arXiv subjects

Tao Guo

Publications and source records attributed to Tao Guo.

At least 55 records · Page 3Linked to original sources

arXiv2022

Formation of a supergiant quantum vortex in a relativistic Bose-Einstein condensate driven by rotation and a parallel magnetic field

Analysis based on the energy spectrum of noninteracting bosons shows that, under the circumstance of parallel rotation and magnetic field, charged bosons form a Bose-Einstein condensate because of the lift of the Landau level degeneracy by rotation [\textcolor{blue}{Y. Liu and I. Zahed, Phys. Rev. Lett. {\bf120}, 032001 (2018)}]. In this work, we study the interaction effect on the ground state of this Bose-Einstein condensate of charged bosons from the viewpoint of spontaneous symmetry breaking. We employ a minimal model for charged bosons with repulsive self-interaction. We find that the ground state of such a Bose-Einstein condensate is a supergiant quantum vortex, i.e., a quantized vortex with a large circulation. The size of the vortex is as large as the system size. The low-energy dispersion of the excitation spectra exhibits quadratic behavior, which is an anisotropic realization of the type-II Goldstone boson. Our study may give some implications to off-central relativistic heavy ion collisions, where large vorticity and magnetic fields can be generated.

nucl-th↗

arXiv2022

Do we need dense matter equation of state in curved spacetime for neutron stars?

Neutron stars are regarded as natural laboratories for the study of dense strong interaction matter. The equation of state (EoS) of dense matter computed in flat spacetime is used to predict the structure of neutron stars by solving the Tolman-Oppenheimer-Volkoff (TOV) equation. Recently, it has been reported that the curved spacetime effect or specifically gravitational time dilation effect on the EoS of dense matter leads to a significant increase of the maximum mass limit of neutron stars [Phys. Rev. D \textbf{104}, 123005 (2021) and J. Cosmol. Astropart. Phys. 02 (2021) 026]. However, in this work, we show that to study the hydrostatic equilibrium of dense matter within the framework of general relativity and relativistic fluid dynamics, the grand canonical EoS of dense matter, $p(T,μ)$, should be the same as that computed in flat spacetime, otherwise it is not consistent with local thermodynamic relations and energy-momentum conservation of the fluid. The gravitation influences the pressure $p$ only through enhancing the temperature $T$ and the chemical potential $μ$, known as Tolman's law and Klein's law. We rewrite the TOV equation as an alternative version so that the grand canonical EoS computed by using field theoretical methods can be used as a direct input. This may provide a tool to study the grand canonical EoS of dense matter via deep learning.

gr-qc↗

arXiv2022

PromptFL: Let Federated Participants Cooperatively Learn Prompts Instead of Models -- Federated Learning in Age of Foundation Model

Quick global aggregation of effective distributed parameters is crucial to federated learning (FL), which requires adequate bandwidth for parameters communication and sufficient user data for local training. Otherwise, FL may cost excessive training time for convergence and produce inaccurate models. In this paper, we propose a brand-new FL framework, PromptFL, that replaces the federated model training with the federated prompt training, i.e., let federated participants train prompts instead of a shared model, to simultaneously achieve the efficient global aggregation and local training on insufficient data by exploiting the power of foundation models (FM) in a distributed way. PromptFL ships an off-the-shelf FM, i.e., CLIP, to distributed clients who would cooperatively train shared soft prompts based on very few local data. Since PromptFL only needs to update the prompts instead of the whole model, both the local training and the global aggregation can be significantly accelerated. And FM trained over large scale data can provide strong adaptation capability to distributed users tasks with the trained soft prompts. We empirically analyze the PromptFL via extensive experiments, and show its superiority in terms of system feasibility, user privacy, and performance.

cs.LG↗

arXiv2022

Semantic Compression with Side Information: A Rate-Distortion Perspective

We consider the semantic rate-distortion problem motivated by task-oriented video compression. The semantic information corresponding to the task, which is not observable to the encoder, shows impacts on the observations through a joint probability distribution. The similarities among intra-frame segments and inter-frames in video compression are formulated as side information available at both the encoder and the decoder. The decoder is interested in recovering the observation and making an inference of the semantic information under certain distortion constraints. We establish the information-theoretic limits for the tradeoff between compression rates and distortions by fully characterizing the rate-distortion function. We further evaluate the rate-distortion function under specific Markov conditions for three scenarios: i) both the task and the observation are binary sources; ii) the task is a binary classification of an integer observation as even and odd; iii) Gaussian correlated task and observation. We also illustrate through numerical results that recovering only the semantic information can reduce the coding rate comparing to recovering the source observation.

cs.IT↗

arXiv2022

Lossy Computing with Side Information via Multi-Hypergraphs

We consider a problem of coding for computing, where the decoder wishes to estimate a function of its local message and the source message at the encoder within a given distortion. We show that the rate-distortion function can be characterized through a characteristic multi-hypergraph, which simplifies the evaluation of the rate-distortion function.

cs.IT↗

arXiv2022

Efficient Attribute Unlearning: Towards Selective Removal of Input Attributes from Feature Representations

Recently, the enactment of privacy regulations has promoted the rise of the machine unlearning paradigm. Existing studies of machine unlearning mainly focus on sample-wise unlearning, such that a learnt model will not expose user's privacy at the sample level. Yet we argue that such ability of selective removal should also be presented at the attribute level, especially for the attributes irrelevant to the main task, e.g., whether a person recognized in a face recognition system wears glasses or the age range of that person. Through a comprehensive literature review, it is found that existing studies on attribute-related problems like fairness and de-biasing learning cannot address the above concerns properly. To bridge this gap, we propose a paradigm of selectively removing input attributes from feature representations which we name `attribute unlearning'. In this paradigm, certain attributes will be accurately captured and detached from the learned feature representations at the stage of training, according to their mutual information. The particular attributes will be progressively eliminated along with the training procedure towards convergence, while the rest of attributes related to the main task are preserved for achieving competitive model performance. Considering the computational complexity during the training process, we not only give a theoretically approximate training method, but also propose an acceleration scheme to speed up the training process. We validate our method by spanning several datasets and models and demonstrate that our design can preserve model fidelity and reach prevailing unlearning efficacy with high efficiency. The proposed unlearning paradigm builds a foundation for future machine unlearning system and will become an essential component of the latest privacy-related legislation.

cs.LG↗

arXiv2022

Mass spectra of doubly heavy tetraquarks in an improved chromomagnetic interaction model

Doubly heavy tetraquark states are the prime candidates for tightly bound exotic states. We present a systematic study of the mass spectra of the $S$-wave doubly heavy tetraquark states $QQ\bar{q}\bar{q}$ ($q=u, d, s$ and $Q=c, b$) with different quantum numbers $J^P=0^+$, $1^+$, and $2^+$ in the framework of the improved chromomagnetic interaction (ICMI) model. The parameters in the ICMI model are obtained by fitting the conventional hadron spectra and are used directly to predict the masses of the tetraquark states. For heavy quarks, the uncertainties of the parameters are obtained by comparing the masses of doubly and triply heavy baryons with those given by lattice QCD, QCD sum rules, and potential models. Several compact and stable bound states are found in both the doubly charmed and doubly bottomed tetraquark systems. The predicted mass of the $cc\bar u\bar d$ state is consistent with the recent measurement from the LHCb collaboration.

hep-ph↗

arXiv2021

Structural Entropy of the Stochastic Block Models

With the rapid expansion of graphs and networks and the growing magnitude of data from all areas of science, effective treatment and compression schemes of context-dependent data is extremely desirable. A particularly interesting direction is to compress the data while keeping the "structural information" only and ignoring the concrete labelings. Under this direction, Choi and Szpankowski introduced the structures (unlabeled graphs) which allowed them to compute the structural entropy of the Erdős--Rényi random graph model. Moreover, they also provided an asymptotically optimal compression algorithm that (asymptotically) achieves this entropy limit and runs in expectation in linear time. In this paper, we consider the Stochastic Block Models with an arbitrary number of parts. Indeed, we define a partitioned structural entropy for Stochastic Block Models, which generalizes the structural entropy for unlabeled graphs and encodes the partition information as well. We then compute the partitioned structural entropy of the Stochastic Block Models, and provide a compression scheme that asymptotically achieves this entropy limit.

cs.IT↗

arXiv2021

DialogueBERT: A Self-Supervised Learning based Dialogue Pre-training Encoder

With the rapid development of artificial intelligence, conversational bots have became prevalent in mainstream E-commerce platforms, which can provide convenient customer service timely. To satisfy the user, the conversational bots need to understand the user's intention, detect the user's emotion, and extract the key entities from the conversational utterances. However, understanding dialogues is regarded as a very challenging task. Different from common language understanding, utterances in dialogues appear alternately from different roles and are usually organized as hierarchical structures. To facilitate the understanding of dialogues, in this paper, we propose a novel contextual dialogue encoder (i.e. DialogueBERT) based on the popular pre-trained language model BERT. Five self-supervised learning pre-training tasks are devised for learning the particularity of dialouge utterances. Four different input embeddings are integrated to catch the relationship between utterances, including turn embedding, role embedding, token embedding and position embedding. DialogueBERT was pre-trained with 70 million dialogues in real scenario, and then fine-tuned in three different downstream dialogue understanding tasks. Experimental results show that DialogueBERT achieves exciting results with 88.63% accuracy for intent recognition, 94.25% accuracy for emotion recognition and 97.04% F1 score for named entity recognition, which outperforms several strong baselines by a large margin.

cs.CL↗

arXiv2021

A generative adversarial approach to facilitate archival-quality histopathologic diagnoses from frozen tissue sections

In clinical diagnostics and research involving histopathology, formalin fixed paraffin embedded (FFPE) tissue is almost universally favored for its superb image quality. However, tissue processing time (more than 24 hours) can slow decision-making. In contrast, fresh frozen (FF) processing (less than 1 hour) can yield rapid information but diagnostic accuracy is suboptimal due to lack of clearing, morphologic deformation and more frequent artifacts. Here, we bridge this gap using artificial intelligence. We synthesize FFPE-like images ,virtual FFPE, from FF images using a generative adversarial network (GAN) from 98 paired kidney samples derived from 40 patients. Five board-certified pathologists evaluated the results in a blinded test. Image quality of the virtual FFPE data was assessed to be high and showed a close resemblance to real FFPE images. Clinical assessments of disease on the virtual FFPE images showed a higher inter-observer agreement compared to FF images. The nearly instantaneously generated virtual FFPE images can not only reduce time to information but can facilitate more precise diagnosis from routine FF images without extraneous costs and effort.

eess.IV↗

arXiv2021

Mass spectra and decays of open-heavy tetraquark states

Open-heavy tetraquark states, especially those contain four different quarks have drawn much attention in both theoretical and experimental fields. In the framework of the improved chromomagnetic interaction (ICMI) model, we complete a systematic study on the mass spectra and possible strong decay channels of the $S$-wave open-heavy tetraquark states, $qq\bar{q}\bar{Q}$ ($q=u,d,s$ and $Q=c,b$), with different quantum number $J^P=0^+$, $1^+$, and $2^+$. The parameters in the ICMI model are extracted from the conventional hadron spectra and used directly to predict the mass of tetraquark states. Several compact bound states and narrow resonances are found in both charm-strange and bottom-strange tetraquark sectors, most of them as a product of the strong coupling between the different channels. Our results show the recently discovered four different flavors tetraquark candidates $X_0(2900)$ is probably compact $ud\bar{s}\bar{c}$ state with quantum number $J^P=0^+$. The predictions about $X_0(2900)$ and its partners are expected to be better checked with other theories and future experiments.

hep-ph↗

arXiv2021

COSINE: A Web Server for Clonal and Subclonal Structure Inference and Evolution in Cancer Genomics

Cancers evolve from mutation of a single cell with sequential clonal and subclonal expansion of somatic mutation acquisition. Inferring clonal and subclonal structures from bulk or single cell tumor genomic sequencing data has a huge impact on cancer evolution studies. Clonal state and mutational order can provide detailed insight into tumor origin and its future development. In the past decade, a variety of methods have been developed for subclonal reconstruction using bulk tumor sequencing data. As these methods have been developed in different programming languages and using different input data formats, their use and comparison can be problematic. Therefore, we established a web server for clonal and subclonal structure inference and evolution of cancer genomic data (COSINE), which included 12 popular subclonal reconstruction methods. We decomposed each method via a detailed workflow of single processing steps with a user-friendly interface. To the best of our knowledge, this is the first web server providing online subclonal inference, including the most popular subclonal reconstruction methods. COSINE is freely accessible at www.clab-cosine.net or http://bio.rj.run:48996/cun-web.

q-bio.GN↗

arXiv2021

Community aware group testing

In this paper, we propose algorithms that leverage a known community structure to make group testing more efficient. We consider a population organized in disjoint communities: each individual participates in a community, and its infection probability depends on the community (s)he participates in. Use cases include families, students who participate in several classes, and workers who share common spaces. Group testing reduces the number of tests needed to identify the infected individuals by pooling diagnostic samples and testing them together. We show that if we design the testing strategy taking into account the community structure, we can significantly reduce the number of tests needed for adaptive and non-adaptive group testing, and can improve the reliability in cases where tests are noisy.

cs.IT↗

arXiv2021

Group testing for overlapping communities

In this paper, we propose algorithms that leverage a known community structure to make group testing more efficient. We consider a population organized in connected communities: each individual participates in one or more communities, and the infection probability of each individual depends on the communities (s)he participates in. Use cases include students who participate in several classes, and workers who share common spaces. Group testing reduces the number of tests needed to identify the infected individuals by pooling diagnostic samples and testing them together. We show that making testing algorithms aware of the community structure, can significantly reduce the number of tests needed both for adaptive and non-adaptive group testing.

cs.IT↗

arXiv2021

Searching for lepton flavor violating decays tau to Pl in Minimal R-symmetric Supersymmetric Standard Model

We analyze the lepton flavor violating decays $τ\rightarrow Pl$ ($P=π,η,η';\;l=e,μ$) in the scenario of the minimal R-symmetric supersymmetric standard model. The prediction on the branching ratios BR$(τ\rightarrow P e)$ and BR$(τ\rightarrow P μ)$ is affected by the mass insertion parameters $δ^{13}$ and $δ^{23}$, respectively. These parameters are constrained by the experimental bounds on the branching ratios BR($τ\rightarrow e (μ) γ$) and BR($τ\rightarrow 3e(μ)$). The result shows $Z$ penguin dominates the prediction on BR($τ\rightarrow Pl$) in a large region of the parameter space. The branching ratios for BR($τ\rightarrow Pl$) are predicted to be, at least, five orders of magnitude smaller than present experimental bounds and three orders of magnitude smaller than future experimental sensitivities.

hep-ph↗

arXiv2020

Thermoelectric performance of P-N-P abrupt heterostructures vertical to temperature gradient

We present a model for P-N-P abrupt heterostructures vertical to temperature gradient to improve the thermoelectric performance. The P-N-P heterostructure is considered as an abrupt bipolar junction transistor due to an externally applied temperature gradient paralleled to depletion layers. Taking Bi2Te3 and Bi0.5Sb1.5Te3 as N-type and P-type thermoelectric materials respectively for example, we achieve the purpose of controlling the Seebeck coefficient and the electrical conductivity independently while amplifying operation power. The calculated results show that the Seebeck coefficient can reach 3312V/K, and the ZTmax values of this model are 45 or 425, which are tens or even hundreds of times greater than those of bulk materials and films.

cond-mat.mtrl-sci↗

arXiv2020

New Results on the Storage-Retrieval Tradeoff in Private Information Retrieval Systems

In a private information retrieval (PIR) system, the user needs to retrieve one of the possible messages from a set of storage servers, but wishes to keep the identity of requested message private from any given server. Existing efforts in this area have made it clear that the efficiency of the retrieval will be impacted significantly by the amount of the storage space allowed at the servers. In this work, we consider the tradeoff between the storage cost and the retrieval cost. We first present three fundamental results: 1) a regime-wise 2-approximate characterization of the optimal tradeoff, 2) a cyclic permutation lemma that can produce more sophisticated codes from simpler ones, and 3) a relaxed entropic linear program (LP) lower bound that has a polynomial complexity. Equipped with the cyclic permutation lemma, we then propose two novel code constructions, and by applying the lemma, obtain new storage-retrieval points. Furthermore, we derive more explicit lower bounds by utilizing only a subset of the constraints in the relaxed entropic LP in a systematic manner. Though the new upper bound and lower bound do not lead to a more precise approximate characterization in general, they are significantly tighter than the existing art.

cs.IT↗

arXiv2020

Weakly Secure Symmetric Multilevel Diversity Coding

Multilevel diversity coding is a classical coding model where multiple mutually independent information messages are encoded, such that different reliability requirements can be afforded to different messages. It is well known that {\em superposition coding}, namely separately encoding the independent messages, is optimal for symmetric multilevel diversity coding (SMDC) (Yeung-Zhang 1999). In the current paper, we consider weakly secure SMDC where security constraints are injected on each individual message, and provide a complete characterization of the conditions under which superposition coding is sum-rate optimal. Two joint coding strategies, which lead to rate savings compared to superposition coding, are proposed, where some coding components for one message can be used as the encryption key for another. By applying different variants of Han's inequality, we show that the lack of opportunity to apply these two coding strategies directly implies the optimality of superposition coding. It is further shown that under a set of particular security constraints, one of the proposed joint coding strategies can be used to construct a code that achieves the optimal rate region.

cs.IT↗