arXiv subjects

Cheng Zhang

Publications and source records attributed to Cheng Zhang.

At least 343 records · Page 19Linked to original sources

arXiv2023

Azimuthal decorrelation for photon induced dijet production in ultra-peripheral collisions of heavy ions

We study the azimuthal angular decorrelation of the dijet production via photon fusion in ultra-peripheral heavy ion collisions. The impact parameter dependent cross section of quark-antiquark pairs production is derived using the equivalent photon approximation, and the contribution from final-state QCD radiations to the azimuthal angular distribution are calculated within Soft-Collinear Effective Theory. We carry out the QCD resummation of large logarithms of the azimuthal angle as well as the jet radius at next-to-leading logarithmic accuracy. In the end we present the normalized differential cross section for azimuthal decorrelation of the dijet pair and find that our results are consistent with the measurements reported by the ATLAS collaboration.

hep-ph↗

arXiv2023

Nonlinear Schrödinger equation on the half-line without a conserved number of solitons

We explore the phenomena of absorption/emission of solitons by an integrable boundary for the nonlinear Schrödinger equation on the half-line. This is based on the investigation of time-dependent reflection matrices which satisfy the boundary zero curvature equation. In particular, this leads to absorption/emission processes at the boundary that can take place for solitons and higher-order solitons. As a consequence, the usual charges on the half-line are no longer conserved but we show explicitly how to restore an infinite set of conserved quantities by taking the boundary into account. The Hamiltonian description and Poisson structure of the model are presented, which allows us to derive for the first time a classical version of the boundary algebra used originally in the context of the quantum nonlinear Schrödinger equation.

nlin.SI↗

arXiv2022

Sharp $L^p$ estimates and size of nodal sets for generalized Steklov eigenfunctions

We prove sharp $L^p$ estimates for the Steklov eigenfunctions on compact manifolds with boundary in terms of their $L^2$ norms on the boundary. We prove it by establishing $L^p$ bounds for the harmonic extension operators as well as the spectral projection operators on the boundary. Moreover, we derive lower bounds on the size of nodal sets for a variation of the Steklov spectral problem. We consider a generalized version of the Steklov problem by adding a non-smooth potential on the boundary but some of our results are new even without potential.

math.AP↗

arXiv2022

Gravitational-wave bursts from spin-precessing black holes in binary systems

Gravitational waves from precessing binary black holes exhibit new features that are absent in non-precessionary systems. All current waveform models take into account only the modulation of the signal due to precession. In this letter, we find that this effect has its own signature, by gravitational emission of a short and transient signal, or burst. The frequency of the burst is comparable to that of the late stage of the inspiral. We show that under certain conditions, this signal is strong enough to be detected by Advanced LIGO. For third-generation detectors like the Einstein telescope, the calculated signal-to-noise ratio can reach higher values. Measurements of precession would provide valuable insights into the intrinsic structure of black holes, and therefore into astrophysical binary formation mechanisms.

gr-qc↗

arXiv2022

WSC-Trans: A 3D network model for automatic multi-structural segmentation of temporal bone CT

Cochlear implantation is currently the most effective treatment for patients with severe deafness, but mastering cochlear implantation is extremely challenging because the temporal bone has extremely complex and small three-dimensional anatomical structures, and it is important to avoid damaging the corresponding structures when performing surgery. The spatial location of the relevant anatomical tissues within the target area needs to be determined using CT prior to the procedure. Considering that the target structures are too small and complex, the time required for manual segmentation is too long, and it is extremely challenging to segment the temporal bone and its nearby anatomical structures quickly and accurately. To overcome this difficulty, we propose a deep learning-based algorithm, a 3D network model for automatic segmentation of multi-structural targets in temporal bone CT that can automatically segment the cochlea, facial nerve, auditory tubercle, vestibule and semicircular canal. The algorithm combines CNN and Transformer for feature extraction and takes advantage of spatial attention and channel attention mechanisms to further improve the segmentation effect, the experimental results comparing with the results of various existing segmentation algorithms show that the dice similarity scores, Jaccard coefficients of all targets anatomical structures are significantly higher while HD95 and ASSD scores are lower, effectively proving that our method outperforms other advanced methods.

eess.IV↗

arXiv2022

Discovering the Unknown Knowns: Turning Implicit Knowledge in the Dataset into Explicit Training Examples for Visual Question Answering

Visual question answering (VQA) is challenging not only because the model has to handle multi-modal information, but also because it is just so hard to collect sufficient training examples -- there are too many questions one can ask about an image. As a result, a VQA model trained solely on human-annotated examples could easily over-fit specific question styles or image contents that are being asked, leaving the model largely ignorant about the sheer diversity of questions. Existing methods address this issue primarily by introducing an auxiliary task such as visual grounding, cycle consistency, or debiasing. In this paper, we take a drastically different approach. We found that many of the "unknowns" to the learned VQA model are indeed "known" in the dataset implicitly. For instance, questions asking about the same object in different images are likely paraphrases; the number of detected or annotated objects in an image already provides the answer to the "how many" question, even if the question has not been annotated for that image. Building upon these insights, we present a simple data augmentation pipeline SimpleAug to turn this "known" knowledge into training examples for VQA. We show that these augmented examples can notably improve the learned VQA models' performance, not only on the VQA-CP dataset with language prior shifts but also on the VQA v2 dataset without such shifts. Our method further opens up the door to leverage weakly-labeled or unlabeled images in a principled way to enhance VQA models. Our code and data are publicly available at https://github.com/heendung/simpleAUG.

cs.CV↗

arXiv2022

Rhino: Deep Causal Temporal Relationship Learning With History-dependent Noise

Discovering causal relationships between different variables from time series data has been a long-standing challenge for many domains such as climate science, finance, and healthcare. Given the complexity of real-world relationships and the nature of observations in discrete time, causal discovery methods need to consider non-linear relations between variables, instantaneous effects and history-dependent noise (the change of noise distribution due to past actions). However, previous works do not offer a solution addressing all these problems together. In this paper, we propose a novel causal relationship learning framework for time-series data, called Rhino, which combines vector auto-regression, deep learning and variational inference to model non-linear relationships with instantaneous effects while allowing the noise distribution to be modulated by historical observations. Theoretically, we prove the structural identifiability of Rhino. Our empirical results from extensive synthetic experiments and two real-world benchmarks demonstrate better discovery performance compared to relevant baselines, with ablation studies revealing its robustness under model misspecification.

cs.LG↗

arXiv2022

Tag-Set-Sequence Learning for Generating Question-Answer Pairs

Transformer-based QG models can generate question-answer pairs (QAPs) with high qualities, but may also generate silly questions for certain texts. We present a new method called tag-set sequence learning to tackle this problem, where a tag-set sequence is a sequence of tag sets to capture the syntactic and semantic information of the underlying sentence, and a tag set consists of one or more language feature tags, including, for example, semantic-role-labeling, part-of-speech, named-entity-recognition, and sentiment-indication tags. We construct a system called TSS-Learner to learn tag-set sequences from given declarative sentences and the corresponding interrogative sentences, and derive answers to the latter. We train a TSS-Learner model for the English language using a small training dataset and show that it can indeed generate adequate QAPs for certain texts that transformer-based models do poorly. Human evaluation on the QAPs generated by TSS-Learner over SAT practice reading tests is encouraging.

cs.CL↗

arXiv2022

DNA Scaffold Carrier Assisted Protein Translocation Through Solid-State Nanopore

The detection of biomolecules at the single molecular level have important applications in the fields of biosensing and biomedical diagnosis. Solid state nanopore (SS-nanopore) is an effective tool to perform the single molecular detection, due to its unique properties of label-free and less sample consumption. The current SS-nanopore translocations of small biomolecules are driven by the electronic field force, thus easily influenced by the charges, shapes and sizes of the target molecules. Therefore, it remains a great challenge to control biomolecules to translocate through SS-nanopore, particularly for the protein with complex conformations and different charges. Towards this problem, we developed a DNA scaffold carrier assisted strategy to help protein translocation through SS-nanopore, thus facilitating the target protein detection. Taking advantages of a DNA carrier loading effect, the current signal to noise ratios are significantly improved. Our proposed method is sensitive, convenient and can detect real-time sample nanopore translocations. This method opens up a wide range of applications for SS-nanopore detection and promote the development of single molecular detections.

physics.bio-ph↗

arXiv2022

Toward 6G TK$μ$ Extreme Connectivity: Architecture, Key Technologies and Experiments

Sixth-generation (6G) networks are evolving towards new features and order-of-magnitude enhancement of systematic performance metrics compared to the current 5G. In particular, the 6G networks are expected to achieve extreme connectivity performance with Tbps-scale data rate, Kbps/Hz-scale spectral efficiency, and $μ$s-scale latency. To this end, an original three-layer 6G network architecture is designed to realise uniform full-spectrum cell-free radio access and provide task-centric agile proximate support for diverse applications. The designed architecture is featured by super edge node (SEN) which integrates connectivity, computing, AI, data, etc. On this basis, a technological framework of pervasive multi-level (PML) AI is established in the centralised unit to enable task-centric near-real-time resource allocation and network automation. We then introduce a radio access network (RAN) architecture of full spectrum uniform cell-free networks, which is among the most attractive RAN candidates for 6G TK$μ$ extreme connectivity. A few most promising key technologies, i.e., cell-free massive MIMO, photonics-assisted Terahertz wireless access and spatiotemporal two-dimensional channel coding are further discussed. A testbed is implemented and extensive trials are conducted to evaluate innovative technologies and methodologies. The proposed 6G network architecture and technological framework demonstrate exciting potentials for full-service and full-scenario applications.

cs.NI↗

arXiv2022

Revealing Spatial-temporal Taxi Demand Patterns after Vaccination in COVID-19 Pandemic

The COVID-19 pandemic has had an unprecedented impact on our daily lives. With the increase in vaccination rate, normalcy gradually returns, so is the taxi demand. However, the changes in the spatial-temporal taxi demand pattern and factors impacting the recovery of this demand after COVID-19 vaccination started remain unclear. With the multisource time-series data from Chicago, including pandemic severity, vaccination progress and taxi trip volume, the recovery pattern of taxi demand is analyzed. The result reveals the taxi trip volume and average travel distance increased in most community areas in the city of Chicago after taking the COVID-19 vaccine. Taxi demand recovers relatively faster in the airport and area near the central downtown than in other areas in Chicago. Considering the asynchrony of data, the Pearson coefficient and Dynamic Time Warping (DTW) are both applied to investigate the correlations among different time series. It found that the recovery of taxi demand is not only related to the pandemic severity but strongly correlated with vaccination progress. Then, the leading and lagging relationship between vaccination progress and taxi demand is investigated by Time Lagging Cross-Correlation method. Taxi demand starts to recover after the first-dose vaccination period started and before the second-dose vaccination period. However, it was not until mid-April that the recovery rate of taxi demand exceeded the growth of the vaccination rate.

stat.AP↗

arXiv2022

Learning with Free Object Segments for Long-Tailed Instance Segmentation

One fundamental challenge in building an instance segmentation model for a large number of classes in complex scenes is the lack of training examples, especially for rare objects. In this paper, we explore the possibility to increase the training examples without laborious data collection and annotation. We find that an abundance of instance segments can potentially be obtained freely from object-centric images, according to two insights: (i) an object-centric image usually contains one salient object in a simple background; (ii) objects from the same class often share similar appearances or similar contrasts to the background. Motivated by these insights, we propose a simple and scalable framework FreeSeg for extracting and leveraging these "free" object foreground segments to facilitate model training in long-tailed instance segmentation. Concretely, we investigate the similarity among object-centric images of the same class to propose candidate segments of foreground instances, followed by a novel ranking of segment quality. The resulting high-quality object segments can then be used to augment the existing long-tailed datasets, e.g., by copying and pasting the segments onto the original training images. Extensive experiments show that FreeSeg yields substantial improvements on top of strong baselines and achieves state-of-the-art accuracy for segmenting rare object categories.

cs.CV↗

arXiv2022

Topological Lifshitz transition and one-dimensional Weyl mode in HfTe5

Landau band crossings typically stem from the intra-band evolution of electronic states in magnetic fields and enhance the interaction effect in their vicinity. Here in the extreme quantum limit of topological insulator HfTe5, we report the observation of a topological Lifshitz transition from inter-band Landau level crossings using magneto-infrared spectroscopy. By tracking the Landau level transitions, we demonstrate that band inversion drives the zeroth Landau bands to cross with each other after 4.5 T and forms one-dimensional Weyl mode with fundamental gap persistently closed. The unusual reduction of the zeroth Landau level transition activity suggests a topological Lifshitz transition at 21 T which shifts the Weyl mode close to Fermi level. As a result, a broad and asymmetric absorption feature emerges due to the Pauli blocking effect in one dimension, along with a distinctive negative magneto-resistivity. Our results provide a strategy for realizing one-dimensional Weyl quasiparticles in bulk crystals.

cond-mat.mtrl-sci↗

arXiv2022

Self-referencing 3D characterization of ultrafast optical-vortex beams using tilted interference TERMITES technique

Femtosecond light pulses carrying optical angular momentums (OAMs), possessing intriguing properties of helical phase fronts and ultrafast temporal profiles, enable many applications in nonlinear optics, strong-field physics and laser micro-machining. While generation of OAM-carrying ultrafast pulses and their interactions with matters have been intensively studied in experiments, three-dimensional characterization of ultrafast OAM-carrying light beams in spatio-temporal domain has, however, proved difficult to achieve. Conventional measurement schemes rely on the use of a reference pulsed light beam which needs to be well-characterized in its phase front and to have sufficient overlap and coherence with the beam under test, largely limiting practical applications of these schemes. Here we demonstrate a self-referencing set-up based on a tilted interferometer that can be used to measure complete spatio-temporal information of OAM-carrying femtosecond pulses with different topological charges. Through scanning one interferometer arm, the spectral phase over the pulse spatial profile can be obtained using the tilted interference signal, and the temporal envelope of the light field at one particular position around its phase singularity can be retrieved simultaneously, enabling three-dimensional beam reconstruction. This self-referencing technique, capable of measuring spatio-temporal ultrafast optical-vortex beams, may find many applications in fields of nonlinear optics and light-matter interactions.

physics.optics↗

arXiv2022

Optimistic Optimization of Gaussian Process Samples

Bayesian optimization is a popular formalism for global optimization, but its computational costs limit it to expensive-to-evaluate functions. A competing, computationally more efficient, global optimization framework is optimistic optimization, which exploits prior knowledge about the geometry of the search space in form of a dissimilarity function. We investigate to which degree the conceptual advantages of Bayesian Optimization can be combined with the computational efficiency of optimistic optimization. By mapping the kernel to a dissimilarity, we obtain an optimistic optimization algorithm for the Bayesian Optimization setting with a run-time of up to $\mathcal{O}(N \log N)$. As a high-level take-away we find that, when using stationary kernels on objectives of relatively low evaluation cost, optimistic optimization can be strongly preferable over Bayesian optimization, while for strongly coupled and parametric models, good implementations of Bayesian optimization can perform much better, even at low evaluation cost. We argue that there is a new research domain between geometric and probabilistic search, i.e. methods that run drastically faster than traditional Bayesian optimization, while retaining some of the crucial functionality of Bayesian optimization.

cs.LG↗

arXiv2022

NeurIPS Competition Instructions and Guide: Causal Insights for Learning Paths in Education

In this competition, participants will address two fundamental causal challenges in machine learning in the context of education using time-series data. The first is to identify the causal relationships between different constructs, where a construct is defined as the smallest element of learning. The second challenge is to predict the impact of learning one construct on the ability to answer questions on other constructs. Addressing these challenges will enable optimisation of students' knowledge acquisition, which can be deployed in a real edtech solution impacting millions of students. Participants will run these tasks in an idealised environment with synthetic data and a real-world scenario with evaluation data collected from a series of A/B tests.

cs.CY↗

arXiv2022

On Incorrectness Logic and Kleene Algebra with Top and Tests

Kleene algebra with tests (KAT) is a foundational equational framework for reasoning about programs, which has found applications in program transformations, networking and compiler optimizations, among many other areas. In his seminal work, Kozen proved that KAT subsumes propositional Hoare logic, showing that one can reason about the (partial) correctness of while programs by means of the equational theory of KAT. In this work, we investigate the support that KAT provides for reasoning about incorrectness, instead, as embodied by Ohearn's recently proposed incorrectness logic. We show that KAT cannot directly express incorrectness logic. The main reason for this limitation can be traced to the fact that KAT cannot express explicitly the notion of codomain, which is essential to express incorrectness triples. To address this issue, we study Kleene Algebra with Top and Tests (TopKAT), an extension of KAT with a top element. We show that TopKAT is powerful enough to express a codomain operation, to express incorrectness triples, and to prove all the rules of incorrectness logic sound. This shows that one can reason about the incorrectness of while-like programs by means of the equational theory of TopKAT.

cs.PL↗

arXiv2022

Exploring gluon tomography with polarization dependent diffractive J/$ψ$ production

We study azimuthal asymmetries in diffractive J/$ψ$ production in ultraperipheral heavy-ion collisions at RHIC and LHC energies using the color glass condensate effective theory. Our calculation successfully describes azimuthal averaged $J/ψ$ production cross section measured by STAR and ALICE. We further predict very large $\cos 2ϕ$ and $\cos 4ϕ$ azimuthal asymmetries for diffractive $J/ψ$ production both in UPCs at RHIC and LHC energies and in eA collisions at EIC energy. These novel polarization dependent observables may provide complementary information for constraining gluon transverse spatial distribution inside large nuclei. As compared to all previous analysis of diffractive $J/ψ$ production, the essential new elements integrated in our theoretical calculations are: the double-slit interference effect, the linear polarization of coherent photons, and the final state soft photon radiation effect.

hep-ph↗