arXiv subjects

Tao Wang

Publications and source records attributed to Tao Wang.

At least 523 records · Page 29Linked to original sources

arXiv2023

A Novel Two-Layer Codebook Based Near-Field Beam Training for Intelligent Reflecting Surface

In this paper, we study the codebook-based near-field beam training for intelligent reflecting surfaces (IRSs) aided wireless system. In the considered model, the near-field beam training is critical to focus signals at the location of user equipment (UE) to obtain prominent IRS array gain. However, existing codebook schemes cannot achieve low training overhead and high receiving power simultaneously. To tackle this issue, a novel two-layer codebook based beam training scheme is proposed. The layer-1 codebook is designed based on the omnidirectionality of a random-phase beam pattern, which estimates the UE distance with training overhead equivalent to that of one DFT codeword. Then, based on the estimated UE distance, the layer-2 codebook is generated to scan candidate UE locations and obtain the optimal codeword for IRS beamforming. Numerical results show that compared with benchmarks, the proposed two-layer beam training scheme achieves more accurate UE distance and angle estimation, higher data rate, and smaller training overhead.

cs.IT↗

arXiv2023

Visual Content Privacy Protection: A Survey

Vision is the most important sense for people, and it is also one of the main ways of cognition. As a result, people tend to utilize visual content to capture and share their life experiences, which greatly facilitates the transfer of information. Meanwhile, it also increases the risk of privacy violations, e.g., an image or video can reveal different kinds of privacy-sensitive information. Researchers have been working continuously to develop targeted privacy protection solutions, and there are several surveys to summarize them from certain perspectives. However, these surveys are either problem-driven, scenario-specific, or technology-specific, making it difficult for them to summarize the existing solutions in a macroscopic way. In this survey, a framework that encompasses various concerns and solutions for visual privacy is proposed, which allows for a macro understanding of privacy concerns from a comprehensive level. It is based on the fact that privacy concerns have corresponding adversaries, and divides privacy protection into three categories, based on computer vision (CV) adversary, based on human vision (HV) adversary, and based on CV \& HV adversary. For each category, we analyze the characteristics of the main approaches to privacy protection, and then systematically review representative solutions. Open challenges and future directions for visual privacy protection are also discussed.

cs.CR↗

arXiv2023

Weak degeneracy of planar graphs without 4- and 6-cycles

A graph is $k$-degenerate if every subgraph $H$ has a vertex $v$ with $d_{H}(v) \leq k$. The class of degenerate graphs plays an important role in the graph coloring theory. Observed that every $k$-degenerate graph is $(k + 1)$-choosable and $(k + 1)$-DP-colorable. Bernshteyn and Lee defined a generalization of $k$-degenerate graphs, which is called \emph{weakly $k$-degenerate}. The weak degeneracy plus one is an upper bound for many graph coloring parameters, such as choice number, DP-chromatic number and DP-paint number. In this paper, we give two sufficient conditions for a plane graph without $4$- and $6$-cycles to be weakly $2$-degenerate, which implies that every such graph is $3$-DP-colorable and near-bipartite, where a graph is near-bipartite if its vertex set can be partitioned into an independent set and an acyclic set.

math.CO↗

arXiv2023

Decomposition of planar graphs with forbidden configurations

A $(d,h)$-decomposition of a graph $G$ is an ordered pair $(D, H)$ such that $H$ is a subgraph of $G$ of maximum degree at most $h$ and $D$ is an acyclic orientation of $G-E(H)$ with maximum out-degree at most $d$. In this paper, we prove that for $l \in \{5, 6, 7, 8, 9\}$, every planar graph without $4$- and $l$-cycles is $(2,1)$-decomposable. As a consequence, for every planar graph $G$ without $4$- and $l$-cycles, there exists a matching $M$, such that $G - M$ is $3$-DP-colorable and has Alon-Tarsi number at most $3$. In particular, $G$ is $1$-defective $3$-DP-colorable, $1$-defective $3$-paintable and 1-defective 3-choosable. These strengthen the results in [Discrete Appl. Math. 157~(2) (2009) 433--436] and [Discrete Math. 343 (2020) 111797].

math.CO↗

arXiv2023

Weak degeneracy of planar graphs and locally planar graphs

Weak degeneracy is a variation of degeneracy which shares many nice properties of degeneracy. In particular, if a graph $G$ is weakly $d$-degenerate, then for any $(d + 1)$-list assignment $L$ of $G$, one can construct an $L$-coloring of $G$ by a modified greedy coloring algorithm. It is known that planar graphs of girth 5 are 3-choosable and locally planar graphs are 5-choosable. This paper strengthens these results and proves that planar graphs of girth 5 are weakly 2-degenerate and locally planar graphs are weakly 4-degenerate.

math.CO↗

arXiv2023

Prolate-oblate asymmetric shape phase transition in the interacting boson model with SU (3) higher-order interactions

Prolate-oblate shape phase transition is an interesting topic in nuclear structure, which is useful for understanding the intrinsic interactions between nucleons. Recently, the interacting boson model with $SU(3)$ higher-order interactions was proposed, in which the prolate shape and the oblate shape are not described in a mirror symmetric way. This asymmetric description seems more realistic. The level evolutions, $B(E2)$ values and other important indicators showing the prolate-oblate asymmetric transitions are investigated in detail, and realistic structure evolutions from $^{180}$Hf to $^{200}$Hg are compared. A key finding is that, the average deformation of the prolate shape is nearly twice the one of the oblate shape. These results, together with the successful description of the $B(E2)$ anomaly in $^{168,170}$Os, $^{172}$Pt, the $γ$-soft properties of $^{196}$Pt, $^{82}$Kr and the normal states of $^{110}$Cd, support the validity of the new model.

nucl-th↗

arXiv2023

SN 2018gk Revisited: the Photosphere, the Central Engine, And the Putative Dust

In this paper, we perform a comprehensive study for the physical properties of SN 2018gk which is a luminous type IIb supernova (SN). We find that the early-time photospheric velocity vary from a larger value to a smaller value before the photosphere reach a temperature floor. We generalize the photosphere modulus and fit the multi-band light curves (LCs) of SN 2018gk. We find that the $^{56}$Ni mass model require $\sim0.90$ M$_\odot$ of $^{56}$Ni which is larger than the derived ejecta mass ($\sim0.10$ M$_\odot$). Alternatively, we use the magnetar plus $^{56}$Ni and the fallback plus $^{56}$Ni models to fit the LCs of SN 2018gk, finding that the two models can fit the LCs. We favor the magnetar plus $^{56}$Ni since the parameters are rather reasonable ($M_{\rm ej} =1.65$ M$_\odot$, $M_{\rm Ni}=0.05$ M$_\odot$ which is smaller than the upper limit of the value of the $^{56}$Ni mass can by synthesized by the neutrino-powered core collapse SNe $B=6.52\times10^{14}$ G which is comparable to those of luminous and superluminous SNe studied in the literature, and $P_0=10.42$ ms which is comparable to those of luminous SNe), while the validity of the fallback plus $^{56}$Ni model depends on the accretion efficiency ($η$). Therefore, we suggest that SN 2018gk might be a SN IIb mainly powered by a central engine. Finally, we confirm the NIR excesses of the spectral energy distributions (SEDs) of SN 2018gk at some epochs and constrain the physical properties of the putative dust using the blackbody plus dust emission model.

astro-ph.HE↗

arXiv2023

Noise2Music: Text-conditioned Music Generation with Diffusion Models

We introduce Noise2Music, where a series of diffusion models is trained to generate high-quality 30-second music clips from text prompts. Two types of diffusion models, a generator model, which generates an intermediate representation conditioned on text, and a cascader model, which generates high-fidelity audio conditioned on the intermediate representation and possibly the text, are trained and utilized in succession to generate high-fidelity music. We explore two options for the intermediate representation, one using a spectrogram and the other using audio with lower fidelity. We find that the generated audio is not only able to faithfully reflect key elements of the text prompt such as genre, tempo, instruments, mood, and era, but goes beyond to ground fine-grained semantics of the prompt. Pretrained large language models play a key role in this story -- they are used to generate paired text for the audio of the training set and to extract embeddings of the text prompts ingested by the diffusion models. Generated examples: https://google-research.github.io/noise2music

cs.SD↗

arXiv2023

Achieving ferroelectricity in a centrosymmetric high-performance semiconductor by strain engineering

Phase engineering by strains in 2D semiconductors is of great importance for a variety of applications. Here, we present a study of strain induced ferroelectric (FE) transition on bismuth oxyselenide (Bi$_2$O$_2$Se) films, a high-performance (HP) semiconductor for next-generation electronics. Bi$_2$O$_2$Se is non-FE at ambient. Upon a loading force $\gtrsim 400$ nN, piezoelectric force responses exhibit butterfly loops on magnitude and 180$^\textrm{o}$ phase switching. By carefully ruling out extrinsic factors, these features are attributed to a transition to FE phase. The transition is further proved by the appearance of a sharp peak on optical second harmonic generation under an uniaxial strain. Fundamentally, solids with paraelectric at ambient and FE under strains are scarce. FE transition is discussed with the help of first-principle calculations and theoretical simulations. The switching of FE polarization acts as a knob for Schottky barrier engineering at contacts and serves as basis for a memristor with a huge switching ratio of 10$^6$. Our work endows a new degree of freedom to a HP electronic/optoelectronic semiconductor and the integration of FE and HP semiconductivity paving the way for multiple exciting functionalities, including HP neuromorphic computation and bulk piezophotovoltaic.

cond-mat.mtrl-sci↗

arXiv2023

$E(5)$-like emerging $γ$-softness in $^{82}$Kr

Recently the interacting bosom model with $SU(3)$ higher-order interactions was proposed by one of the authors, wherein unexpected $γ$-softness can be emerged in this new model. This stimulates further discussion on the connections of the new $γ$-softness and the realistic $γ$-soft nuclei. In this paper, $E(5)$-like $γ$-softness arises when the $SU(3)$ fourth-order interaction $\hat{C}_{2}^{2}[SU(3)]$ is considered. And the corresponding transitional behaviors are similar to that from the $U(5)$ limit to the $O(6)$ limit in previous IBM-1, which provides a novel perspective for understanding the new model. Low-lying spectra, $B(E2)$ values between the low-lying states and quadrupole moment of the first $2_{1}^{+}$ state are investigated. Experimental data of the $E(5)$-like nucleus $^{82}$Kr are compared with the calculated theoretical results, where the calculated low-lying level energies and the associated $B(E2)$ values fit very well with the experimental data.

nucl-th↗

arXiv2023

The hidden side of cosmic star formation at z > 3: Bridging optically-dark and Lyman break galaxies with GOODS-ALMA

Our current understanding of the cosmic star formation history at z>3 is primarily based on UV-selected galaxies (i.e., LBGs). Recent studies of H-dropouts have revealed that we may be missing a large proportion of star formation that is taking place in massive galaxies at z>3. In this work, we extend the H-dropout criterion to lower masses to select optically dark/faint galaxies (OFGs), in order to complete the census between LBGs and H-dropouts. Our criterion (H> 26.5 mag & [4.5] < 25 mag) combined with a de-blending technique is designed to select not only extremely dust-obscured massive galaxies but also normal star-forming galaxies. In total, we identified 27 OFGs at z_phot > 3 (z_med=4.1) in the GOODS-ALMA field, covering a wide distribution of stellar masses with log($M_{\star}$/$M_{\odot}$) = 9.4-11.1. We find that up to 75% of the OFGs with log($M_{\star}$/$M_{\odot}$) = 9.5-10.5 were neglected by previous LBGs and H-dropout selection techniques. After performing stacking analyses, the OFGs exhibit shorter gas depletion timescales, slightly lower gas fractions, and lower dust temperatures than typical star-forming galaxies. Their SFR_tot (SFR_ IR+SFR_UV) is much larger than SFR_UVcorr (corrected for dust extinction), with SFR_tot/SFR_UVcorr = $8\pm1$, suggesting the presence of hidden dust regions in the OFGs that absorb all UV photons. The average dust size measured by a circular Gaussian model fit is R_e(1.13 mm)=1.01$\pm$0.05 kpc. We find that the cosmic SFRD at z>3 contributed by massive OFGs is at least two orders of magnitude higher than the one contributed by equivalently massive LBGs. Finally, we calculate the combined contribution of OFGs and LBGs to the cosmic SFRD at z=4-5 to be 4 $\times$ 10$^{-2}$ $M_{\odot}$ yr$^{-1}$Mpc$^{-3}$, which is about 0.15 dex (43%) higher than the SFRD derived from UV-selected samples alone at the same redshift.

astro-ph.GA↗

arXiv2023

Mortality Prediction with Adaptive Feature Importance Recalibration for Peritoneal Dialysis Patients: a deep-learning-based study on a real-world longitudinal follow-up dataset

Objective: Peritoneal Dialysis (PD) is one of the most widely used life-supporting therapies for patients with End-Stage Renal Disease (ESRD). Predicting mortality risk and identifying modifiable risk factors based on the Electronic Medical Records (EMR) collected along with the follow-up visits are of great importance for personalized medicine and early intervention. Here, our objective is to develop a deep learning model for a real-time, individualized, and interpretable mortality prediction model - AICare. Method and Materials: Our proposed model consists of a multi-channel feature extraction module and an adaptive feature importance recalibration module. AICare explicitly identifies the key features that strongly indicate the outcome prediction for each patient to build the health status embedding individually. This study has collected 13,091 clinical follow-up visits and demographic data of 656 PD patients. To verify the application universality, this study has also collected 4,789 visits of 1,363 hemodialysis dialysis (HD) as an additional experiment dataset to test the prediction performance, which will be discussed in the Appendix. Results: 1) Experiment results show that AICare achieves 81.6%/74.3% AUROC and 47.2%/32.5% AUPRC for the 1-year mortality prediction task on PD/HD dataset respectively, which outperforms the state-of-the-art comparative deep learning models. 2) This study first provides a comprehensive elucidation of the relationship between the causes of mortality in patients with PD and clinical features based on an end-to-end deep learning model. 3) This study first reveals the pattern of variation in the importance of each feature in the mortality prediction based on built-in interpretability. 4) We develop a practical AI-Doctor interaction system to visualize the trajectory of patients' health status and risk indicators.

cs.LG↗

arXiv2023

Spatio-Temporal Point Process for Multiple Object Tracking

Multiple Object Tracking (MOT) focuses on modeling the relationship of detected objects among consecutive frames and merge them into different trajectories. MOT remains a challenging task as noisy and confusing detection results often hinder the final performance. Furthermore, most existing research are focusing on improving detection algorithms and association strategies. As such, we propose a novel framework that can effectively predict and mask-out the noisy and confusing detection results before associating the objects into trajectories. In particular, we formulate such "bad" detection results as a sequence of events and adopt the spatio-temporal point process}to model such events. Traditionally, the occurrence rate in a point process is characterized by an explicitly defined intensity function, which depends on the prior knowledge of some specific tasks. Thus, designing a proper model is expensive and time-consuming, with also limited ability to generalize well. To tackle this problem, we adopt the convolutional recurrent neural network (conv-RNN) to instantiate the point process, where its intensity function is automatically modeled by the training data. Furthermore, we show that our method captures both temporal and spatial evolution, which is essential in modeling events for MOT. Experimental results demonstrate notable improvements in addressing noisy and confusing detection results in MOT datasets. An improved state-of-the-art performance is achieved by incorporating our baseline MOT algorithm with the spatio-temporal point process model.

cs.CV↗

arXiv2023

Strict Intuitionistic Fuzzy Distance/Similarity Measures Based on Jensen-Shannon Divergence

Being a pair of dual concepts, the normalized distance and similarity measures are very important tools for decision-making and pattern recognition under intuitionistic fuzzy sets framework. To be more effective for decision-making and pattern recognition applications, a good normalized distance measure should ensure that its dual similarity measure satisfies the axiomatic definition. In this paper, we first construct some examples to illustrate that the dual similarity measures of two nonlinear distance measures introduced in [A distance measure for intuitionistic fuzzy sets and its application to pattern classification problems, \emph{IEEE Trans. Syst., Man, Cybern., Syst.}, vol.~51, no.~6, pp. 3980--3992, 2021] and [Intuitionistic fuzzy sets: spherical representation and distances, \emph{Int. J. Intell. Syst.}, vol.~24, no.~4, pp. 399--420, 2009] do not meet the axiomatic definition of intuitionistic fuzzy similarity measure. We show that (1) they cannot effectively distinguish some intuitionistic fuzzy values (IFVs) with obvious size relationship; (2) except for the endpoints, there exist infinitely many pairs of IFVs, where the maximum distance 1 can be achieved under these two distances; leading to counter-intuitive results. To overcome these drawbacks, we introduce the concepts of strict intuitionistic fuzzy distance measure (SIFDisM) and strict intuitionistic fuzzy similarity measure (SIFSimM), and propose an improved intuitionistic fuzzy distance measure based on Jensen-Shannon divergence. We prove that (1) it is a SIFDisM; (2) its dual similarity measure is a SIFSimM; (3) its induced entropy is an intuitionistic fuzzy entropy. Comparative analysis and numerical examples demonstrate that our proposed distance measure is completely superior to the existing ones.

math.GM↗

arXiv2023

Sum-Rate Maximization for Active RIS-Aided Downlink RSMA System

In this paper, the problem of sum-rate maximization for an active reconfigurable intelligent surface (RIS) assisted downlink rate-splitting multiple access (RSMA) transmission system is studied. In the considered model, the active RIS is deployed to overcome severe power attenuation, which is caused by the cumulative product of RIS incidence path loss and the reflection path loss. Since the active RIS can adjust both the phase and the amplitude of the incident signal simultaneously, the RIS control scheme requires delicate design to improve RSMA communication performance. To address this issue, a sum-rate maximization problem is formulated to jointly optimize the beamforming vectors, rate allocation vector, and RIS precoding matrix. To solve this non-convex sum-rate maximization problem, an iterative algorithm based on fractional programming (FP) and quadratic constraint quadratic programming (QCQP) is proposed. In particular, the proposed algorithm firstly decomposes the original problem into two subproblems, namely, 1) beamforming and rate allocation optimization and 2) active RIS precoding optimization. The corresponding variables of the two subproblems are optimized through sequential convex approximation (SCA) and block coordinate descent (BCD), respectively. Numerical results show that the proposed active RIS-aided RSMA system could increase the sum-rate by up to 45% over the conventional passive RIS-aided RSMA system with the same energy consumption.

cs.IT↗

arXiv2023

Jiamusi pulsar observations: IV. The core-weak pattern of PSR B0329+54

The bright pulsar PSR B0329+54 was previously known for many years to have two emission modes. Sensitive observations of individual pulses reveal that the central component of pulse profile, which is called core component, is found to be very weakened occasionally for some periods and then recovered. This is the newly identified core-weak mode. Based on our long observations of PSR B0329+54 by the Jiamusi 66-m telescope at 2250 MHz, we report here that the profile components of individual pulses, including these for the core and the leading and trailing peaks, are relatedly varying over some periods even before and after the core-weak mode, forming a regular pattern in the phase-vs-time plot for a train of period-folded pulses. The pattern has a similar structure for the core-weak mode with a time scale of 3 to 14 periods. It starts with an intensity brightening at the trailing phase of the core component, and then the core intensity declines to a very low level, as if the core component is drifting out from the normal radiation window within one or two periods. Then the intensity for the trailing components is enhanced, and then the leading component appears at an advanced phase. Such a core-weak mode lasts for several periods. Finally, the core-weak mode ends up with an enhanced intensity at the leading phase for the core component, as if the core gradually comes back and finally stays at the phase of the profile center as it used to.

astro-ph.HE↗

arXiv2023

Spatio-Temporal Context Modeling for Road Obstacle Detection

Road obstacle detection is an important problem for vehicle driving safety. In this paper, we aim to obtain robust road obstacle detection based on spatio-temporal context modeling. Firstly, a data-driven spatial context model of the driving scene is constructed with the layouts of the training data. Then, obstacles in the input image are detected via the state-of-the-art object detection algorithms, and the results are combined with the generated scene layout. In addition, to further improve the performance and robustness, temporal information in the image sequence is taken into consideration, and the optical flow is obtained in the vicinity of the detected objects to track the obstacles across neighboring frames. Qualitative and quantitative experiments were conducted on the Small Obstacle Detection (SOD) dataset and the Lost and Found dataset. The results indicate that our method with spatio-temporal context modeling is superior to existing methods for road obstacle detection.

cs.CV↗

arXiv2023

A Multi-Scale Framework for Out-of-Distribution Detection in Dermoscopic Images

The automatic detection of skin diseases via dermoscopic images can improve the efficiency in diagnosis and help doctors make more accurate judgments. However, conventional skin disease recognition systems may produce high confidence for out-of-distribution (OOD) data, which may become a major security vulnerability in practical applications. In this paper, we propose a multi-scale detection framework to detect out-of-distribution skin disease image data to ensure the robustness of the system. Our framework extracts features from different layers of the neural network. In the early layers, rectified activation is used to make the output features closer to the well-behaved distribution, and then an one-class SVM is trained to detect OOD data; in the penultimate layer, an adapted Gram matrix is used to calculate the features after rectified activation, and finally the layer with the best performance is chosen to compute a normality score. Experiments show that the proposed framework achieves superior performance when compared with other state-of-the-art methods in the task of skin disease recognition.

cs.CV↗