < 0.071\,{\rm eV}$ at 95% confidence. DESI data alone measure the modified-gravity parameter that controls the clustering of massive particles, $\mu_0=0.11^{+0.45}_{-0.54}$, while the combination of DESI with the CMB and the clustering and lensing analysis from DESY3 constrains both modified-gravity parameters, giving $\mu_0 = 0.04\pm 0.22$ and $\Sigma_0 = 0.044\pm 0.047$, in agreement with general relativity. [Abridged.], Comment: This DESI Collaboration Key Publication is part of the 2024 publication series using the first year of observations (see https://data.desi.lbl.gov/doc/papers/). 55 pages, 10 figures

We present the samples of galaxies and quasars used for DESI 2024 cosmological analyses, drawn from the DESI Data Release 1 (DR1). We describe the construction of large-scale structure (LSS) catalogs from these samples, which include matched sets of synthetic reference `randoms' and weights that account for variations in the observed density of the samples due to experimental design and varying instrument performance. We detail how we correct for variations in observational completeness, the input `target' densities due to imaging systematics, and the ability to confidently measure redshifts from DESI spectra. We then summarize how remaining uncertainties in the corrections can be translated to systematic uncertainties for particular analyses. We describe the weights added to maximize the signal-to-noise of DESI DR1 2-point clustering measurements. We detail measurement pipelines applied to the LSS catalogs that obtain 2-point clustering measurements in configuration and Fourier space. The resulting 2-point measurements depend on window functions and normalization constraints particular to each sample, and we present the corrections required to match models to the data. We compare the configuration- and Fourier-space 2-point clustering of the data samples to that recovered from simulations of DESI DR1 and find they are, generally, in statistical agreement to within 2\% in the inferred real-space over-density field. The LSS catalogs, 2-point measurements, and their covariance matrices will be released publicly with DESI DR1., Comment: This DESI Collaboration Key Publication is part of the 2024 publication series using the first year of observations (see https://data.desi.lbl.gov/doc/papers/)

We present the measurements and cosmological implications of the galaxy two-point clustering using over 4.7 million unique galaxy and quasar redshifts in the range $0.1

The search for low-cost, durable, and effective catalysts is essential for green hydrogen production and carbon dioxide upcycling to help in the mitigation of climate change. Discovery of new catalysts is currently limited by the gap between what AI-accelerated computational models predict and what experimental studies produce. To make progress, large and diverse experimental datasets are needed that are reproducible and tested at industrially-relevant conditions. We address these needs by utilizing a comprehensive high-throughput characterization and experimental pipeline to create the Open Catalyst Experiments 2024 (OCX24) dataset. The dataset contains 572 samples synthesized using both wet and dry methods with X-ray fluorescence and X-ray diffraction characterization. We prepared 441 gas diffusion electrodes, including replicates, and evaluated them using zero-gap electrolysis for carbon dioxide reduction (CO$_2$RR) and hydrogen evolution reactions (HER) at current densities up to $300$ mA/cm$^2$. To find correlations with experimental outcomes and to perform computational screens, DFT-verified adsorption energies for six adsorbates were calculated on $\sim$20,000 inorganic materials requiring 685 million AI-accelerated relaxations. Remarkably from this large set of materials, a data driven Sabatier volcano independently identified Pt as being a top candidate for HER without having any experimental measurements on Pt or Pt-alloy samples. We anticipate the availability of experimental data generated specifically for AI training, such as OCX24, will significantly improve the utility of computational models in selecting materials for experimental screening., Comment: 38 pages, 22 figures

Text logo design heavily relies on the creativity and expertise of professional designers, in which arranging element layouts is one of the most important procedures. However, few attention has been paid to this specific task which needs to take precise textural details and user constraints into consideration, but only on the broader tasks such as document/poster layout generation. In this paper, we propose a VLM-based framework that generates content-aware text logo layouts by integrating multi-modal inputs with user constraints, supporting a more flexible and stable layout design in real-world applications. We introduce two model techniques to reduce the computation for processing multiple glyph images simultaneously, while does not face performance degradation. To support instruction-tuning of out model, we construct two extensive text logo datasets, which are 5x more larger than the existing public dataset. Except for the geometric annotations (e.g. text masks and character recognition), we also compliment with comprehensive layout descriptions in natural language format, for more effective training to have reasoning ability when dealing with complex layouts and custom user constraints. Experimental studies demonstrate the effectiveness of our proposed model and datasets, when comparing with previous methods in various benchmarks to evaluate geometric aesthetics and human preferences. The code and datasets will be publicly available.

Decision Transformer (DT) can learn effective policy from offline datasets by converting the offline reinforcement learning (RL) into a supervised sequence modeling task, where the trajectory elements are generated auto-regressively conditioned on the return-to-go (RTG).However, the sequence modeling learning approach tends to learn policies that converge on the sub-optimal trajectories within the dataset, for lack of bridging data to move to better trajectories, even if the condition is set to the highest RTG.To address this issue, we introduce Diffusion-Based Trajectory Branch Generation (BG), which expands the trajectories of the dataset with branches generated by a diffusion model.The trajectory branch is generated based on the segment of the trajectory within the dataset, and leads to trajectories with higher returns.We concatenate the generated branch with the trajectory segment as an expansion of the trajectory.After expanding, DT has more opportunities to learn policies to move to better trajectories, preventing it from converging to the sub-optimal trajectories.Empirically, after processing with BG, DT outperforms state-of-the-art sequence modeling methods on D4RL benchmark, demonstrating the effectiveness of adding branches to the dataset without further modifications.

Quantum secure direct communication (QSDC) enables the message sender to directly send secure messages to the receiver through the quantum channel without keys. Device-independent (DI) and measurement-device-independent (MDI) QSDC protocols can enhance QSDC's practical security in theory. DI QSDC requires extremely high global detection efficiency and has quite low secure communication distance. DI and MDI QSDC both require high-quality entanglement. Current entanglement sources prepare entangled photon pairs with low efficiency, largely reducing their practical communication efficiency. In the paper, we propose a single-photon-based receiver-device-independent (RDI) QSDC protocol. It only relies on the trusted single-photon source, which is nearly on-demand under current technology, and treats all the receiving devices in both communication parties as ``black-boxes''. The parties ensure the message security only from the observed statistics. We develop a numerical method to simulate its performance in practical noisy communication situation. RDI QSDC provides the same security level as MDI QSDC. Compared with DI and MDI QSDC, RDI QSDC has some advantages. First, it uses the single-photon source and single-photon measurement, which makes it obtain the practical communication efficiency about 3415 times of that in DI QSDC and easy to implement. The whole protocol is feasible with current technology. Second, it has higher photon loss robustness and noise tolerance than DI QSDC, which enables it to have a secure communication distance about 26 times of that in DI QSDC. Based on above features, the RDI QSDC protocol makes it possible to achieve highly-secure and high-efficient QSDC in the near future.

In the study of cosmology and galaxy evolution, the peculiar velocity and density field of dark matter (DM) play a crucial role in studying many issues. Here, we propose a UNet-based deep learning to reconstruct the real-space DM velocity field from the spatial distribution of a sparse sample of DM halos in redshift space. By comparing and testing various properties, we demonstrate that the reconstructed velocity field is in good agreement with the actual situation. At $k<0.3~h/{\rm Mpc}$, the reconstruction of various velocity field components, including velocity magnitude and divergence, outperforms traditional linear perturbation theory. Additionally, the effects of redshift space distortions (RSD) are well corrected using the UNet model. Compared to the true real-space power spectra, the UNet reconstruction provides an unbiased estimate of the density, velocity, and momentum fields, remaining consistent within $2\sigma$ level. We also demonstrate that the UNet model remains effective even with limited information about halo masses. Thus, our proposed UNet model has a wide range of applications in various aspects of cosmology, such as RSD, cosmic web analysis, the kinetic Sunyaev-Zel'dovich effect, BAO reconstruction, and so on., Comment: 17 pages,14 figures

Pre-trained vision-language models provide a robust foundation for efficient transfer learning across various downstream tasks. In the field of video action recognition, mainstream approaches often introduce additional parameter modules to capture temporal information. While the increased model capacity brought by these additional parameters helps better fit the video-specific inductive biases, existing methods require learning a large number of parameters and are prone to catastrophic forgetting of the original generalizable knowledge. In this paper, we propose a simple yet effective Multi-modal Spatio-Temporal Adapter (MSTA) to improve the alignment between representations in the text and vision branches, achieving a balance between general knowledge and task-specific knowledge. Furthermore, to mitigate over-fitting and enhance generalizability, we introduce a spatio-temporal description-guided consistency constraint. This constraint involves feeding template inputs (i.e., ``a video of $\{\textbf{cls}\}$'') into the trainable language branch, while LLM-generated spatio-temporal descriptions are input into the pre-trained language branch, enforcing consistency between the outputs of the two branches. This mechanism prevents over-fitting to downstream tasks and improves the distinguishability of the trainable branch within the spatio-temporal semantic space. We evaluate the effectiveness of our approach across four tasks: zero-shot transfer, few-shot learning, base-to-novel generalization, and fully-supervised learning. Compared to many state-of-the-art methods, our MSTA achieves outstanding performance across all evaluations, while using only 2-7\% of the trainable parameters in the original model. Code will be avaliable at https://github.com/chenhaoxing/ETL4Video.

Recent advancements of generative AI have significantly promoted content creation and editing, where prevailing studies further extend this exciting progress to video editing. In doing so, these studies mainly transfer the inherent motion patterns from the source videos to the edited ones, where results with inferior consistency to user prompts are often observed, due to the lack of particular alignments between the delivered motions and edited contents. To address this limitation, we present a shape-consistent video editing method, namely StableV2V, in this paper. Our method decomposes the entire editing pipeline into several sequential procedures, where it edits the first video frame, then establishes an alignment between the delivered motions and user prompts, and eventually propagates the edited contents to all other frames based on such alignment. Furthermore, we curate a testing benchmark, namely DAVIS-Edit, for a comprehensive evaluation of video editing, considering various types of prompts and difficulties. Experimental results and analyses illustrate the outperforming performance, visual consistency, and inference efficiency of our method compared to existing state-of-the-art studies., Comment: Project page: https://alonzoleeeooo.github.io/StableV2V, code: https://github.com/AlonzoLeeeooo/StableV2V, model weights: https://huggingface.co/AlonzoLeeeooo/StableV2V, dataset (DAVIS-Edit): https://huggingface.co/datasets/AlonzoLeeeooo/DAVIS-Edit

We investigate the dynamics of a relativistic spin-1/2 particle governed by a one-dimensional time-periodic kicking Dirac equation. We observe distinct oscillatory behavior in the momentum space and quantum tunneling in the vicinity of zero momentum, which are found to be equivalent to the celebrated Bloch oscillations and Landau-Zener tunneling in solid state periodic energy bands. Using the Floquet formalism, we derive an effective Hamiltonian that can accurately predict both the oscillation period and amplitude. The tunneling probability has also been determined analytically. Our analysis extends to the influence of various parameters on the dynamical behavior, might shedding light on how relativistic effects and spin degrees of freedom impact transport properties and localization phenomena in the quantum systems.

Remote sensing (RS) visual grounding aims to use natural language expression to locate specific objects (in the form of the bounding box or segmentation mask) in RS images, enhancing human interaction with intelligent RS interpretation systems. Early research in this area was primarily based on horizontal bounding boxes (HBBs), but as more diverse RS datasets have become available, tasks involving oriented bounding boxes (OBBs) and segmentation masks have emerged. In practical applications, different targets require different grounding types: HBB can localize an object's position, OBB provides its orientation, and mask depicts its shape. However, existing specialized methods are typically tailored to a single type of RS visual grounding task and are hard to generalize across tasks. In contrast, large vision-language models (VLMs) exhibit powerful multi-task learning capabilities but struggle to handle dense prediction tasks like segmentation. This paper proposes GeoGround, a novel framework that unifies support for HBB, OBB, and mask RS visual grounding tasks, allowing flexible output selection. Rather than customizing the architecture of VLM, our work aims to elegantly support pixel-level visual grounding output through the Text-Mask technique. We define prompt-assisted and geometry-guided learning to enhance consistency across different signals. To support model training, we present refGeo, a large-scale RS visual instruction-following dataset containing 161k image-text pairs. Experimental results show that GeoGround demonstrates strong performance across four RS visual grounding tasks, matching or surpassing the performance of specialized methods on multiple benchmarks. Code available at https://github.com/zytx121/GeoGround, Comment: 25 pages, 19 figures

The Structured Dialogue System, referred to as SuDoSys, is an innovative Large Language Model (LLM)-based chatbot designed to provide psychological counseling. SuDoSys leverages the World Health Organization (WHO)'s Problem Management Plus (PM+) guidelines to deliver stage-aware multi-turn dialogues. Existing methods for employing an LLM in multi-turn psychological counseling typically involve direct fine-tuning using generated dialogues, often neglecting the dynamic stage shifts of counseling sessions. Unlike previous approaches, SuDoSys considers the different stages of counseling and stores essential information throughout the counseling process, ensuring coherent and directed conversations. The system employs an LLM, a stage-aware instruction generator, a response unpacker, a topic database, and a stage controller to maintain dialogue flow. In addition, we propose a novel technique that simulates counseling clients to interact with the evaluated system and evaluate its performance automatically. When assessed using both objective and subjective evaluations, SuDoSys demonstrates its effectiveness in generating logically coherent responses. The system's code and program scripts for evaluation are open-sourced., Comment: Accepted to the 16th International Conference on Social Robotic (ICSR 2024)

Scene graphs have proven to be highly effective for various scene understanding tasks due to their compact and explicit representation of relational information. However, current methods often overlook the critical importance of preserving symmetry when generating scene graphs from 3D point clouds, which can lead to reduced accuracy and robustness, particularly when dealing with noisy, multi-view data. This work, to the best of our knowledge, presents the first implementation of an Equivariant Scene Graph Neural Network (ESGNN) to generate semantic scene graphs from 3D point clouds, specifically for enhanced scene understanding. Furthermore, a significant limitation of prior methods is the absence of temporal modeling to capture time-dependent relationships among dynamically evolving entities within a scene. To address this gap, we introduce a novel temporal layer that leverages the symmetry-preserving properties of ESGNN to fuse scene graphs across multiple sequences into a unified global representation by an approximate graph-matching algorithm. Our combined architecture, termed the Temporal Equivariant Scene Graph Neural Network (TESGNN), not only surpasses existing state-of-the-art methods in scene estimation accuracy but also achieves faster convergence. Importantly, TESGNN is computationally efficient and straightforward to implement using existing frameworks, making it well-suited for real-time applications in robotics and computer vision. This approach paves the way for more robust and scalable solutions to complex multi-view scene understanding challenges. Our source code is publicly available at: https://github.com/HySonLab/TESGraph, Comment: arXiv admin note: text overlap with arXiv:2407.00609

Stuttering is a neurodevelopmental speech disorder characterized by common speech symptoms such as pauses, exclamations, repetition, and prolongation. Speech-language pathologists typically assess the type and severity of stuttering by observing these symptoms. Many effective end-to-end methods exist for stuttering detection, but a commonly overlooked challenge is the uncertain relationship between tasks involved in this process. Using a suitable multi-task strategy could improve stuttering detection performance. This paper presents a novel stuttering event detection model designed to help speech-language pathologists assess both the type and severity of stuttering. First, the Conformer model extracts acoustic features from stuttered speech, followed by a Long Short-Term Memory (LSTM) network to capture contextual information. Finally, we explore multi-task learning for stuttering and propose an effective multi-task strategy. Experimental results show that our model outperforms current state-of-the-art methods for stuttering detection. In the SLT 2024 Stuttering Speech Challenge based on the AS-70 dataset [1], our model improved the mean F1 score by 24.8% compared to the baseline method and achieved first place. On this basis, we conducted relevant extensive experiments on LSTM and multi-task learning strategies respectively. The results show that our proposed method improved the mean F1 score by 39.8% compared to the baseline method., Comment: 7 pages, 3 figures, 7 tables

This paper studies the steady-state oscillations and the corresponding energy transfers in the two-DOF mechanical system with a grounded nonlinear energy sink (NES), under an external excitation. Based on the complexification-multiscale method, the slow frequency and the fast frequency of the complex amplitudes on the invariant manifold are analyzed, without omitting any terms. The analytical results show that the complex amplitudes of the primary system responses contain only the slow frequency, while that of the NES responses can contain both the slow frequency and the fast frequency when it triggers the high-order frequency term. In addition, each type of set on the invariant manifold represents the corresponding steady-state oscillations of the system, that is, the fixed point corresponds to the stationary oscillation, the interval corresponds to the nonlinear beat, and the hysteresis loop corresponds to the relaxation oscillation. Accordingly, we rewrite the forms of the complexification system to match the corresponding types of steady-state oscillations. Furthermore, the Hilbert-Huang transform is used to investigate the time-frequency relation of the displacements and the energies of the three steady-state responses. The Hilbert spectrums indicate that there is no energy transfer in the stationary oscillations, the O({\epsilon})/O({\epsilon}) type of energy transfer in the nonlinear beats, and the O({\epsilon})/O({\epsilon}) and the O({\epsilon})/O(1) types of energy transfer in the relaxation oscillations. The theoretical results are confirmed by numerical simulations., Comment: 29 pages,40 figures

The present paper continues the work of [2]. For any symmetrizable generalized Cartan Matrix $C$ and the corresponding quantum group $\mathbf{U}$, we consider the associated quiver $Q$ with an admissible automorphism $a$. We construct the category $\widetilde{\mathcal{Q}/\mathcal{N}}$ of the localization of Lusztig sheaves for the quiver with the automorphism. Its Grothendieck group gives a realization of the integrable highest weight $\mathbf{U}-$module $\Lambda_{\lambda}$, and modulo the traceless ones Lusztig sheaves provide the (signed) canonical basis of $\Lambda_{\lambda}$. As an application, the symmetrizable crystal structures on Nakajima's quiver varieties and Lusztig's nilpotent varieties of preprojective algebras are deduced.

Topic modeling is a fundamental task in natural language processing, allowing the discovery of latent thematic structures in text corpora. While Large Language Models (LLMs) have demonstrated promising capabilities in topic discovery, their direct application to topic modeling suffers from issues such as incomplete topic coverage, misalignment of topics, and inefficiency. To address these limitations, we propose LLM-ITL, a novel LLM-in-the-loop framework that integrates LLMs with many existing Neural Topic Models (NTMs). In LLM-ITL, global topics and document representations are learned through the NTM, while an LLM refines the topics via a confidence-weighted Optimal Transport (OT)-based alignment objective. This process enhances the interpretability and coherence of the learned topics, while maintaining the efficiency of NTMs. Extensive experiments demonstrate that LLM-ITL can help NTMs significantly improve their topic interpretability while maintaining the quality of document representation.

The multi-window observations, including the light curve and the evolutions of the spectral peak energy ($E_p$), the polarization degree (PD) and the polarization angle (PA), are used to infer the model parameters to predict the time-integrated PD in gamma-ray burst (GRB) prompt phase. We select 23 GRBs co-detected by Fermi/GBM and polarization detectors (i.e., GAP, POLAR and AstroSat). In our multi-window fitting, the light curve, $E_p$ curve, PD curve and PA curve are interpreted simultaneously under the synchrotron radiation model in ordered magnetic fields (i.e., the aligned-fields case and the toroidal-fields case). For the bursts with abrupt PA rotations, the predicted time-integrated PD of the aligned-fields case roughly matches the corresponding observed best fit value, while it is higher for the toroidal-fields case. For the bursts without abrupt PA rotation(s), the predicted PDs of the aligned-fields case and the toroidal-fields case are comparable and could interpret the observational data equally well. For GRB 170206A, its observed time-resolved and time-integrated PDs are comparable and both smaller than our predicted upper limits in ordered magnetic fields. So mixed magnetic fields, i.e., the magnetic fields with both ordered and random components, should be reside in the radiation regions of this burst. Except 1 out of the total 23 bursts, the predicted time-integrated PDs, which are around $\sim44\%$ for the aligned-fields case and around $49\%$ for the toroidal-fields case, are consistent with the corresponding observed values. Therefore, consistent with the former study, the models with synchrotron radiation in ordered magnetic fields could interpret most of the current polrization data within $1\sigma$ error bar., Comment: 19 pages, 26 figures, 2 tables

The observed twice abrupt ${90}^{\circ}$ rotations of the polarization angle (PA) in the prompt phase of gamma-ray bursts (GRBs) are difficult to be understandable within the current one-emitting-shell models. Here, we apply a model with multiple emitting shells to solve this new challenging problem. Two configurations of large-scale ordered magnetic fields in the shells are considered: toroidal and aligned. Together with the light curves and the spectral peak-energy evolutions, the twice ${90}^{\circ}$ PA rotations in GRB 170114A and GRB 160821A could be well interpreted with the multi-shell aligned magnetic fields configuration. Our numerical calculations also show that the multiple shells with the toroidal magnetic field configuration could not explain the observed twice ${90}^{\circ}$ PA rotations. An aligned magnetic field configuration in the GRB outflow usually indicate to prefer a magnetar central engine, while a toroidal field configuration is typically related to a central black hole. Therefore, the magnetar central engines for the two GRBs are favored., Comment: 13 pages, 6 figures, 4 tables, ApJ accepted

Symmetry breaking in a layered superconductor with Ising spin-orbit coupling has offered an opportunity to realize unconventional superconductivity. To be more specific, orbital Fulde-Ferrell-Larkin-Ovchinnikov (FFLO) state, exhibiting layer-dependent finite-momentum pairing, may emerge in transition metal dichalcogenides materials (TMDC) in the presence of an in-plane magnetic field. Orbital FFLO state can be more robust against the magnetic field than the conventional superconducting state with zero-momentum pairing. This feature renders its potential in field resilient superconducting functionality. Although, orbital FFLO state has been reported in NbSe2 and MoS2, it is not yet clear if orbital FFLO state can be a general feature of TMDC superconductor. Here, we report the observation of orbital FFLO state in 2H-NbS2 flakes and its dependence on the thickness of flake. We conclude that the relatively weak interlayer coupling is instrumental in stabilizing orbital FFLO state at higher temperature with respect to the critical temperature and lower magnetic field with respect to paramagnetic limit in NbS2 in comparison to its NbSe2 counterpart.

With the rapid development of wearable technology, devices like smartphones, smartwatches, and headphones equipped with IMUs have become essential for applications such as pedestrian positioning. However, traditional pedestrian dead reckoning (PDR) methods struggle with diverse motion patterns, while recent data-driven approaches, though improving accuracy, often lack robustness due to reliance on a single device.In our work, we attempt to enhance the positioning performance using the low-cost commodity IMUs embedded in the wearable devices. We propose a multi-device deep learning framework named Suite-IN, aggregating motion data from Apple Suite for inertial navigation. Motion data captured by sensors on different body parts contains both local and global motion information, making it essential to reduce the negative effects of localized movements and extract global motion representations from multiple devices.

This paper studies offline dynamic pricing without data coverage assumption, thereby allowing for any price including the optimal one not being observed in the offline data. Previous approaches that rely on the various coverage assumptions such as that the optimal prices are observable, would lead to suboptimal decisions and consequently, reduced profits. We address this challenge by framing the problem to a partial identification framework. Specifically, we establish a partial identification bound for the demand parameter whose associated price is unobserved by leveraging the inherent monotonicity property in the pricing problem. We further incorporate pessimistic and opportunistic strategies within the proposed partial identification framework to derive the estimated policy. Theoretically, we establish rate-optimal finite-sample regret guarantees for both strategies. Empirically, we demonstrate the superior performance of the newly proposed methods via a synthetic environment. This research provides practitioners with valuable insights into offline pricing strategies in the challenging no-coverage setting, ultimately fostering sustainable growth and profitability of the company.

Soft- and hard-constrained Physics Informed Neural Networks (PINNs) have achieved great success in solving partial differential equations (PDEs). However, these methods still face great challenges when solving the Navier-Stokes equations (NSEs) with complex boundary conditions. To address these challenges, this paper introduces a novel complementary scheme combining soft and hard constraint PINN methods. The soft-constrained part is thus formulated to obtain the preliminary results with a lighter training burden, upon which refined results are then achieved using a more sophisticated hard-constrained mechanism with a primary network and a distance metric network. Specifically, the soft-constrained part focuses on boundary points, while the primary network emphasizes inner domain points, primarily through PDE loss. Additionally, the novel distance metric network is proposed to predict the power function of the distance from a point to the boundaries, which serves as the weighting factor for the first two components. This approach ensures accurate predictions for both boundary and inner domain areas. The effectiveness of the proposed method on the NSEs problem with complex boundary conditions is demonstrated by solving a 2D cylinder wake problem and a 2D blocked cavity flow with a segmented inlet problem, achieving significantly higher accuracy compared to traditional soft- and hard-constrained PINN approaches. Given PINN's inherent advantages in solving the inverse and the large-scale problems, which are challenging for traditional computational fluid dynamics (CFD) methods, this approach holds promise for the inverse design of required flow fields by specifically-designed boundary conditions and the reconstruction of large-scale flow fields by adding a limited number of training input points. The code for our approach will be made publicly available.

While 3D content generation has advanced significantly, existing methods still face challenges with input formats, latent space design, and output representations. This paper introduces a novel 3D generation framework that addresses these challenges, offering scalable, high-quality 3D generation with an interactive Point Cloud-structured Latent space. Our framework employs a Variational Autoencoder (VAE) with multi-view posed RGB-D(epth)-N(ormal) renderings as input, using a unique latent space design that preserves 3D shape information, and incorporates a cascaded latent diffusion model for improved shape-texture disentanglement. The proposed method, GaussianAnything, supports multi-modal conditional 3D generation, allowing for point cloud, caption, and single/multi-view image inputs. Notably, the newly proposed latent space naturally enables geometry-texture disentanglement, thus allowing 3D-aware editing. Experimental results demonstrate the effectiveness of our approach on multiple datasets, outperforming existing methods in both text- and image-conditioned 3D generation., Comment: project page: https://nirvanalan.github.io/projects/GA/

The new gauge boson $Z'$ in $U(1)_{B-L}$ is widely considered as the mediator of dark matter. In this paper, we propose the conversion-driven dark matter in $U(1)_{B-L}$. The dark sector contains two Dirac fermions $\tilde{\chi}_1$ and $\tilde{\chi}_2$ with $U(1)_{B-L}$ charge 0 and $-1$, respectively. A $Z_2$ symmetry is also introduced to ensure the stability of dark matter. The mass term $\delta m \bar{\tilde{\chi}}_1\tilde{\chi}_2$ induces the mixing of dark fermion. Then the lightest dark fermion $\chi_1$ becomes the dark matter candidate, whose coupling to $Z'$ is suppressed by the mixing angle $\theta$. Instead of freezing-out via pair annihilation, we show that the observed relic abundance can be obtained through the conversion processes. We then explore the feasible parameter space of conversion-driven dark matter in $U(1)_{B-L}$. Under various experimental constraints, the conversion-driven dark matter prefers the region with $3\times10^{-6}\lesssim g'\lesssim2\times10^{-4}$ and $0.02~\text{GeV}\lesssim m_{Z'}\lesssim10$~GeV, which is within the reach of future Belle II, FASER and SHiP., Comment: 15 pages, 5 figures

Hallucinations, the tendency to produce irrelevant/incorrect responses, are prevalent concerns in generative AI-based tools like ChatGPT. Although hallucinations in ChatGPT are studied for textual responses, it is unknown how ChatGPT hallucinates for technical texts that contain both textual and technical terms. We surveyed 47 software engineers and produced a benchmark of 412 Q&A pairs from the bug reports of two OSS projects. We find that a RAG-based ChatGPT (i.e., ChatGPT tuned with the benchmark issue reports) is 36.4% correct when producing answers to the questions, due to two reasons 1) limitations to understand complex technical contents in code snippets like stack traces, and 2) limitations to integrate contexts denoted in the technical terms and texts. We present CHIME (ChatGPT Inaccuracy Mitigation Engine) whose underlying principle is that if we can preprocess the technical reports better and guide the query validation process in ChatGPT, we can address the observed limitations. CHIME uses context-free grammar (CFG) to parse stack traces in technical reports. CHIME then verifies and fixes ChatGPT responses by applying metamorphic testing and query transformation. In our benchmark, CHIME shows 30.3% more correction over ChatGPT responses. In a user study, we find that the improved responses with CHIME are considered more useful than those generated from ChatGPT without CHIME.

We present learning-based implicit shape representations designed for real-time avatar collision queries arising in the simulation of clothing. Signed distance functions (SDFs) have been used for such queries for many years due to their computational efficiency. Recently deep neural networks have been used for implicit shape representations (DeepSDFs) due to their ability to represent multiple shapes with modest memory requirements compared to traditional representations over dense grids. However, the computational expense of DeepSDFs prevents their use in real-time clothing simulation applications. We design a learning-based representation of SDFs for human avatars whoes bodies change shape kinematically due to joint-based skinning. Rather than using a single DeepSDF for the entire avatar, we use a collection of extremely computationally efficient (shallow) neural networks that represent localized deformations arising from changes in body shape induced by the variation of a single joint. This requires a stitching process to combine each shallow SDF in the collection together into one SDF representing the signed closest distance to the boundary of the entire body. To achieve this we augment each shallow SDF with an additional output that resolves whether or not the individual shallow SDF value is referring to a closest point on the boundary of the body, or to a point on the interior of the body (but on the boundary of the individual shallow SDF). Our model is extremely fast and accurate and we demonstrate its applicability with real-time simulation of garments driven by animated characters., Comment: Preprint

Federated learning (FL) has become a cornerstone in decentralized learning, where, in many scenarios, the incoming data distribution will change dynamically over time, introducing continuous learning (CL) problems. This continual federated learning (CFL) task presents unique challenges, particularly regarding catastrophic forgetting and non-IID input data. Existing solutions include using a replay buffer to store historical data or leveraging generative adversarial networks. Nevertheless, motivated by recent advancements in the diffusion model for generative tasks, this paper introduces DCFL, a novel framework tailored to address the challenges of CFL in dynamic distributed learning environments. Our approach harnesses the power of the conditional diffusion model to generate synthetic historical data at each local device during communication, effectively mitigating latent shifts in dynamic data distribution inputs. We provide the convergence bound for the proposed CFL framework and demonstrate its promising performance across multiple datasets, showcasing its effectiveness in tackling the complexities of CFL tasks.

Tidal disruption events (TDEs), where stars are captured or tidally disrupted by supermassive black holes, are potential sources of high-energy neutrinos. We report the discovery of a potential neutrino flare that is spatially and temporally associated with X-ray emission from TDE ATLAS17jrp. The best-fit spectrum of the neutrino flare follows a power-law with an index of $\rm{\gamma=2.7\pm0.4}$ and a flux normalization of $\rm{\Phi_0 =1.7^{+6.3}_{-1.5}\times 10^{-18}\;GeV^{-1} cm^{-2} s^{-1}}$ at 100 TeV based on an analysis of 10-year track data from IceCube, and the flare duration is 61 days. We calculate that the probability of this association occurring by chance is $0.17\%$. Therefore, ATLAS17jrp is the second TDE (not including candidates) associated with high-energy neutrinos, following TDE AT2019dsg associated with an IceCube neutrino alert. This association can be attributed to the interaction of X-ray photons produced by the hot corona with high-energy particles accelerated by disk winds or outflows, resulting in the production of neutrinos., Comment: 8 pages, 2 figures, submitted to ApJL

Realistic audio synthesis that captures accurate acoustic phenomena is essential for creating immersive experiences in virtual and augmented reality. Synthesizing the sound received at any position relies on the estimation of impulse response (IR), which characterizes how sound propagates in one scene along different paths before arriving at the listener's position. In this paper, we present Acoustic Volume Rendering (AVR), a novel approach that adapts volume rendering techniques to model acoustic impulse responses. While volume rendering has been successful in modeling radiance fields for images and neural scene representations, IRs present unique challenges as time-series signals. To address these challenges, we introduce frequency-domain volume rendering and use spherical integration to fit the IR measurements. Our method constructs an impulse response field that inherently encodes wave propagation principles and achieves state-of-the-art performance in synthesizing impulse responses for novel poses. Experiments show that AVR surpasses current leading methods by a substantial margin. Additionally, we develop an acoustic simulation platform, AcoustiX, which provides more accurate and realistic IR simulations than existing simulators. Code for AVR and AcoustiX are available at https://zitonglan.github.io/avr., Comment: NeurIPS 2024 Spotlight

The collective flow is crucial for understanding the anisotropic expansion of particles produced in heavy-ion collisions and is sensitive to the equation of state of nuclear matter in high baryon density regions. In this paper, we use the hadronic transport model SMASH to study the elliptic flow ($v_2$), quadrangular flow ($v_4$), and their ratio ($v_{4}/v_{2}^{2}$) in Au+Au collisions at high baryon density. Our results show that the inclusion of baryonic mean-field potential in the model successfully reproduces experimental data from the HADES experiment, indicating that baryonic interactions play an important role in shaping anisotropic flow. In addition to comparing the transverse momentum ($p_T$), rapidity, and centrality dependence of $v_{4}/v_{2}^{2}$ between HADES data and model calculations, we also explore its time evolution and energy dependence from $\sqrt{s_{NN}} =$ 2.4 to 4.5 GeV. We find that the ratio $v_{4}/v_{2}^{2}$ is close to 0.5, as expected from hydrodynamic behavior. These results suggest that the early-stage evolution in the high baryon density region resembles ideal fluid behavior.

Skyrmions are topological defects belonging to nontrivial homotopy classes in particle theory, and are recognized as a suitable unit in the high-density, low-dissipation microelectronic devices in condensed matter physics. Their remarkably stable topology has recently been observed in electromagnetic waves. For the evanescent fields near a surface, this has been realized so far only for elementary optical skyrmions, with a fixed skyrmion number. Here we introduce the concept of moir\'e optical skyrmion clusters, where multi-skyrmions are nested to form a large optical skyrmion cluster that is either crystallized or quasi-crystallized as a consequence of the twisted nanostructures. The rapid inverting of optical skyrmion number is achieved in the imperfectly aligned composite nanostructures. This moir\'e optical skyrmion interaction mechanism is described by a lattice model. Further, the nucleation and collapse of optical skyrmion are studied, where their nanoscale dynamics are revealed with a tiny change of the twist angle. The sudden reversal of the on-chip skyrmion can serve as a precise beacon of the relative alignment deviation between twisted composite nanostructures.

Recent advances in generative artificial intelligence (AI) have shown promise in accurately grading open-ended student responses. However, few prior works have explored grading handwritten responses due to a lack of data and the challenge of combining visual and textual information. In this work, we leverage state-of-the-art multi-modal AI models, in particular GPT-4o, to automatically grade handwritten responses to college-level math exams. Using real student responses to questions in a probability theory exam, we evaluate GPT-4o's alignment with ground-truth scores from human graders using various prompting techniques. We find that while providing rubrics improves alignment, the model's overall accuracy is still too low for real-world settings, showing there is significant room for growth in this task.

The recent progress in text-to-image models pretrained on large-scale datasets has enabled us to generate various images as long as we provide a text prompt describing what we want. Nevertheless, the availability of these models is still limited when we expect to generate images that fall into a specific domain either hard to describe or just unseen to the models. In this work, we propose DomainGallery, a few-shot domain-driven image generation method which aims at finetuning pretrained Stable Diffusion on few-shot target datasets in an attribute-centric manner. Specifically, DomainGallery features prior attribute erasure, attribute disentanglement, regularization and enhancement. These techniques are tailored to few-shot domain-driven generation in order to solve key issues that previous works have failed to settle. Extensive experiments are given to validate the superior performance of DomainGallery on a variety of domain-driven generation scenarios. Codes are available at https://github.com/Ldhlwh/DomainGallery., Comment: NeurIPS 2024

In the context of modern life, particularly in Industry 4.0 within the online space, emotions and moods are frequently conveyed through social media posts. The trend of sharing stories, thoughts, and feelings on these platforms generates a vast and promising data source for Big Data. This creates both a challenge and an opportunity for research in applying technology to develop more automated and accurate methods for detecting stress in social media users. In this study, we developed a real-time system for stress detection in online posts, using the "Dreaddit: A Reddit Dataset for Stress Analysis in Social Media," which comprises 187,444 posts across five different Reddit domains. Each domain contains texts with both stressful and non-stressful content, showcasing various expressions of stress. A labeled dataset of 3,553 lines was created for training. Apache Kafka, PySpark, and AirFlow were utilized to build and deploy the model. Logistic Regression yielded the best results for new streaming data, achieving 69,39% for measuring accuracy and 68,97 for measuring F1-scores., Comment: 6 pages, 4 figures

Deep regression models are used in a wide variety of safety-critical applications, but are vulnerable to backdoor attacks. Although many defenses have been proposed for classification models, they are ineffective as they do not consider the uniqueness of regression models. First, the outputs of regression models are continuous values instead of discretized labels. Thus, the potential infected target of a backdoored regression model has infinite possibilities, which makes it impossible to be determined by existing defenses. Second, the backdoor behavior of backdoored deep regression models is triggered by the activation values of all the neurons in the feature space, which makes it difficult to be detected and mitigated using existing defenses. To resolve these problems, we propose DRMGuard, the first defense to identify if a deep regression model in the image domain is backdoored or not. DRMGuard formulates the optimization problem for reverse engineering based on the unique output-space and feature-space characteristics of backdoored deep regression models. We conduct extensive evaluations on two regression tasks and four datasets. The results show that DRMGuard can consistently defend against various backdoor attacks. We also generalize four state-of-the-art defenses designed for classifiers to regression models, and compare DRMGuard with them. The results show that DRMGuard significantly outperforms all those defenses.