arXiv subjects

Zhe Zhang

Publications and source records attributed to Zhe Zhang.

At least 145 records · Page 8Linked to original sources

arXiv2024

How to Cache Important Contents for Multi-modal Service in Dynamic Networks: A DRL-based Caching Scheme

With the continuous evolution of networking technologies, multi-modal services that involve video, audio, and haptic contents are expected to become the dominant multimedia service in the near future. Edge caching is a key technology that can significantly reduce network load and content transmission latency, which is critical for the delivery of multi-modal contents. However, existing caching approaches only rely on a limited number of factors, e.g., popularity, to evaluate their importance for caching, which is inefficient for caching multi-modal contents, especially in dynamic network environments. To overcome this issue, we propose a content importance-based caching scheme which consists of a content importance evaluation model and a caching model. By leveraging dueling double deep Q networks (D3QN) model, the content importance evaluation model can adaptively evaluate contents' importance in dynamic networks. Based on the evaluated contents' importance, the caching model can easily cache and evict proper contents to improve caching efficiency. The simulation results show that the proposed content importance-based caching scheme outperforms existing caching schemes in terms of caching hit ratio (at least 15% higher), reduced network load (up to 22% reduction), average number of hops (up to 27% lower), and unsatisfied requests ratio (more than 47% reduction).

cs.NI↗

arXiv2024

A Robust Super-resolution Gridless Imaging Framework for UAV-borne SAR Tomography

Synthetic aperture radar (SAR) tomography (TomoSAR) retrieves three-dimensional (3-D) information from multiple SAR images, effectively addresses the layover problem, and has become pivotal in urban mapping. Unmanned aerial vehicle (UAV) has gained popularity as a TomoSAR platform, offering distinct advantages such as the ability to achieve 3-D imaging in a single flight, cost-effectiveness, rapid deployment, and flexible trajectory planning. The evolution of compressed sensing (CS) has led to the widespread adoption of sparse reconstruction techniques in TomoSAR signal processing, with a focus on $\ell _1$ norm regularization and other grid-based CS methods. However, the discretization of illuminated scene along elevation introduces modeling errors, resulting in reduced reconstruction accuracy, known as the "off-grid" effect. Recent advancements have introduced gridless CS algorithms to mitigate this issue. This paper presents an innovative gridless 3-D imaging framework tailored for UAV-borne TomoSAR. Capitalizing on the pulse repetition frequency (PRF) redundancy inherent in slow UAV platforms, a multiple measurement vectors (MMV) model is constructed to enhance noise immunity without compromising azimuth-range resolution. Given the sparsely placed array elements due to mounting platform constraints, an atomic norm soft thresholding algorithm is proposed for partially observed MMV, offering gridless reconstruction capability and super-resolution. An efficient alternative optimization algorithm is also employed to enhance computational efficiency. Validation of the proposed framework is achieved through computer simulations and flight experiments, affirming its efficacy in UAV-borne TomoSAR applications.

eess.SP↗

arXiv2024

Syllable-level lyrics generation from melody exploiting character-level language model

The generation of lyrics tightly connected to accompanying melodies involves establishing a mapping between musical notes and syllables of lyrics. This process requires a deep understanding of music constraints and semantic patterns at syllable-level, word-level, and sentence-level semantic meanings. However, pre-trained language models specifically designed at the syllable level are publicly unavailable. To solve these challenging issues, we propose to exploit fine-tuning character-level language models for syllable-level lyrics generation from symbolic melody. In particular, our method endeavors to incorporate linguistic knowledge of the language model into the beam search process of a syllable-level Transformer generator network. Additionally, by exploring ChatGPT-based evaluation for generated lyrics, along with human subjective evaluation, we demonstrate that our approach enhances the coherence and correctness of the generated lyrics, eliminating the need to train expensive new language models.

cs.CL↗

arXiv2024

Two-pass Endpoint Detection for Speech Recognition

Endpoint (EP) detection is a key component of far-field speech recognition systems that assist the user through voice commands. The endpoint detector has to trade-off between accuracy and latency, since waiting longer reduces the cases of users being cut-off early. We propose a novel two-pass solution for endpointing, where the utterance endpoint detected from a first pass endpointer is verified by a 2nd-pass model termed EP Arbitrator. Our method improves the trade-off between early cut-offs and latency over a baseline endpointer, as tested on datasets including voice-assistant transactional queries, conversational speech, and the public SLURP corpus. We demonstrate that our method shows improvements regardless of the first-pass EP model used.

eess.AS↗

arXiv2024

Enhanced α particle generation via proton-boron fusion reactions in laser-modulated plasma

Aneutronic and nonradioactive properties make the proton-boron fusion a prospective candidate for fusion energy production through reactions following p+$^{11}$B$\rightarrow$3$α$ (p-$^{11}$B). However, it is difficult to achieve a thermal fusion ignition, since the low reaction cross-sections for center-of-mass energy below $\sim$100 keV. To realize fusion energy gain, it is essential to consider utilization of the maximum cross-section at the resonant peak of p-$^{11}$B fusion, and explore the nuclear reactions in plasma environment. In this work, p-$^{11}$B reactions triggered by interactions between energetic proton beams and laser-ablated boron plasma have been investigated. More than 200 times enhancement of $α$ particle emission efficiency (number ratio of escaping $α$ particles and boron nuclei) in plasma has been observed, compared with the cold boron. The proton beam transport path modulated by strong electro-magnetic fields in plasma could dominate the enhanced $α$ particle generation, due to a longer collisional length. In addition, an $α$ particle yield up to 1$\times$10$^{10}$ /sr has been measured via the pitcher-catcher scheme in plasma. This work could benefit understanding of the plasma effects on nuclear reaction dynamics, and also enable opportunities to explore physics in laser fusion associated with advanced fusion fuels.

physics.plasm-ph↗

arXiv2024

HyperGANStrument: Instrument Sound Synthesis and Editing with Pitch-Invariant Hypernetworks

GANStrument, exploiting GANs with a pitch-invariant feature extractor and instance conditioning technique, has shown remarkable capabilities in synthesizing realistic instrument sounds. To further improve the reconstruction ability and pitch accuracy to enhance the editability of user-provided sound, we propose HyperGANStrument, which introduces a pitch-invariant hypernetwork to modulate the weights of a pre-trained GANStrument generator, given a one-shot sound as input. The hypernetwork modulation provides feedback for the generator in the reconstruction of the input sound. In addition, we take advantage of an adversarial fine-tuning scheme for the hypernetwork to improve the reconstruction fidelity and generation diversity of the generator. Experimental results show that the proposed model not only enhances the generation capability of GANStrument but also significantly improves the editability of synthesized sounds. Audio examples are available at the online demo page.

cs.SD↗

arXiv2023

Refined analysis of $Ω^{-} \barΩ^{+}$ polarization in electron-positron annihilation process

We investigate the production of spin-3/2 hyperon pairs, $Ω^- \barΩ^+$, in electron-positron annihilation within the helicity amplitude formalism. A refined selection of helicity basis matrices is proposed to relate polarization expansion coefficients and spin density matrix elements and to illuminate their inherent physical interpretations and symmetrical properties. With a novel parametrization scheme of helicity amplitudes, we perform an analysis of polarization correlation coefficients for double-tag $Ω^- \barΩ^+$ pairs. We present three sets of expressions to describe the decay of $Ω^{-}$ hyperons, and further address the existing tension in the measurements of its decay parameters, particularly $ϕ_Ω$. The method and the framework developed in this paper can also be applied to studies of the production and decay mechanisms of other spin-3/2 particles.

hep-ph↗

arXiv2023

Identification of morphological fingerprint in perinatal brains using quasi-conformal mapping and contrastive learning

The morphological fingerprint in the brain is capable of identifying the uniqueness of an individual. However, whether such individual patterns are present in perinatal brains, and which morphological attributes or cortical regions better characterize the individual differences of ne-onates remain unclear. In this study, we proposed a deep learning framework that projected three-dimensional spherical meshes of three morphological features (i.e., cortical thickness, mean curvature, and sulcal depth) onto two-dimensional planes through quasi-conformal mapping, and employed the ResNet18 and contrastive learning for individual identification. We used the cross-sectional structural MRI data of 682 infants, incorporating with data augmentation, to train the model and fine-tuned the parameters based on 60 infants who had longitudinal scans. The model was validated on 30 longitudinal scanned infant data, and remarkable Top1 and Top5 accuracies of 71.37% and 84.10% were achieved, respectively. The sensorimotor and visual cortices were recognized as the most contributive regions in individual identification. Moreover, the folding morphology demonstrated greater discriminative capability than the cortical thickness, which could serve as the morphological fingerprint in perinatal brains. These findings provided evidence for the emergence of morphological fingerprints in the brain at the beginning of the third trimester, which may hold promising implications for understanding the formation of in-dividual uniqueness in the brain during early development.

cs.LG↗

arXiv2023

Measurement of Stimulated Raman Side-Scattering Predominance in Directly Driven Experiment

Due to its particular geometry, stimulated Raman side-scattering (SRSS) drives scattered light emission at non-conventional directions, leading to scarce and complex experimental observations. Direct-irradiation campaigns at the SG-II UP facility have measured the scattered light driven by SRSS over a wide range of angles. It indicated an emission at large polar angles over a broad azimuthal range, sensitive to the plasma profile and laser polarization, resulting in a loss of about 5\% of the total laser energy. Direct comparison with back-scattering measurement has evidenced SRSS as the dominant Raman scattering process. The predominance of SRSS was confirmed by 2D particle-in-cell simulations, and its angular spread has been corroborated by ray-tracing simulations. The main implication is that a complete characterization of the SRS instability and an accurate measurement of the energy losses require the collection of the scattered light in a broad range of directions. Otherwise, spatially limited measurement could lead to an underestimation of the energetic importance of stimulated Raman scattering.

physics.plasm-ph↗

arXiv2023

Small limit cycles bifurcating in pendulum systems under trigonometric perturbations

In this paper, we consider the bifurcation of small-amplitude limit cycles near the origin in perturbed pendulum systems of the form $\dot x= y$, $\dot y=-\sin(x)+\varepsilon Q(x,y)$, where $Q(x,y)$ is a smooth or piecewise smooth polynomial in the triple $(\sin(x),\cos(x), y)$ with free coefficients. We obtain the sharp upper bound on the number of positive zeros of its associated first order Melnikov function near $h=0$ for $Q(x,y)$ being smooth and piecewise smooth with the discontinuity at $y=0$, respectively.

math.DS↗

arXiv2023

A Product on Lorenz Hulls, Zonoids, and Vector Measure Ranges

A Lorenz hull is the convex hull of the range of an $n$-dimensional vector of finite signed measures defined on a common measurable space. We show that the set of $n$-dimensional Lorenz hulls is endowed with a natural product that is commutative, associative, and distributive over Minkowski sums. The same holds with "zonoid" in place of "Lorenz hull" as the two concepts give rise to the same set of subsets of $\mathbb{R}^n$. The product is defined via the common notion of a product measure.

math.DS↗

arXiv2023

Coincident Learning for Unsupervised Anomaly Detection

Anomaly detection is an important task for complex systems (e.g., industrial facilities, manufacturing, large-scale science experiments), where failures in a sub-system can lead to low yield, faulty products, or even damage to components. While complex systems often have a wealth of data, labeled anomalies are typically rare (or even nonexistent) and expensive to acquire. Unsupervised approaches are therefore common and typically search for anomalies either by distance or density of examples in the input feature space (or some associated low-dimensional representation). This paper presents a novel approach called CoAD, which is specifically designed for multi-modal tasks and identifies anomalies based on \textit{coincident} behavior across two different slices of the feature space. We define an \textit{unsupervised} metric, $\hat{F}_β$, out of analogy to the supervised classification $F_β$ statistic. CoAD uses $\hat{F}_β$ to train an anomaly detection algorithm on \textit{unlabeled data}, based on the expectation that anomalous behavior in one feature slice is coincident with anomalous behavior in the other. The method is illustrated using a synthetic outlier data set and a MNIST-based image data set, and is compared to prior state-of-the-art on two real-world tasks: a metal milling data set and a data set from a particle accelerator.

cs.LG↗

arXiv2023

Laboratory observation of ion acceleration via reflection off laser-produced magnetized collisionless shocks

Fermi acceleration by collisionless shocks is believed to be the primary mechanism to produce high energy charged particles in the Universe,where charged particles gain energy successively from multiple reflections off the shock front.Here,we present the first direct experimental evidence of ion energization from reflection off a supercritical quasi perpendicular collisionless shock,an essential component of Fermi acceleration in a laser produced magnetized plasma. We observed a quasi monoenergetic ion beam with 2,4 times the shock velocity in the upstream flow using time of flight method. Our related kinetic simulations reproduced the energy gain and showed that these ions were first reflected and then accelerated mainly by the motional electric field associated with the shock. This mechanism can also explain the quasi monoenergetic fast ion component observed in the Earth's bow shock.

physics.plasm-ph↗

arXiv2023

Mani-GPT: A Generative Model for Interactive Robotic Manipulation

In real-world scenarios, human dialogues are multi-round and diverse. Furthermore, human instructions can be unclear and human responses are unrestricted. Interactive robots face difficulties in understanding human intents and generating suitable strategies for assisting individuals through manipulation. In this article, we propose Mani-GPT, a Generative Pre-trained Transformer (GPT) for interactive robotic manipulation. The proposed model has the ability to understand the environment through object information, understand human intent through dialogues, generate natural language responses to human input, and generate appropriate manipulation plans to assist the human. This makes the human-robot interaction more natural and humanized. In our experiment, Mani-GPT outperforms existing algorithms with an accuracy of 84.6% in intent recognition and decision-making for actions. Furthermore, it demonstrates satisfying performance in real-world dialogue tests with users, achieving an average response accuracy of 70%.

cs.RO↗

arXiv2023

Points-to-3D: Bridging the Gap between Sparse Points and Shape-Controllable Text-to-3D Generation

Text-to-3D generation has recently garnered significant attention, fueled by 2D diffusion models trained on billions of image-text pairs. Existing methods primarily rely on score distillation to leverage the 2D diffusion priors to supervise the generation of 3D models, e.g., NeRF. However, score distillation is prone to suffer the view inconsistency problem, and implicit NeRF modeling can also lead to an arbitrary shape, thus leading to less realistic and uncontrollable 3D generation. In this work, we propose a flexible framework of Points-to-3D to bridge the gap between sparse yet freely available 3D points and realistic shape-controllable 3D generation by distilling the knowledge from both 2D and 3D diffusion models. The core idea of Points-to-3D is to introduce controllable sparse 3D points to guide the text-to-3D generation. Specifically, we use the sparse point cloud generated from the 3D diffusion model, Point-E, as the geometric prior, conditioned on a single reference image. To better utilize the sparse 3D points, we propose an efficient point cloud guidance loss to adaptively drive the NeRF's geometry to align with the shape of the sparse 3D points. In addition to controlling the geometry, we propose to optimize the NeRF for a more view-consistent appearance. To be specific, we perform score distillation to the publicly available 2D image diffusion model ControlNet, conditioned on text as well as depth map of the learned compact geometry. Qualitative and quantitative comparisons demonstrate that Points-to-3D improves view consistency and achieves good shape controllability for text-to-3D generation. Points-to-3D provides users with a new way to improve and control text-to-3D generation.

cs.CV↗

arXiv2023

Nanoscale soil-water retention mechanism of unsaturated clay via MD and machine learning

In this article, we investigate the nanoscale soil-water retention mechanism of unsaturated clay through molecular dynamics and machine learning. Pyrophyllite was chosen due to its stable structure and as the precursor of other 2:1 clay minerals. A series of molecular dynamics simulations of clay at low degrees of saturation were conducted. Soil water was represented by a point cloud through the center-of-mass method. Water-air interface area was measured numerically by the alpha-shape method. The soil-water retention mechanism at the nanoscale was analyzed by distinguishing adsorptive pressure and capillary pressure at different mass water contents and considering the apparent capillary interface area (i.e., water-air interface area per unit water volume). The water number density profile was used to quantify the adsorption effect. A neural-network based machine learning technique was utilized to construct function relationships among matric suction, the mass water content, and the apparent water-air interface area. Our numerical results have demonstrated from a nanoscale perspective that the adsorption effect is dominated by the van der Waals force and hydroxyl hydration between the clay surface and water. As the mass water content increases, the adsorption pressure decreases, and capillarity plays a prominent role in the soil-water retention mechanism at the nanoscale.

cond-mat.mtrl-sci↗

arXiv2023

Spin Density Matrix for $Ω^-$ and its Polarization Alignment in $ψ(3686) \to Ω^- \barΩ^+$

We investigate the spin density matrix of $Ω^-$ in the Cartesian coordinate system of baryon-antibaryon pairs produced in $e^{+}e^{-}$ annihilation. Using the helicity formalism of Jacob and Wick, we derive the expression for the spin-3/2 density matrices. Our analysis is based on the angular distribution of the process $e^+e^-\rightarrowψ(3686)\rightarrowΩ^-\barΩ^+$ in the BESIII experiment. By decomposing the polarization state of $Ω^-$ particles along different coordinate axes, we examine the polarization dependence of the cross-section. Our results demonstrate that $Ω^-$ particles exhibit varying degrees of tensor polarization along the x-, y-, and z-axes, as well as weak vector polarization and rank-3 tensor polarization along the y-axis. To our knowledge, this is the first study to calculate the polarization dependence of the cross-section distributions for the annihilation process $e^+e^-\rightarrowΩ^-\barΩ^+$. Our theoretical predictions are in good agreement with the experimental measurements.

hep-ex↗

arXiv2023

Controllable Lyrics-to-Melody Generation

Lyrics-to-melody generation is an interesting and challenging topic in AI music research field. Due to the difficulty of learning the correlations between lyrics and melody, previous methods suffer from low generation quality and lack of controllability. Controllability of generative models enables human interaction with models to generate desired contents, which is especially important in music generation tasks towards human-centered AI that can facilitate musicians in creative activities. To address these issues, we propose a controllable lyrics-to-melody generation network, ConL2M, which is able to generate realistic melodies from lyrics in user-desired musical style. Our work contains three main novelties: 1) To model the dependencies of music attributes cross multiple sequences, inter-branch memory fusion (Memofu) is proposed to enable information flow between multi-branch stacked LSTM architecture; 2) Reference style embedding (RSE) is proposed to improve the quality of generation as well as control the musical style of generated melodies; 3) Sequence-level statistical loss (SeqLoss) is proposed to help the model learn sequence-level features of melodies given lyrics. Verified by evaluation metrics for music quality and controllability, initial study of controllable lyrics-to-melody generation shows better generation quality and the feasibility of interacting with users to generate the melodies in desired musical styles when given lyrics.

cs.SD↗