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In a recent work \cite{biggs2024comparing}, the effective field theory (EFT) is adopted to consider the quantum decoherence of a near-horizon Unrhu-DeWitt (UDW) charged qubit in a macroscopic cat state. We generalize this EFT approach to study the relativistic quantum information (RQI) of two static UDW-charged qubits with or without a black hole. This EFT is obtained by integrating out a massless mediator field, yielding the direct Coulombic interactions among intrinsic multipole moments of UDW detectors and the induced one on the black hole. The RQI of the quantum state of the mediator field can be probed by the reduced final states of UDW detectors by tracing out the induced internal states of the black hole. From the reduced final state, we find the patterns of entanglement harvesting agree with the ones obtained by the conventional approach based on master theory. However, the more detailed study suggests that the contextual meanings of (non-)locality may or may not be the same in quantum field theory (QFT) and RQI. To explore the contextual meanings of quantumness and locality more, we also calculate quantum discord and locality bound of the Bell-type experiments, with the former characterizing the non-classical correlations and the latter the (non-)locality in the hidden-variable context of RQI. We find that QFT and RQI agree on quantumness based on different physical reasons but may not agree on locality., Comment: 31 pages and 12 figures

Neural decoding, the process of understanding how brain activity corresponds to different stimuli, has been a primary objective in cognitive sciences. Over the past three decades, advancements in functional Magnetic Resonance Imaging and machine learning have greatly improved our ability to map visual stimuli to brain activity, especially in the visual cortex. Concurrently, research has expanded into decoding more complex processes like language and memory across the whole brain, utilizing techniques to handle greater variability and improve signal accuracy. We argue that "seeing" involves more than just mapping visual stimuli onto the visual cortex; it engages the entire brain, as various emotions and cognitive states can emerge from observing different scenes. In this paper, we develop algorithms to enhance our understanding of visual processes by incorporating whole-brain activation maps while individuals are exposed to visual stimuli. We utilize large-scale fMRI encoders and Image generative models pre-trained on large public datasets, which are then fine-tuned through Image-fMRI contrastive learning. Our models hence can decode visual experience across the entire cerebral cortex, surpassing the traditional confines of the visual cortex. We first compare our method with state-of-the-art approaches to decoding visual processing and show improved predictive semantic accuracy by 43%. A network ablation analysis suggests that beyond the visual cortex, the default mode network contributes most to decoding stimuli, in line with the proposed role of this network in sense-making and semantic processing. Additionally, we implemented zero-shot imagination decoding on an extra validation dataset, achieving a p-value of 0.0206 for mapping the reconstructed images and ground-truth text stimuli, which substantiates the model's capability to capture semantic meanings across various scenarios.

Current 3D human motion reconstruction methods from monocular videos rely on features within the current reconstruction window, leading to distortion and deformations in the human structure under local occlusions or blurriness in video frames. To estimate realistic 3D human mesh sequences based on incomplete features, we propose Temporally-alignable Probability Guided Graph Topological Modeling for 3D Human Reconstruction (ProGraph). For missing parts recovery, we exploit the explicit topological-aware probability distribution across the entire motion sequence. To restore the complete human, Graph Topological Modeling (GTM) learns the underlying topological structure, focusing on the relationships inherent in the individual parts. Next, to generate blurred motion parts, Temporal-alignable Probability Distribution (TPDist) utilizes the GTM to predict features based on distribution. This interactive mechanism facilitates motion consistency, allowing the restoration of human parts. Furthermore, Hierarchical Human Loss (HHLoss) constrains the probability distribution errors of inter-frame features during topological structure variation. Our Method achieves superior results than other SOTA methods in addressing occlusions and blurriness on 3DPW.

Embeddings have become a cornerstone in the functionality of large language models (LLMs) due to their ability to transform text data into rich, dense numerical representations that capture semantic and syntactic properties. These embedding vector databases serve as the long-term memory of LLMs, enabling efficient handling of a wide range of natural language processing tasks. However, the surge in popularity of embedding vector databases in LLMs has been accompanied by significant concerns about privacy leakage. Embedding vector databases are particularly vulnerable to embedding inversion attacks, where adversaries can exploit the embeddings to reverse-engineer and extract sensitive information from the original text data. Existing defense mechanisms have shown limitations, often struggling to balance security with the performance of downstream tasks. To address these challenges, we introduce Eguard, a novel defense mechanism designed to mitigate embedding inversion attacks. Eguard employs a transformer-based projection network and text mutual information optimization to safeguard embeddings while preserving the utility of LLMs. Our approach significantly reduces privacy risks, protecting over 95% of tokens from inversion while maintaining high performance across downstream tasks consistent with original embeddings.

The massive semi-detached binary with a long-term decreasing orbital period may involve a rapid mass-transfer phase in Case A, and thus they are good astrophysical laboratories for investigating the evolution of massive binary stars. In this work, by using the long-term observational light curves from the OGLE project and other data in the low-metallicity LMC, four semi-detached massive binaries with long-term decreases in the orbital periods are detected from 165 EB-type close binaries. It is found that the more massive component in S07798 is filling its Roche lobe where the period decrease is caused by mass transfer from the primary to the secondary. However, the other three (S03065, S12631, S16873) are semi-detached binaries with a lobe-filling secondary where the mass transfer between the components should cause the period to increase if the angular momentum is conservative. The long-term period decreases in these three systems may be caused by the angular momentum loss. Additionally, the orbital periods of three systems (S03065, S07798, S16873) are detected to show cyclic variation with periods shorter than 11 years, which can be plausibly explained by the presence of close-in third bodies in these massive binaries. Based on all of these results, it is suggested that the detected four semi-detached binaries almost have multiplicity. The companion stars are crucial for the origin and evolution of these massive close binaries.

In the rapidly evolving field of information visualization, rigorous evaluation is essential for validating new techniques, understanding user interactions, and demonstrating the effectiveness and usability of visualizations. Faithful evaluations provide valuable insights into how users interact with and perceive the system, enabling designers to identify potential weaknesses and make informed decisions about design choices and improvements. However, an emerging trend of multiple evaluations within a single research raises critical questions about the sustainability, feasibility, and methodological rigor of such an approach. New researchers and students, influenced by this trend, may believe -- multiple evaluations are necessary for a study, regardless of the contribution types. However, the number of evaluations in a study should depend on its contributions and merits, not on the trend of including multiple evaluations to strengthen a paper. So, how many evaluations are enough? This is a situational question and cannot be formulaically determined. Our objective is to summarize current trends and patterns to assess the distribution of evaluation methods over different paper contribution types. In this paper, we identify this trend through a non-exhaustive literature survey of evaluation patterns in 214 papers in the two most recent years' VIS issues in IEEE TVCG from 2023 and 2024. We then discuss various evaluation strategy patterns in the information visualization field to guide practical choices and how this paper will open avenues for further discussion.

In the era of big data, machine learning (ML) has become a powerful tool in various fields, notably impacting structural dynamics. ML algorithms offer advantages by modeling physical phenomena based on data, even in the absence of underlying mechanisms. However, uncertainties such as measurement noise and modeling errors can compromise the reliability of ML predictions, highlighting the need for effective uncertainty awareness to enhance prediction robustness. This paper presents a comprehensive review on navigating uncertainties in ML, categorizing uncertainty-aware approaches into probabilistic methods (including Bayesian and frequentist perspectives) and non-probabilistic methods (such as interval learning and fuzzy learning). Bayesian neural networks, known for their uncertainty quantification and nonlinear mapping capabilities, are emphasized for their superior performance and potential. The review covers various techniques and methodologies for addressing uncertainties in ML, discussing fundamentals and implementation procedures of each method. While providing a concise overview of fundamental concepts, the paper refrains from in-depth critical explanations. Strengths and limitations of each approach are examined, along with their applications in structural dynamic forward problems like response prediction, sensitivity assessment, and reliability analysis, and inverse problems like system identification, model updating, and damage identification. Additionally, the review identifies research gaps and suggests future directions for investigations, aiming to provide comprehensive insights to the research community. By offering an extensive overview of both probabilistic and non-probabilistic approaches, this review aims to assist researchers and practitioners in making informed decisions when utilizing ML techniques to address uncertainties in structural dynamic problems., Comment: 114 pages, 27 figures, 6 tables, references added

Extensive research has revealed that adversarial examples (AE) pose a significant threat to voice-controllable smart devices. Recent studies have proposed black-box adversarial attacks that require only the final transcription from an automatic speech recognition (ASR) system. However, these attacks typically involve many queries to the ASR, resulting in substantial costs. Moreover, AE-based adversarial audio samples are susceptible to ASR updates. In this paper, we identify the root cause of these limitations, namely the inability to construct AE attack samples directly around the decision boundary of deep learning (DL) models. Building on this observation, we propose ALIF, the first black-box adversarial linguistic feature-based attack pipeline. We leverage the reciprocal process of text-to-speech (TTS) and ASR models to generate perturbations in the linguistic embedding space where the decision boundary resides. Based on the ALIF pipeline, we present the ALIF-OTL and ALIF-OTA schemes for launching attacks in both the digital domain and the physical playback environment on four commercial ASRs and voice assistants. Extensive evaluations demonstrate that ALIF-OTL and -OTA significantly improve query efficiency by 97.7% and 73.3%, respectively, while achieving competitive performance compared to existing methods. Notably, ALIF-OTL can generate an attack sample with only one query. Furthermore, our test-of-time experiment validates the robustness of our approach against ASR updates., Comment: Published in the 2024 IEEE Symposium on Security and Privacy (SP)

The CPT invariant vacuum states of a scalar field in de Sitter space, called $\alpha$-vacua, are not unique. We explore the $\alpha$-vacua from the quantum information perspective by a pair of static Unruh-DeWitt (UDW) detectors coupled to a scalar field with either monopole or dipole coupling, which are in time-like zero separation or space-like antipodal separation. The analytical form of the reduced final state of the UDW detector is derived. We study the entanglement harvesting and quantum discord of the reduced state, which characterize the quantum entanglement and quantum correlation of the underlying $\alpha$-vacua, respectively. Our results imply that the quantum entanglement gravitated by de Sitter gravity behaves quite differently for time-like and space-like separations. It experiences ``sudden death" for the former and grows for the latter as the measuring time or the value of $\alpha$ increases. This demonstrates the nonlocal nature of quantum entanglement. For the quantum discord, we find no ``sudden death" behavior, and it experiences superhorizon suppression, which explains the superhorizon decoherence in the inflationary universe scenario. Overall, the time-like or space-like quantum entanglement and correlation behave differently on their dependence of $\alpha$, measuring time and spectral gaps, with details discussed in this work., Comment: 32 pages, 19 figures, Typos corrected, Matched with the version accepted by JHEP

Soft hairs are an intrinsic infrared feature of a black hole, which may also affect near-horizon physics. In this work, we study some of the subtleties surrounding one of the primary observables with which we can study their effects in the context of Einstein's gravity: the black hole shadow. First, we clarify the singular pathology associated with black holes with soft hairs and demonstrate the metrics of linearly superrotated black holes are free of near-zone pathologies due to appropriate asymptotic falloff conditions imposed on the event horizon. We then analytically construct the photon orbits around such black holes and derive the shadow equation, and find that the linear superrotation hairs will deform the circular shadow of a bald Schwarzchild black hole into ellipses. This is in sharp contrast to their supertranslated counterparts, which only shift the position of the center of the circular shadow but do not change its shape. Our results suggest a richness to the observable effects due to the infrared structures of Einstein's gravity and demand their observations by future black hole imaging projects., Comment: 10 pages, 2 figures, v2. Clarify the near-horizon pathology and reinterpret the regular metric we consider as the one with the near-horizon superrotation hairs. The main result of the shadow deformation does not change

Video recognition remains an open challenge, requiring the identification of diverse content categories within videos. Mainstream approaches often perform flat classification, overlooking the intrinsic hierarchical structure relating categories. To address this, we formalize the novel task of hierarchical video recognition, and propose a video-language learning framework tailored for hierarchical recognition. Specifically, our framework encodes dependencies between hierarchical category levels, and applies a top-down constraint to filter recognition predictions. We further construct a new fine-grained dataset based on medical assessments for rehabilitation of stroke patients, serving as a challenging benchmark for hierarchical recognition. Through extensive experiments, we demonstrate the efficacy of our approach for hierarchical recognition, significantly outperforming conventional methods, especially for fine-grained subcategories. The proposed framework paves the way for hierarchical modeling in video understanding tasks, moving beyond flat categorization.

In real-world road scenes, diverse material properties lead to complex light reflection phenomena, making accurate color reproduction crucial for enhancing the realism and safety of simulated driving environments. However, existing methods often struggle to capture the full spectrum of lighting effects, particularly in dynamic scenarios where viewpoint changes induce significant material color variations. To address this challenge, we introduce NieR (Normal-Based Lighting Scene Rendering), a novel framework that takes into account the nuances of light reflection on diverse material surfaces, leading to more precise rendering. To simulate the lighting synthesis process, we present the LD (Light Decomposition) module, which captures the lighting reflection characteristics on surfaces. Furthermore, to address dynamic lighting scenes, we propose the HNGD (Hierarchical Normal Gradient Densification) module to overcome the limitations of sparse Gaussian representation. Specifically, we dynamically adjust the Gaussian density based on normal gradients. Experimental evaluations demonstrate that our method outperforms state-of-the-art (SOTA) methods in terms of visual quality and exhibits significant advantages in performance indicators. Codes are available at https://wanghongsheng01.github.io/NieR/.

Single-view clothed human reconstruction holds a central position in virtual reality applications, especially in contexts involving intricate human motions. It presents notable challenges in achieving realistic clothing deformation. Current methodologies often overlook the influence of motion on surface deformation, resulting in surfaces lacking the constraints imposed by global motion. To overcome these limitations, we introduce an innovative framework, Motion-Based 3D Clo}thed Humans Synthesis (MOSS), which employs kinematic information to achieve motion-aware Gaussian split on the human surface. Our framework consists of two modules: Kinematic Gaussian Locating Splatting (KGAS) and Surface Deformation Detector (UID). KGAS incorporates matrix-Fisher distribution to propagate global motion across the body surface. The density and rotation factors of this distribution explicitly control the Gaussians, thereby enhancing the realism of the reconstructed surface. Additionally, to address local occlusions in single-view, based on KGAS, UID identifies significant surfaces, and geometric reconstruction is performed to compensate for these deformations. Experimental results demonstrate that MOSS achieves state-of-the-art visual quality in 3D clothed human synthesis from monocular videos. Notably, we improve the Human NeRF and the Gaussian Splatting by 33.94% and 16.75% in LPIPS* respectively. Codes are available at https://wanghongsheng01.github.io/MOSS/., Comment: arXiv admin note: text overlap with arXiv:1710.03746 by other authors

Reconstructing 3D human bodies from realistic motion sequences remains a challenge due to pervasive and complex occlusions. Current methods struggle to capture the dynamics of occluded body parts, leading to model penetration and distorted motion. RemoCap leverages Spatial Disentanglement (SD) and Motion Disentanglement (MD) to overcome these limitations. SD addresses occlusion interference between the target human body and surrounding objects. It achieves this by disentangling target features along the dimension axis. By aligning features based on their spatial positions in each dimension, SD isolates the target object's response within a global window, enabling accurate capture despite occlusions. The MD module employs a channel-wise temporal shuffling strategy to simulate diverse scene dynamics. This process effectively disentangles motion features, allowing RemoCap to reconstruct occluded parts with greater fidelity. Furthermore, this paper introduces a sequence velocity loss that promotes temporal coherence. This loss constrains inter-frame velocity errors, ensuring the predicted motion exhibits realistic consistency. Extensive comparisons with state-of-the-art (SOTA) methods on benchmark datasets demonstrate RemoCap's superior performance in 3D human body reconstruction. On the 3DPW dataset, RemoCap surpasses all competitors, achieving the best results in MPVPE (81.9), MPJPE (72.7), and PA-MPJPE (44.1) metrics. Codes are available at https://wanghongsheng01.github.io/RemoCap/.

In the animation industry, 3D modelers typically rely on front and back non-overlapped concept designs to guide the 3D modeling of anime characters. However, there is currently a lack of automated approaches for generating anime characters directly from these 2D designs. In light of this, we explore a novel task of reconstructing anime characters from non-overlapped views. This presents two main challenges: existing multi-view approaches cannot be directly applied due to the absence of overlapping regions, and there is a scarcity of full-body anime character data and standard benchmarks. To bridge the gap, we present Non-Overlapped Views for 3D \textbf{A}nime Character Reconstruction (NOVA-3D), a new framework that implements a method for view-aware feature fusion to learn 3D-consistent features effectively and synthesizes full-body anime characters from non-overlapped front and back views directly. To facilitate this line of research, we collected the NOVA-Human dataset, which comprises multi-view images and accurate camera parameters for 3D anime characters. Extensive experiments demonstrate that the proposed method outperforms baseline approaches, achieving superior reconstruction of anime characters with exceptional detail fidelity. In addition, to further verify the effectiveness of our method, we applied it to the animation head reconstruction task and improved the state-of-the-art baseline to 94.453 in SSIM, 7.726 in LPIPS, and 19.575 in PSNR on average. Codes and datasets are available at https://wanghongsheng01.github.io/NOVA-3D/.

Although 3D Gaussian Splatting (3DGS) has recently made progress in 3D human reconstruction, it primarily relies on 2D pixel-level supervision, overlooking the geometric complexity and topological relationships of different body parts. To address this gap, we introduce the Hierarchical Graph Human Gaussian Control (HUGS) framework for achieving high-fidelity 3D human reconstruction. Our approach involves leveraging explicitly semantic priors of body parts to ensure the consistency of geometric topology, thereby enabling the capture of the complex geometrical and topological associations among body parts. Additionally, we disentangle high-frequency features from global human features to refine surface details in body parts. Extensive experiments demonstrate that our method exhibits superior performance in human body reconstruction, particularly in enhancing surface details and accurately reconstructing body part junctions. Codes are available at https://wanghongsheng01.github.io/HUGS/.

Inspired by the recent discovery of a violation of strong cosmic censorship (SCC) for the near-extremal Reissner-Nordstr\"om black holes in de Sitter space (RN-dS), we investigate if the weak cosmic censorship conjecture (WCCC) can also be violated in RN-dS with a fixed cosmological constant. Our method is based on the recent formulation of examining WCCC by requiring the second law to hold, which requires the sum of areas of the event and cosmic horizons cannot decrease during the infall process of Wald's gedanken experiment. We find that the WCCC can be violated for the near-extremal RN-dS in some regimes of second-order perturbation of field configurations. Given the charge parameter of RN-dS, we can find the lowest value of the sub-extremality parameter, beyond which the WCCC holds. Our results imply that violations of SCC and WCCC could be correlated. Because of a lack of an unambiguous relation between the gravitational mass and matter's kinematic mass in asymptotically de Sitter space, we cannot compare the corresponding regimes of parameter space for the violations of SCC and WCCC. We also discuss the subtlety in formulating both the first-law and second-law approaches to examine WCCC., Comment: 12 pages, 6 figures

Software process models are essential to facilitate collaboration and communication among software teams to solve complex development tasks. Inspired by these software engineering practices, we present FlowGen - a code generation framework that emulates software process models based on multiple Large Language Model (LLM) agents. We emulate three process models, FlowGenWaterfall, FlowGenTDD, and FlowGenScrum, by assigning LLM agents to embody roles (i.e., requirement engineer, architect, developer, tester, and scrum master) that correspond to everyday development activities and organize their communication patterns. The agents work collaboratively using chain-of-thought and prompt composition with continuous self-refinement to improve the code quality. We use GPT3.5 as our underlying LLM and several baselines (RawGPT, CodeT, Reflexion) to evaluate code generation on four benchmarks: HumanEval, HumanEval-ET, MBPP, and MBPP-ET. Our findings show that FlowGenScrum excels compared to other process models, achieving a Pass@1 of 75.2, 65.5, 82.5, and 56.7 in HumanEval, HumanEval-ET, MBPP, and MBPP-ET, respectively (an average of 15% improvement over RawGPT). Compared with other state-of-the-art techniques, FlowGenScrum achieves a higher Pass@1 in MBPP compared to CodeT, with both outperforming Reflexion. Notably, integrating CodeT into FlowGenScrum resulted in statistically significant improvements, achieving the highest Pass@1 scores. Our analysis also reveals that the development activities impacted code smell and exception handling differently, with design and code review adding more exception handling and reducing code smells. Finally, FlowGen models maintain stable Pass@1 scores across GPT3.5 versions and temperature values, highlighting the effectiveness of software process models in enhancing the quality and stability of LLM-generated code., Comment: ICSE 2025

Deepfake technology has given rise to a spectrum of novel and compelling applications. Unfortunately, the widespread proliferation of high-fidelity fake videos has led to pervasive confusion and deception, shattering our faith that seeing is believing. One aspect that has been overlooked so far is that current deepfake detection approaches may easily fall into the trap of overfitting, focusing only on forgery clues within one or a few local regions. Moreover, existing works heavily rely on neural networks to extract forgery features, lacking theoretical constraints guaranteeing that sufficient forgery clues are extracted and superfluous features are eliminated. These deficiencies culminate in unsatisfactory accuracy and limited generalizability in real-life scenarios. In this paper, we try to tackle these challenges through three designs: (1) We present a novel framework to capture broader forgery clues by extracting multiple non-overlapping local representations and fusing them into a global semantic-rich feature. (2) Based on the information bottleneck theory, we derive Local Information Loss to guarantee the orthogonality of local representations while preserving comprehensive task-relevant information. (3) Further, to fuse the local representations and remove task-irrelevant information, we arrive at a Global Information Loss through the theoretical analysis of mutual information. Empirically, our method achieves state-of-the-art performance on five benchmark datasets.Our code is available at \url{https://github.com/QingyuLiu/Exposing-the-Deception}, hoping to inspire researchers., Comment: AAAI2024

Most of existing category-level object pose estimation methods devote to learning the object category information from point cloud modality. However, the scale of 3D datasets is limited due to the high cost of 3D data collection and annotation. Consequently, the category features extracted from these limited point cloud samples may not be comprehensive. This motivates us to investigate whether we can draw on knowledge of other modalities to obtain category information. Inspired by this motivation, we propose CLIPose, a novel 6D pose framework that employs the pre-trained vision-language model to develop better learning of object category information, which can fully leverage abundant semantic knowledge in image and text modalities. To make the 3D encoder learn category-specific features more efficiently, we align representations of three modalities in feature space via multi-modal contrastive learning. In addition to exploiting the pre-trained knowledge of the CLIP's model, we also expect it to be more sensitive with pose parameters. Therefore, we introduce a prompt tuning approach to fine-tune image encoder while we incorporate rotations and translations information in the text descriptions. CLIPose achieves state-of-the-art performance on two mainstream benchmark datasets, REAL275 and CAMERA25, and runs in real-time during inference (40FPS)., Comment: 14 pages, 4 figures, 9 tables

High-pressure synchrotron X-ray diffraction (XRD) studies have been conducted on three types of Si particles (micron, 100 nm, and 30 nm). The pressure for initiation of Si-I->Si-II phase transformation (PT) essentially increases with a reduction in particle size. For 30 nm Si particles, Si-I directly transforms to Si-XI by skipping the intermediate Si-II phase, which appears during the pressure release. The evolution of phase fractions of Si particles under hydrostatic compression is studied. The equation of state (EOS) of Si-I, Si-II, Si-V, and Si-XI for all three particle sizes is determined, and the results are compared with other studies. A simple iterative procedure is suggested to extract the EOS of Si-XI and Si-II from the data for a mixture of two and three phases with different pressures in each phase. Using previous atomistic simulations, EOS for Si-II is extended to ambient pressure, which is important for plastic strain-induced phase transformations. Surprisingly, the EOS of micron and 30 nm Si are identical, but different from 100 nm particles. In particular, the Si-I phase of 100 nm Si is less compressible than that of micron and 30 nm Si. The reverse Si-V->Si-I PT is observed for the first time after complete pressure release to the ambient for 100 nm particles., Comment: 19 pages, 10 figures, 2 tables

This research aims to accelerate the inference speed of large language models (LLMs) with billions of parameters. We propose \textbf{S}mart \textbf{P}arallel \textbf{A}uto-\textbf{C}orrect d\textbf{E}coding (SPACE), an innovative approach designed for achieving lossless acceleration of LLMs. By integrating semi-autoregressive inference and speculative decoding capabilities, SPACE uniquely enables autoregressive LLMs to parallelize token generation and verification. This is realized through a specialized semi-autoregressive supervised fine-tuning process that equips existing LLMs with the ability to simultaneously predict multiple tokens. Additionally, an auto-correct decoding algorithm facilitates the simultaneous generation and verification of token sequences within a single model invocation. Through extensive experiments on a range of LLMs, SPACE has demonstrated inference speedup ranging from 2.7x-4.0x on HumanEval-X while maintaining output quality., Comment: Accepted by ACL 2024 Findings

The widespread smart devices raise people's concerns of being eavesdropped on. To enhance voice privacy, recent studies exploit the nonlinearity in microphone to jam audio recorders with inaudible ultrasound. However, existing solutions solely rely on energetic masking. Their simple-form noise leads to several problems, such as high energy requirements and being easily removed by speech enhancement techniques. Besides, most of these solutions do not support authorized recording, which restricts their usage scenarios. In this paper, we design an efficient yet robust system that can jam microphones while preserving authorized recording. Specifically, we propose a novel phoneme-based noise with the idea of informational masking, which can distract both machines and humans and is resistant to denoising techniques. Besides, we optimize the noise transmission strategy for broader coverage and implement a hardware prototype of our system. Experimental results show that our system can reduce the recognition accuracy of recordings to below 50\% under all tested speech recognition systems, which is much better than existing solutions., Comment: 14 pages, 28 figures; submitted to IEEE TDSC

Large language models (LLMs) commonly employ autoregressive generation during inference, leading to high memory bandwidth demand and consequently extended latency. To mitigate this inefficiency, we present Bi-directional Tuning for lossless Acceleration (BiTA), an innovative method expediting LLMs via streamlined semi-autoregressive generation and draft verification. Inspired by the concept of prompt tuning, we enhance LLMs with a parameter-efficient design called bi-directional tuning for the capability in semi-autoregressive generation. Employing efficient tree-based decoding, the models perform draft candidate generation and verification in parallel, ensuring outputs identical to their autoregressive counterparts under greedy sampling. BiTA serves as a lightweight plug-in module, seamlessly boosting the inference efficiency of existing LLMs without requiring additional assistance models or incurring significant extra memory costs. Applying the proposed BiTA, LLaMA-2-70B-Chat achieves a 2.7$\times$ speedup on the MT-Bench benchmark. Extensive experiments confirm our method surpasses state-of-the-art acceleration techniques., Comment: An appendix has been included. Source code at https://github.com/linfeng93/BiTA

Following a recent proposal to describe inelastic eikonal scattering processes in terms of gravitationally dressed elastic eikonal amplitudes, we motivate a collinear double graviton dressing and investigate its properties. This is derived from a generalized Wilson line operator in the worldline formalism by integrating over fluctuations of the eikonal trajectories of external particles in gravitationally interacting theories. The dressing can be expressed as a product of exponential terms -- a coherent piece with contributions to all odd orders in the gravitational coupling constant and a term quadratic in graviton modes, with the former providing classical gravitational wave observables. In particular, the coherent dressing involves $\mathcal{O}(\kappa^3)$ subleading double graviton corrections to the Weinberg soft factor. We use this dressing to derive expressions for the waveform, radiative momentum spectrum and angular momentum. In a limiting case of the waveform, we derive the nonlinear memory effect resulting from the emission of nearly soft gravitons from a scattering process., Comment: Matches published version; 49 pages, 1 figure, revisions of Sec. 2.3, Sec. 4.1 and Sec. 5 with broader literature review, minor revision of notation and typos corrected