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Kai Wang

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preprint2026arXiv

Audio-Visual Intelligence in Large Foundation Models

Audio-Visual Intelligence (AVI) has emerged as a central frontier in artificial intelligence, bridging auditory and visual modalities to enable machines that can perceive, generate, and interact in the multimodal real world. In the era of large foundation models, joint modeling of audio and vision has become increasingly crucial, i.e., not only for understanding but also for controllable generation and reasoning across dynamic, temporally grounded signals. Recent advances, such as Meta MovieGen and Google Veo-3, highlight the growing industrial and academic focus on unified audio-vision architectures that learn from massive multimodal data. However, despite rapid progress, the literature remains fragmented, spanning diverse tasks, inconsistent taxonomies, and heterogeneous evaluation practices that impede systematic comparison and knowledge integration. This survey provides the first comprehensive review of AVI through the lens of large foundation models. We establish a unified taxonomy covering the broad landscape of AVI tasks, ranging from understanding (e.g., speech recognition, sound localization) to generation (e.g., audio-driven video synthesis, video-to-audio) and interaction (e.g., dialogue, embodied, or agentic interfaces). We synthesize methodological foundations, including modality tokenization, cross-modal fusion, autoregressive and diffusion-based generation, large-scale pretraining, instruction alignment, and preference optimization. Furthermore, we curate representative datasets, benchmarks, and evaluation metrics, offering a structured comparison across task families and identifying open challenges in synchronization, spatial reasoning, controllability, and safety. By consolidating this rapidly expanding field into a coherent framework, this survey aims to serve as a foundational reference for future research on large-scale AVI.

preprint2026arXiv

SkillSafetyBench: Evaluating Agent Safety under Skill-Facing Attack Surfaces

Reusable skills are becoming a common interface for extending large language model agents, packaging procedural guidance with access to files, tools, memory, and execution environments. However, this modularity introduces attack surfaces that are largely missed by existing safety evaluations: even when the user request is benign, task-relevant skill materials or local artifacts can steer an agent toward unsafe actions. We present SkillSafetyBench, a runnable benchmark for evaluating such skill-mediated safety failures. SkillSafetyBench includes 155 adversarial cases across 47 tasks, 6 risk domains, and 30 safety categories, each evaluated with a case-specific rule-based verifier. Experiments with multiple CLI agents and model backends show that localized non-user attacks can consistently induce unsafe behavior, with distinct failure patterns across domains, attack methods, and scaffold-model pairings. Our findings suggest that agent safety depends not only on model-level alignment, but also on how agents interpret skills, trust workflow context, and act through executable environments.

preprint2025arXiv

Observation of the $γ$-ray Emission from W43 with LHAASO

In this paper, we report the detection of the very-high-energy (VHE, $ 100{\rm\ GeV} < E < 100{\rm\ TeV} $) and ultra-high-energy (UHE, $E > 100\rm\ TeV$) $γ$-ray emissions from the direction of the young star-forming region W43, observed by the Large High Altitude Air Shower Observation (LHAASO). The extended $γ$-ray source was detected with a significance of ${\sim}16\,σ$ by KM2A and ${\sim}17\,σ$ by WCDA, respectively. The angular extension of this $γ$-ray source is about 0.5 degrees, corresponding to a physical size of about 50 pc. We discuss the origin of the $γ$-ray emission and possible cosmic ray acceleration in the W43 region using multi-wavelength data. Our findings suggest that W43 is likely another young star cluster capable of accelerating cosmic rays (CRs) to at least several hundred TeV.

preprint2025arXiv

Study of Ultra-High-Energy Gamma-Ray Source 1LHAASO J0056+6346u and Its Possible Origins

We report a dedicated study of the newly discovered extended UHE $γ$-ray source 1LHAASO J0056+6346u. Analyzing 979 days of LHAASO-WCDA data and 1389 days of LHAASO-KM2A data, we observed a significant excess of $γ$-ray events with both WCDA and KM2A. Assuming a point power-law source with a fixed spectral index, the significance maps reveal excesses of ${\sim}12.65\,σ$, ${\sim}22.18\,σ$, and ${\sim}10.24\,σ$ in the energy ranges of 1--25 TeV, 25--100 TeV, and $> 100$ TeV, respectively. We use a 3D likelihood algorithm to derive the morphological and spectral parameters, and the source is detected with significances of $12.65\,σ$ by WCDA and $25.27\,σ$ by KM2A. The best-fit positions derived from WCDA and KM2A data are (R.A. = $13.96^\circ\pm0.09^\circ$, Decl. = $63.92^\circ\pm0.05^\circ$) and (R.A. = $14.00^\circ\pm0.05^\circ$, Decl. = $63.79^\circ\pm0.02^\circ$), respectively. The angular size ($r_{39}$) of 1LHAASO J0056+6346u is $0.34^\circ\pm0.04^\circ$ at 1--25 TeV and $0.24^\circ\pm0.02^\circ$ at $> 25$ TeV. The differential flux of this UHE $γ$-ray source can be described by an exponential cutoff power-law function: $(2.67\pm0.25) \times 10^{-15} (E/20\,\text{TeV})^{-1.97\pm0.10} e^{-E/(55.1\pm7.2)\,\text{TeV}} \,\text{TeV}^{-1}\,\text{cm}^{-2}\,\text{s}^{-1}$. To explore potential sources of $γ$-ray emission, we investigated the gas distribution around 1LHAASO J0056+6346u. 1LHAASO J0056+6346u is likely to be a TeV PWN powered by an unknown pulsar, which would naturally explain both its spatial and spectral properties. Another explanation is that this UHE $γ$-ray source might be associated with gas content illuminated by a nearby CR accelerator, possibly the SNR candidate G124.0+1.4.

preprint2025arXiv

Ultrahigh-Energy Gamma-ray Emission Associated with Black Hole-Jet Systems

Black holes (BH), one of the most intriguing objects in the universe, can manifest themselves through electromagnetic radiation initiated by the accretion flow. Some stellar-mass BHs drive relativistic jets when accreting matter from their companion stars, forming microquasars. Non-thermal emission from the radio to tera-electronvolt (TeV) gamma-ray band has been observed from microquasars, indicating the acceleration of relativistic particles. Here we report detection of four microquasars (SS 433, V4641 Sgr, GRS 1915+105, MAXI J1820+070) of spectrum extending to the ultrahigh-energy (UHE; photon energy $E>100$ TeV) band and one microquasar (Cygnus X-1) of spectrum approaching 100 TeV, using the Large High Altitude Air Shower Observatory (LHAASO). Notably, the total emission associated with SS 433 cannot be interpreted with a single leptonic component. In the UHE band, its emission is in spatial coincidence with a giant atomic cloud, which is consistent with a hadronic origin. An elongated source is discovered from V4641 Sgr with the spectrum continuing up to 800 TeV. The detection of UHE gamma rays demonstrates that accreting BHs and their environments can operate as extremely efficient accelerators of particles out of 1 peta-electronvolt (PeV), suggesting microquasars to be important contributors to Galactic cosmic rays especially around the `knee' region.

preprint2024arXiv

Mutual Information as Intrinsic Reward of Reinforcement Learning Agents for On-demand Ride Pooling

The emergence of on-demand ride pooling services allows each vehicle to serve multiple passengers at a time, thus increasing drivers' income and enabling passengers to travel at lower prices than taxi/car on-demand services (only one passenger can be assigned to a car at a time like UberX and Lyft). Although on-demand ride pooling services can bring so many benefits, ride pooling services need a well-defined matching strategy to maximize the benefits for all parties (passengers, drivers, aggregation companies and environment), in which the regional dispatching of vehicles has a significant impact on the matching and revenue. Existing algorithms often only consider revenue maximization, which makes it difficult for requests with unusual distribution to get a ride. How to increase revenue while ensuring a reasonable assignment of requests brings a challenge to ride pooling service companies (aggregation companies). In this paper, we propose a framework for vehicle dispatching for ride pooling tasks, which splits the city into discrete dispatching regions and uses the reinforcement learning (RL) algorithm to dispatch vehicles in these regions. We also consider the mutual information (MI) between vehicle and order distribution as the intrinsic reward of the RL algorithm to improve the correlation between their distributions, thus ensuring the possibility of getting a ride for unusually distributed requests. In experimental results on a real-world taxi dataset, we demonstrate that our framework can significantly increase revenue up to an average of 3\% over the existing best on-demand ride pooling method.

preprint2024arXiv

Summarizing Stream Data for Memory-Constrained Online Continual Learning

Replay-based methods have proved their effectiveness on online continual learning by rehearsing past samples from an auxiliary memory. With many efforts made on improving training schemes based on the memory, however, the information carried by each sample in the memory remains under-investigated. Under circumstances with restricted storage space, the informativeness of the memory becomes critical for effective replay. Although some works design specific strategies to select representative samples, by only employing a small number of original images, the storage space is still not well utilized. To this end, we propose to Summarize the knowledge from the Stream Data (SSD) into more informative samples by distilling the training characteristics of real images. Through maintaining the consistency of training gradients and relationship to the past tasks, the summarized samples are more representative for the stream data compared to the original images. Extensive experiments are conducted on multiple online continual learning benchmarks to support that the proposed SSD method significantly enhances the replay effects. We demonstrate that with limited extra computational overhead, SSD provides more than 3% accuracy boost for sequential CIFAR-100 under extremely restricted memory buffer. Code in https://github.com/vimar-gu/SSD.

preprint2023arXiv

OneRing: A Simple Method for Source-free Open-partial Domain Adaptation

In this paper, we investigate Source-free Open-partial Domain Adaptation (SF-OPDA), which addresses the situation where there exist both domain and category shifts between source and target domains. Under the SF-OPDA setting, which aims to address data privacy concerns, the model cannot access source data anymore during target adaptation. We propose a novel training scheme to learn a (n+1)-way classifier to predict the n source classes and the unknown class, where samples of only known source categories are available for training. Furthermore, for target adaptation, we simply adopt a weighted entropy minimization to adapt the source pretrained model to the unlabeled target domain without source data. In experiments, we show our simple method surpasses current OPDA approaches which demand source data during adaptation. When augmented with a closed-set domain adaptation approach during target adaptation, our source-free method further outperforms the current state-of-the-art OPDA method by 2.5%, 7.2% and 13% on Office-31, Office-Home and VisDA respectively.

preprint2023arXiv

Smoothed Online Combinatorial Optimization Using Imperfect Predictions

Smoothed online combinatorial optimization considers a learner who repeatedly chooses a combinatorial decision to minimize an unknown changing cost function with a penalty on switching decisions in consecutive rounds. We study smoothed online combinatorial optimization problems when an imperfect predictive model is available, where the model can forecast the future cost functions with uncertainty. We show that using predictions to plan for a finite time horizon leads to regret dependent on the total predictive uncertainty and an additional switching cost. This observation suggests choosing a suitable planning window to balance between uncertainty and switching cost, which leads to an online algorithm with guarantees on the upper and lower bounds of the cumulative regret. Empirically, our algorithm shows a significant improvement in cumulative regret compared to other baselines in synthetic online distributed streaming problems.

preprint2022arXiv

An Efficient Training Approach for Very Large Scale Face Recognition

Face recognition has achieved significant progress in deep learning era due to the ultra-large-scale and welllabeled datasets. However, training on the outsize datasets is time-consuming and takes up a lot of hardware resource. Therefore, designing an efficient training approach is indispensable. The heavy computational and memory costs mainly result from the million-level dimensionality of thefully connected (FC) layer. To this end, we propose a novel training approach, termed Faster Face Classification (F2C), to alleviate time and cost without sacrificing the performance. This method adopts Dynamic Class Pool (DCP) for storing and updating the identities features dynamically, which could be regarded as a substitute for the FC layer. DCP is efficiently time-saving and cost-saving, as its smaller size with the independence from the whole face identities together. We further validate the proposed F2C method across several face benchmarks and private datasets, and display comparable results, meanwhile the speed is faster than state-of-the-art FC-based methods in terms of recognition accuracy and hardware costs. Moreover, our method is further improved by a well-designed dual data loader including indentity-based and instancebased loaders, which makes it more efficient for the updating DCP parameters.

preprint2022arXiv

Anisotropic Electrene T'-Ca2P with Electron Gas Magnetic Coupling as Anode Material for Na/K Ion Batteries

There is an urgently need for the high-performance rechargeable electrical storage devices as supplement or substitutions of lithium ion batteries due to the shortage of lithium in nature. Herein we propose a stable 2D electrene T'-Ca2P as anode material for Na/K ion batteries by first-principle calculations. Our calculated results show that T'-Ca2P monolayer is an antiferromagnetic semiconducting electrene with spin-polarized electron gas. It exhibits suitable adsorption for both Na and K atoms, and its anisotropic migration energy barriers are 0.050/0.101 eV and 0.037/0.091 eV in b/a direction, respectively. The theoretical capacities for Na and K are both 482 mAh/g, while the average working voltage platforms are 0.171-0.226 V and 0.013-0.267 V, respectively. All the results reveal that the T'-Ca2P monolayer has promised application prospects as anode materials for Na/K ion batteries.

preprint2022arXiv

Background-penalty-free waveguide enhancement of CARS signal in air-filled anti-resonance hollow-core fiber

We study coherent anti-Stokes Raman spectroscopy in air-filled anti-resonance hollow-core photonic crystal fiber, otherwise known as 'revolver' fiber. We compare the vibrational coherent anti-Stokes Raman signal of N$_2$, at 2331 cm$^{-1}$, generated in ambient air (no fiber present), with the one generated in a 2.96 cm of a revolver fiber. We show a 170 times enhancement for the signal produced in the fiber, due to an increased interaction path. Remarkably, the N$_2$ signal obtained in the revolver fiber shows near-zero non-resonant background, due to near-zero overlap between the laser field and the fiber cladding. Through our study, we find that the revolver fiber properties make it an ideal candidate for the coherent Raman spectroscopy signal enhancement.

preprint2022arXiv

CAFE: Learning to Condense Dataset by Aligning Features

Dataset condensation aims at reducing the network training effort through condensing a cumbersome training set into a compact synthetic one. State-of-the-art approaches largely rely on learning the synthetic data by matching the gradients between the real and synthetic data batches. Despite the intuitive motivation and promising results, such gradient-based methods, by nature, easily overfit to a biased set of samples that produce dominant gradients, and thus lack global supervision of data distribution. In this paper, we propose a novel scheme to Condense dataset by Aligning FEatures (CAFE), which explicitly attempts to preserve the real-feature distribution as well as the discriminant power of the resulting synthetic set, lending itself to strong generalization capability to various architectures. At the heart of our approach is an effective strategy to align features from the real and synthetic data across various scales, while accounting for the classification of real samples. Our scheme is further backed up by a novel dynamic bi-level optimization, which adaptively adjusts parameter updates to prevent over-/under-fitting. We validate the proposed CAFE across various datasets, and demonstrate that it generally outperforms the state of the art: on the SVHN dataset, for example, the performance gain is up to 11%. Extensive experiments and analyses verify the effectiveness and necessity of proposed designs.

preprint2022arXiv

Cohomology theory of Rota-Baxter pre-Lie algebras of arbitrary weights

This paper is devoted to studying deformation, cohomology theory of Rota-Baxter pre-Lie algebras of arbitrary weights. First we give the notion of a new representation of a Rota-Baxter pre-Lie algebra of arbitrary weight and define the cohomology theory of a Rota-Baxter pre-Lie algebra of arbitrary weight. Then we study formal deformations by lower degree cohomology groups. Finally, we classify abelian extensions of Rota-Baxter pre-Lie algebras of arbitrary weight using the second cohomology group, and classify skeletal Rota-Baxter pre-Lie 2-algebra of arbitrary weight using the third cohomology group as applications.

preprint2022arXiv

Could TDE outflows produce the PeV neutrino events?

A tidal disruption event (TDE), AT2019dsg, was observed to be associated with a PeV neutrino event, IceCube-191001A, lagging the optical outburst by a half year. It is known that TDEs may generate ultrafast outflows. If the TDE occurs in a cloudy environment, the outflow-cloud interactions may form shock waves which generate accelerated protons and hence delayed neutrinos from hadronic interactions in clouds. Here we investigate the neutrino production in AT2019dsg by examining the TDE outflow-cloud interaction model. We find that, for an outflow with a velocity of 0.07c and a kinetic luminosity of $10^{45}\rm erg\ s^{-1}$, protons may be accelerated up to $\sim$ 60 PeV by the bow shocks, and generate PeV neutrinos by interactions with clouds. The predicted neutrino number in this model depends on the uncertainties of model parameters and in order to match the observations, some challenging values of parameters have been involved. The PeV neutrino event number can be $\sim 4\times10^{-3}$ for a hard proton index $Γ=1.5$.

preprint2022arXiv

Crafting Better Contrastive Views for Siamese Representation Learning

Recent self-supervised contrastive learning methods greatly benefit from the Siamese structure that aims at minimizing distances between positive pairs. For high performance Siamese representation learning, one of the keys is to design good contrastive pairs. Most previous works simply apply random sampling to make different crops of the same image, which overlooks the semantic information that may degrade the quality of views. In this work, we propose ContrastiveCrop, which could effectively generate better crops for Siamese representation learning. Firstly, a semantic-aware object localization strategy is proposed within the training process in a fully unsupervised manner. This guides us to generate contrastive views which could avoid most false positives (i.e., object vs. background). Moreover, we empirically find that views with similar appearances are trivial for the Siamese model training. Thus, a center-suppressed sampling is further designed to enlarge the variance of crops. Remarkably, our method takes a careful consideration of positive pairs for contrastive learning with negligible extra training overhead. As a plug-and-play and framework-agnostic module, ContrastiveCrop consistently improves SimCLR, MoCo, BYOL, SimSiam by 0.4% ~ 2.0% classification accuracy on CIFAR-10, CIFAR-100, Tiny ImageNet and STL-10. Superior results are also achieved on downstream detection and segmentation tasks when pre-trained on ImageNet-1K.

preprint2022arXiv

Creating boundaries along a synthetic frequency dimension

Synthetic dimensions have garnered widespread interest for implementing high dimensional classical and quantum dynamics on lower dimensional geometries. Synthetic frequency dimensions, in particular, have been used to experimentally realize a plethora of bulk physics effects, such as effective gauge potentials, nontrivial Hermitian as well as non-Hermitian topology, spin-momentum locking, complex long-range coupling, unidirectional frequency conversion, and four-dimensional lattices. However, in synthetic frequency dimensions there has not been any demonstration of boundary effects which are of paramount importance in topological physics due to the bulk edge correspondence, since systems exhibiting synthetic frequency dimensions do not support well-defined sharp boundaries. Here we theoretically elucidate a method to construct boundaries in the synthetic frequency dimension of dynamically modulated ring resonators by strongly coupling it to an auxiliary ring, and provide an experimental demonstration of this method. We experimentally explore various physics effects associated with the creation of such boundaries in the synthetic frequency dimension, including confinement of the spectrum of light, the discretization of the band structure, and the interaction of such boundaries with the topologically protected one-way chiral modes in a quantum Hall ladder. The incorporation of boundaries allows us to observe topologically robust transport of light along the frequency axis, which shows that the frequency of light can be controlled through topological concepts. Our demonstration of such sharp boundaries fundamentally expands the capability of exploring topological physics, and is also of importance for other applications such as classical and quantum information processing in synthetic frequency dimensions.

preprint2022arXiv

Deep Q-learning of global optimizer of multiply model parameters for viscoelastic imaging

Objective: Estimation of the global optima of multiple model parameters is valuable in imaging to form a reliable diagnostic image. Given non convexity of the objective function, it is challenging to avoid from different local minima. Methods: We first formulate the global searching of multiply parameters to be a k-D move in the parametric space, and convert parameters updating to be state-action decision-making problem. We proposed a novel Deep Q-learning of Model Parameters (DQMP) method for global optimization of model parameters by updating the parameter configurations through actions that maximize a Q-value, which employs a Deep Reward Network designed to learn global reward values from both visible curve fitting errors and hidden parameter errors. Results: The DQMP method was evaluated by viscoelastic imaging on soft matter by Kelvin-Voigt fractional derivative (KVFD) modeling. In comparison to other methods, imaging of parameters by DQMP yielded the smallest errors (< 2%) to the ground truth images. DQMP was applied to viscoelastic imaging on biological tissues, which indicated a great potential of imaging on physical parameters in diagnostic applications. Conclusions: DQMP method is able to achieve global optima, yielding accurate model parameter estimates in viscoelastic imaging. Assessment of DQMP by simulation imaging and ultrasound breast imaging demonstrated the consistency, reliability of the imaged parameters, and powerful global searching ability of DQMP. Significance: DQMP method is promising for imaging of multiple parameters, and can be generalized to global optimization for many other complex nonconvex functions and imaging of physical parameters.

preprint2022arXiv

Deformations and Cohomology Theory of Rota-Baxter Systems

Inspired by the work of Wang and Zhou [4] for Rota-Baxter algebras, we develop a cohomology theory of Rota-Baxter systems and justify it by interpreting the lower degree cohomology groups as formal deformations and as abelian extensions of Rota-Baxter systems. A further study on an $L_\infty$-algebra structure associated to this cohomology theory will be given in a subsequent paper.

preprint2022arXiv

Detection of diffuse gamma-ray emission towards a massive star forming region hosting Wolf-Rayet stars

Isotopic and elemental abundances seen in Galactic cosmic rays imply that $\sim20\%$ of the cosmic-ray (CR) nuclei are probably synthesized by massive Wolf-Rayet (WR) stars. Massive star clusters hosting WR and OB-type stars have been proposed as potential Galactic cosmic-ray accelerators for decades, in particular via diffusive shock acceleration at wind termination shocks. Here we report the analysis of {\em Fermi} Large Area Telescope's data towards the direction of Masgomas-6a, a young massive star cluster candidate hosting two WR stars. We detect an extended $γ$-ray source with $\rm{TS}=183$ in the vicinity of Masgomas-6a, spatially coincident with two unassociated {\em Fermi} 4FGL sources. We also present the CO observational results of molecular clouds in this region, using the data from the Milky Way Imaging Scroll Painting project. The $γ$-ray emission intensity correlates well with the distribution of molecular gas at the distance of Masgomas-6a, indicating that these gamma rays may be produced by CRs accelerated by massive stars in Masgomas-6a. At the distance of $3.9{\rm \ kpc}$ of Masgomas-6a, the luminosity of the extended source is $(1.81\pm0.02)\times 10^{35}{\rm \ erg \ s^{-1}}$. With a kinetic luminosity of $\sim 10^{37}{\rm erg \ s^{-1}}$ in the stellar winds, the WR stars are capable of powering the $γ$-ray emission via neutral pion decay resulted from cosmic ray $pp$ interactions. The size of the GeV source and the energetic requirement suggests a CR diffusion coefficient smaller than that in the Galactic interstellar medium, indicating strong suppression of CR diffusion in the molecular cloud.

preprint2022arXiv

Detection of Prompt Fast-Variable Thermal Component in Multi-Pulse Short Gamma-Ray Burst 170206A

We report the detection of a strong thermal component in the short Gamma-Ray Burst 170206A with three intense pulses in its light curves, throughout which the fluxes of this thermal component exhibit fast temporal variability same as that of the accompanying non-thermal component. The values of the time-resolved low-energy photon index in the non-thermal component are between about -0.79 and -0.16, most of which are harder than -2/3 excepted in the synchrotron emission process. In addition, we found a common evolution between the thermal component and the non-thermal component, $E_{\rm p,CPL} \propto kT_{\rm BB}^{0.95\pm0.28}$, and $F_{\rm CPL} \propto F_{\rm BB}^{0.67\pm0.18}$, where $E_{\rm p,CPL}$ and $F_{\rm CPL}$ are the peak photon energy and corresponding flux of the non-thermal component, and $ kT_{\rm BB}$ and $F_{BB}$ are the temperature and corresponding flux of the thermal component, respectively. Finally, we proposed that the photospheric thermal emission and the Comptonization of thermal photons may be responsible for the observational features of GRB 170206A.

preprint2022arXiv

Divide to Adapt: Mitigating Confirmation Bias for Domain Adaptation of Black-Box Predictors

Domain Adaptation of Black-box Predictors (DABP) aims to learn a model on an unlabeled target domain supervised by a black-box predictor trained on a source domain. It does not require access to both the source-domain data and the predictor parameters, thus addressing the data privacy and portability issues of standard domain adaptation. Existing DABP approaches mostly rely on model distillation from the black-box predictor, \emph{i.e.}, training the model with its noisy target-domain predictions, which however inevitably introduces the confirmation bias accumulated from the prediction noises. To mitigate such bias, we propose a new method, named BETA, to incorporate knowledge distillation and noisy label learning into one coherent framework. This is enabled by a new divide-to-adapt strategy. BETA divides the target domain into an easy-to-adapt subdomain with less noise and a hard-to-adapt subdomain. Then it deploys mutually-teaching twin networks to filter the predictor errors for each other and improve them progressively, from the easy to hard subdomains. As such, BETA effectively purifies the noisy labels and reduces error accumulation. We theoretically show that the target error of BETA is minimized by decreasing the noise ratio of the subdomains. Extensive experiments demonstrate BETA outperforms existing methods on all DABP benchmarks, and is even comparable with the standard domain adaptation methods that use the source-domain data.

preprint2022arXiv

FaceMAE: Privacy-Preserving Face Recognition via Masked Autoencoders

Face recognition, as one of the most successful applications in artificial intelligence, has been widely used in security, administration, advertising, and healthcare. However, the privacy issues of public face datasets have attracted increasing attention in recent years. Previous works simply mask most areas of faces or synthesize samples using generative models to construct privacy-preserving face datasets, which overlooks the trade-off between privacy protection and data utility. In this paper, we propose a novel framework FaceMAE, where the face privacy and recognition performance are considered simultaneously. Firstly, randomly masked face images are used to train the reconstruction module in FaceMAE. We tailor the instance relation matching (IRM) module to minimize the distribution gap between real faces and FaceMAE reconstructed ones. During the deployment phase, we use trained FaceMAE to reconstruct images from masked faces of unseen identities without extra training. The risk of privacy leakage is measured based on face retrieval between reconstructed and original datasets. Experiments prove that the identities of reconstructed images are difficult to be retrieved. We also perform sufficient privacy-preserving face recognition on several public face datasets (i.e. CASIA-WebFace and WebFace260M). Compared to previous state of the arts, FaceMAE consistently \textbf{reduces at least 50\% error rate} on LFW, CFP-FP and AgeDB.

preprint2022arXiv

Fermi Observations of GRB 220426A: a burst similar to GRB 090902B

We report on a very bright, long-duration gamma-ray burst (GRB), GRB~220426A, observed by \emph{Fermi} satellite. GRB~220426A with total duration of $T_{90}=6$~s is composed with two main pulses and some sub-peaks. The spectral analysis of this burst with Band function reveals that both the time-integrated and the time-resolved spectra are very narrow with high $α\gtrsim 0.2$ and low $β\lesssim -3.1$. It is strong reminiscent of GRB~090902B, a special GRB with identification of the photospheric emission. Then, we perform the spectral analysis of this burst based on a non-dissipated photospheric emission, which can be well modelled as the multicolor-blackbody with a cutoff power-law distribution of the thermal temperature. The spectral fittings reveal that the photospheric emission can well describe the radiation spectrum of this burst. We conclude that this burst would be a second burst in the class of GRB~090902B observed by \emph{Fermi} satellite. We also discuss the physics of photosphere and the origin of the high-energy component in GRB~220426A .

preprint2022arXiv

Grasping the Arrow of Time from the Singularity: Decoding Micromotion in Low-dimensional Latent Spaces from StyleGAN

The disentanglement of StyleGAN latent space has paved the way for realistic and controllable image editing, but does StyleGAN know anything about temporal motion, as it was only trained on static images? To study the motion features in the latent space of StyleGAN, in this paper, we hypothesize and demonstrate that a series of meaningful, natural, and versatile small, local movements (referred to as "micromotion", such as expression, head movement, and aging effect) can be represented in low-rank spaces extracted from the latent space of a conventionally pre-trained StyleGAN-v2 model for face generation, with the guidance of proper "anchors" in the form of either short text or video clips. Starting from one target face image, with the editing direction decoded from the low-rank space, its micromotion features can be represented as simple as an affine transformation over its latent feature. Perhaps more surprisingly, such micromotion subspace, even learned from just single target face, can be painlessly transferred to other unseen face images, even those from vastly different domains (such as oil painting, cartoon, and sculpture faces). It demonstrates that the local feature geometry corresponding to one type of micromotion is aligned across different face subjects, and hence that StyleGAN-v2 is indeed "secretly" aware of the subject-disentangled feature variations caused by that micromotion. We present various successful examples of applying our low-dimensional micromotion subspace technique to directly and effortlessly manipulate faces, showing high robustness, low computational overhead, and impressive domain transferability. Our codes are available at https://github.com/wuqiuche/micromotion-StyleGAN.

preprint2022arXiv

Groups and protocluster candidates in the CLAUDS and HSC-SSP joint deep surveys

Using the extended halo-based group finder developed by Yang et al. (2021), which is able to deal with galaxies via spectroscopic and photometric redshifts simultaneously, we construct galaxy group and candidate protocluster catalogs in a wide redshift range ($0 < z < 6$) from the joint CFHT Large Area $U$-band Deep Survey (CLAUDS) and Hyper Suprime-Cam Subaru Strategic Program (HSC-SSP) deep data set. Based on a selection of 5,607,052 galaxies with $i$-band magnitude $m_{i} < 26$ and a sky coverage of $34.41\ {\rm deg}^2$, we identify a total of 2,232,134 groups, within which 402,947 groups have at least three member galaxies. We have visually checked and discussed the general properties of those richest groups at redshift $z>2.0$. By checking the galaxy number distributions within a $5-7\ h^{-1}\mathrm{Mpc}$ projected separation and a redshift difference $Δz \le 0.1$ around those richest groups at redshift $z>2$, we identified a list of 761, 343 and 43 protocluster candidates in the redshift bins $2\leq z<3$, $3\leq z<4$ and $z \geq 4$, respectively. In general, these catalogs of galaxy groups and protocluster candidates will provide useful environmental information in probing galaxy evolution along the cosmic time.

preprint2022arXiv

Improving Non-native Word-level Pronunciation Scoring with Phone-level Mixup Data Augmentation and Multi-source Information

Deep learning-based pronunciation scoring models highly rely on the availability of the annotated non-native data, which is costly and has scalability issues. To deal with the data scarcity problem, data augmentation is commonly used for model pretraining. In this paper, we propose a phone-level mixup, a simple yet effective data augmentation method, to improve the performance of word-level pronunciation scoring. Specifically, given a phoneme sequence from lexicon, the artificial augmented word sample can be generated by randomly sampling from the corresponding phone-level features in training data, while the word score is the average of their GOP scores. Benefit from the arbitrary phone-level combination, the mixup is able to generate any word with various pronunciation scores. Moreover, we utilize multi-source information (e.g., MFCC and deep features) to further improve the scoring system performance. The experiments conducted on the Speechocean762 show that the proposed system outperforms the baseline by adding the mixup data for pretraining, with Pearson correlation coefficients (PCC) increasing from 0.567 to 0.61. The results also indicate that proposed method achieves similar performance by using 1/10 unlabeled data of baseline. In addition, the experimental results also demonstrate the efficiency of our proposed multi-source approach.

preprint2022arXiv

Investigating Accuracy-Novelty Performance for Graph-based Collaborative Filtering

Recent years have witnessed the great accuracy performance of graph-based Collaborative Filtering (CF) models for recommender systems. By taking the user-item interaction behavior as a graph, these graph-based CF models borrow the success of Graph Neural Networks (GNN), and iteratively perform neighborhood aggregation to propagate the collaborative signals. While conventional CF models are known for facing the challenges of the popularity bias that favors popular items, one may wonder "Whether the existing graph-based CF models alleviate or exacerbate popularity bias of recommender systems?" To answer this question, we first investigate the two-fold performances w.r.t. accuracy and novelty for existing graph-based CF methods. The empirical results show that symmetric neighborhood aggregation adopted by most existing graph-based CF models exacerbate the popularity bias and this phenomenon becomes more serious as the depth of graph propagation increases. Further, we theoretically analyze the cause of popularity bias for graph-based CF. Then, we propose a simple yet effective plugin, namely r-AdjNorm, to achieve an accuracy-novelty trade-off by controlling the normalization strength in the neighborhood aggregation process. Meanwhile, r-AdjNorm can be smoothly applied to the existing graph-based CF backbones without additional computation. Finally, experimental results on three benchmark datasets show that our proposed method can improve novelty without sacrificing accuracy under various graph-based CF backbones.

preprint2022arXiv

Investigation of variable temperature Mössbauer spectrum of YFe$_{0.5}$Cr$_{0.5}$O$_3$ perovskite

In this paper, we reported the preparation of YFe$_{0.5}$Cr$_{0.5}$O$_3$ by the sol-gel method and studied its structure and Mössbauer spectrum at variable temperatures. X-ray diffraction(XRD) analysis exhibits that the sample has the orthorhombic structure with the Pnma space group, and the energy dispersive spectroscopy (EDS) analysis shows that the sample has Fe/Cr = 1:1, indicating that the sample is Fe half-doped YCrO$_3$. The hyperfine parameters of the Mössbauer spectrum at room temperature confirm that the characteristics of 57Fe in the sample were trivalent hexacoordinated high-spin(s=5/2), and the coexistence of doublet and the sextets at 250K indicate that the sample has superparamagnetic relaxation. The Mössbauer spectrum records the magnetic phase transition in the temperature range of 250K-300K.

preprint2022arXiv

Labeling Trick: A Theory of Using Graph Neural Networks for Multi-Node Representation Learning

In this paper, we provide a theory of using graph neural networks (GNNs) for multi-node representation learning (where we are interested in learning a representation for a set of more than one node, such as link). We know that GNN is designed to learn single-node representations. When we want to learn a node set representation involving multiple nodes, a common practice in previous works is to directly aggregate the single-node representations obtained by a GNN into a joint node set representation. In this paper, we show a fundamental constraint of such an approach, namely the inability to capture the dependence between nodes in the node set, and argue that directly aggregating individual node representations does not lead to an effective joint representation for multiple nodes. Then, we notice that a few previous successful works for multi-node representation learning, including SEAL, Distance Encoding, and ID-GNN, all used node labeling. These methods first label nodes in the graph according to their relationships with the target node set before applying a GNN. Then, the node representations obtained in the labeled graph are aggregated into a node set representation. By investigating their inner mechanisms, we unify these node labeling techniques into a single and most general form -- labeling trick. We prove that with labeling trick a sufficiently expressive GNN learns the most expressive node set representations, thus in principle solves any joint learning tasks over node sets. Experiments on one important two-node representation learning task, link prediction, verified our theory. Our work explains the superior performance of previous node-labeling-based methods, and establishes a theoretical foundation of using GNNs for multi-node representation learning.

preprint2022arXiv

Learning MDPs from Features: Predict-Then-Optimize for Sequential Decision Problems by Reinforcement Learning

In the predict-then-optimize framework, the objective is to train a predictive model, mapping from environment features to parameters of an optimization problem, which maximizes decision quality when the optimization is subsequently solved. Recent work on decision-focused learning shows that embedding the optimization problem in the training pipeline can improve decision quality and help generalize better to unseen tasks compared to relying on an intermediate loss function for evaluating prediction quality. We study the predict-then-optimize framework in the context of sequential decision problems (formulated as MDPs) that are solved via reinforcement learning. In particular, we are given environment features and a set of trajectories from training MDPs, which we use to train a predictive model that generalizes to unseen test MDPs without trajectories. Two significant computational challenges arise in applying decision-focused learning to MDPs: (i) large state and action spaces make it infeasible for existing techniques to differentiate through MDP problems, and (ii) the high-dimensional policy space, as parameterized by a neural network, makes differentiating through a policy expensive. We resolve the first challenge by sampling provably unbiased derivatives to approximate and differentiate through optimality conditions, and the second challenge by using a low-rank approximation to the high-dimensional sample-based derivatives. We implement both Bellman--based and policy gradient--based decision-focused learning on three different MDP problems with missing parameters, and show that decision-focused learning performs better in generalization to unseen tasks.

preprint2022arXiv

Magnetic field tuning of the valley population in the Weyl phase of Nd$_2$Ir$_2$O$_7$

The frustrated magnet Nd$_2$Ir$_2$O$_7$, where strong correlations together with spin-orbit coupling play a crucial role, is predicted to be a Weyl semimetal and to host topological pairs of bulk Dirac-like valleys. Here we use an external magnetic field to manipulate the localized rare earth 4f moments coupled to the 5d electronic bands. Low energy optical spectroscopy reveals that a field of only a few teslas suffices to create charge compensating pockets of holes and electrons in different regions of momentum space, thus introducing a valley population shift that can be tuned with the field.

preprint2022arXiv

Modeling Motion with Multi-Modal Features for Text-Based Video Segmentation

Text-based video segmentation aims to segment the target object in a video based on a describing sentence. Incorporating motion information from optical flow maps with appearance and linguistic modalities is crucial yet has been largely ignored by previous work. In this paper, we design a method to fuse and align appearance, motion, and linguistic features to achieve accurate segmentation. Specifically, we propose a multi-modal video transformer, which can fuse and aggregate multi-modal and temporal features between frames. Furthermore, we design a language-guided feature fusion module to progressively fuse appearance and motion features in each feature level with guidance from linguistic features. Finally, a multi-modal alignment loss is proposed to alleviate the semantic gap between features from different modalities. Extensive experiments on A2D Sentences and J-HMDB Sentences verify the performance and the generalization ability of our method compared to the state-of-the-art methods.

preprint2022arXiv

Monitoring AGNs with H$β$ Asymmetry. III. Long-term Reverberation Mapping Results of 15 Palomar-Green Quasars

In this third paper of the series reporting on the reverberation mapping (RM) campaign of active galactic nuclei with asymmetric H$β$ emission-line profiles, we present results for 15 Palomar-Green (PG) quasars using spectra obtained between the end of 2016 to May 2021. This campaign combines long time spans with relatively high cadence. For 8 objects, both the time lags obtained from the entire light curves and the measurements from individual observing seasons are provided. Reverberation mapping of 9 of our targets has been attempted for the first time, while the results for 6 others can be compared with previous campaigns. We measure the H$β$ time lags over periods of years and estimate their black hole masses. The long duration of the campaign enables us to investigate their broad line region (BLR) geometry and kinematics for different years by using velocity-resolved lags, which demonstrate signatures of diverse BLR geometry and kinematics. The BLR geometry and kinematics of individual objects are discussed. In this sample, the BLR kinematics of Keplerian/virialized motion and inflow is more common than outflow.

preprint2022arXiv

MSDN: Mutually Semantic Distillation Network for Zero-Shot Learning

The key challenge of zero-shot learning (ZSL) is how to infer the latent semantic knowledge between visual and attribute features on seen classes, and thus achieving a desirable knowledge transfer to unseen classes. Prior works either simply align the global features of an image with its associated class semantic vector or utilize unidirectional attention to learn the limited latent semantic representations, which could not effectively discover the intrinsic semantic knowledge e.g., attribute semantics) between visual and attribute features. To solve the above dilemma, we propose a Mutually Semantic Distillation Network (MSDN), which progressively distills the intrinsic semantic representations between visual and attribute features for ZSL. MSDN incorporates an attribute$\rightarrow$visual attention sub-net that learns attribute-based visual features, and a visual$\rightarrow$attribute attention sub-net that learns visual-based attribute features. By further introducing a semantic distillation loss, the two mutual attention sub-nets are capable of learning collaboratively and teaching each other throughout the training process. The proposed MSDN yields significant improvements over the strong baselines, leading to new state-of-the-art performances on three popular challenging benchmarks, i.e., CUB, SUN, and AWA2. Our codes have been available at: \url{https://github.com/shiming-chen/MSDN}.

preprint2022arXiv

Multi-label classification for biomedical literature: an overview of the BioCreative VII LitCovid Track for COVID-19 literature topic annotations

The COVID-19 pandemic has been severely impacting global society since December 2019. Massive research has been undertaken to understand the characteristics of the virus and design vaccines and drugs. The related findings have been reported in biomedical literature at a rate of about 10,000 articles on COVID-19 per month. Such rapid growth significantly challenges manual curation and interpretation. For instance, LitCovid is a literature database of COVID-19-related articles in PubMed, which has accumulated more than 200,000 articles with millions of accesses each month by users worldwide. One primary curation task is to assign up to eight topics (e.g., Diagnosis and Treatment) to the articles in LitCovid. Despite the continuing advances in biomedical text mining methods, few have been dedicated to topic annotations in COVID-19 literature. To close the gap, we organized the BioCreative LitCovid track to call for a community effort to tackle automated topic annotation for COVID-19 literature. The BioCreative LitCovid dataset, consisting of over 30,000 articles with manually reviewed topics, was created for training and testing. It is one of the largest multilabel classification datasets in biomedical scientific literature. 19 teams worldwide participated and made 80 submissions in total. Most teams used hybrid systems based on transformers. The highest performing submissions achieved 0.8875, 0.9181, and 0.9394 for macro F1-score, micro F1-score, and instance-based F1-score, respectively. The level of participation and results demonstrate a successful track and help close the gap between dataset curation and method development. The dataset is publicly available via https://ftp.ncbi.nlm.nih.gov/pub/lu/LitCovid/biocreative/ for benchmarking and further development.

preprint2022arXiv

Multi-label topic classification for COVID-19 literature with Bioformer

We describe Bioformer team's participation in the multi-label topic classification task for COVID-19 literature (track 5 of BioCreative VII). Topic classification is performed using different BERT models (BioBERT, PubMedBERT, and Bioformer). We formulate the topic classification task as a sentence pair classification problem, where the title is the first sentence, and the abstract is the second sentence. Our results show that Bioformer outperforms BioBERT and PubMedBERT in this task. Compared to the baseline results, our best model increased micro, macro, and instance-based F1 score by 8.8%, 15.5%, 7.4%, respectively. Bioformer achieved the highest micro F1 and macro F1 scores in this challenge. In post-challenge experiments, we found that pretraining of Bioformer on COVID-19 articles further improves the performance.

preprint2022arXiv

Progressive Multi-scale Consistent Network for Multi-class Fundus Lesion Segmentation

Effectively integrating multi-scale information is of considerable significance for the challenging multi-class segmentation of fundus lesions because different lesions vary significantly in scales and shapes. Several methods have been proposed to successfully handle the multi-scale object segmentation. However, two issues are not considered in previous studies. The first is the lack of interaction between adjacent feature levels, and this will lead to the deviation of high-level features from low-level features and the loss of detailed cues. The second is the conflict between the low-level and high-level features, this occurs because they learn different scales of features, thereby confusing the model and decreasing the accuracy of the final prediction. In this paper, we propose a progressive multi-scale consistent network (PMCNet) that integrates the proposed progressive feature fusion (PFF) block and dynamic attention block (DAB) to address the aforementioned issues. Specifically, PFF block progressively integrates multi-scale features from adjacent encoding layers, facilitating feature learning of each layer by aggregating fine-grained details and high-level semantics. As features at different scales should be consistent, DAB is designed to dynamically learn the attentive cues from the fused features at different scales, thus aiming to smooth the essential conflicts existing in multi-scale features. The two proposed PFF and DAB blocks can be integrated with the off-the-shelf backbone networks to address the two issues of multi-scale and feature inconsistency in the multi-class segmentation of fundus lesions, which will produce better feature representation in the feature space. Experimental results on three public datasets indicate that the proposed method is more effective than recent state-of-the-art methods.

preprint2022arXiv

Prompt Vision Transformer for Domain Generalization

Though vision transformers (ViTs) have exhibited impressive ability for representation learning, we empirically find that they cannot generalize well to unseen domains with previous domain generalization algorithms. In this paper, we propose a novel approach DoPrompt based on prompt learning to embed the knowledge of source domains in domain prompts for target domain prediction. Specifically, domain prompts are prepended before ViT input tokens from the corresponding source domain. Each domain prompt learns domain-specific knowledge efficiently since it is optimized only for one domain. Meanwhile, we train a prompt adapter to produce a suitable prompt for each input image based on the learned source domain prompts. At test time, the adapted prompt generated by the prompt adapter can exploit the similarity between the feature of the out-of-domain image and source domains to properly integrate the source domain knowledge. Extensive experiments are conducted on four benchmark datasets. Our approach achieves 1.4% improvements in the averaged accuracy, which is 3.5 times the improvement of the state-of-the-art algorithm with a ViT backbone.

preprint2022arXiv

QuickSkill: Novice Skill Estimation in Online Multiplayer Games

Matchmaking systems are vital for creating fair matches in online multiplayer games, which directly affects players' satisfactions and game experience. Most of the matchmaking systems largely rely on precise estimation of players' game skills to construct equitable games. However, the skill rating of a novice is usually inaccurate, as current matchmaking rating algorithms require considerable amount of games for learning the true skill of a new player. Using these unreliable skill scores at early stages for matchmaking usually leads to disparities in terms of team performance, which causes negative game experience. This is known as the ''cold-start'' problem for matchmaking rating algorithms. To overcome this conundrum, this paper proposes QuickSKill, a deep learning based novice skill estimation framework to quickly probe abilities of new players in online multiplayer games. QuickSKill extracts sequential performance features from initial few games of a player to predict his/her future skill rating with a dedicated neural network, thus delivering accurate skill estimation at the player's early game stage. By employing QuickSKill for matchmaking, game fairness can be dramatically improved in the initial cold-start period. We conduct experiments in a popular mobile multiplayer game in both offline and online scenarios. Results obtained with two real-world anonymized gaming datasets demonstrate that proposed QuickSKill delivers precise estimation of game skills for novices, leading to significantly lower team skill disparities and better player game experience. To the best of our knowledge, proposed QuickSKill is the first framework that tackles the cold-start problem for traditional skill rating algorithms.

preprint2022arXiv

Reliable Label Correction is a Good Booster When Learning with Extremely Noisy Labels

Learning with noisy labels has aroused much research interest since data annotations, especially for large-scale datasets, may be inevitably imperfect. Recent approaches resort to a semi-supervised learning problem by dividing training samples into clean and noisy sets. This paradigm, however, is prone to significant degeneration under heavy label noise, as the number of clean samples is too small for conventional methods to behave well. In this paper, we introduce a novel framework, termed as LC-Booster, to explicitly tackle learning under extreme noise. The core idea of LC-Booster is to incorporate label correction into the sample selection, so that more purified samples, through the reliable label correction, can be utilized for training, thereby alleviating the confirmation bias. Experiments show that LC-Booster advances state-of-the-art results on several noisy-label benchmarks, including CIFAR-10, CIFAR-100, Clothing1M and WebVision. Remarkably, under the extreme 90\% noise ratio, LC-Booster achieves 92.9\% and 48.4\% accuracy on CIFAR-10 and CIFAR-100, surpassing state-of-the-art methods by a large margin.

preprint2022arXiv

Skeleton-based Action Recognition via Temporal-Channel Aggregation

Skeleton-based action recognition methods are limited by the semantic extraction of spatio-temporal skeletal maps. However, current methods have difficulty in effectively combining features from both temporal and spatial graph dimensions and tend to be thick on one side and thin on the other. In this paper, we propose a Temporal-Channel Aggregation Graph Convolutional Networks (TCA-GCN) to learn spatial and temporal topologies dynamically and efficiently aggregate topological features in different temporal and channel dimensions for skeleton-based action recognition. We use the Temporal Aggregation module to learn temporal dimensional features and the Channel Aggregation module to efficiently combine spatial dynamic channel-wise topological features with temporal dynamic topological features. In addition, we extract multi-scale skeletal features on temporal modeling and fuse them with an attention mechanism. Extensive experiments show that our model results outperform state-of-the-art methods on the NTU RGB+D, NTU RGB+D 120, and NW-UCLA datasets.

preprint2022arXiv

Solving Mission-Wide Chance-Constrained Optimal Control Using Dynamic Programming

This paper aims to provide a Dynamic Programming (DP) approach to solve the Mission-Wide Chance-Constrained Optimal Control Problems (MWCC-OCP). The mission-wide chance constraint guarantees that the probability that the entire state trajectory lies within a constraint/safe region is higher than a prescribed level, and is different from the stage-wise chance constraints imposed at individual time steps. The control objective is to find an optimal policy sequence that achieves both (i) satisfaction of a mission-wide chance constraint, and (ii) minimization of a cost function. By transforming the stage-wise chance-constrained problem into an unconstrained counterpart via Lagrangian method, standard DP can then be deployed. Yet, for MWCC-OCP, this methods fails to apply, because the mission-wide chance constraint cannot be easily formulated using stage-wise chance constraints due to the time-correlation between the latter (individual states are coupled through the system dynamics). To fill this gap, firstly, we detail the conditions required for a classical DP solution to exist for this type of problem; secondly, we propose a DP solution to the MWCC-OCP through state augmentation by introducing an additional functional state variable.

preprint2022arXiv

Swift and Sure: Hardness-aware Contrastive Learning for Low-dimensional Knowledge Graph Embeddings

Knowledge graph embedding (KGE) has shown great potential in automatic knowledge graph (KG) completion and knowledge-driven tasks. However, recent KGE models suffer from high training cost and large storage space, thus limiting their practicality in real-world applications. To address this challenge, based on the latest findings in the field of Contrastive Learning, we propose a novel KGE training framework called Hardness-aware Low-dimensional Embedding (HaLE). Instead of the traditional Negative Sampling, we design a new loss function based on query sampling that can balance two important training targets, Alignment and Uniformity. Furthermore, we analyze the hardness-aware ability of recent low-dimensional hyperbolic models and propose a lightweight hardness-aware activation mechanism. The experimental results show that in the limited training time, HaLE can effectively improve the performance and training speed of KGE models on five commonly-used datasets. After training just a few minutes, the HaLE-trained models are competitive compared to the state-of-the-art models in both low- and high-dimensional conditions.

preprint2022arXiv

Takagi Topological Insulator on the Honeycomb Lattice

Recently, real topological phases protected by $PT$ symmetry have been actively investigated. In two dimensions, the corresponding topological invariant is the Stiefel-Whitney number. A recent theoretical advance is that in the presence of the sublattice symmetry, the Stiefel-Whitney number can be equivalently formulated in terms of Takagi's factorization. The topological invariant gives rise to a novel second-order topological insulator with odd $PT$-related pairs of corner zero modes. In this article, we review the elements of this novel second-order topological insulator, and demonstrate the essential physics by a simple model on the honeycomb lattice.

preprint2022arXiv

Tensor Theory for Higher Dimensional Chern Insulators with Large Chern Numbers

Recent advances in topological artificial systems open the door to realizing topological states in dimensions higher than the usual three-dimensional space. Here, we present a "tensor product" theory, which offers a method to construct Chern insulators with arbitrarily high dimensions and Chern numbers. Particularly, we show that the tensor product of a $d_A$D Chern insulator $\langle \mathcal{H}_A^{(κ_{A})}, C_A\rangle$ with a $d_B$D Chern insulator $\langle \mathcal{H}_B^{(κ_B)}, C_B\rangle$ leads to a $(d_A+d_B)$D Chern insulator $\langle \mathcal{H}_{A B}^{(κ_A\star κ_B)},-2C_AC_B\rangle $, where in the brackets, $\mathcal{H}^{(κ)}$ is the $d$D Hamiltonian with $d$ even, $C$ is the corresponding $(d/2)$th Chern number, and $κ$ labels the five non-chiral Altland-Zirnbauer symmetry classes A, AI, D, AII and C. The four real classes AI, D, AII and C form a Klein four-group under the multiplication `$\star$' with class AI the identity, and class A is the zero element. Our theory leads to novel higher-dimensional topological physics. (i) The construction can generate large higher-order Chern numbers, e.g., for some cases the resultant classification is $8\mathbb{Z}$. (ii) Fascinatingly, the boundary states feature flat nodal hypersurfaces with nontrivial Chern charges. For the constructed $(d_A+d_B)$D Chern insulator, a boundary perpendicular to a direction of $\mathcal{H}_A$ generically hosts $|C_A|$ $d_B$D nodal hypersurfaces, each of which has topological charge $\pm 2C_B$. Under perturbations, each nodal hypersurface bursts into stable unit nodal points, with the total Chern charge conserved. Examples are given to demonstrate our theory, which can be experimentally realized in artificial systems such as acoustic crystals, electric circuit arrays, ultracold atoms, or mechanical networks.

preprint2022arXiv

The Large High Altitude Air Shower Observatory (LHAASO) Science Book (2021 Edition)

Since the science white paper of the Large High Altitude Air Shower Observatory (LHAASO) published on arXiv in 2019 [e-Print: 1905.02773 (astro-ph.HE)], LHAASO has completed the transition from a project to an operational gamma-ray astronomical observatory LHAASO is a new generation multi-component facility located in Daocheng, Sichuan province of China, at an altitude of 4410 meters. It aims at measuring with unprecedented sensitivity the spectrum, composition, and anisotropy of cosmic rays in the energy range between 10$^{12}$ and 10$^{18}$~eV, and acting simultaneously as a wide aperture (one stereoradiant) continuously operating gamma-ray telescope in the energy range between 10$^{11}$ and $10^{15}$~eV with the designed sensitivity of 1.3\% of the Crab Unit (CU) above 100 TeV. LHAASO's capability of measuring simultaneously different shower components (electrons, muons, and Cherenkov/fluorescence light), will allow it to investigate the origin, acceleration, and propagation of CR through measurement of the energy spectrum, elemental composition, and anisotropy with unprecedented resolution. The remarkable sensitivity of LHAASO will play a key role in CR physics and gamma-ray astronomy for a general and comprehensive exploration of the high energy universe and will allow important studies of fundamental physics (such as indirect dark matter search, Lorentz invariance violation, quantum gravity) and solar and heliospheric physics. The LHAASO Collaboration organized an editorial working group and finished all editorial work of this science book, to summarize the instrumental features and outline the prospects of scientific researches with the LHAASO experiment.

preprint2022arXiv

The Shape Part Slot Machine: Contact-based Reasoning for Generating 3D Shapes from Parts

We present the Shape Part Slot Machine, a new method for assembling novel 3D shapes from existing parts by performing contact-based reasoning. Our method represents each shape as a graph of ``slots,'' where each slot is a region of contact between two shape parts. Based on this representation, we design a graph-neural-network-based model for generating new slot graphs and retrieving compatible parts, as well as a gradient-descent-based optimization scheme for assembling the retrieved parts into a complete shape that respects the generated slot graph. This approach does not require any semantic part labels; interestingly, it also does not require complete part geometries -- reasoning about the slots proves sufficient to generate novel, high-quality 3D shapes. We demonstrate that our method generates shapes that outperform existing modeling-by-assembly approaches regarding quality, diversity, and structural complexity.

preprint2022arXiv

Truncations of random unitary matrices drawn from Hua-Pickrell distribution

Let $U$ be a random unitary matrix drawn from the Hua-Pickrell distribution $μ_{\mathrm{U}(n+m)}^{(δ)}$ on the unitary group $\mathrm{U}(n+m)$. We show that the eigenvalues of the truncated unitary matrix $[U_{i,j}]_{1\leq i,j\leq n}$ form a determinantal point process $\mathscr{X}_n^{(m,δ)}$ on the unit disc $\mathbb{D}$ for any $δ\in\mathbb{C}$ satisfying $\mathrm{Re}\,δ>-1/2$. We also prove that the limiting point process taken by $n\to\infty$ of the determinantal point process $\mathscr{X}_n^{(m,δ)}$ is always $\mathscr{X}^{[m]}$, independent of $δ$. Here $\mathscr{X}^{[m]}$ is the determinantal point process on $\mathbb{D}$ with weighted Bergman kernel \begin{equation*} \begin{split} K^{[m]}(z,w)=\frac{1}{(1-z\overline w)^{m+1}} \end{split} \end{equation*} with respect to the reference measure $dμ^{[m]}(z)=\frac{m}π(1-|z|)^{m-1}dσ(z)$, where $dσ(z)$ is the Lebesgue measure on $\mathbb{D}$.

preprint2022arXiv

Trust-SIoT: Towards Trustworthy Object Classification in the Social Internet of Things

The recent emergence of the promising paradigm of the Social Internet of Things (SIoT) is a result of an intelligent amalgamation of the social networking concepts with the Internet of Things (IoT) objects (also referred to as "things") in an attempt to unravel the challenges of network discovery, navigability, and service composition. This is realized by facilitating the IoT objects to socialize with one another, i.e., similar to the social interactions amongst the human beings. A fundamental issue that mandates careful attention is to thus establish, and over time, maintain trustworthy relationships amongst these IoT objects. Therefore, a trust framework for SIoT must include object-object interactions, the aspects of social relationships, credible recommendations, etc., however, the existing literature has only focused on some aspects of trust by primarily relying on the conventional approaches that govern linear relationships between input and output. In this paper, an artificial neural network-based trust framework, Trust-SIoT, has been envisaged for identifying the complex non-linear relationships between input and output in a bid to classify the trustworthy objects. Moreover, Trust-SIoT has been designed for capturing a number of key trust metrics as input, i.e., direct trust by integrating both current and past interactions, reliability, and benevolence of an object, credible recommendations, and the degree of relationship by employing a knowledge graph embedding. Finally, we have performed extensive experiments to evaluate the performance of Trust-SIoT vis-a-vis state-of-the-art heuristics on two real-world datasets. The results demonstrate that Trust-SIoT achieves a higher F1 and lower MAE and MSE scores.

preprint2022arXiv

Ultra-High Lithium Storage Capacity of Al2C Monolayer under Restricted Multilayered Growth Mechanism

Designing anode materials with high lithium specific capacity is crucial to the development of high energy-density lithium ion batteries. Herein, a distinctive lithium growth mechanism, namely, the restricted multilayered growth for lithium, and a strategy for lithium storage are proposed to achieve the balance between the ultra-high specific capacity and the need to avert uncontrolled dendritic growth of lithium. In particular, based on first-principles computation, we show that the Al2C monolayer with planar tetracoordinate carbon structure can be an ideal platform for realizing the restricted multilayered growth mechanism as a 2D anode material. Furthermore, the Al2C monolayer exhibits ultra-high specific capacity of lithium of 4059 mAh/g, yet with a low dif-fusion barrier of 0.039-0.17 eV as well as low open circuit voltage in the range of 0.002-0.34 V. These novel properties endow the Al2C monolayer a promising anode material for future lithium ion batteries. Our study offers a new way to design promising 2D anode materials with high specific capacity, fast lithium-ion diffusion, and safe lithium storage mechanism.

preprint2022arXiv

Weighted $L_p$ Markov factors with doubling weights on the ball

Let $L_{p,w},\ 1 \le p<\infty,$ denote the weighted $L_p$ space of functions on the unit ball $\Bbb B^d$ with a doubling weight $w$ on $\Bbb B^d$. The Markov factor for $L_{p,w}$ on a polynomial $P$ is defined by $\frac{\|\, |\nabla P|\,\|_{p,w}}{\|P\|_{p,w}}$, where $\nabla P$ is the gradient of $P$. We investigate the worst case Markov factors for $L_{p,w}\ (1\le p<\infty)$ and obtain that the degree of these factors are at most $2$. In particular, for the Jacobi weight $w_μ(x)=(1-|x|^2)^{μ-1/2}, \ μ\ge0$, the exponent $2$ is sharp. We also study the average case Markov factor for $L_{2,w}$ on random polynomials with independent $N(0, σ^2)$ coefficients and obtain that the upper bound of the average (expected) Markov factor is order degree to the $3/2$, as compared to the degree squared worst case upper bound.

preprint2021arXiv

A Benchmark of Ocular Disease Intelligent Recognition: One Shot for Multi-disease Detection

In ophthalmology, early fundus screening is an economic and effective way to prevent blindness caused by ophthalmic diseases. Clinically, due to the lack of medical resources, manual diagnosis is time-consuming and may delay the condition. With the development of deep learning, some researches on ophthalmic diseases have achieved good results, however, most of them are just based on one disease. During fundus screening, ophthalmologists usually give diagnoses of multi-disease on binocular fundus image, so we release a dataset with 8 diseases to meet the real medical scene, which contains 10,000 fundus images from both eyes of 5,000 patients. We did some benchmark experiments on it through some state-of-the-art deep neural networks. We found simply increasing the scale of network cannot bring good results for multi-disease classification, and a well-structured feature fusion method combines characteristics of multi-disease is needed. Through this work, we hope to advance the research of related fields.

preprint2021arXiv

Applications of Deep Learning in Fundus Images: A Review

The use of fundus images for the early screening of eye diseases is of great clinical importance. Due to its powerful performance, deep learning is becoming more and more popular in related applications, such as lesion segmentation, biomarkers segmentation, disease diagnosis and image synthesis. Therefore, it is very necessary to summarize the recent developments in deep learning for fundus images with a review paper. In this review, we introduce 143 application papers with a carefully designed hierarchy. Moreover, 33 publicly available datasets are presented. Summaries and analyses are provided for each task. Finally, limitations common to all tasks are revealed and possible solutions are given. We will also release and regularly update the state-of-the-art results and newly-released datasets at https://github.com/nkicsl/Fundus Review to adapt to the rapid development of this field.

preprint2021arXiv

Gaussian limit for determinantal point processes with $J$-Hermitian kernels

We show that the central limit theorem for linear statistics over determinantal point processes with $J$-Hermitian kernels holds under fairly general conditions. In particular, We establish Gaussian limit for linear statistics over determinantal point processes on union of two copies of $\mathbb{R}^d$ when the correlation kernels are $J$-Hermitian translation-invariant.

preprint2021arXiv

Simple and effective localized attribute representations for zero-shot learning

Zero-shot learning (ZSL) aims to discriminate images from unseen classes by exploiting relations to seen classes via their semantic descriptions. Some recent papers have shown the importance of localized features together with fine-tuning the feature extractor to obtain discriminative and transferable features. However, these methods require complex attention or part detection modules to perform explicit localization in the visual space. In contrast, in this paper we propose localizing representations in the semantic/attribute space, with a simple but effective pipeline where localization is implicit. Focusing on attribute representations, we show that our method obtains state-of-the-art performance on CUB and SUN datasets, and also achieves competitive results on AWA2 dataset, outperforming generally more complex methods with explicit localization in the visual space. Our method can be implemented easily, which can be used as a new baseline for zero shot-learning. In addition, our localized representations are highly interpretable as attribute-specific heatmaps.

preprint2020arXiv

Aesthetic Quality Assessment for Group photograph

Image aesthetic quality assessment has got much attention in recent years, but not many works have been done on a specific genre of photos: Group photograph. In this work, we designed a set of high-level features based on the experience and principles of group photography: Opened-eye, Gaze, Smile, Occluded faces, Face Orientation, Facial blur, Character center. Then we combined them and 83 generic aesthetic features to build two aesthetic assessment models. We also constructed a large dataset of group photographs - GPD- annotated with the aesthetic score. The experimental result shows that our features perform well for categorizing professional photos and snapshots and predicting the distinction of multiple group photographs of diverse human states under the same scene.

preprint2020arXiv

Benchmarking calculations with spectroscopic accuracy of excitation energies and wavelengths in sulfur-like tungsten

Atomic properties of S-like W are evaluated through a state-of-the-art method, namely, the multi-configuration Dirac-Hartree-Fock (MCDHF) method combined with the relativistic configuration interaction (RCI) approach. The level energies, wavelengths, and transition parameters involving the 88 lowest levels of W$^{+58}$ (W LIX) are calculated. We discuss in detail the relative importance of the valence- and core-valence electron correlation effects, the Breit interaction, the higher order retardation correction beyond the Breit interaction through the transverse photon interaction, and the quantum electrodynamical (QED) corrections. The present level energies are highly accurate, with uncertainties close to what can be achieved from spectroscopy. As such, they provide benchmark tests for other theoretical calculations of S-like W and should assist the spectroscopists in their assignment/identification of observed lines in complex spectra.

preprint2020arXiv

Bookworm continual learning: beyond zero-shot learning and continual learning

We propose bookworm continual learning(BCL), a flexible setting where unseen classes can be inferred via a semantic model, and the visual model can be updated continually. Thus BCL generalizes both continual learning (CL) and zero-shot learning (ZSL). We also propose the bidirectional imagination (BImag) framework to address BCL where features of both past and future classes are generated. We observe that conditioning the feature generator on attributes can actually harm the continual learning ability, and propose two variants (joint class-attribute conditioning and asymmetric generation) to alleviate this problem.

preprint2020arXiv

Boundary criticality of $PT$-invariant topology and second-order nodal-line semimetals

For conventional topological phases, the boundary gapless modes are determined by bulk topological invariants. Based on developing an analytic method to solve higher-order boundary modes, we present $PT$-invariant $2$D topological insulators and $3$D topological semimetals that go beyond this bulk-boundary correspondence framework. With unchanged bulk topological invariant, their first-order boundaries undergo transitions separating different phases with second-order-boundary zero-modes. For the $2$D topological insulator, the helical edge modes appear at the transition point for two second-order topological insulator phases with diagonal and off-diagonal corner zero-modes, respectively. Accordingly, for the $3$D topological semimetal, the criticality corresponds to surface helical Fermi arcs of a Dirac semimetal phase. Interestingly, we find that the $3$D system generically belongs to a novel second-order nodal-line semimetal phase, possessing gapped surfaces but a pair of diagonal or off-diagonal hinge Fermi arcs.

preprint2020arXiv

CardioLearn: A Cloud Deep Learning Service for Cardiac Disease Detection from Electrocardiogram

Electrocardiogram (ECG) is one of the most convenient and non-invasive tools for monitoring peoples' heart condition, which can use for diagnosing a wide range of heart diseases, including Cardiac Arrhythmia, Acute Coronary Syndrome, et al. However, traditional ECG disease detection models show substantial rates of misdiagnosis due to the limitations of the abilities of extracted features. Recent deep learning methods have shown significant advantages, but they do not provide publicly available services for those who have no training data or computational resources. In this paper, we demonstrate our work on building, training, and serving such out-of-the-box cloud deep learning service for cardiac disease detection from ECG named CardioLearn. The analytic ability of any other ECG recording devices can be enhanced by connecting to the Internet and invoke our open API. As a practical example, we also design a portable smart hardware device along with an interactive mobile program, which can collect ECG and detect potential cardiac diseases anytime and anywhere.

preprint2020arXiv

Distinguishing between dynamical and static Rashba effects in hybrid perovskite nanocrystals using transient absorption spectroscopy

The dynamical and static Rashba effects in hybrid methylammonium (MA) lead halide perovskites have recently been theoretically predicted. However, only the static effect was experimentally confirmed so far. Here we report on the dynamical Rashba effect observed using snapshot transient absorption spectral imaging with 400 nm pumping for a fully encapsulated film of 20-nm-sized 3D MAPbBr3 nanocrystals. The effect causes a 240 meV splitting of the lowest-energy absorption bleaching band, initially appearing over sub-ps timescale and progressively stabilizing to 60 meV during 500 ps. The integrated intensities of the split subbands demonstrate a photon-helicity-dependent asymmetry, thus proving the Rashba-type splitting and providing direct experimental evidence for the Rashba spin-split edge states in lead halide perovskite materials. The ultrafast dynamics is governed by the relaxation of two-photon-excited electrons in the Rashba spin-split system caused by a built-in electric field originating from dynamical charge separation in the entire MAPbBr3 nanocrystal.

preprint2020arXiv

Efficient Bitruss Decomposition for Large-scale Bipartite Graphs

Cohesive subgraph mining in bipartite graphs becomes a popular research topic recently. An important structure k-bitruss is the maximal cohesive subgraph where each edge is contained in at least k butterflies (i.e., (2, 2)-bicliques). In this paper, we study the bitruss decomposition problem which aims to find all the k-bitrusses for k >= 0. The existing bottom-up techniques need to iteratively peel the edges with the lowest butterfly support. In this peeling process, these techniques are time-consuming to enumerate all the supporting butterflies for each edge. To relax this issue, we first propose a novel online index -- the BE-Index which compresses butterflies into k-blooms (i.e., (2, k)-bicliques). Based on the BE-Index, the new bitruss decomposition algorithm BiT-BU is proposed, along with two batch-based optimizations, to accomplish the butterfly enumeration of the peeling process in an efficient way. Furthermore, the BiT-PC algorithm is devised which is more efficient against handling the edges with high butterfly supports. We theoretically show that our new algorithms significantly reduce the time complexities of the existing algorithms. Also, we conduct extensive experiments on real datasets and the result demonstrates that our new techniques can speed up the state-of-the-art techniques by up to two orders of magnitude.

preprint2020arXiv

Energy Levels, Transition Rates and Electron Impact Excitation Rates for B-like Kr XXXII

Energy levels and transition rates for electric-dipole, electric-quadrupole, electric-octupole, magnetic-dipole, and magnetic-quadrupole transitions among the levels arising from the $n\ \leq$ 5 configurations in B-like Kr XXXII are calculated by using two state-of-the-art methods, namely, the multi-configuration Dirac-Hartree-Fock (MCDHF) approach and the second-order many-body perturbation theory (RMBPT). Our results are compared with several available experimental and other theoretical values. Electron-impact excitation (EIE) collision strengths are calculated via the independent process and isolated resonance approximation using distorted-wave (denoted by IPIRDW). Radiation damping effects on the resonance excitation contributions are included. Effective collision strengths are calculated as a function of electron temperature by assuming a Maxwellian electron velocity distribution. Spectral line intensities are modeled by using collision radiative model, and several line pairs pointed out might be useful for density diagnostics.

preprint2020arXiv

Exploring Cohesive Subgraphs with Vertex Engagement and Tie Strength in Bipartite Graphs

We propose a novel cohesive subgraph model called $τ$-strengthened $(α,β)$-core (denoted as $(α,β)_τ$-core), which is the first to consider both tie strength and vertex engagement on bipartite graphs. An edge is a strong tie if contained in at least $τ$ butterflies ($2\times2$-bicliques). $(α,β)_τ$-core requires each vertex on the upper or lower level to have at least $α$ or $β$ strong ties, given strength level $τ$. To retrieve the vertices of $(α,β)_τ$-core optimally, we construct index $I_{α,β,τ}$ to store all $(α,β)_τ$-cores. Effective optimization techniques are proposed to improve index construction. To make our idea practical on large graphs, we propose 2D-indexes $I_{α,β}, I_{β,τ}$, and $I_{α,τ}$ that selectively store the vertices of $(α,β)_τ$-core for some $α,β$, and $τ$. The 2D-indexes are more space-efficient and require less construction time, each of which can support $(α,β)_τ$-core queries. As query efficiency depends on input parameters and the choice of 2D-index, we propose a learning-based hybrid computation paradigm by training a feed-forward neural network to predict the optimal choice of 2D-index that minimizes the query time. Extensive experiments show that ($1$) $(α,β)_τ$-core is an effective model capturing unique and important cohesive subgraphs; ($2$) the proposed techniques significantly improve the efficiency of index construction and query processing.

preprint2020arXiv

Gamma-ray Burst Spectrum with a Time-dependent Injection Rate of High-energy Electrons

Although the physical origin of prompt emission in gamma-ray bursts (GRBs) remains inconclusive, previous studies have considered the synchrotron radiation of relativistic electrons as a promising mechanism. These works usually adopted a invariable injection rate of electrons ($Q$) which may be discordant with that in a Poynting-flux dominated jet. In a Poynting-flux dominated jet (e.g., ICMART model, Zhang & Yan 2011), the number of magnetic reconnections occurred simultaneously may grow rapidly with time and results in an increase of $Q$ with time. This paper is dedicated to study the synchrotron radiation spectrum in this scenario. It is found that the radiation spectrum would obviously get harder if an increasing $Q$ is adopted and a Band-like radiation spectrum can be obtained if the increase of $Q$ is fast enough. The latter is related to the fact that a bump-shape rather than a power-law spectrum appears in the low-energy regime of the obtained electron spectrum. This effect can strongly harden the low-energy radiation spectrum. It indicates that an increasing $Q$ can help to alleviate the "fast-cooling problem" of synchrotron radiation for GRBs. Our studies also reveal that a Poynting-flux dominated jet with a large emission radius, a small length of the magnetic reconnection region, or a low-minimum energy of injected electron would prefer to form a Band-like radiation spectrum. We suggest that the Band spectrum found in GRBs may be the synchrotron emission of the electrons with a bump-shape distribution in its low-energy regime.

preprint2020arXiv

High-order Time Stepping Schemes for Semilinear Subdiffusion Equations

The aim of this paper is to develop and analyze high-order time stepping schemes for solving semilinear subdiffusion equations. We apply the $k$-step BDF convolution quadrature to discretize the time-fractional derivative with order $α\in (0,1)$, and modify the starting steps in order to achieve optimal convergence rate. This method has already been well-studied for the linear fractional evolution equations in Jin, Li and Zhou \cite{JinLiZhou:correction}, while the numerical analysis for the nonlinear problem is still missing in the literature. By splitting the nonlinear potential term into an irregular linear part and a smoother nonlinear part, and using the generating function technique, we prove that the convergence order of the corrected BDF$k$ scheme is $O(τ^{\min(k,1+2α-ε)})$, without imposing further assumption on the regularity of the solution. Numerical examples are provided to support our theoretical results.

preprint2020arXiv

Multi-Domain Dialogue Acts and Response Co-Generation

Generating fluent and informative responses is of critical importance for task-oriented dialogue systems. Existing pipeline approaches generally predict multiple dialogue acts first and use them to assist response generation. There are at least two shortcomings with such approaches. First, the inherent structures of multi-domain dialogue acts are neglected. Second, the semantic associations between acts and responses are not taken into account for response generation. To address these issues, we propose a neural co-generation model that generates dialogue acts and responses concurrently. Unlike those pipeline approaches, our act generation module preserves the semantic structures of multi-domain dialogue acts and our response generation module dynamically attends to different acts as needed. We train the two modules jointly using an uncertainty loss to adjust their task weights adaptively. Extensive experiments are conducted on the large-scale MultiWOZ dataset and the results show that our model achieves very favorable improvement over several state-of-the-art models in both automatic and human evaluations.

preprint2020arXiv

Multidimensional synthetic chiral-tube lattices via nonlinear frequency conversion

Geometrical dimensionality plays a fundamentally important role in the topological effects arising in discrete lattices. While direct experiments are limited by three spatial dimensions, the research topic of synthetic dimensions implemented by the frequency degree of freedom in photonics is rapidly advancing. The manipulation of light in such artificial lattices is typically realized through electro-optic modulation, yet their operating bandwidth imposes practical constraints on the range of interactions between different frequency components. Here we propose and experimentally realize all-optical synthetic dimensions involving specially tailored simultaneous short- and long-range interactions between discrete spectral lines mediated by frequency conversion in a nonlinear waveguide. We realize triangular chiral-tube lattices in three-dimensional space and explore their four-dimensional generalization. We implement a synthetic gauge field with nonzero magnetic flux and observe the associated multidimensional dynamics of frequency combs, all within one physical spatial port. We anticipate that our method will provide a new means for the fundamental study of high-dimensional physics and act as an important step towards using topological effects in optical devices operating in the time and frequency domains.

preprint2020arXiv

Multilayer Structures of Graphene and Pt Nanoparticles -- a Multiscale Computational Study

We report results of a multiscale simulation study of multilayer structures consisting of graphene sheets with embedded Pt nanoparticles. Density functional theory is used to understand the energetics of Pt-graphene interfaces and provide reference data for the parameterization of a Pt-graphene interaction potential. Molecular dynamics simulations then provide the conformation and energetics of graphene sheets with embedded Pt nanoparticles of varying density, form and size. These results are interpreted using a continuum mechanical model of sheet deformation, and serve to parameterize a meso-scale Monte Carlo model to investigate the question under which conditions the free volume around the Pt nanoparticles forms a percolating cluster, such that the structures can be used in catalytic applications. We conclude with a discussion of potential applications of such multilayer structures.

preprint2020arXiv

Photo-production of Higgs Boson at the LHeC

As one category of vector boson fusion, photo-production is one important production mechanism at $e$-$p$ colliders. A future $e$-$p$ collider -- Large Hadron-electron Collider (LHeC) has been discussed as a "Higgs factory" candidate where the Higgs boson produced via weak boson fusion (WBF) at the LHeC plays an important role in precision measurement of Yukawa couplings. On the other hand, a measurement of photo-production of the Higgs boson, if possible, might be complementary to the measurement of Higgs to di-photon partial decay width $Γ({h\toγγ})$. In this paper, we study the possibility of measuring this production process at the LHeC with the help of the photon PDFs published in recent years. This process has a clean final state without additional colored particles in the detectable region other than the decay products of the Higgs. We compute the cross sections of all related processes and find that the production rate is at the same order as the neutral current WBF production of Higgs boson with missing forward jets. However, a detailed phenomenological study of various Higgs decay channels shows that even in the most promising semi-leptonic $WW$ channel, the feasibility of identifying such photo-production is negative due to an irreducible photo-production of $W^{+}W^{-}$.

preprint2020arXiv

Photospheric Emission in Gamma-ray Bursts: Variability

It is generally believed that the variability of photospheric emission in gamma-ray bursts (GRBs) traces that of the jet power. This work further investigates the variability of photospheric emission in a variable jet. By setting a constant $η$ (dimensionless entropy of the jet), we find that the light curve of the photospheric emission shows a ``tracking'' pattern on the time profile of jet power. However, the relative variability is significantly low in the photospheric emission compared with that in the jet power. If the $η$ is genetic variable, the variability of the photospheric emission is not only limited by the jet power but also affected by $η$ strongly. It becomes complex and is generally different from that of the jet power. Moreover, the opposite phase may stand in the variabilities of the photospheric emission at different photon energies. We also find that the relative variability does not remain constant over the photon energies with an obvious reduction at a certain energy. This is consistent with the analysis of GRB 090902B in which an appreciable thermal component has been detected in a wide energy range. For several other GRBs coupling with the thermal component, we conservatively evaluate the variability of the thermal and non-thermal emission, respectively. Our results show that the relative variability of the thermal emission is likely comparable to that of the non-thermal emission for these bursts. In addition, the analysis of GRB~120323A reveals that the variability of the photospheric emission may be of the opposite phase from that of the non-thermal emission.

preprint2020arXiv

Probing the trilinear Higgs boson self-coupling via single Higgs production at the LHeC

The determination of the Higgs self coupling is one of the key ingredients for understanding the mechanism behind the electroweak symmetry breaking. An indirect method for constraining the Higgs trilinear self coupling via single Higgs production at next-to-leading order (NLO) has been proposed in order to avoid the drawbacks of studies with double Higgs production. In this paper we study the Higgs self interaction through the vector boson fusion (VBF) process $e^{-} p \to ν_{e} h j$ at the future LHeC. At NLO level, we compute analytically the scattering amplitudes for relevant processes, in particular those induced by the Higgs self interaction. A Monte Carlo simulation and a statistical analysis utilizing the analytic results are then carried out for Higgs production through VBF and decay to $b\bar{b}$, which yield for the trilinear Higgs self-coupling rescaling parameter $κ_λ$ the limit [-0.57, 2.98] with $2~\text{ab}^{-1}$ integrated luminosity. If we assume about 10% of the signal survives the event selection cuts, and include all the background, the constraint will be broadened to [-2.11, 4.63].

preprint2020arXiv

Quantum wave-particle superposition in a delayed-choice experiment

Wave-particle duality epitomizes the counterintuitive character of quantum physics. A striking illustration is the quantum delay-choice experiment, which is based on Wheeler's classic delayed-choice gedanken experiment, but with the addition of a quantum-controlled device enabling wave-to-particle transitions. Here we realize a quantum delayed-choice experiment in which we control the wave and the particle states of photons and in particular the phase between them, thus directly establishing the created quantum nature of the wave-particle. We generate three-photon entangled states and inject one photon into a Mach--Zehnder interferometer embedded in a 187-m-long two-photon Hong-Ou-Mandel interferometer. The third photon is sent 141m away from the interferometers and remotely prepares a two-photon quantum gate according to independent active choices under Einstein locality conditions. We have realized transitions between wave and particle states in both classical and quantum scenarios, and therefore tests of the complementarity principle that go fundamentally beyond earlier implementations.

preprint2020arXiv

Relating the structure of dark matter halos to their assembly and environment

We use a large $N$-body simulation to study the relation of the structural properties of dark matter halos to their assembly history and environment. The complexity of individual halo assembly histories can be well described by a small number of principal components (PCs), which, compared to formation times, provide a more complete description of halo assembly histories and have a stronger correlation with halo structural properties. Using decision trees built with the random ensemble method, we find that about $60\%$, $10\%$, and $20\%$ of the variances in halo concentration, axis ratio, and spin, respectively, can be explained by combining four dominating predictors: the first PC of the assembly history, halo mass, and two environment parameters. Halo concentration is dominated by halo assembly. The local environment is found to be important for the axis ratio and spin but is degenerate with halo assembly. The small percentages of the variance in the axis ratio and spin that are explained by known assembly and environmental factors suggest that the variance is produced by many nuanced factors and should be modeled as such. The relations between halo intrinsic properties and environment are weak compared to their variances, with the anisotropy of the local tidal field having the strongest correlation with halo properties. Our method of dimension reduction and regression can help simplify the characterization of the halo population and clarify the degeneracy among halo properties.

preprint2020arXiv

Relational Graph Attention Network for Aspect-based Sentiment Analysis

Aspect-based sentiment analysis aims to determine the sentiment polarity towards a specific aspect in online reviews. Most recent efforts adopt attention-based neural network models to implicitly connect aspects with opinion words. However, due to the complexity of language and the existence of multiple aspects in a single sentence, these models often confuse the connections. In this paper, we address this problem by means of effective encoding of syntax information. Firstly, we define a unified aspect-oriented dependency tree structure rooted at a target aspect by reshaping and pruning an ordinary dependency parse tree. Then, we propose a relational graph attention network (R-GAT) to encode the new tree structure for sentiment prediction. Extensive experiments are conducted on the SemEval 2014 and Twitter datasets, and the experimental results confirm that the connections between aspects and opinion words can be better established with our approach, and the performance of the graph attention network (GAT) is significantly improved as a consequence.

preprint2020arXiv

Rigidity of determinantal point processes on the unit disc with sub-Bergman kernels

We give natural constructions of number rigid determinantal point processes on the unit disc $\mathbb{D}$ with sub-Bergman kernels of the form \[ K_Λ(z, w) = \sum_{n\in Λ}(n+1) z^n \bar{w}^n, \quad z, w \in \mathbb{D}, \] with $Λ$ an infinite subset of the set of non-negative integers. Our constructions are given both in a deterministic method and a probabilisitc method. In the deterministic method, our proofs involve the classical Bloch functions.

preprint2020arXiv

Semantic Drift Compensation for Class-Incremental Learning

Class-incremental learning of deep networks sequentially increases the number of classes to be classified. During training, the network has only access to data of one task at a time, where each task contains several classes. In this setting, networks suffer from catastrophic forgetting which refers to the drastic drop in performance on previous tasks. The vast majority of methods have studied this scenario for classification networks, where for each new task the classification layer of the network must be augmented with additional weights to make room for the newly added classes. Embedding networks have the advantage that new classes can be naturally included into the network without adding new weights. Therefore, we study incremental learning for embedding networks. In addition, we propose a new method to estimate the drift, called semantic drift, of features and compensate for it without the need of any exemplars. We approximate the drift of previous tasks based on the drift that is experienced by current task data. We perform experiments on fine-grained datasets, CIFAR100 and ImageNet-Subset. We demonstrate that embedding networks suffer significantly less from catastrophic forgetting. We outperform existing methods which do not require exemplars and obtain competitive results compared to methods which store exemplars. Furthermore, we show that our proposed SDC when combined with existing methods to prevent forgetting consistently improves results.

preprint2020arXiv

ShapeAssembly: Learning to Generate Programs for 3D Shape Structure Synthesis

Manually authoring 3D shapes is difficult and time consuming; generative models of 3D shapes offer compelling alternatives. Procedural representations are one such possibility: they offer high-quality and editable results but are difficult to author and often produce outputs with limited diversity. On the other extreme are deep generative models: given enough data, they can learn to generate any class of shape but their outputs have artifacts and the representation is not editable. In this paper, we take a step towards achieving the best of both worlds for novel 3D shape synthesis. We propose ShapeAssembly, a domain-specific "assembly-language" for 3D shape structures. ShapeAssembly programs construct shapes by declaring cuboid part proxies and attaching them to one another, in a hierarchical and symmetrical fashion. Its functions are parameterized with free variables, so that one program structure is able to capture a family of related shapes. We show how to extract ShapeAssembly programs from existing shape structures in the PartNet dataset. Then we train a deep generative model, a hierarchical sequence VAE, that learns to write novel ShapeAssembly programs. The program captures the subset of variability that is interpretable and editable. The deep model captures correlations across shape collections that are hard to express procedurally. We evaluate our approach by comparing shapes output by our generated programs to those from other recent shape structure synthesis models. We find that our generated shapes are more plausible and physically-valid than those of other methods. Additionally, we assess the latent spaces of these models, and find that ours is better structured and produces smoother interpolations. As an application, we use our generative model and differentiable program interpreter to infer and fit shape programs to unstructured geometry, such as point clouds.

preprint2020arXiv

Spectral mapping of polarization-correlated photon-pair sources using quantum-classical correspondence

Direct spectral characterization of a quantum photon-pair source usually involves cumbersome, costly, and time-consuming detection issues. In this study, we experimentally characterize the spectral properties of a type-II phase-matched spontaneous parametric down-conversion (SPDC) source based on a titanium-diffused periodically poled lithium niobate (Ti:PPLN) waveguide. The characterization of the spectral information of the generated cross-polarized photon pairs is of importance for the use of such sources in applications including quantum information and communication. We demonstrate that the joint spectral intensity of the cross-polarized photon-pair source can be fully reconstructed using the quantum-classical correspondence through classical sum-frequency generation (SFG) measurements. This technique, which uses a much less complex detection system for visible light, opens the possibility of fast monitoring and control of the quantum state of (polarization-correlated) photon-pair sources to facilitate the realization of a stable and high-usability quantum source.

preprint2020arXiv

Suppressing Uncertainties for Large-Scale Facial Expression Recognition

Annotating a qualitative large-scale facial expression dataset is extremely difficult due to the uncertainties caused by ambiguous facial expressions, low-quality facial images, and the subjectiveness of annotators. These uncertainties lead to a key challenge of large-scale Facial Expression Recognition (FER) in deep learning era. To address this problem, this paper proposes a simple yet efficient Self-Cure Network (SCN) which suppresses the uncertainties efficiently and prevents deep networks from over-fitting uncertain facial images. Specifically, SCN suppresses the uncertainty from two different aspects: 1) a self-attention mechanism over mini-batch to weight each training sample with a ranking regularization, and 2) a careful relabeling mechanism to modify the labels of these samples in the lowest-ranked group. Experiments on synthetic FER datasets and our collected WebEmotion dataset validate the effectiveness of our method. Results on public benchmarks demonstrate that our SCN outperforms current state-of-the-art methods with \textbf{88.14}\% on RAF-DB, \textbf{60.23}\% on AffectNet, and \textbf{89.35}\% on FERPlus. The code will be available at \href{https://github.com/kaiwang960112/Self-Cure-Network}{https://github.com/kaiwang960112/Self-Cure-Network}.

preprint2020arXiv

Synthetic photonic lattice for single-shot reconstruction of frequency combs

We formulate theoretically and demonstrate experimentally an all-optical method for reconstruction of the amplitude, phase and coherence of frequency combs from a single-shot measurement of the spectral intensity. Our approach exploits synthetic frequency lattices with pump-induced spectral short- and long-range couplings between different signal components across a broad bandwidth of of hundreds GHz in a single nonlinear fiber. When combined with ultra-fast signal conversion techniques, this approach has the potential to provide real-time measurement of pulse-to-pulse variations in the spectral phase and coherence properties of exotic light sources.

preprint2020arXiv

The 1st Agriculture-Vision Challenge: Methods and Results

The first Agriculture-Vision Challenge aims to encourage research in developing novel and effective algorithms for agricultural pattern recognition from aerial images, especially for the semantic segmentation task associated with our challenge dataset. Around 57 participating teams from various countries compete to achieve state-of-the-art in aerial agriculture semantic segmentation. The Agriculture-Vision Challenge Dataset was employed, which comprises of 21,061 aerial and multi-spectral farmland images. This paper provides a summary of notable methods and results in the challenge. Our submission server and leaderboard will continue to open for researchers that are interested in this challenge dataset and task; the link can be found here.

preprint2020arXiv

The Lp-Lq problems of Bergman-type operators

Let $\mathbb{B}^d$ be the unit ball on the complex space $\mathbb{C}^d$ with normalized Lebesgue measure $dv.$ For $α\in\mathbb{R},$ denote $k_α(z,w)=\frac{1}{(1-\langle z,w\rangle)^α},$ the Bergman-type integral operator $K_α$ on $L^1(\mathbb{B}^d,dv)$ is defined by $$ K_αf(z)=\int_{\mathbb{B}^d}k_α(z,w)f(w)dv(w).$$ It is an important class of operators in the holomorphic function space theory over the unit ball. We also consider the integral operator $K_α^+$ on $L^1(\mathbb{B}^d,dv)$ which is given by $$ K_α^+ f(z)=\int_{\mathbb{B}^d}\vert k_α(z,w)\vert f(w)dv(w).$$ In this paper, we completely characterize the $L^p$-$L^q$ boundedness of $K_α,K_α^+$ and $L^p$-$L^q$ compactness of $K_α.$ The results of boundedness are in fact the Hardy-Littlewood-Sobolev theorem but also prove the conjecture of G. Cheng et al [Trans. Amer. Math. Soc. (2017), MR3710638 ] in the case of bounded domain $\mathbb{B}^d.$ Meanwhile, a trace formula and some sharp norm estimates of $K_α,K_α^+$ are given.

preprint2019arXiv

A Survey on Face Data Augmentation

The quality and size of training set have great impact on the results of deep learning-based face related tasks. However, collecting and labeling adequate samples with high quality and balanced distributions still remains a laborious and expensive work, and various data augmentation techniques have thus been widely used to enrich the training dataset. In this paper, we systematically review the existing works of face data augmentation from the perspectives of the transformation types and methods, with the state-of-the-art approaches involved. Among all these approaches, we put the emphasis on the deep learning-based works, especially the generative adversarial networks which have been recognized as more powerful and effective tools in recent years. We present their principles, discuss the results and show their applications as well as limitations. Different evaluation metrics for evaluating these approaches are also introduced. We point out the challenges and opportunities in the field of face data augmentation, and provide brief yet insightful discussions.

preprint2019arXiv

A two-zone model for blazar emission: implications for TXS 0506+056 and the neutrino event IceCube-170922A

A high-energy muon neutrino event, IceCube-170922A, was recently discovered in both spatial and temporal coincidence with a gamma-ray flare of the blazar TXS 0506+056. It has been shown, with standard one-zone models, that neutrinos can be produced in the blazar jet via hadronic interactions, but with a flux which is mostly limited by the X-ray data. In this work, we explore the neutrino production from TXS 0506+056 by invoking two physically distinct emission zones in the jet, separated by the broad line region (BLR). Using the Doppler-boosted radiation of the BLR as the target photon field, the inner zone accounts for the neutrino and gamma-ray emission via $pγ$ interactions and inverse Compton scattering respectively, while the outer zone produces the optical and X-ray emission via synchrotron and synchrotron self-Compton processes. The different conditions of the two zones allow us to suppress the X-ray emission from the electromagnetic cascade, and set a much higher upper limit on the muon neutrino flux (i.e., $\sim 10^{-11}\rm erg~cm^{-2}s^{-1}$) than in one-zone models. We compare, in detail, our scenario with one-zone models discussed in the literature, and argue that differentiating between such scenarios will become possible with next generation neutrino telescopes, such as IceCube-Gen2.

preprint2019arXiv

Experimental parity-time symmetry quantum walks on a directed graph

Quantum walks (QW) are of crucial importance in the development of quantum information processing algorithms. Recently, several quantum algorithms have been proposed to implement network analysis, in particular to rank the centrality of nodes in networks represented by graphs. Employing QW in centrality ranking is advantageous comparing to certain widely used classical algorithms (e.g. PageRank) because QW approach can lift the vertex rank degeneracy in certain graphs. However, it is challenging to implement a directed graph via QW, since it corresponds to a non-Hermitian Hamiltonian and thus cannot be accomplished by conventional QW. Here we report the realizations of centrality rankings of both a three-vertex and four-vertex directed graphs with parity-time (PT) symmetric quantum walks. To achieve this, we use high-dimensional photonic quantum states, optical circuitries consisting of multiple concatenated interferometers and dimension dependent loss. Importantly, we demonstrate the advantage of QW approach experimentally by breaking the vertex rank degeneracy in a four-vertex graph. Our work shows that PT-symmetric quantum walks may be useful for realizing advanced algorithm in a quantum network.

preprint2016arXiv

Dynamical co-existence of excitons and free carriers in perovskite probed by density-resolved fluorescent spectroscopic method

Using transient fluorescent spectra at time-zero, we develop a density-resolved fluorescent spectroscopic method for investigating photoproducts in CH3NH3PbI3 perovskite and related photophysics. The density dependent dynamical co-existence of excitons and free carriers over a wide density range is experimentally observed for the first time. The exciton binding energy (EB) and the effective mass of electron-hole pair can be estimated based on such co-existence. No ionic polarization is found contributing to photophysical behavior. It also solves the conflict between the large experimentally measured EB and the small predicted values. The spectroscopic method also helps to detect the true free carrier density under continuous illumination without the interference of ionic conductivity. Our methods and results profoundly enrich the study and understanding of the photophysics in perovskite materials for photovoltaic applications.

preprint2016arXiv

Exceptional Points and Asymmetric Mode Switching in Plasmonic Waveguides

We investigate the exceptional points (EPs) in a non-Hermitian system composed of a pair of graphene sheets with different losses. There are two surface plasmon polaritons (SPP) modes in the graphene waveguide. By varying the distance between two graphene sheets and chemical potential of graphene, the EPs appear as the eigenvalues, that is, the wave vectors of the two modes coalesce. The cross conversion of eigenmodes and variation of geometric phase can be observed by encircling the EP in the parametric space formed by the geometric parameters and chemical potential of graphene. At the same time, a certain input SPP mode may lead to completely different output. The study paves a way to the development of nanoscale sensitive optical switches and sensors.

preprint2016arXiv

Extended relativistic configuration interaction and many-body perturbation calculations of spectroscopic data for the $n \leq 6$ configurationsin ne-like ions between Cr XV and Kr XXVII

Level energies, wavelengths, electric dipole, magnetic dipole, electric quadrupole, and magnetic quadrupole transition rates, oscillator strengths, and line strengths from combined relativistic configuration interaction and many-body perturbation calculations are reported for the 201 fine-structure states of the $2s^2 2p^6$, $2s^2 2p^5 3l$, $2s 2p^6 3l$, $2s^2 2p^5 4l$, $2s 2p^6 4l$, $2s^2 2p^5 5l$, and $2s^2 2p^5 6l$ configurations in all Ne-like ions between Cr XV and Kr XXVII. Calculated level energies and transition data are compared with experiments from the NIST and CHIANTI databases, and other recent benchmark calculations. The mean energy difference with the NIST experiments is only 0.05%. The present calculations significantly increase the amount of accurate spectroscopic data for the $n >3$ states in a number of Ne-like ions of astrophysics interest. A complete dataset should be helpful in analyzing new observations from the solar and other astrophysical sources, and is also likely to be useful for modeling and diagnosing a variety of plasmas including astronomical and fusion plasma.

preprint2016arXiv

Generation and reverse transformation of twisted light by spatial light modulator

A spatial light modulator (SLM) is one of the most useful and convenient device to generate structural light beams such as twisted light and complexed images used in modern optical science. The unbounded dimension of twisted light makes it promising in harnessing information carrying ability of a single photon, which greatly enhances the channel capacity in optical communications. We perform a detail theoretical study of the birth, evolution and reverse transformation of twisted light generated from a phase-only SLM based on diffraction theory, analytical expressions are obtained to show the special evolution behaviors of the light beam with the propagation distance. Beam intensity distributions calculated theoretically are in well agreement with experimental observations. Our findings clearly reveal how twisted light gradually emerges from a Gaussian spatial shape to a ring shape, and also the ring shape will gradually evolve to a quasi-Gaussian shape conversely, in the reverse transformation. These results will provide guidelines for using SLM in many optical experiments and OAM-mode multiplexing in optical communications.

preprint2016arXiv

Leveraging Crowd for Game-based Learning: A Case Study of Privacy Education Game Design and Evaluation by Crowdsourcing

As the Internet grows in importance, it is vital to develop methods and techniques for educating end-users to improve their awareness of online privacy. Web-based education tools have been proven effective in many domains and have been increasingly adopted by many online professional and educational services. However, the design and development of Web-based education tools for online privacy is still in the early stage. The traditional solutions always involve privacy experts who have sophisticated expertise. Such involvement can make the tool development costly. Furthermore, it is not clear how inspiring and effective these education tools are to general users of varying backgrounds, specially to novice users who have rarely dealt with online privacy issues before. In this paper, we design, develop, and evaluate a game-based privacy learning system by leveraging the wisdom of a crowd of non-experts on Amazon Mechanic Turk. Empirical study demonstrates that the crowd can provide high-quality ideas of designing and developing a practical, educational privacy learning game.

preprint2016arXiv

Light-effect transistor (LET) with multiple independent gating controls for optical logic gates and optical amplification

Modern electronics are developing electronic-optical integrated circuits, while their electronic backbone, e.g. field-effect transistors (FETs), remains the same. However, further FET down scaling is facing physical and technical challenges. A light-effect transistor (LET) offers electronic-optical hybridization at the component level, which can continue Moore's law to the quantum region without requiring a FET's fabrication complexity, e.g. a physical gate and doping, by employing optical gating and photoconductivity. Multiple independent gates are therefore readily realized to achieve unique functionalities without increasing chip space. Here we report LET device characteristics and novel digital and analog applications, such as optical logic gates and optical amplification. Prototype CdSe-nanowire-based LETs show output and transfer characteristics resembling advanced FETs, e.g. on/off ratios up to ~1.0x10^6 with a source-drain voltage of ~1.43 V, gate-power of ~260 nW, and subthreshold swing of ~0.3 nW/decade (excluding losses). Our work offers new electronic-optical integration strategies and electronic and optical computing approaches.

preprint2016arXiv

Probing the statistical properties of CMB $B$-mode polarization through Minkowski Functionals

The detection of the magnetic type $B$-mode polarization is the main goal of future cosmic microwave background (CMB) experiments. In the standard model, the $B$-mode map is a strongly non-gaussian field due to the lensed component. Besides the two-point correlation function, the other statistics are also very important to dig the information of the polarization map. In this paper, we employ the Minkowski functionals to study the morphological properties of the lensed $B$-mode maps. We find that the deviations from Gaussianity are very significant for both full and partial-sky surveys. As an application of the analysis, we investigate the morphological imprints of the foreground residuals in the $B$-mode map. We find that even for very tiny foreground residuals, the effects on the map can be detected by the Minkowski functional analysis. Therefore, it provides a complementary way to investigate the foreground contaminations in the CMB studies.

preprint2016arXiv

Reproducibility of Retinal Thickness Measurements across Spectral-Domain Optical Coherence Tomography Devices using Iowa Reference Algorithm

PURPOSE: Establishing and obtaining consistent quantitative indices of retinal thickness from a variety of clinically used Spectral-Domain Optical Coherence Tomography scanners. DESIGN: Retinal images from five Spectral-Domain Optical Coherence Tomography scanners were used to determine total retinal thickness with scanner-specific correction factors establishing consistency of thickness measurement across devices. PARTICIPANTS: 55 Fovea-centered Spectral-Domain Optical Coherence Tomography volumes from eleven subjects were analyzed, obtained from Cirrus HD-OCT, RS-3000, Heidelberg Spectralis, RTVue and Topcon2000, seven subjects with retinal diseases and four normal controls. METHOD: The Iowa Reference Algorithm measured total retinal thickness. Nonlinear model of total retinal thickness measurement comparisons was derived and used for device-specific comparisons. Bland-Altman plots and pairwise predictive equations yielded pairwise scanner-specific differences. Mendel test determined whether measurement biases were constant for each scanner pair. RESULTS: Mendel test revealed that all pairwise scanner differences of total retinal thickness were constant across the cohort (p=0.992). Individual measurements can thus be bias-corrected and the Iowa Reference Algorithm serve as a scanner-agnostic independent standard of total retinal thickness across the five tested SD-OCT scanners. CONCLUSIONS: Combination of the Iowa Reference Algorithm with scanner-specific bias correction yields cross-scanner consistency of total retinal thickness measurements, facilitating scanning-device independent quantitative assessment of total retinal thickness, longitudinal follow-up quantification without requiring patients to be imaged on the same scanner model, and allowing for multi-center studies with heterogeneous device utilization when using the Iowa Reference Algorithm.

preprint2016arXiv

Reverberation Mapping of the Broad-line Region in NGC 5548: Evidence for Radiation Pressure?

NGC 5548 is the best-observed reverberation-mapped active galactic nucleus with long-term, intensive monitoring. Here we report results from a new observational campaign between January and July, 2015. We measure the centroid time lag of the broad H$β$ emission line with respect to the 5100 Åcontinuum and obtain $τ_{\rm cent} = 7.20^{+1.33}_{-0.35}$ days in the rest frame. This yields a black hole mass of $M_{\bullet}=8.71^{+3.21}_{-2.61} $x$ 10^{7}M_{\odot}$ using a broad H$β$ line dispersion of $3124\pm302$ km s$^{-1}$ and a virial factor of $f_{_{\rm BLR}}=6.3\pm1.5$ for the broad-line region (BLR), consistent with the mass measurements from previous H$β$ campaigns. The high-quality data allow us to construct a velocity-binned delay map for the broad H$β$ line, which shows a symmetric response pattern around the line center, a plausible kinematic signature of virialized motion of the BLR. Combining all the available measurements of H$β$ time lags and the associated mean 5100 Å luminosities over 18 campaigns between 1989 and 2015, we find that the H$β$ BLR size varies with the mean optical luminosity, but, interestingly, with a possible delay of $2.35_{-1.25}^{+3.47}$ yrs. This delay coincides with the typical BLR dynamical timescale of NGC 5548, indicating that the BLR undergoes dynamical changes, possibly driven by radiation pressure.

preprint2016arXiv

Spectroscopic Indication for a Centi-parsec Supermassive Black Hole Binary in the Galactic Center of NGC 5548

As a natural consequence of cosmological hierarchical structure formation, sub-parsec supermassive black hole binaries (SMBHBs) should be common in galaxies but thus far have eluded spectroscopic identification. Based on four decades of optical spectroscopic monitoring, we report that the nucleus of NGC 5548, a nearby Seyfert galaxy long suspected to have experienced a major merger about one billion years ago, exhibits long-term variability with a period of 14 years in the optical continuum and broad Hbeta emission line. Remarkably, the double-peaked profile of Hbeta shows systematic velocity changes with a similar period. These pieces of observations plausibly indicate that a SMBHB resides in the center of NGC 5548. The complex, secular variations in the line profiles can be explained by orbital motion of a binary with equal mass and a semi-major axis of 22 light-days (corresponding to 18 milli-parsec). At a distance of 75 Mpc, NGC 5548 is one of the nearest sub-parsec SMBHB candidates that offers an ideal laboratory for gravitational wave detection.

preprint2016arXiv

Squarkonium, diquarkonium and octetonium at the LHC and their di-photon decays

Motivated by the recent di-photon excess by both ATLAS and CMS collaborations at the LHC, we systematically investigate the production and di-photon decay of onia formed by pair of all possible color exotic scalars in minimal extension. When such scalar massive meta-stable colored and charged (MMCC) particles are produced in pair near threshold, $η$ onium can be formed and decay into di-photon through annihilation as $pp\to η\to γγ$. Squarkonium is formed by meta-stable squarks in supersymmetric models such as stoponium. Diquarkonium is formed by meta-stable color sextet diquarks which may be realized in the Pati-Salam model. Octetonium is formed by color octet scalars bosons as in the Manohar-Wise model. Stoponium prediction is much smaller than the required signal to account for the di-photon excess. Due to the enhancement factor from color and electric charge, predictions of diquarkonium and octetonium are of $\cal O$(10 fb) which are significantly greater than the stoponium prediction. Since the color enhancement also results in large production at the colliders, such light color exotic states of $\cal O$(375 GeV) suffer from severe direct search constraints. On the other hand, if their dominant decay mode involve top quark, they may be buried in the $t\bar{t}$ plus jets samples and can potentially be searched via $t+j$ resonance.

preprint2016arXiv

Supermassive Black Holes with High Accretion Rates in Active Galactic Nuclei. V. A New Size-Luminosity Scaling Relation for the Broad-Line Region

This paper reports results of the third-year campaign of monitoring super-Eddington accreting massive black holes (SEAMBHs) in active galactic nuclei (AGNs) between 2014-2015. Ten new targets were selected from quasar sample of Sloan Digital Sky Survey (SDSS), which are generally more luminous than the SEAMBH candidates in last two years. H$β$ lags ($τ_{_{\rm Hβ}}$) in five of the 10 quasars have been successfully measured in this monitoring season. We find that the lags are generally shorter, by large factors, than those of objects with same optical luminosity, in light of the well-known $R_{_{\rm Hβ}}-L_{5100}$ relation. The five quasars have dimensionless accretion rates of $\dot{\mathscr{M}}=10-10^3$. Combining measurements of the previous SEAMBHs, we find that the reduction of H$β$ lags tightly depends on accretion rates, $τ_{_{\rm Hβ}}/τ_{_{R-L}}\propto\dot{\mathscr{M}}^{-0.42}$, where $τ_{_{R-L}}$ is the H$β$ lag from the normal $R_{_{\rm Hβ}}-L_{5100}$ relation. Fitting 63 mapped AGNs, we present a new scaling relation for the broad-line region: $R_{_{\rm Hβ}}=α_1\ell_{44}^{β_1}\,\min\left[1,\left(\dot{\mathscr{M}}/\dot{\mathscr{M}}_c\right)^{-γ_1}\right]$, where $\ell_{44}=L_{5100}/10^{44}\,\rm erg~s^{-1}$ is 5100 Å continuum luminosity, and coefficients of $α_1=(29.6_{-2.8}^{+2.7})$ lt-d, $β_1=0.56_{-0.03}^{+0.03}$, $γ_1=0.52_{-0.16}^{+0.33}$ and $\dot{\mathscr{M}}_c=11.19_{-6.22}^{+2.29}$. This relation is applicable to AGNs over a wide range of accretion rates, from $10^{-3}$ to $10^3$. Implications of this new relation are briefly discussed.

preprint2016arXiv

Test of \textit{Topmetal-${II}^-$} In Liquid Nitrogen For Cryogenic Temperature TPCs

\textit{Topmetal-${II}^-$} is a highly pixelated direct charge sensor that contains a 72${\times}$72 pixel array of 83$μ$m pitch size. The key feature of \textit{Topmetal-${II}^-$} is that it can directly collect charges via metal nodes of each pixel to form two-dimensional images of charge cloud distributions. \textit{Topmetal-${II}^-$} was proved to measure charged particles without amplification at room temperature. To measure its performance at cryogenic temperature, a \textit{Topmetal-${II}^-$} sensor is embedded into a liquid nitrogen dewar. The results presented in this paper show that \textit{Topmetal-${II}^-$} can also operate well at this low temperature with a noise (ENC) of 12 e$^-$ lower than that at room temperature (13 e$^-$). From the noise perspective, \textit{Topmetal-${II}^-$} is a promising candidate for the next generation readout of liquid argon and xenon Time Projection Chamber (TPC) used in experiments searching for neutrinoless double beta decay and dark matter.

preprint2016arXiv

Utility of potential energy span as an approximate free energy proxy

Free energy calculation is critical in predictive tasks such as protein folding, docking and design. However, rigorous calculation of free energy change is prohibitively expensive in these practical applications. The minimum potential energy is therefore widely utilized to approximate free energy. In this study, based on analysis of extensive molecular dynamics (MD) simulation trajectories of a few native globular proteins, we found that change of minimum and corresponding maximum potential energy terms exhibit similar level of correlation with change of free energy. More importantly, we demonstrated that change of span (maximum - minimum) of potential energy terms, which engender negligible additional computational cost, exhibit considerably stronger correlations with change of free energy than the corresponding change of minimum and maximum potential energy terms. Therefore, potential energy span may serve as an alternative efficient approximate free energy proxy.

preprint2015arXiv

Configurational space discretization and free energy calculation in complex molecular systems

Trajectories provide dynamical information that is discarded in free energy calculations, for which we sought to design a scheme with the hope of saving cost for generating dynamical information. We first demonstrated that snapshots in a converged trajectory set are associated with implicit conformers that have invariant statistical weight distribution (ISWD). Based on the thought that infinite number of sets of implicit conformers with ISWD may be created through independent converged trajectory sets, we hypothesized that explicit conformers with ISWD may be constructed for complex molecular systems through systematic increase of conformer fineness, and tested the hypothesis in lipid molecule palmitoyloleoylphosphatidylcholine (POPC). Furthermore, when explicit conformers with ISWD were utilized as basic states to define conformational entropy, change of which between two given macrostates was found to be equivalent to change of free energy except a mere difference of a negative temperature factor, and change of enthalpy essentially cancels corresponding change of average intra-conformer entropy. These findings suggest that entropy enthalpy compensation is inherently a local phenomenon in configurational space. By implicitly taking advantage of entropy enthalpy compensation and forgoing all dynamical information, constructing explicit conformers with ISWD and counting thermally accessible number of which for interested end macrostates is likely to be an efficient and reliable alternative end point free energy calculation strategy.

preprint2015arXiv

Dixmier Trace for Toeplitz Operators on Symmetric Domains

For Toeplitz operators on bounded symmetric domains of arbitrary rank, we define a Hilbert quotient module corresponding to partitions of length $1$ and prove that it belongs to the Macaev class ${\mathcal{L}}^{n,\infty}$. We next obtain an explicit formula for the Dixmier trace of Toeplitz commutators in terms of the underlying boundary geometry.

preprint2015arXiv

Highly-sensitive detection of the lattice distortion in single bent ZnO nanowires by second-harmonic generation microscopy

Nanogenerators based on ZnO nanowires (NWs) realize the energy conversion at nanoscale, which are ascribed to the piezoelectric property caused by the lattice distortion of the ZnO NWs. The lattice distortion can significantly tune the electronic and optical properties, and requires a sensitive and convenient measurement. However, high-resolution transmission electron microscopy (HRTEM) technique provides a limited sensitivity of 0.01 nm on the variation of the lattice spacing and requires vacuum conditions. Here we demonstrate a highly-sensitive detection of the lattice distortion in single bent ZnO NWs by second-harmonic generation (SHG) microscopy. As the curvature of the single bent ZnO NW increases to 21 mm-1 (<4% bending distortion), it shows a significant decrease (~70%) in the SHG intensity ratio between perpendicular and parallel excitation polarization with respect to c-axis of ZnO NWs. Importantly, the extraordinary non-axisymmetrical SHG polarimetric patterns are also observed, indicating the twisting distortion around c-axis of ZnO NWs. Thus, SHG microscopy provides a sensitive all-optical and non-invasive method for in situ detecting the lattice distortion under various circumstances.

preprint2015arXiv

Ideal gas behavior of rotamerically defined conformers in native globular proteins

Protein conformational transitions, which are essential for function, may be driven either by entropy or enthalpy when molecular systems comprising solute and solvent molecules are the focus. Revealing thermodynamic origin of a given molecular process is an important but difficult task, and general principles governing protein conformational distributions remain elusive. Here we demonstrate that when protein molecules are taken as thermodynamic systems and solvents being treated as the environment, conformational entropy is an excellent proxy for free energy and is sufficient to explain protein conformational distributions. Specifically, by defining each unique combination of side chain torsional state as a conformer, the population distribution (or free energy) on an arbitrarily given order parameter is approximately a linear function of conformational entropy. Additionally, span of various microscopic potential energy terms is observed to be highly correlated with both conformational entropy and free energy. Presently widely utilized free energy proxies, including minimum potential energy, average potential energy terms by themselves or in combination with vibrational entropy\cite, are found to correlate with free energy rather poorly. Therefore, our findings provide a fundamentally new theoretical base for development of significantly more reliable and efficient next generation computational tools, where the number of available conformers,rather than poential energy of microscopic configurations, is the central focus. We anticipate that many related research fields, including structure based drug design and discovery, protein design, docking and prediction of general intermolecular interactions involving proteins, are expected to benefit greatly.

preprint2015arXiv

Light Higgsino from $A_t$ Dilemma in Rare $B$-decays

In the Minimal Supersymmetric Standard Model (MSSM), large chiral symmetry breaking term $A_t$, which plays an important role in Higgs mass, may significantly contribute in flavor changing neutral current (FCNC) processes $B\to X_{s}γ$ and $B_{s}\to μ^{+}μ^{-}$. Though the above processes can both be categorized as $b\to s$ transitions, the two rare decays behave completely different in MSSM. With an on-shell photon in the final state, helicity of initial state $b$-quark and final state $s$-quark must be flipped in $B\to X_{s}γ$, which corresponds to the simultaneous breaking of chiral symmetry and electroweak symmetry. The common feature is shared by fermion mass generation. Same as radiative mass generation in MSSM, Peccei-Quinn and $R$ symmetry breaking contributions, for example from a Higgsino-stop loop when $μA_t<0$, may significantly cancel the contribution from charged Higgs and reduce the prediction of $B\to X_{s}γ$. For the latter process, including Babu-Kolda FCNC proportional to $μA_t$, $B_{s}\to μ^{+}μ^{-}$ is mediated by a scalar $H_d$ boson which corresponds to chiral symmetry breaking. In addition, as a result of interference among the Higgs extension sector and $Z$ contributions, in the region of $μA_t <0$ which is favored by $B\to X_{s}γ$, there may simultaneously exist large enhancement in $B_{s}\to μ^{+}μ^{-}$. However, we still find viable parameter region with light Higgsino of a few hundreds GeV when charged Higgs contribution is not negligible with $M_A$ of 400 GeV.

preprint2015arXiv

Measuring the Aharonov-Anandan phase in photonics

The Aharonov-Anandan phase is a description of the geometric nature in non-adiabatic cyclic evolutions of quantum states. Here we report on a measurement of the Aharonov-Anandan phase in photonics. We consider a time-independent quantum driven harmonic oscillator that is initially prepared at the vacuum state. We utilize evanescently coupled waveguides to realize this physical model and achieve a measurement of the Aharonov-Anandan phase via integrated interferometry.

preprint2015arXiv

Pressure-induced reversible phase transition and amorphization of CH$_3$NH$_3$PbI$_3$

Recent advances of highly efficient solar cells based on organic-inorganic halide perovskites have triggered intense research efforts to establish the fundamental properties of these materials. In this work, we utilized diamond anvil cell to investigate the pressure-induced structural and electronic transformations in methylammonium lead iodide (CH$_3$NH$_3$PbI$_3$) up to 7 GPa at room temperature. The synchrotron X-ray diffraction experiment show that the sample transformed from tetragonal to orthorhombic phase at 0.3 GPa and amorphized above 4 GPa. Further high pressure IR spectroscopy experiments illustrated the high pressure behavior of organic (CH$_3$NH$_3$)$^+$ cations. We also analyzed the pressure dependence of the band gap energy based on the optical absorption and photoluminescence (PL) results. Moreover, all the observed changes were fully reversible when the pressure was completely released. Our in situ high pressure studies provide essential information for the intrinsic properties and stability of organic-inorganic halide perovskites, which significantly affect the performance of perovskite solar cells.

preprint2015arXiv

Smoothing methods comparison for CMB E- and B-mode separation

The anisotropies of the B-mode polarization in the cosmic microwave background radiation play a crucial role for the study of the very early Universe. However, in the real observation, the mixture of the E-mode and B-mode can be caused by the partial sky surveys, which must be separated before applied to the cosmological explanation. The separation method developed by Smith (\citealt{PhysRevD.74.083002}) has been widely adopted, where the edge of the top-hat mask should be smoothed to avoid the numerical errors. In this paper, we compare three different smoothing methods, and investigate the leakage residuals of the E-B mixture. We find that, if the less information loss is needed and the smaller region is smoothed in the analysis, the \textit{sin}- and \textit{cos}-smoothing methods are better. However, if we need a clean constructed B-mode map, the larger region around the mask edge should be smoothed. In this case, the \textit{Gaussian}-smoothing method becomes much better. In addition, we find that the leakage caused by the numerical errors in the \textit{Gaussian}-smoothing method mostly concentrates on two bands, which is quite easy to be reduced for the further E-B separations.

preprint2015arXiv

Supplementary Material to: Realization of Three-port Spring Networks with Inerter for Effective Mechanical Control

This is a supplementary material to "Realization of three-port spring networks with inerter for effective mechanical control" [1], which provides the detailed proofs of some results. For more background information, refer to [2]-[32] and references therein.

preprint2015arXiv

Supplementary Material to: Realizations of a Special Class of Admittances with Strictly Lower Complexity than Canonical Forms

This is supplementary material to "Realizations of a special class of admittances with strictly lower complexity than canonical forms" [1], which presents the detailed proofs of some results. For more background information, refer to [2]-[22] and references therein.

preprint2014arXiv

Characterizing the Impact of the Workload on the Value of Dynamic Resizing in Data Centers

Energy consumption imposes a significant cost for data centers; yet much of that energy is used to maintain excess service capacity during periods of predictably low load. Resultantly, there has recently been interest in developing designs that allow the service capacity to be dynamically resized to match the current workload. However, there is still much debate about the value of such approaches in real settings. In this paper, we show that the value of dynamic resizing is highly dependent on statistics of the workload process. In particular, both slow time-scale non-stationarities of the workload (e.g., the peak-to-mean ratio) and the fast time-scale stochasticity (e.g., the burstiness of arrivals) play key roles. To illustrate the impact of these factors, we combine optimization-based modeling of the slow time-scale with stochastic modeling of the fast time scale. Within this framework, we provide both analytic and numerical results characterizing when dynamic resizing does (and does not) provide benefits.

preprint2014arXiv

Light Top Squark in Precision Top Quark Sample

The uncertainty of $t\bar{t}$ production cross section measurement at LHC is at a-few-percent level which still allows the stop pair production $\tilde{t}\tilde{t}^{*}$ with identical final states $2b+\ell+nj+{E}_{T}$. In this paper, we attempt to use the existing measurement of $W$-polarization in top quark decay to improve the distinction between stop and top quark states. We apply the ATLAS $χ^2$ method of $W$-polarization measurement in semi-leptonic $t\bar{t}$ final state to semi-leptonic stop pair samples and study its prediction. We find that the faked top events from stop mostly contribute to the left-handed polarized $W$ due to the reconstruction. The benchmark point with maximal contribution to top events only changes $F_{L}$ by 1%. After comparing with the current experiments, we conclude that the current measurement of $W$-polarization in $t$ decay cannot exclude the light stop scenario.

preprint2014arXiv

Polarizations of $χ_{c1}$ and $χ_{c2}$ in prompt production at the LHC

Prompt $χ_c$ production at hadron colliders may provide a unique test for the color-octet mechanism in nonrelativistic QCD. We present an analysis for the polarization observables of $χ_{c1}$ and $χ_{c2}$ at next-to-leading order in $α_S$, and propose to measure them at the LHC, which is expected to be important for testing the validity of NRQCD.

preprint2014arXiv

Pure leptonic proposal to $W^{+}W^{-}$ excess and neutrino mass generation

We investigate the TeV models for neutrino mass generation as candidate models to explain the recent 2$σ$ excess of leptonic $W^{+}W^{-}$ pair production at LHC. Several models with singly charged exotic states that may explain the excess require light masses completely excluded by LEP experiments. One possible model with new lepton doublets can fit the observation and evade all direct search bounds but with tuned Yukawa structure to satisfy lepton universality. The new exotic leptons $L^{\pm}$ decay into $L^{\pm}\to \ell^{\pm} ϕ$ where $ϕ$ is a light singlet scalar of $\cal O$(MeV) that decays into neutrinos. Drell-Yan production of $L^{+}L^{-}\to \ell^{+}\ell^{-}+\cancel{E}_{T}$ fits the excess and $L^{\pm}L^{0}\to \ell^{\pm}+\cancel{E}_{T}$ is completely buried in SM background.

preprint2013arXiv

Comprehensive Constraints on a Spin-3/2 Singlet Particle as a Dark Matter Candidate

We consider the proposal that dark matter (DM) is composed of a spin-3/2 particle that is a singlet of the standard model (SM). Its leading effective interactions with ordinary matter involve a pair of their fields and a pair of SM fermions, in the form of products of chiral currents. We make a comprehensive analysis on possible phenomenological effects of the interactions in various experiments and observations. These include collider searches for monojet plus missing transverse energy events, direct detections of DM scattering off nuclei, possible impacts on the gamma rays and antiproton-to-proton flux ratio in cosmic rays, and the observed relic density. The current data already set strong constraints on the effective interactions in a complementary manner. The constraint from collider searches is most effective at a relatively low mass of DM, and the antiproton-to-proton flux ratio offers the best bound for a heavy DM, while the spin-independent direct detection is the best in between. For DM mass of order 10 GeV to 1 TeV, the effective interaction scale is constrained to be typically above a few tens TeV.

preprint2013arXiv

Flavor dependence of annihilation parameters in QCD factorization

For $B_{d,s} \to π^\mp K^\pm$ and $K^{(\ast)} K^{(\ast)}$ decays, the flavor symmetry breaking effects may be particularly small since the final state interactions should be the same between the corresponding $B_d$ and $B_s$ decays due to the charge conjugation symmetry of the final states. This is consistent with the newly measured direct CP asymmetry of $B_s \to π^+ K^-$. These decays are thus supposed to be important in testing the Standard Model and in probing new physics effects. However, the observation of pure annihilation decay $B_s \to π^+ π^-$ appears to imply a large annihilation scenario with $ρ_A \sim 3$, in contrast to the case of $ρ_A \sim 1$ in $B_{u,d}$ decays in the framework of QCD factorization. This seems to indicate unexpectedly large flavor symmetry breaking effects between the annihilation amplitudes of $B_s$ and $B_{u,d}$ decays. This apparent contradiction could be resolved by noticing that there is a priori no reason to justify the common practice of assuming the universality of annihilation parameters for different Dirac structures of effective operators. We then argue that, for $B_{d,s} \to π^\mp K^\pm$ decays, the flavor symmetry breaking effects of annihilation amplitudes have all been included in the initial state decay constants and are thus small. But the flavor symmetry breaking effects in $B_{d,s} \to K^{(\ast)} K^{(\ast)}$ decays are likely to be much larger, as part of the annihilation topologies of $B_s \to K K$ decay could be related to $B_s \to π^+ π^-$ decay. Therefore when new physics effects are searched for in these decay channels, care must be taken to consider the potentially large flavor symmetry breaking effects in more details.

preprint2013arXiv

Higgs Precision Measurements and Flavor Physics: A Supersymmetric Example

[Review for "Chinese Science Bulletin" and CEPC+SPPC Proposal] Rare decays in flavor physics often suffer from Helicity suppress and Loop suppress. Helicity flip is a direct consequence of chiral $U(3)$ symmetry breaking and electroweak symmetry breaking. The identical feature is also shared by the mass generation of SM fermions. In this review, we use MSSM as an example to illustrate an explicit connection between bottom Yukawa coupling and rare decay process of $b\to sγ$. We take a symmetry approach to study the common symmetry breaking in supersymmetric correction to bottom quark mass generation and $b\to sγ$. We show that Large Peccei-Quinn symmetry breaking effect and $R$-symmetry breaking effect required by $b\to sγ$ inevitably lead to significant reduction of bottom Yukawa ${y}_{b}$. To compromise the reduction in $b\bar{b}$, a new decay is also needed to keep the Higgs total width as the SM value.

preprint2013arXiv

Is polarization effect visible in leptonic SUSY searches?

On-shell effective theory approach has been widely used in search of various supersymmetric signals, in particular, gluino/squark pairs with long cascade decay chains in which complete matrix element calculations may encounter over-20 dimensional integrations. On the other hand, leptons from polarized chargino decays may show significant boost or anti-boost effect in some scenarios and simulation without polarization information may underestimate or overestimate the lepton $p_{T}$ cut efficiencies in the first place. We study the polarization effects in supersymmetry searches of multi-jets plus leptons final states. We find it justifiable for first two generations to only use on-shell effective theories. While for measurements related to third generation squarks, for instance, polarization effect of charginos from stop may reduce the lepton $p_{T}$ cut efficiencies in cross section measurements by $25\%$ when slepton contributions dominate in chargino decay or $W$ are on-shell. The signal is then underestimated if only on-shell effective theory approach is taken in simulation of signal and the real bound on squark/gluino should be more stringent.

preprint2013arXiv

Laterally emitted surface second harmonic generation in a single ZnTe nanowire

We report a direct observation on the unique laterally emitted surface second harmonic generation (SHG) in a single ZnTe nanowire. The highly directional surface SHG signal that polarizes along the direction vertical to the nanowire growth axis, is significantly larger than the bulk SHG contribution, indicating a high efficiency of surface SHG. Two strong SHG peaks are observed on both sides of the nanowire surfaces in the far-field scanning images, which is further supported by FDTD simulations, demonstrating that the unique laterally emitted signal is ascribed to surface SHG in the ZnTe nanowire. The surface SHG in a single ZnTe nanowire with unique lateral emission and high conversion efficiency shows great potential applications in short-wavelength nanolasers, nonlinear microscopy and polarization dependent photonic integrating.

preprint2013arXiv

Revisit to Non-decoupling MSSM

Dipole operator $\bar{s}σ_{μν}F^{μν}b$ requires the helicity flip in the involving quark states thus the breaking of chiral $U(3)_{Q}\times U(3)_{d}$. On the other hand, the $b$-quark mass generation is also a consequence of chiral $U(3)_{Q}\times U(3)_{d}$ symmetry breaking. Therefore, in many models, there might be strong correlation between the $b\to sγ$ and $b$ quark Yukawa coupling. We use non-decoupling MSSM model to illustrate this feature. The light Higgs boson may evade the direct search experiments at LEPII or Tevatron while the 125 GeV Higgs-like boson is identified as the heavy Higgs boson in the spectrum. A light charged Higgs is close to the heavy Higgs boson which is of 125 GeV and its contribution to $b\to s γ$ requires large supersymmetric correction with large PQ and $R$ symmetry breaking. The large supersymmetric contribution at the same time significantly modifies the $b$ quark Yukawa co upling. With combined flavor constraints $B\to X_{s}γ$ and $B_{s}\to μ^{+}μ^{-}$ and direct constraints on Higgs properties, we find best fit scenarios with light stop of $\cal O$(500 GeV), negative $A_{t}$ around -750 GeV and large $μ$-term of 2-3 TeV. In addition, reduction in $b\bar{b}$ partial width may also result in large enhancement of $ττ$ decay branching fraction. Large parameter region in the survival space under all bounds may be further constrained by $H\to ττ$ if no excess of $ττ$ is confirmed at LHC. We only identify a small parameter region with significant $H\to hh$ decay that is consistent with all bounds and reduced $ττ$ decay branching fraction.

preprint2012arXiv

$Υ(1S)$ prompt production at the Tevatron and LHC in nonrelativistic QCD

With nonrelativistic QCD factorization, we calculate the $Υ(1S)$ prompt production at hadron colliders at next-to-leading order in $α_s$. In addition to the color-singlet contribution, color-octet channels (especially the P-wave channel) up to $O(v^4)$ are all considered. Aside from direct production, the feed-down contributions from higher excited S-wave and P-wave $b\bar b$ states to $Υ(1S)$ production are also included. We use the potential model estimates as input for color-singlet long-distance matrix elements (LDMEs). While for color-octet contributions, we find they can be approximately described by three LDMEs: $\mo{}{3}{S}{1}{8}$, $\mo{}{1}{S}{0}{8}$ and $\mo{}{3}{P}{0}{8}$. By fitting the Tevatron data we can determine some linear combinations of these LDMEs, and then use them to predict $Υ(1S)$ production at the LHC. Our predictions are consistent with the new experimental data of CMS and LHCb.

preprint2012arXiv

Entropically Dominant State of Proteins

Configurational entropy is an important factor in the free energy change of many macromolecular recognition and binding processes, and has been intensively studied. Despite great progresses that have been made, the global sampling remains to be a grand challenge in computational analysis of relevant processes. Here we propose and demonstrate an entropy estimation method that is based on physical partition of configurational space and can be readily combined with currently available methodologies. Tests with two globular proteins suggest that for flexible macromolecules with large and complex configurational space, accurate configurational entropy estimation may be achieved simply by considering the entropically most important subspace. This conclusion effectively converts an exhaustive sampling problem into a local sampling one, and defines entropically dominant state for proteins and other complex macromolecules. The conceptional breakthrough is likely to positively impact future theoretical analysis, computational algorithm development and experimental design of diverse chemical and biological molecular systems.

preprint2012arXiv

J/psi polarization at hadron colliders in nonrelativistic QCD

With nonrelativistic QCD factorization, we present a full next-to-leading order computation of the polarization observable for $J/psi$ production at hadron colliders including all important Fock states, i.e. $^3S_1[1,8]$, $^1S_0[8]$, and $^3P_J[8]$. We find the $^3P_J[8]$ channel contributes a positive longitudinal component and a negative transverse component. So the $J/psi$ polarization puzzle may be understood as the transverse components canceling between $^3S_1[8]$ and $^3P_J[8]$ channels, which results in mainly the unpolarized (even slightly longitudinally polarized) $J/psi$. This may give a possible solution to the long-standing $J/psi$ polarization puzzle. Predictions for $J/psi$ polarization at the LHC are also presented.

preprint2012arXiv

Searching SUSY Leptonic Partner at the CERN LHC

Motivated by the observed excess of the di-photon signal in Higgs searches, $σ_{γγ}/σ_{\rm SM}\simeq 1.5$, we argue that models with enhanced $Γ(h\to γγ)$ alone are the most favorable scenarios when the latest LHC/Tevatron results are all taken into account. We study the phenomenology of a supersymmetric scenario of light stau first proposed by \textit{Carena et. al. \cite{carlos}} that predicts a 125 GeV SM-like Higgs boson with enhanced diphoton decay through light stau loops. Since it is extremely challenging to search the Drell-Yan stau pair at the LHC due to the small production rate, we focus on the parameter space with enhanced production of inclusive stau pairs, in particular, via $b\bar{b}$ fusion or gaugino pairs. We study its phenomenology in both pure leptonic tau $τ^\pm_\ell$ channels and hadronic tau tagged $τ_h$ channels. We find the same-sign dilepton from $\tildeχ^\pm_1 \tildeχ^0_2 \to τ^\pm_\ell τ^\pm_\ell+X$ may significantly improve the discovery potential with even 7--8 TeV LHC of ${\cal O}(30 \text{fb}^{-1})$ data. In the case of hadronic tau pair, we use the final state $j+τ_h τ_h +\cancel{E}_T$ to search and find that even with the most optimistic region of $M_2\sim 200-300$ GeV, it requires at least 50 fb$^{-1}$ data of 14 TeV LHC to reach a significance of 3.5 $σ$. Therefore, we conclude it is difficult to claim discovery only through hadronic tau based on the data by the 2012 shut-down. 5 $σ$ reach for our most optimistic region then requires 100 fb$^{-1}$ data with 14 TeV running.

preprint2012arXiv

Some Remarks On Essentially Normal Submodules

Given a *-homomorphism $σ: C(M)\to \mathscr{L}(\mathcal{H})$ on a Hilbert space $\mathcal{H}$ for a compact metric space $M$, a projection $P$ onto a subspace $\mathcal{P}$ in $\mathcal{H}$ is said to be essentially normal relative to $σ$ if $[σ(φ),P]\in \mathcal{K}$ for $φ\in C(M)$, where $\mathcal{K}$ is the ideal of compact operators on $\mathcal{H}$. In this note we consider two notions of span for essentially normal projections $P$ and $Q$, and investigate when they are also essentially normal. First, we show the representation theorem for two projections, and relate these results to Arveson's conjecture for the closure of homogenous polynomial ideals on the Drury-Arveson space. Finally, we consider the relation between the relative position of two essentially normal projections and the $K$ homology elements defined for them.

preprint2012arXiv

What if bb does not dominate the decay of the Higgs-like boson?

The dominant decay mode of standard model Higgs at 126 GeV $b\bar{b}$ suffers from severe SM background at the LHC even in associated productions $Wh_{\rm SM}$ or $Z h_{\rm SM}$. The precision measurement of BR($ϕ\to b\bar{b})$ requires more data to reduce its large error bar. We investigate the possibility of this channel with largest uncertainty not dominating the decay of Higgs-like boson discovered at the LHC. In such scenarios, the Higgs signal shows highly suppressed $b\bar{b}$, slightly reduced $τ^{+}τ^{-}$ and moderately enhanced gauge bosons comparing with the SM predictions. The model requires two different sources of electroweak symmetry breaking and radiative correction to $m_{b}$ strongly enhanced. However, large reduction in $b\bar{b}$ usually results large enhancement in $τ^{+}τ^{-}$ mode in particular. The reduction of $τ^{+}τ^{-}$ therefore implies that a new decay mode is inevitable. We find that a non-decoupling MSSM Higgs decay into lighter Higgs $H\to hh$ may fit the signature. Here, MSSM $H$ is identified as the 126 GeV resonance while $h$ is below $M_{H}/2$ and can evade the direct search bound at LEPII and Tevatron. Large $PQ$ and $R$ symmetry breaking effects mediated by strong interaction can strongly enhance radiative corrections in $m_b$. However, the scenario can only be realized in highly fine-tuned parameter region where $G_{Hhh}$ is tiny. Nevertheless, we discuss the discovery potential of this highly fine-tuned $H\to hh$ at the LHC.

preprint2011arXiv

A complete NLO calculation of the $J/ψ$ and $ψ'$ production at hadron colliders

A complete next-to-leading order (NLO) calculation in $α_s$ for the $\jpsi$ and $\psip$ prompt production at the Tevatron, LHC, and RHIC in nonrelativistic QCD is presented. We argue that the next-to-next-to-leading order (NNLO) color-singlet contribution may not be so important as to resolve the large discrepancy between theory and experiment in $\jpsi$ large $p_T$ production cross sections. Therefore, a complete NLO calculation, including both color-singlet and color-octet contribution, is necessary and essential to give a good description for $\jpsi$ and $\psip$ production. We also study the methods to fit the long-distance matrix elements using either two linear combined matrix elements or three matrix elements, and find these two methods can give consistent results. Compared with the measurements at the LHC and RHIC for prompt $\jpsi$ and $\psip$ production, our predictions are found to agree with all data. In particular, the recently released large $p_T$ data (up to 60-70 GeV) at the LHC are in good agreement with our predictions. Our results imply that the universality of color-octet matrix elements holds approximately in charmonium hadroproduction, when one uses fixed order perturbative calculation to describe data (the data in small $p_T$ region are not included). Our work may provide a new test for the universality of color-octet matrix elements, and the color-octet mechanism in general.

preprint2011arXiv

A harmonic analysis approach to essential normality of principal submodules

Guo and the second author have shown that the closure $[I]$ in the Drury-Arveson space of a homogeneous principal ideal $I$ in $\mathbb{C}[z_1,...,z_n]$ is essentially normal. In this note, the authors extend this result to the closure of any principal polynomial ideal in the Bergman space. In particular, the commutators and cross-commutators of the restrictions of the multiplication operators are shown to be in the Schatten $p $-class for $p>n$. The same is true for modules generated by polynomials with vector-valued coefficients. Further, the maximal ideal space $X_I$ of the resulting $C^\ast$-algebra for the quotient module is shown to be contained in $Z(I)\cap \partial\mathbb{B}_n$, where $Z(I)$ is the zero variety for $I$, and to contain all points in $\partial\mathbb{B}_n$ that are limit points of $Z(I)\cap \mathbb{B}_n$. Finally, the techniques introduced enable one to study a certain class of weight Bergman spaces on the ball.

preprint2011arXiv

A Revisit to Top Quark Forward-Backward Asymmetry

We analyze various models for the top quark forward-backward asymmetry ($A^t_{FB}$) at the Tevatron, using the latest CDF measurements on different $A^t_{FB}$s and the total cross section. The axigluon model in Ref. \cite{paul} has difficulties in explaining the large rapidity dependent asymmetry and mass dependent asymmetry simultaneously and the parameter space relevant to $A^t_{FB}$ is ruled out by the latest dijet search at ATLAS. In contrast to Ref. \cite{cp}, we demonstrate that the large parameter space in this model with a $U(1)_d$ flavor symemtry is not ruled out by flavor physics. The $t$-channel flavor-violating $Z^{\prime}$ \cite{hitoshi}, $W^{\prime}$\cite{waiyee} and diquark \cite{tim} models all have parameter regions that satisfy different $A_{FB}$ measurements within 1 $σ$. However, the heavy $Z^{\prime}$ model which can be marginally consistent with the total cross section is severely constrained by the Tevatron direct search of same-sign top quark pair. The diquark model suffers from too large total cross section and is difficult to fit the $t \bar{t}$ invariant mass distribution. The electroweak precision constraints on the $W'$ model based on $Z'$-$Z$ mixings is estimated and the result is rather weak ($m_{Z'} > 450$ GeV). Therefore, the heavy $W^{\prime}$ model seems to give the best fit for all the measurements. The $W^{\prime}$ model predicts the $t\bar{t}+j$ signal from $tW^{\prime}$ production and is 10%-50% of SM $t\bar{t}$ at the 7 TeV LHC. Such $t+j$ resonance can serve as the direct test of the $W^{\prime}$ model.

preprint2011arXiv

Can Up FCNC solve the $ΔA_{CP}$ puzzle?

We investigate the attempt using flavor violation gauge interaction in the up sector to explain the LHCb recently observed large $ΔA_{CP}$ ($A_{CP}(D^{0}\to K^{+}K^{-})-A_{CP}(D^{0}\to π^{+}π^{-})$). We study an Abelian model that only right-handed up quarks is charged under it and the 1-3 coupling is maximized. The simultaneous 1-3 2-3 mixing is realized by a quark mixing of 1-2 generation. Given the easy identification of top quark, the model can be directly tested by $ΔF=1$ and $ΔF=2$ processes at the hadron colliders as associated top production $g c \to t Z^{\prime}$ or same-sign top scattering $u u\to t t$. The direct search bounds are still consistent with the assumption that $ut$ and $ct$ couplings are equal but the same-sign top scattering bound is expected to be reached very soon. However, since there is no CKM-like suppression, the corresponding parameter space for generating $ΔA_{CP}$ is completely excluded by the $D^{0}-\bar{D}^{0}$ mixing. We conclude that the up FCNC type models cannot explain the $ΔA_{CP}$ while to be consistent with the $D^{0}-\bar{D}^{0}$ mixing constraint at the same time. On the other hand, a model as SM with fourth family extension has better chance to explain the large $ΔA_{CP}$ consistently.

preprint2011arXiv

Higgs search and flavor-safe fermion mass generation

We study a scenario of electroweak symmetry breaking where the weak gauge boson masses arises significantly from a fermiophobic source. To minimize flavor violation, the fermion mass generation is still due to one light doublet scalar. One of the realizations is the Bosonic Technicolor model. In these scenarios, the Yukawa couplings between the light scalar and the standard model fermions are in general enhanced while the couplings between the light scalar and weak gauge bosons are reduced. Even though the flavor violation induced by the neutral scalar at the tree level can be avoided, the charged scalar state inevitably mediate flavor changing neutral current processes. With the enhancement in the Yukawa couplings, one expects serious constraints of such models from flavor violating effects. We find that the most severe bound comes from neutral meson mixing of $B^{0}_{d}-\bar{B^{0}_{d}}$. Large parameter space is excluded if the weak gauge boson mass generation is dominated by the fermophobic sector. However, the correlation between the Yukawa coupling and charged scalar mass show that a factor of two enhancement in top Yukawa coupling is still allowed for charged scalar heavier than 500 GeV. We use this as a benchmark point to study the phenomenology of the light scalar. It is interesting that the destructive interference between the top quark loop and the $W$-boson loop in the di-photon channel becomes significant and makes the channel negligible. In the light scalar region, the search becomes much more challenging than the conventional SM Higgs boson.

preprint2011arXiv

Inverse seesaw in supersymmetry

We study a mechanism where tiny neutrino masses arise only from radiative contribution in a supersymmetric model. In each generation, the tree-level light neutrino mass is rotated away by introducing a second SM singlet $s_{L}$ that forms a Dirac mass term with the right-handed neutrino $n_{R}$. Even with non-zero Majorana neutrino mass for the right-handed neutrinos $M_{R} \bar{n^{c}_{R}} n_{R}$, the lightest neutrino remains massless at tree level due to an approximate symmetry as $U(1)_{ν-s}$. Holomorphic feature of superpotential ensures that the Majorana neutrino masses $M_{R} \bar{n^{c}_{R}} n_{R}$ and $M^{*}_{R} \bar{s^{c}_{L}} s_{L}$ are not generated simultaneously. However, the $U(1)_{ν-s}$ is not respected by the SM gauge interactions or interaction with Higgs. Consequently, tiny neutrino masses arise from radiative contributions. It is also shown that the right-handed neutrino Majorana mass $M_{R}$ can be at $\cal O$(KeV) to obtain the proper light neutrino mass.

preprint2011arXiv

J/psi (psi') production at the Tevatron and LHC at O(α_s^4v^4) in nonrelativistic QCD

We present a complete evaluation for $\jpsi(\psip)$ prompt production at the Tevatron and LHC at next-to-leading order in nonrelativistic QCD, including color-singlet, color-octet, and higher charmonia feeddown contributions. The short-distance coefficients of $\pj$ at next-to-leading order are found to be larger than leading order by more than an order of magnitude but with a minus sign at high transverse momentum $p_T$. Two new linear combinations of color-octet matrix elements are obtained from the CDF data, and used to predict $\jpsi$ production at the LHC, which agrees with the CMS data. The possibility of $\sa$ dominance and the $\jpsi$ polarization puzzle are also discussed.

preprint2011arXiv

QCD corrections to e^+ e^- to J/ψ(ψ(2S))+χ_{cJ} (J=0,1,2) at B Factories

We analytically calculate the cross sections of double charmonium production in $e^+ e^- \to J/ψ(ψ(2S))χ_{cJ}$ (J=0,1,2) at next-to-leading order (NLO) in $α_s$ in nonrelativistic QCD, and confirm factorization of these processes. In contrast to $χ_{c0}$ production, for which the NLO correction is large and positive, the NLO corrections for $χ_{c1,2}$ production can be negative, resulting in decreased $K$ factors of 0.91 and 0.78 for J=1 and 2 respectively when $μ= 2 m_{c}$. Consequently, the NLO QCD corrections markedly enlarge the difference between cross sections of $χ_{c0}$ and $χ_{c1,2}$. This may explain why $e^+ e^- \to J/ψ(ψ(2S))χ_{c0}$ but not $e^+ e^- \to J/ψ(ψ(2S))χ_{c1,2}$ is observed experimentally. Moreover, for $J/ψ(ψ(2S))χ_{c1,2}$, the NLO QCD corrections substantially reduce the $μ$ dependence and lead to predictions with small theoretical uncertainties.

preprint2011arXiv

QCD radiative corrections to $χ_{cJ}$ production at hadron colliders

To clarify the outstanding problem in charmonium production that existing theories cannot explain the observed cross sections of $χ_{cJ}$ (J=0,1,2) and ratio $R_{χ_c}=σ_{χ_{c2}}/σ_{χ_{c1}}\approx 0.75$ (in contrast to the spin counting value 5/3) at the Tevatron, we study the complete next-to-leading order radiative corrections in nonrelativistic QCD, and find next-to-leading order contributions of $^3P_J^{[1]}$ are more important than leading order at high $p_T$, and $^3P_1^{[1]}$ decreases slower than $^3P_2^{[1]}$, implying a natural explanation for the $R_{χ_c}$ puzzle. By fitting $R_{χ_c}$, the predicted cross sections of $χ_{cJ}$ are found to agree with data. The result indicates color-octet contribution is crucially needed, thus providing a unique test for heavy quarkonium production mechanisms. Feed-down contributions of $χ_{cJ}$ to prompt $J/ψ$ production are estimated to be substantial, about $30-40%$ at $p_T=20$ GeV. Production of $χ_{cJ}$ (J=0,1,2) at the LHC is also predicted.

preprint2011arXiv

Reducing subspaces for analytic multipliers of the Bergman space

We answer affirmatively the problem left open in \cite{DSZ,GSZZ} and prove that for a finite Blaschke product $ϕ$, the minimal reducing subspaces of the Bergman space multiplier $M_ϕ$ are pairwise orthogonal and their number is equal to the number $q$ of connected components of the Riemann surface of $ϕ^{-1}\circ ϕ$. In particular, the double commutant $\{M_ϕ,M_ϕ^\ast\}'$ is abelian of dimension $q$. An analytic/arithmetic description of the minimal reducing subspaces of $M_ϕ$ is also provided, along with a list of all possible cases in degree of $ϕ$ equal to eight.

preprint2011arXiv

RNASEQR - A streamlined and accurate RNA-seq sequence analysis program

The paper has been withdrawn by the authors.

preprint2010arXiv

Axigluon as Possible Explanation for $p\bar{p} \to t\bar{t}$ Forward-Backward Asymmetry

A flavor-nonuniversal chiral color model is introduced. It is used for comparison to the recent data on $\bar{p} p \to \bar{t} t$. We concluded that the data are consistent with interpretation as an axigluon exchange within 1$σ$ and a unique rise and fall behavior is predicated with regard to the asymmetry $A^t_{FB}$ as a function of $t \bar{t}$ invariant mass, which can distinguish our model from others before one discovers the axigluon resonance. Further aspects of the model are discussed.

preprint2010arXiv

Invisible Higgs decay with B\to Kν\barν constraint

If the Higgs boson were the only particle within the LHC accessible range, precision measurement of the Higgs's properties would play a unique role in studying electroweak symmetry breaking as well as possible new physics. We try to use low energy experiments such as rare B decay to constrain a challenging decay mode of Higgs, in which a Higgs decays to a pair of light (\approx 1 \sim 2 GeV) SM singlet S and becomes invisible. By using the current experimental bound of rare decay B\to Kν\barν and computing the contribution of B\to K SS to (the) B\to K+\cancel{E}, we obtain an upper bound on the Higgs coupling to such light singlet. It is interesting that the partial width of the invisible decay mode h\to SS by taking the upper bound value of coupling is at a comparable level with h\to WW/ZZ or WW^(*) decay modes, making the Higgs identifiable but with a different predicted decay BR from the standard model Higgs decay. It will then have an impact on precision measurement of the Higgs's properties. We also study the implication for cosmology from such a light singlet and propose a solution to the potential problem.

preprint2010arXiv

Multivariate dependence and genetic networks inference

A critical task in systems biology is the identification of genes that interact to control cellular processes by transcriptional activation of a set of target genes. Many methods have been developed to use statistical correlations in high-throughput datasets to infer such interactions. However, cellular pathways are highly cooperative, often requiring the joint effect of many molecules, and few methods have been proposed to explicitly identify such higher-order interactions, partially due to the fact that the notion of multivariate statistical dependency itself remains imprecisely defined. We define the concept of dependence among multiple variables using maximum entropy techniques and introduce computational tests for their identification. Synthetic network results reveal that this procedure uncovers dependencies even in undersampled regimes, when the joint probability distribution cannot be reliably estimated. Analysis of microarray data from human B cells reveals that third-order statistics, but not second-order ones, uncover relationships between genes that interact in a pathway to cooperatively regulate a common set of targets.

preprint2010arXiv

Nearly Degenerate Gauginos and Dark Matter at the LHC

Motivated by dark-matter considerations in supersymmetric theories, we investigate in a fairly model-independent way the detection at the LHC of nearly degenerate gauginos with mass differences between a few GeV and about 30 GeV. Due to the degeneracy of gaugino states, the conventional leptonic signals are likely lost. We first consider the leading signal from gluino production and decay. We find that it is quite conceivable to reach a large statistical significance for the multi-jet plus missing energy signal with an integrated luminosity about 50 pb^-1 (50 fb^-1) for a gluino mass of 500 GeV (1 TeV). If gluinos are not too heavy, less than about 1.5 TeV, this channel can typically probe gaugino masses up to about 100 GeV below the gluino mass. We then study the Drell-Yan type of gaugino pair production in association with a hard QCD jet, for gaugino masses in the range of 100-150 GeV. The signal observation may be statistically feasible with about 10 fb^-1, but systematically challenging due to the lack of distinctive features for the signal distributions. By exploiting gaugino pair production through weak boson fusion, signals of large missing energy plus two forward-backward jets may be observable at a 4-6σlevel above the large SM backgrounds with an integrated luminosity of 100-300 fb^-1. Finally, we point out that searching for additional isolated soft muons in the range p_T ~3-10 GeV in the data samples discussed above may help to enrich the signal and to control the systematics. Significant efforts are made to explore the connection between the signal kinematics and the relevant masses for the gluino and gauginos, to probe the mass scales of the superpartners, in particular the LSP dark matter.

preprint2010arXiv

NLSP Gluino Search at the Tevatron and early LHC

We investigate the collider phenomenology of gluino-bino co-annihilation scenario both at the Tevatron and 7 TeV LHC. This scenario can be realized, for example, in a class of realistic supersymmetric models with non-universal gaugino masses and t-b-τYukawa unification. The NLSP gluino and LSP bino should be nearly degenerate in mass, so that the typical gluino search channels involving leptons or hard jets are not available. Consequently, the gluino can be lighter than various bounds on its mass from direct searches. We propose a new search for NLSP gluino involving multi-b final states, arising from the three-body decay \tilde{g}-> b\bar{b}\tildeχ_1^0. We identify two realistic models with gluino mass of around 300 GeV for which the three-body decay is dominant, and show that a 4.5 σobservation sensitivity can be achieved at the Tevatron with an integrated luminosity of 10 fb^{-1}. For the 7 TeV LHC with 50 pb^{-1} of integrated luminosity, the number of signal events for the two models is O(10), to be compared with negligible SM background event.

preprint2010arXiv

TeV scale horizontal gauge symmetry and its implications in B-physics

We propose a gauged $U(1)_{H}$ horizontal symmetry around TeV scale that is a subgroup of a $SU(3)_{H}$ horizontal gauge symmetry broken at ${\cal O}(10^{14}) \GeV$. The breaking generates right-handed Majorana neutrino masses through a $SU(3)_H$ sextet scalar. A particular Majorana right-handed neutrino mass matrix explicitly determines the remnant $U(1)_{H}$ at low energy which only couples to $b-s$ and $μ-τ$ in the gauge eigenstate. The dangerous $K-\bar{K}$, $D-\bar{D}$ mixing and $B_s \rightarrow μ^+ μ^-$ are kept to be safe because the relevant couplings are suppressed through high powers of small mixing angles in the fermion rotation matrix. Our analysis which applies to the general case shows that the Tevatron di-muon anomaly can be explained through the $B_{s}$ and $B_{d}$ mixing while keeping all the other experimental constraints within 90 \% C. L. For the $B$ meson decay, the $B_{s}\to μ^{\pm}τ^{\mp}$ is the leading leptonic decay channel which is several orders of magnitude below current experimental bound.

preprint2009arXiv

Explorations of the Top Quark Forward-Backward Asymmetry at the Tevatron

We consider the recent measurement of the top quark forward-backward asymmetry at the Fermilab Tevatron, which shows a discrepancy of slightly more than 2$σ$ compared to the SM prediction. We find that $t$-channel exchange of a color sextet or triplet scalar particle can explain the measurement, while leaving the cross section for $t \bar{t}$ production within measured uncertainties. Such particles have good discovery prospects by study of the kinematic structure of $t \bar{t}$+jets at the LHC.

preprint2009arXiv

Like-sign Di-lepton Signals in Higgsless Models at the LHC

We study the potential LHC discovery of the Z1 KK gauge boson unitarizing longitudinal W+W- scattering amplitude. In particular, we explore the decay mode Z1->t tbar along with Z1-> W+W- without specifying the branching fractions. We propose to exploit the associated production pp-> W Z1, and select the final state of like-sign dileptons plus multijets and large missing energy. We conclude that it is possible to observe the Z1 resonance at a 5 sigma level with an integrated luminosity of 100 inverse fb at the LHC upto 650 GeV for a dominant WW channel, and 560 GeV for a dominant ttbar channel.

preprint2009arXiv

Neutrino mass from a hidden world and its phenomenological implications

We propose a model of neutrino mass generation in extra dimension. Allowing a large lepton number violation on a distant brane spatially separated from the standard model brane, a small neutrino mass is naturally generated due to an exponential suppression of the messenger field in the 5D bulk. The model accommodates a large Yukawa coupling with the singlet neutrino (n_R) which may change the standard Higgs search and can simultaneously accommodate visible lepton number violation at the electroweak scale, which leads to very interesting phenomenology at the CERN Large Hadron Collider.

preprint2009arXiv

QCD radiative correction to color-octet $J/ψ$ inclusive production at B Factories

In nonrelativistic Quantum Chromodynamics (NRQCD), we study the next-to-leading order (NLO) QCD radiative correction to the color-octet $J/ψ$ inclusive production at B Factories. Compared with the leading-order (LO) result, the NLO QCD corrections are found to enhance the short-distance coefficients in the color-octet $J/ψ$ production $ e^+ e^-\to c \bar c (^3P_0^{(8)} {\rm or} ^3P_0^{(8)})g$ by a factor of about 1.9. Moreover, the peak at the endpoint in the $J/ψ$ energy distribution predicted at LO can be smeared by the NLO corrections, but the major color-octet contribution still comes from the large energy region of $J/ψ$. By fitting the latest data of $σ(e^{+}e^{-}\to J/ψ+X_{\mathrm{non-c\bar{c}}})$ observed by Belle, we find that the values of color-octet matrix elements are much smaller than expected earlier by using the naive velocity scaling rules or extracted from fitting experimental data with LO calculations. As the most stringent constraint by setting the color-singlet contribution to be zero in $e^{+}e^{-}\to J/ψ+X_{\mathrm{non-c\bar{c}}}$, we get an upper limit of the color-octet matrix element, $<0| {\cal O}^{J/ψ}[{}^1S_0^{(8)}]|0> + 4.0 <0| {\cal O}^{J/ψ} [{}^3P_0^{(8)}]|0>/m_c^2 <(2.0 \pm 0.6)\times 10^{-2} {\rm GeV}^3$ at NLO in $α_s$.

preprint2008arXiv

GeV Majorana Neutrinos in Top-quark Decay at the LHC

We explore the ΔL=2 same-sign dilepton signal from top-quark decay via a Majorana neutrino at the LHC in the top anti-top pair production samples. The signature is same-sign dilepton plus multi-jets with no significant missing energy. The most optimistic region lies where the Majorana neutrino mass is between 15-65 GeV. For 300 fb^-1 integrated luminosity, it is possible to probe S_{ij}, the effective mixing parameter, to order of 10^-5.

preprint2002arXiv

Natural R-Parity, μ-term, and Fermion Mass Hierarchy From Discrete Gauge Symmetries

In the minimal supersymmetric Standard Model with seesaw neutrino masses we show how R-parity can emerge naturally as a discrete gauge symmetry. The same discrete symmetry explains the smallness of the μ-term (the Higgsino mass parameter) via the Giudice--Masiero mechanism. The discrete gauge anomalies are cancelled by a discrete version of the Green--Schwarz mechanism. The simplest symmetry group is found to be Z_4 with a charge assignment that is compatible with grand unification. Several other Z_N gauge symmetries are found for N=10,12,18,36 etc, with some models employing discrete anomaly cancellation at higher Kac-Moody levels. Allowing for a flavor structure in Z_N, we show that the same gauge symmetry can also explain the observed hierarchy in the fermion masses and mixings.

preprint2002arXiv

Stabilizing the Axion by Discrete Gauge Symmetries

The axion solution to the strong CP problem makes use of a global Peccei-Quinn U(1) symmetry which is susceptible to violations from quantum gravitational effects. We show how discrete gauge symmetries can protect the axion from such violations. PQ symmetry emerges as an approximate global symmetry from discrete gauge symmetries. Simple models based on Z_N symmetries with N =11,12, etc are presented realizing the DFSZ axion and the KSVZ axion. The discrete gauge anomalies are cancelled by a discrete version of the Green-Schwarz mechanism. In the supersymmetric extension our models provide a natural link between the SUSY breaking scale, the axion scale, and the SUSY-preserving μterm.

Kai Wang

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151 published item(s)

Audio-Visual Intelligence in Large Foundation Models

SkillSafetyBench: Evaluating Agent Safety under Skill-Facing Attack Surfaces

Observation of the $γ$-ray Emission from W43 with LHAASO

Study of Ultra-High-Energy Gamma-Ray Source 1LHAASO J0056+6346u and Its Possible Origins

Ultrahigh-Energy Gamma-ray Emission Associated with Black Hole-Jet Systems

Mutual Information as Intrinsic Reward of Reinforcement Learning Agents for On-demand Ride Pooling

Summarizing Stream Data for Memory-Constrained Online Continual Learning

OneRing: A Simple Method for Source-free Open-partial Domain Adaptation

Smoothed Online Combinatorial Optimization Using Imperfect Predictions

An Efficient Training Approach for Very Large Scale Face Recognition

Anisotropic Electrene T'-Ca2P with Electron Gas Magnetic Coupling as Anode Material for Na/K Ion Batteries

Background-penalty-free waveguide enhancement of CARS signal in air-filled anti-resonance hollow-core fiber

CAFE: Learning to Condense Dataset by Aligning Features

Cohomology theory of Rota-Baxter pre-Lie algebras of arbitrary weights

Could TDE outflows produce the PeV neutrino events?

Crafting Better Contrastive Views for Siamese Representation Learning

Creating boundaries along a synthetic frequency dimension

Deep Q-learning of global optimizer of multiply model parameters for viscoelastic imaging

Deformations and Cohomology Theory of Rota-Baxter Systems

Detection of diffuse gamma-ray emission towards a massive star forming region hosting Wolf-Rayet stars

Detection of Prompt Fast-Variable Thermal Component in Multi-Pulse Short Gamma-Ray Burst 170206A

Divide to Adapt: Mitigating Confirmation Bias for Domain Adaptation of Black-Box Predictors

FaceMAE: Privacy-Preserving Face Recognition via Masked Autoencoders

Fermi Observations of GRB 220426A: a burst similar to GRB 090902B

Grasping the Arrow of Time from the Singularity: Decoding Micromotion in Low-dimensional Latent Spaces from StyleGAN

Groups and protocluster candidates in the CLAUDS and HSC-SSP joint deep surveys

Improving Non-native Word-level Pronunciation Scoring with Phone-level Mixup Data Augmentation and Multi-source Information

Investigating Accuracy-Novelty Performance for Graph-based Collaborative Filtering

Investigation of variable temperature Mössbauer spectrum of YFe$_{0.5}$Cr$_{0.5}$O$_3$ perovskite

Labeling Trick: A Theory of Using Graph Neural Networks for Multi-Node Representation Learning

Learning MDPs from Features: Predict-Then-Optimize for Sequential Decision Problems by Reinforcement Learning

Magnetic field tuning of the valley population in the Weyl phase of Nd$_2$Ir$_2$O$_7$

Modeling Motion with Multi-Modal Features for Text-Based Video Segmentation

Monitoring AGNs with H$β$ Asymmetry. III. Long-term Reverberation Mapping Results of 15 Palomar-Green Quasars

MSDN: Mutually Semantic Distillation Network for Zero-Shot Learning

Multi-label classification for biomedical literature: an overview of the BioCreative VII LitCovid Track for COVID-19 literature topic annotations

Multi-label topic classification for COVID-19 literature with Bioformer

Progressive Multi-scale Consistent Network for Multi-class Fundus Lesion Segmentation

Prompt Vision Transformer for Domain Generalization

QuickSkill: Novice Skill Estimation in Online Multiplayer Games

Reliable Label Correction is a Good Booster When Learning with Extremely Noisy Labels

Skeleton-based Action Recognition via Temporal-Channel Aggregation

Solving Mission-Wide Chance-Constrained Optimal Control Using Dynamic Programming

Swift and Sure: Hardness-aware Contrastive Learning for Low-dimensional Knowledge Graph Embeddings

Takagi Topological Insulator on the Honeycomb Lattice

Tensor Theory for Higher Dimensional Chern Insulators with Large Chern Numbers

The Large High Altitude Air Shower Observatory (LHAASO) Science Book (2021 Edition)

The Shape Part Slot Machine: Contact-based Reasoning for Generating 3D Shapes from Parts

Truncations of random unitary matrices drawn from Hua-Pickrell distribution

Trust-SIoT: Towards Trustworthy Object Classification in the Social Internet of Things

Ultra-High Lithium Storage Capacity of Al2C Monolayer under Restricted Multilayered Growth Mechanism

Weighted $L_p$ Markov factors with doubling weights on the ball

A Benchmark of Ocular Disease Intelligent Recognition: One Shot for Multi-disease Detection

Applications of Deep Learning in Fundus Images: A Review

Gaussian limit for determinantal point processes with $J$-Hermitian kernels

Simple and effective localized attribute representations for zero-shot learning

Aesthetic Quality Assessment for Group photograph

Benchmarking calculations with spectroscopic accuracy of excitation energies and wavelengths in sulfur-like tungsten

Bookworm continual learning: beyond zero-shot learning and continual learning

Boundary criticality of $PT$-invariant topology and second-order nodal-line semimetals

CardioLearn: A Cloud Deep Learning Service for Cardiac Disease Detection from Electrocardiogram

Distinguishing between dynamical and static Rashba effects in hybrid perovskite nanocrystals using transient absorption spectroscopy

Efficient Bitruss Decomposition for Large-scale Bipartite Graphs

Energy Levels, Transition Rates and Electron Impact Excitation Rates for B-like Kr XXXII

Exploring Cohesive Subgraphs with Vertex Engagement and Tie Strength in Bipartite Graphs

Gamma-ray Burst Spectrum with a Time-dependent Injection Rate of High-energy Electrons

High-order Time Stepping Schemes for Semilinear Subdiffusion Equations

Multi-Domain Dialogue Acts and Response Co-Generation

Multidimensional synthetic chiral-tube lattices via nonlinear frequency conversion

Multilayer Structures of Graphene and Pt Nanoparticles -- a Multiscale Computational Study

Photo-production of Higgs Boson at the LHeC

Photospheric Emission in Gamma-ray Bursts: Variability

Probing the trilinear Higgs boson self-coupling via single Higgs production at the LHeC

Quantum wave-particle superposition in a delayed-choice experiment