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He Zhang

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preprint2026arXiv

BioHuman: Learning Biomechanical Human Representations from Video

Understanding human motion beyond surface kinematics is crucial for motion analysis, rehabilitation, and injury risk assessment. However, progress in this domain is limited by the lack of large-scale datasets with biomechanical annotations, and by existing approaches that cannot directly infer internal biomechanical states from visual observations. In this paper, we introduce a simulation-based framework for estimating muscle activations from existing motion capture datasets, resulting in BioHuman10M, a large-scale dataset with synchronized video, motion, and activations. Building on BioHuman10M, we propose BioHuman, an end-to-end model that takes monocular video as input and jointly predicts human motion and muscle activations, effectively bridging visual observations and internal biomechanical states. Extensive experiments demonstrate that BioHuman enables accurate reconstruction of both kinematic motion and muscle activity, and generalizes across diverse subjects and motions. We believe our approach establishes a new benchmark for video-based biomechanical understanding and opens up new possibilities for physically grounded human modeling.

preprint2025arXiv

OmniVCus: Feedforward Subject-driven Video Customization with Multimodal Control Conditions

Existing feedforward subject-driven video customization methods mainly study single-subject scenarios due to the difficulty of constructing multi-subject training data pairs. Another challenging problem that how to use the signals such as depth, mask, camera, and text prompts to control and edit the subject in the customized video is still less explored. In this paper, we first propose a data construction pipeline, VideoCus-Factory, to produce training data pairs for multi-subject customization from raw videos without labels and control signals such as depth-to-video and mask-to-video pairs. Based on our constructed data, we develop an Image-Video Transfer Mixed (IVTM) training with image editing data to enable instructive editing for the subject in the customized video. Then we propose a diffusion Transformer framework, OmniVCus, with two embedding mechanisms, Lottery Embedding (LE) and Temporally Aligned Embedding (TAE). LE enables inference with more subjects by using the training subjects to activate more frame embeddings. TAE encourages the generation process to extract guidance from temporally aligned control signals by assigning the same frame embeddings to the control and noise tokens. Experiments demonstrate that our method significantly surpasses state-of-the-art methods in both quantitative and qualitative evaluations. Video demos are at our project page: https://caiyuanhao1998.github.io/project/OmniVCus/. Our code, models, data are released at https://github.com/caiyuanhao1998/Open-OmniVCus

preprint2023arXiv

A Programmable Spatiotemporal Quantum Parametric Mode Sorter

We experimentally demonstrate a programmable parametric mode sorter of high-dimensional signals in a composite spatiotemporal Hilbert space through mode-selective quantum frequency up-conversion. As a concrete example and with quantum communication applications in mind, we consider the Laguerre-Gaussian and Hermite-Gaussian modes as the spatial and temporal state basis for the signals, respectively. By modulating the spatiotemporal profiles of the up-conversion pump, we demonstrate the faithful selection of single photons in those modes and their superposition modes. Our results show an improvement in the quantum mode-sorting performance by coupling the up-converted light into a single-mode fiber and/or operating the upconversion at the edge of phase matching. By optimizing pump temporal profiles only, we achieve more than 12 dB extinction for mutually unbiased basis (MUB) sets of the spatiotemporal modes. This fully programmable and efficient system could serve as a viable resource for quantum communications, quantum computation, and quantum metrology.

preprint2023arXiv

Metrics for Software Process Simulation Modeling

Background: Software Process Simulation (SPS) has become an effective tool for software process management and improvement. However, its adoption in industry is less than what the research community expected due to the burden of measurement cost and the high demand for domain knowledge. The difficulty of extracting appropriate metrics with real data from process enactment is one of the great challenges. Objective: We aim to provide evidence-based support of the process metrics for software process (simulation) modeling. Method: A systematic literature review was performed by extending our previous review series to draw a comprehensive understanding of the metrics for process modeling following a meta-model of ontology of metrics in SPS. Results: We identified 145 process modeling studies that collectively involve 2130 metrics and classified them using the coding technique. Two diagrams which illustrate the high frequency causal relationships used between metrics are proposed in terms of two hierarchical levels of modeling purposes. We revisited the data issues encountered in SPS data preparing phases, as well as identified the corresponding strategies. Conclusion: The results of this study provide process modelers with an evidence-based reference of the identification and the use of metrics in SPS modeling, and further contribute to the development of the body of knowledge on software metrics in the context of process modeling. Furthermore, this study is not limited to process simulation but can be extended to software process modeling, in general. Taking simulation metrics as standards and references can further motivate and guide software developers to improve the collection, governance, and application of process data in practice.

preprint2022arXiv

A Cross-Company Ethnographic Study on Software Teams for DevOps and Microservices: Organization, Benefits, and Issues

Context: DevOps and microservices are acknowledged to be important new paradigms to tackle contemporary software demands and provide capabilities for rapid and reliable software development. Industrial reports show that they are quickly adopted together in massive software companies. However, because of the technical and organizational requirements, many difficulties against efficient implementation of the both emerge in real software teams. Objectives: This study aims to discover the organization, benefits and issues of software teams using DevOps & microservices from an immersive perspective. Method: An ethnographic study was carried out in three companies with different business, size, products, customers, and degree of globalization. All the three companies claimed their adoption of DevOps and microservices. Seven months (cumulative) of participant observations and nine interviews with practitioners were conducted to collect the data of software teams related to DevOps and microservices. A cross-company empirical investigation using grounded theory was done by analyzing the archive data. Results: The adoption of DevOps and microservices brings benefits to rapid delivery, ability improvements and burden reduction, whilst the high cost and lack of practical guidance were emerged. Moreover, our observations and interviews reflect that in software teams, the relationship between DevOps and microservices is not significant, which differs from the relationship described in the previous studies. Four lessons for practitioners and four implications for researchers were discussed based on our findings. Conclusion: Our findings contribute to the understanding of the organization, benefits and issues of adopting DevOps and microservices from an immersive perspective of software teams.

preprint2022arXiv

A Flexible Diffusion Model

Diffusion (score-based) generative models have been widely used for modeling various types of complex data, including images, audios, and point clouds. Recently, the deep connection between forward-backward stochastic differential equations (SDEs) and diffusion-based models has been revealed, and several new variants of SDEs are proposed (e.g., sub-VP, critically-damped Langevin) along this line. Despite the empirical success of the hand-crafted fixed forward SDEs, a great quantity of proper forward SDEs remain unexplored. In this work, we propose a general framework for parameterizing the diffusion model, especially the spatial part of the forward SDE. An abstract formalism is introduced with theoretical guarantees, and its connection with previous diffusion models is leveraged. We demonstrate the theoretical advantage of our method from an optimization perspective. Numerical experiments on synthetic datasets, MINIST and CIFAR10 are also presented to validate the effectiveness of our framework.

preprint2022arXiv

A predictor-corrector deep learning algorithm for high dimensional stochastic partial differential equations

In this paper, we present a deep learning-based numerical method for approximating high dimensional stochastic partial differential equations (SPDEs). At each time step, our method relies on a predictor-corrector procedure. More precisely, we decompose the original SPDE into a degenerate SPDE and a deterministic PDE. Then in the prediction step, we solve the degenerate SPDE with the Euler scheme, while in the correction step we solve the second-order deterministic PDE by deep neural networks via its equivalent backward stochastic differential equation (BSDE). Under standard assumptions, error estimates and the rate of convergence of the proposed algorithm are presented. The efficiency and accuracy of the proposed algorithm are illustrated by numerical examples.

preprint2022arXiv

An Industrial Experience Report on Retro-inspection

To reinforce the quality of code delivery, especially to improve future coding quality, one global Information and Communication Technology (ICT) enterprise has institutionalized a retrospective style inspection (namely retro-inspection), which is similar to Fagan inspection but differs in terms of stage, participants, etc. This paper reports an industrial case study that aims to investigate the experiences and lessons from this software practice. To this end, we collected and analyzed various empirical evidence for data triangulation. The results reflect that retro-inspection distinguishes itself from peer code review by identifying more complicated and underlying defects, providing more indicative and suggestive comments. Many experienced inspectors indicate defects together with their rationale behind and offer suggestions for correction and prevention. As a result, retro-inspection can benefit not only quality assurance (like Fagan inspection), but also internal audit, inter-division communication, and competence promotion. On the other side, we identify several lessons of retro-inspection at this stage, e.g., developers' acceptance and organizers' predicament, for next-step improvement of this practice. To be specific, some recommendations are discussed for retro-inspection, e.g., more adequate preparation and more careful publicity. This study concludes that most of the expected benefits of retro-inspection can be empirically confirmed in this enterprise and its value on the progress to continuous maturity can be recognized organization-wide. The experiences on executing this altered practice in a large enterprise provide reference value on code quality assurance to other software organizations.

preprint2022arXiv

Automorphisms and representations of quasi Laurent polynomial algebras

We study automorphisms and representations of quasi polynomial algebras (QPAs) and quasi Laurent polynomial algebras (QLPAs). For any QLPA defined by an arbitrary skew symmetric integral matrix, we explicitly describe its automorphism groups at generic $q$ and at roots of unity. Any QLPA is isomorphic to the tensor product of copies of the QLPA of degree $2$ at different powers of $q$ and the centre, thus the study of representations of QPAs and QLPAs largely reduces to that of ${\mathcal L}_q(2)$ and ${\mathcal A}_q(2)$, the QLPA and QPA of degree $2$. We study a category of ${\mathcal A}_q(2)$-modules which have finite covers by submodules with natural local finiteness properties and satisfy some condition under localisation, determining its blocks, classifying the simple objects and providing two explicitly constructions for the simples. One construction produces the simple ${\mathcal A}_q(2)$-modules from ${\mathcal L}_q(2)$-modules via monomorphisms composed of the natural embedding of ${\mathcal A}_q(2)$ in ${\mathcal L}_q(2)$ and automorphisms of ${\mathcal L}_q(2)$, and the other explores a class of holonomic ${\mathcal D}_q$-modules for the algebra ${\mathcal D}_q$ of $q$-differential operators.

preprint2022arXiv

Boosting Robustness of Image Matting with Context Assembling and Strong Data Augmentation

Deep image matting methods have achieved increasingly better results on benchmarks (e.g., Composition-1k/alphamatting.com). However, the robustness, including robustness to trimaps and generalization to images from different domains, is still under-explored. Although some works propose to either refine the trimaps or adapt the algorithms to real-world images via extra data augmentation, none of them has taken both into consideration, not to mention the significant performance deterioration on benchmarks while using those data augmentation. To fill this gap, we propose an image matting method which achieves higher robustness (RMat) via multilevel context assembling and strong data augmentation targeting matting. Specifically, we first build a strong matting framework by modeling ample global information with transformer blocks in the encoder, and focusing on details in combination with convolution layers as well as a low-level feature assembling attention block in the decoder. Then, based on this strong baseline, we analyze current data augmentation and explore simple but effective strong data augmentation to boost the baseline model and contribute a more generalizable matting method. Compared with previous methods, the proposed method not only achieves state-of-the-art results on the Composition-1k benchmark (11% improvement on SAD and 27% improvement on Grad) with smaller model size, but also shows more robust generalization results on other benchmarks, on real-world images, and also on varying coarse-to-fine trimaps with our extensive experiments.

preprint2022arXiv

Controllable Shadow Generation Using Pixel Height Maps

Shadows are essential for realistic image compositing. Physics-based shadow rendering methods require 3D geometries, which are not always available. Deep learning-based shadow synthesis methods learn a mapping from the light information to an object's shadow without explicitly modeling the shadow geometry. Still, they lack control and are prone to visual artifacts. We introduce pixel heigh, a novel geometry representation that encodes the correlations between objects, ground, and camera pose. The pixel height can be calculated from 3D geometries, manually annotated on 2D images, and can also be predicted from a single-view RGB image by a supervised approach. It can be used to calculate hard shadows in a 2D image based on the projective geometry, providing precise control of the shadows' direction and shape. Furthermore, we propose a data-driven soft shadow generator to apply softness to a hard shadow based on a softness input parameter. Qualitative and quantitative evaluations demonstrate that the proposed pixel height significantly improves the quality of the shadow generation while allowing for controllability.

preprint2022arXiv

DoubleField: Bridging the Neural Surface and Radiance Fields for High-fidelity Human Reconstruction and Rendering

We introduce DoubleField, a novel framework combining the merits of both surface field and radiance field for high-fidelity human reconstruction and rendering. Within DoubleField, the surface field and radiance field are associated together by a shared feature embedding and a surface-guided sampling strategy. Moreover, a view-to-view transformer is introduced to fuse multi-view features and learn view-dependent features directly from high-resolution inputs. With the modeling power of DoubleField and the view-to-view transformer, our method significantly improves the reconstruction quality of both geometry and appearance, while supporting direct inference, scene-specific high-resolution finetuning, and fast rendering. The efficacy of DoubleField is validated by the quantitative evaluations on several datasets and the qualitative results in a real-world sparse multi-view system, showing its superior capability for high-quality human model reconstruction and photo-realistic free-viewpoint human rendering. Data and source code will be made public for the research purpose. Please refer to our project page: http://www.liuyebin.com/dbfield/dbfield.html.

preprint2022arXiv

HVTR: Hybrid Volumetric-Textural Rendering for Human Avatars

We propose a novel neural rendering pipeline, Hybrid Volumetric-Textural Rendering (HVTR), which synthesizes virtual human avatars from arbitrary poses efficiently and at high quality. First, we learn to encode articulated human motions on a dense UV manifold of the human body surface. To handle complicated motions (e.g., self-occlusions), we then leverage the encoded information on the UV manifold to construct a 3D volumetric representation based on a dynamic pose-conditioned neural radiance field. While this allows us to represent 3D geometry with changing topology, volumetric rendering is computationally heavy. Hence we employ only a rough volumetric representation using a pose-conditioned downsampled neural radiance field (PD-NeRF), which we can render efficiently at low resolutions. In addition, we learn 2D textural features that are fused with rendered volumetric features in image space. The key advantage of our approach is that we can then convert the fused features into a high-resolution, high-quality avatar by a fast GAN-based textural renderer. We demonstrate that hybrid rendering enables HVTR to handle complicated motions, render high-quality avatars under user-controlled poses/shapes and even loose clothing, and most importantly, be efficient at inference time. Our experimental results also demonstrate state-of-the-art quantitative results.

preprint2022arXiv

Interactive Portrait Harmonization

Current image harmonization methods consider the entire background as the guidance for harmonization. However, this may limit the capability for user to choose any specific object/person in the background to guide the harmonization. To enable flexible interaction between user and harmonization, we introduce interactive harmonization, a new setting where the harmonization is performed with respect to a selected \emph{region} in the reference image instead of the entire background. A new flexible framework that allows users to pick certain regions of the background image and use it to guide the harmonization is proposed. Inspired by professional portrait harmonization users, we also introduce a new luminance matching loss to optimally match the color/luminance conditions between the composite foreground and select reference region. This framework provides more control to the image harmonization pipeline achieving visually pleasing portrait edits. Furthermore, we also introduce a new dataset carefully curated for validating portrait harmonization. Extensive experiments on both synthetic and real-world datasets show that the proposed approach is efficient and robust compared to previous harmonization baselines, especially for portraits. Project Webpage at \href{https://jeya-maria-jose.github.io/IPH-web/}{https://jeya-maria-jose.github.io/IPH-web/}

preprint2022arXiv

Leveraging Cross Feedback of User and Item Embeddings with Attention for Variational Autoencoder based Collaborative Filtering

Matrix factorization (MF) has been widely applied to collaborative filtering in recommendation systems. Its Bayesian variants can derive posterior distributions of user and item embeddings, and are more robust to sparse ratings. However, the Bayesian methods are restricted by their update rules for the posterior parameters due to the conjugacy of the priors and the likelihood. Variational autoencoders (VAE) can address this issue by capturing complex mappings between the posterior parameters and the data. However, current research on VAEs for collaborative filtering only considers the mappings based on the explicit data information while the implicit embedding information is overlooked. In this paper, we first derive evidence lower bounds (ELBO) for Bayesian MF models from two viewpoints: user-oriented and item-oriented. Based on the ELBOs, we propose a VAE-based Bayesian MF framework. It leverages not only the data but also the embedding information to approximate the user-item joint distribution. As suggested by the ELBOs, the approximation is iterative with cross feedback of user and item embeddings into each other's encoders. More specifically, user embeddings sampled at the previous iteration are fed to the item-side encoders to estimate the posterior parameters for the item embeddings at the current iteration, and vice versa. The estimation also attends to the cross-fed embeddings to further exploit useful information. The decoder then reconstructs the data via the matrix factorization over the currently re-sampled user and item embeddings.

preprint2022arXiv

Representations of Quantum Coordinate Algebras at Generic $q$ and Wiring Diagrams

This paper is devoted to the representation theory of quantum coordinate algebra $\mathbb{C}_q[G]$, for a semisimple Lie group $G$ and a generic parameter $q$. By inspecting the actions of normal elements on tensor modules, we generalize a result of Levendorski and Soibelman in [22] for highest weight modules. For a double Bruhat cell $G^{w_1,w_2}$, we describe the primitive spectra $\mathrm{prim}\,\mathbb{C}_q[G]_{w_1,w_2}$ in a new fashion, and construct a bundle of $(w_1,w_2)$ type simple modules onto $\mathrm{prim}\,\mathbb{C}_q[G]_{w_1,w_2}$, provided $\mathrm{Supp}(w_1)\cap\mathrm{Supp}(w_2)=\varnothing$ or enough pivot elements. The fibers of the bundle are shown to be products of the spectrums of simple modules of 2-dimensional quantum torus $L_q(2)$. As an application of our theory, we deduce an equivalent condition for the tensor module to be simple, and construct some simple modules for each primitive ideal when $G=SL_3(\mathbb{C})$. This completes the Dixmier's program for $\mathbb{C}_q[SL_3]$. The wiring diagrams, introduced by Fomin and Zelevinsky in their study of total positivity (cf. [3,9]), is the main tool to compute the action of generalized quantum minors on tensor modules in the type A case. We obtain a quantum version of Lindström's lemma, which plays an important role in transforming representation problems into combinatorial ones of wiring diagrams.

preprint2022arXiv

Threshold solutions for nonlocal reaction diffusion equations

We study the Cauchy problem for nonlocal reaction diffusion equations with bistable nonlinearity in 1D spatial domain and investigate the asymptotic behaviors of solutions with a one-parameter family of monotonically increasing and compactly supported initial data. We show that for small values of the parameter the corresponding solutions decay to 0, while for large values the related solutions converge to 1 uniformly on compacts. Moreover, we prove that the transition from extinction (converging to 0) to propagation (converging to 1) is sharp. Numerical results are provided to verify the theoretical results.

preprint2021arXiv

A Driving Behavior Recognition Model with Bi-LSTM and Multi-Scale CNN

In autonomous driving, perceiving the driving behaviors of surrounding agents is important for the ego-vehicle to make a reasonable decision. In this paper, we propose a neural network model based on trajectories information for driving behavior recognition. Unlike existing trajectory-based methods that recognize the driving behavior using the hand-crafted features or directly encoding the trajectory, our model involves a Multi-Scale Convolutional Neural Network (MSCNN) module to automatically extract the high-level features which are supposed to encode the rich spatial and temporal information. Given a trajectory sequence of an agent as the input, firstly, the Bi-directional Long Short Term Memory (Bi-LSTM) module and the MSCNN module respectively process the input, generating two features, and then the two features are fused to classify the behavior of the agent. We evaluate the proposed model on the public BLVD dataset, achieving a satisfying performance.

preprint2020arXiv

Single-Pixel Pattern Recognition with Coherent Nonlinear Optics

We propose and experimentally demonstrate a nonlinear-optics approach to pattern recognition with single-pixel imaging and deep neural network. It employs mode selective image up-conversion to project a raw image onto a set of coherent spatial modes, whereby its signature features are extracted nonlinear-optically. With 40 projection modes, the classification accuracy reaches a high value of 99.49% for the MNIST handwritten digit images, and up to 95.32% even when they are mixed with strong noise. Our experiment harnesses rich coherent processes in nonlinear optics for efficient machine learning, with potential applications in online classification of large size images, fast lidar data analyses, complex pattern recognition, and so on.

preprint2020arXiv

SummPip: Unsupervised Multi-Document Summarization with Sentence Graph Compression

Obtaining training data for multi-document summarization (MDS) is time consuming and resource-intensive, so recent neural models can only be trained for limited domains. In this paper, we propose SummPip: an unsupervised method for multi-document summarization, in which we convert the original documents to a sentence graph, taking both linguistic and deep representation into account, then apply spectral clustering to obtain multiple clusters of sentences, and finally compress each cluster to generate the final summary. Experiments on Multi-News and DUC-2004 datasets show that our method is competitive to previous unsupervised methods and is even comparable to the neural supervised approaches. In addition, human evaluation shows our system produces consistent and complete summaries compared to human written ones.

preprint2020arXiv

Synergy between Machine/Deep Learning and Software Engineering: How Far Are We?

Since 2009, the deep learning revolution, which was triggered by the introduction of ImageNet, has stimulated the synergy between Machine Learning (ML)/Deep Learning (DL) and Software Engineering (SE). Meanwhile, critical reviews have emerged that suggest that ML/DL should be used cautiously. To improve the quality (especially the applicability and generalizability) of ML/DL-related SE studies, and to stimulate and enhance future collaborations between SE/AI researchers and industry practitioners, we conducted a 10-year Systematic Literature Review (SLR) on 906 ML/DL-related SE papers published between 2009 and 2018. Our trend analysis demonstrated the mutual impacts that ML/DL and SE have had on each other. At the same time, however, we also observed a paucity of replicable and reproducible ML/DL-related SE studies and identified five factors that influence their replicability and reproducibility. To improve the applicability and generalizability of research results, we analyzed what ingredients in a study would facilitate an understanding of why a ML/DL technique was selected for a specific SE problem. In addition, we identified the unique trends of impacts of DL models on SE tasks, as well as five unique challenges that needed to be met in order to better leverage DL to improve the productivity of SE tasks. Finally, we outlined a road-map that we believe can facilitate the transfer of ML/DL-based SE research results into real-world industry practices.

preprint2020arXiv

ThreshKnot: Thresholded ProbKnot for Improved RNA Secondary Structure Prediction

RNA structure prediction is a challenging problem, especially with pseudoknots. Recently, there has been a shift from the classical minimum free energy-based methods (MFE) to partition function-based ones that assemble structures using base-pairing probabilities. Two examples of the latter group are the popular maximum expected accuracy (MEA) method and the ProbKnot method. ProbKnot is a fast heuristic that pairs nucleotides that are reciprocally most probable pairing partners, and unlike MEA, can also predict structures with pseudoknots. However, ProbKnot's full potential has been largely overlooked. In particular, when introduced, it did not have an MEA-like hyperparameter that can balance between positive predictive value (PPV) and sensitivity. We show that a simple thresholded version of ProbKnot, which we call ThreshKnot, leads to more accurate overall predictions by filtering out unlikely pairs whose probabilities fall under a given threshold. We also show that on three widely-used folding engines (RNAstructure, Vienna RNAfold, and CONTRAfold), ThreshKnot always outperforms the much more involved MEA algorithm in (1) its higher structure prediction accuracy, (2) its capability to predict pseudoknots, and (3) its faster runtime and easier implementation. This suggests that ThreshKnot should replace MEA as the default partition function-based structure prediction algorithm. ThreshKnot is already available in the widely used RNAstructure software package version 6.2 (released November 27, 2019): https://rna.urmc.rochester.edu/RNAstructure.html

preprint2020arXiv

Traffic Agent Trajectory Prediction Using Social Convolution and Attention Mechanism

The trajectory prediction is significant for the decision-making of autonomous driving vehicles. In this paper, we propose a model to predict the trajectories of target agents around an autonomous vehicle. The main idea of our method is considering the history trajectories of the target agent and the influence of surrounding agents on the target agent. To this end, we encode the target agent history trajectories as an attention mask and construct a social map to encode the interactive relationship between the target agent and its surrounding agents. Given a trajectory sequence, the LSTM networks are firstly utilized to extract the features for all agents, based on which the attention mask and social map are formed. Then, the attention mask and social map are fused to get the fusion feature map, which is processed by the social convolution to obtain a fusion feature representation. Finally, this fusion feature is taken as the input of a variable-length LSTM to predict the trajectory of the target agent. We note that the variable-length LSTM enables our model to handle the case that the number of agents in the sensing scope is highly dynamic in traffic scenes. To verify the effectiveness of our method, we widely compare with several methods on a public dataset, achieving a 20% error decrease. In addition, the model satisfies the real-time requirement with the 32 fps.

preprint2020arXiv

Wide-field, high-resolution lensless on-chip microscopy via near-field blind ptychographic modulation

We report a novel lensless on-chip microscopy platform based on near-field blind ptychographic modulation. In this platform, we place a thin diffuser in between the object and the image sensor for light wave modulation. By blindly scanning the unknown diffuser to different x-y positions, we acquire a sequence of modulated intensity images for quantitative object recovery. Different from previous ptychographic implementations, we employ a unit magnification configuration with a Fresnel number of ~50,000, which is orders of magnitude higher than previous ptychographic setups. The unit magnification configuration allows us to have the entire sensor area, 6.4 mm by 4.6 mm, as the imaging field of view. The ultra-high Fresnel number enables us to directly recover the positional shift of the diffuser in the phase retrieval process, addressing the positioning accuracy issue plagued in regular ptychographic experiments. In our implementation, we use a low-cost, DIY scanning stage to perform blind diffuser modulation. Precise mechanical scanning that is critical in conventional ptychography experiments is no longer needed in our setup. We further employ an up-sampling phase retrieval scheme to bypass the resolution limit set by the imager pixel size and demonstrate a half-pitch resolution of 0.78 micron. We validate the imaging performance via in vitro cell cultures, transparent and stained tissue sections, and a thick biological sample. We show that the recovered quantitative phase map can be used to perform effective cell segmentation of the dense yeast culture. We also demonstrate 3D digital refocusing of the thick biological sample based on the recovered wavefront. The reported platform provides a cost-effective and turnkey solution for large field-of-view, high-resolution, and quantitative on-chip microscopy.

preprint2019arXiv

LinearFold: linear-time approximate RNA folding by 5'-to-3' dynamic programming and beam search

Motivation: Predicting the secondary structure of an RNA sequence is useful in many applications. Existing algorithms (based on dynamic programming) suffer from a major limitation: their runtimes scale cubically with the RNA length, and this slowness limits their use in genome-wide applications. Results: We present a novel alternative $O(n^3)$-time dynamic programming algorithm for RNA folding that is amenable to heuristics that make it run in $O(n)$ time and $O(n)$ space, while producing a high-quality approximation to the optimal solution. Inspired by incremental parsing for context-free grammars in computational linguistics, our alternative dynamic programming algorithm scans the sequence in a left-to-right (5'-to-3') direction rather than in a bottom-up fashion, which allows us to employ the effective beam pruning heuristic. Our work, though inexact, is the first RNA folding algorithm to achieve linear runtime (and linear space) without imposing constraints on the output structure. Surprisingly, our approximate search results in even higher overall accuracy on a diverse database of sequences with known structures. More interestingly, it leads to significantly more accurate predictions on the longest sequence families in that database (16S and 23S Ribosomal RNAs), as well as improved accuracies for long-range base pairs (500+ nucleotides apart), both of which are well known to be challenging for the current models. Availability: Our source code is available at https://github.com/LinearFold/LinearFold, and our webserver is at http://linearfold.org (sequence limit: 100,000nt).

preprint2019arXiv

Polarimetric Thermal to Visible Face Verification via Attribute Preserved Synthesis

Thermal to visible face verification is a challenging problem due to the large domain discrepancy between the modalities. Existing approaches either attempt to synthesize visible faces from thermal faces or extract robust features from these modalities for cross-modal matching. In this paper, we take a different approach in which we make use of the attributes extracted from the visible image to synthesize the attribute-preserved visible image from the input thermal image for cross-modal matching. A pre-trained VGG-Face network is used to extract the attributes from the visible image. Then, a novel Attribute Preserved Generative Adversarial Network (AP-GAN) is proposed to synthesize the visible image from the thermal image guided by the extracted attributes. Finally, a deep network is used to extract features from the synthesized image and the input visible image for verification. Extensive experiments on the ARL Polarimetric face dataset show that the proposed method achieves significant improvements over the state-of-the-art methods.

preprint2016arXiv

Linear absorption coefficient of in-plane graphene on a silicon microring resonator

We demonstrate that linear absorption coefficient (LAC) of a graphene-silicon hybrid waveguide (GSHW) is determined by the optical transmission spectra of a graphene coated symmetrically coupled add-drop silicon microring resonator (SC-ADSMR), of which the value is around 0.23 dB/um. In contrast to the traditional cut-back method, the measured results are not dependent on the coupling efficiency of the fiber tip and the waveguide. Moreover, precision evaluation of graphene coated silicon microring resonator (SMR) is crucial for the optoelectronic devices targeting for compact footprint and low power consumption.

preprint2016arXiv

Towards Evidence-Based Ontology for Supporting Systematic Literature Review

[Background]: Systematic Literature Review (SLR) has become an important software engineering research method but costs tremendous efforts. [Aim]: This paper proposes an approach to leverage on empirically evolved ontology to support automating key SLR activities. [Method]: First, we propose an ontology, SLRONT, built on SLR experiences and best practices as a groundwork to capture common terminologies and their relationships during SLR processes; second, we present an extended version of SLRONT, the COSONT and instantiate it with the knowledge and concepts extracted from structured abstracts. Case studies illustrate the details of applying it for supporting SLR steps. [Results]: Results show that through using COSONT, we acquire the same conclusion compared with sheer manual works, but the efforts involved is significantly reduced. [Conclusions]: The approach of using ontology could effectively and efficiently support the conducting of systematic literature review.

preprint2014arXiv

Constraining Sterile Neutrinos Using Reactor Neutrino Experiments

Models of neutrino mixing involving one or more sterile neutrinos have resurrected their importance in the light of recent cosmological data. In this case, reactor antineutrino experiments offer an ideal place to look for signatures of sterile neutrinos due to their impact on neutrino flavor transitions. In this work, we show that the high-precision data of the Daya Bay experi\-ment constrain the 3+1 neutrino scenario imposing upper bounds on the relevant active-sterile mixing angle $\sin^2 2 θ_{14} \lesssim 0.06$ at 3$σ$ confidence level for the mass-squared difference $Δm^2_{41}$ in the range $(10^{-3},10^{-1}) \, {\rm eV^2}$. The latter bound can be improved by six years of running of the JUNO experiment, $\sin^22θ_{14} \lesssim 0.016$, although in the smaller mass range $ Δm^2_{41} \in (10^{-4} ,10^{-3}) \, {\rm eV}^2$. We have also investigated the impact of sterile neutrinos on precision measurements of the standard neutrino oscillation parameters $θ_{13}$ and $Δm^2_{31}$ (at Daya Bay and JUNO), $θ_{12}$ and $Δm^2_{21}$ (at JUNO), and most importantly, the neutrino mass hierarchy (at JUNO). We find that, except for the obvious situation where $Δm^2_{41}\sim Δm^2_{31}$, sterile states do not affect these measurements substantially.

preprint2014arXiv

Nonstandard interaction effects on neutrino parameters at medium-baseline reactor antineutrino experiments

Precision measurements of leptonic mixing parameters and the determination of the neutrino mass hierarchy are the primary goals of the forthcoming medium-baseline reactor antineutrino experiments, such as JUNO and RENO-50. In this work, we investigate the impact of nonstandard neutrino interactions (NSIs) on the measurements of {sin^2 theta_{12}, Delta m^2_{21}} and {sin^2 theta_{13}, Delta m^2_{31}}, and on the sensitivity to the neutrino mass hierarchy, at the medium-baseline reactor experiments by assuming a typical experimental setup. It turns out that the true mixing parameter sin^2 theta_{12} can be excluded at a more than 3 sigma level if the NSI parameter varepsilon_{e mu} or varepsilon_{e tau} is as large as 2% in the most optimistic case. However, the discovery reach of NSI effects has been found to be small, and depends crucially on the CP-violating phases. Finally, we show that NSI effects could enhance or reduce the discrimination power of the JUNO and RENO-50 experiments between the normal and inverted neutrino mass hierarchies.

preprint2014arXiv

Reducing θ_13 to 9 degrees

We propose to consider the possibility that the observed value of $θ_{13}$ is not the result of a correction from an initially vanishing value, but rather the result of a correction from an initially larger value. As an explicit example of this approach, we consider analytically and numerically well-known CKM-like charged lepton corrections to a neutrino diagonalization matrix that corresponds to a certain mixing scheme. Usually this results in generating $θ_{13} = 9^\circ$ from zero. We note here, however, that 9 is not only given by $0 + 9$, but also by $18 - 9$. Hence, the extreme case of an initial value of 18 degrees, reduced by charged lepton corrections to 9 degrees, is possible. For some cases under study new sum rules for the mixing parameters, and correlations with CP phases are found.

preprint2014arXiv

Renormalization Group Running of the Neutrino Mass Operator in Extra Dimensions

We study the renormalization group (RG) running of the neutrino masses and the leptonic mixing parameters in two different extra-dimensional models, namely, the Universal Extra Dimensions (UED) model and a model, where the Standard Model (SM) bosons probe an extra dimension and the SM fermions are confined to a four-dimensional brane. In particular, we derive the beta function for the neutrino mass operator in the UED model. We also rederive the beta function for the charged-lepton Yukawa coupling, and confirm some of the existing results in the literature. The generic features of the RG running of the neutrino parameters within the two models are analyzed and, in particular, we observe a power-law behavior for the running. We note that the running of the leptonic mixing angle θ_{12} can be sizable, while the running of θ_{23} and θ_{13} is always negligible. In addition, we show that the tri-bimaximal and the bimaximal mixing patterns at a high-energy scale are compatible with low-energy experimental data, while a tri-small mixing pattern is not. Finally, we perform a numerical scan over the low-energy parameter space to infer the high-energy distribution of the parameters. Using this scan, we also demonstrate how the high-energy θ_{12} is correlated with the smallest neutrino mass and the Majorana phases.

preprint2014arXiv

Status of the Zee-Babu model for neutrino mass and possible tests at a like-sign linear collider

We provide an updated scan of the allowed parameter space of the two-loop Zee-Babu model for neutrino mass. Taking into account most recent experimental data on $μ\to eγ$ as well as the mixing angle $θ_{13}$ we obtain lower bounds on the masses of the singly and doubly charged scalars of between 1 to 2 TeV, with some dependence on perturbativity and fine-tuning requirements. This makes the scalars difficult to observe at LHC with 14 TeV even with optimistic assumptions on the luminosity, and would require a multi-TeV linear collider to see the scalar resonances. We point out, however, that a sub-TeV linear collider in the like-sign mode may be able to observe lepton flavour violating processes such as $e^- e^- \to μ^- μ^-$ due to contact interactions induced by the doubly charged scalar with masses up to around 10 TeV. We investigate the possibility to distinguish the Zee-Babu model from the Higgs triplet model using such processes.

preprint2013arXiv

A Factor Framework for Experimental Design for Performance Evaluation of Commercial Cloud Services

Given the diversity of commercial Cloud services, performance evaluations of candidate services would be crucial and beneficial for both service customers (e.g. cost-benefit analysis) and providers (e.g. direction of service improvement). Before an evaluation implementation, the selection of suitable factors (also called parameters or variables) plays a prerequisite role in designing evaluation experiments. However, there seems a lack of systematic approaches to factor selection for Cloud services performance evaluation. In other words, evaluators randomly and intuitively concerned experimental factors in most of the existing evaluation studies. Based on our previous taxonomy and modeling work, this paper proposes a factor framework for experimental design for performance evaluation of commercial Cloud services. This framework capsules the state-of-the-practice of performance evaluation factors that people currently take into account in the Cloud Computing domain, and in turn can help facilitate designing new experiments for evaluating Cloud services.

preprint2013arXiv

Building an Expert System for Evaluation of Commercial Cloud Services

Commercial Cloud services have been increasingly supplied to customers in industry. To facilitate customers' decision makings like cost-benefit analysis or Cloud provider selection, evaluation of those Cloud services are becoming more and more crucial. However, compared with evaluation of traditional computing systems, more challenges will inevitably appear when evaluating rapidly-changing and user-uncontrollable commercial Cloud services. This paper proposes an expert system for Cloud evaluation that addresses emerging evaluation challenges in the context of Cloud Computing. Based on the knowledge and data accumulated by exploring the existing evaluation work, this expert system has been conceptually validated to be able to give suggestions and guidelines for implementing new evaluation experiments. As such, users can conveniently obtain evaluation experiences by using this expert system, which is essentially able to make existing efforts in Cloud services evaluation reusable and sustainable.

preprint2013arXiv

Circumstantial-Evidence-Based Judgment for Software Effort Estimation

Expert judgment for software effort estimation is oriented toward direct evidences that refer to actual effort of similar projects or activities through experts' experiences. However, the availability of direct evidences implies the requirement of suitable experts together with past data. The circumstantial-evidence-based judgment proposed in this paper focuses on the development experiences deposited in human knowledge, and can then be used to qualitatively estimate implementation effort of different proposals of a new project by rational inference. To demonstrate the process of circumstantial-evidence-based judgment, this paper adopts propositional learning theory based diagnostic reasoning to infer and compare different effort estimates when implementing a Web service composition project with some different techniques and contexts. The exemplar shows our proposed work can help determine effort tradeoff before project implementation. Overall, circumstantial-evidence-based judgment is not an alternative but complementary to expert judgment so as to facilitate and improve software effort estimation.

preprint2013arXiv

Effects of nonstandard neutrino interactions at PINGU

Neutrino oscillation experiments in the past decades have greatly improved our knowledge on neutrinos by measuring the fundamental neutrino parameters. The ongoing and upcoming neutrino oscillation experiments are intended to pin down the neutrino mass hierarchy and to discover the leptonic CP violation. By means of neutrino oscillograms, we analyze the impact of non-standard neutrino interactions on neutrino oscillations in the Earth matter. The standard neutrino oscillation probabilities may be significantly changed by non-standard interaction parameters, and in particular, the CP-violating effects in the energy range E = 1 ~ 20 GeV are greatly enhanced. In addition, the event rates of muon neutrinos in the proposed huge atmospheric neutrino experiment, PINGU at the South Pole, have been estimated in the presence of non-standard neutrino interactions. It has been found that the PINGU experiment has very good sensitivities to the non-standard neutrino interaction parameters.

preprint2013arXiv

Exotic Charges, Multicomponent Dark Matter and Light Sterile Neutrinos

Generating small sterile neutrino masses via the same seesaw mechanism that suppresses active neutrino masses requires a specific structure in the neutral fermion mass matrix. We present a model where this structure is enforced by a new U(1)' gauge symmetry, spontaneously broken at the TeV scale. In order not to spoil the neutrino structure, the additional fermions necessary for anomaly cancellations need to carry exotic charges, and turn out to form multicomponent cold dark matter. The active-sterile mixing then connects the new particles and the Standard Model---opening a new portal in addition to the usual Higgs- and kinetic-mixing portals---which leads to dark matter annihilation almost exclusively into neutrinos.

preprint2013arXiv

Meta-Analysis of Gene Level Association Tests

The vast majority of connections between complex disease and common genetic variants were identified through meta-analysis, a powerful approach that enables large samples sizes while protecting against common artifacts due to population structure, repeated small sample analyses, and/or limitations with sharing individual level data. As the focus of genetic association studies shifts to rare variants, genes and other functional units are becoming the unit of analysis. Here, we propose and evaluate new approaches for meta-analysis of rare variant association. We show that our approach retains useful features of single variant meta-analytic approaches and demonstrate its utility in a study of blood lipid levels in ~18,500 individuals genotyped with exome arrays.

preprint2013arXiv

On a Catalogue of Metrics for Evaluating Commercial Cloud Services

Given the continually increasing amount of commercial Cloud services in the market, evaluation of different services plays a significant role in cost-benefit analysis or decision making for choosing Cloud Computing. In particular, employing suitable metrics is essential in evaluation implementations. However, to the best of our knowledge, there is not any systematic discussion about metrics for evaluating Cloud services. By using the method of Systematic Literature Review (SLR), we have collected the de facto metrics adopted in the existing Cloud services evaluation work. The collected metrics were arranged following different Cloud service features to be evaluated, which essentially constructed an evaluation metrics catalogue, as shown in this paper. This metrics catalogue can be used to facilitate the future practice and research in the area of Cloud services evaluation. Moreover, considering metrics selection is a prerequisite of benchmark selection in evaluation implementations, this work also supplements the existing research in benchmarking the commercial Cloud services.

preprint2013arXiv

Probing the leptonic Dirac CP-violating phase in neutrino oscillation experiments

The discovery of leptonic CP violation is one of the primary goals of next-generation neutrino oscillation experiments, which is feasible due to the recent measurement of a relatively large leptonic mixing angle θ_{13}. We suggest two new working observables ΔA^{\rm m}_{αβ} \equiv \max[A^{\rm CP}_{αβ}(δ)] - \min[A^{\rm CP}_{αβ}(δ)] and ΔA^{\rm CP}_{αβ}(δ) \equiv A^{\rm CP}_{αβ}(δ) - A^{\rm CP}_{αβ}(0) to describe the CP-violating effects in long-baseline and atmospheric neutrino oscillation experiments. The former signifies the experimental sensitivity to the leptonic Dirac CP-violating phase $δ$ and can be used to optimize the experimental setup, while the latter measures the intrinsic leptonic CP violation and can be used to extract $δ$ directly from the experimental observations. Both analytical and numerical analyses are carried out to illustrate their main features. It turns out that an intense neutrino beam with sub-GeV energies and a baseline of a few 100 km may serve as an optimal experimental setup for probing leptonic CP violation.

preprint2013arXiv

Radiative corrections to the leptonic Dirac CP-violating phase

Since the smallest leptonic mixing angle theta_{13} has been measured to be relatively large, it is quite promising to constrain or determine the leptonic Dirac CP-violating phase delta in future neutrino oscillation experiments. Given some typical values of delta = pi/2, pi, and 3pi/2 at the low-energy scale, as well as current experimental results of the other neutrino parameters, we perform a systematic study of the radiative corrections to delta by using the one-loop renormalization group equations in the minimal supersymmetric standard model and the universal extra-dimensional model. It turns out that delta is rather stable against radiative corrections in both models, except for the minimal supersymmetric standard model with a very large value of tan beta. Both cases of Majorana and Dirac neutrinos are discussed. In addition, we use the preliminary indication of delta = (1.08^{+0.28}_{-0.31}) pi or delta = (1.67^{+0.37}_{-0.77}) pi from the latest global-fit analyses of data from neutrino oscillation experiments to illustrate how it will be modified by radiative corrections.

preprint2013arXiv

The Cloud's Cloudy Moment: A Systematic Survey of Public Cloud Service Outage

Inadequate service availability is the top concern when employing Cloud computing. It has been recognized that zero downtime is impossible for large-scale Internet services. By learning from the previous and others' mistakes, nevertheless, it is possible for Cloud vendors to minimize the risk of future downtime or at least keep the downtime short. To facilitate summarizing lessons for Cloud providers, we performed a systematic survey of public Cloud service outage events. This paper reports the result of this survey. In addition to a set of findings, our work generated a lessons framework by classifying the outage root causes. The framework can in turn be used to arrange outage lessons for reference by Cloud providers. By including potentially new root causes, this lessons framework will be smoothly expanded in our future work.

preprint2013arXiv

Towards a Taxonomy of Performance Evaluation of Commercial Cloud Services

Cloud Computing, as one of the most promising computing paradigms, has become increasingly accepted in industry. Numerous commercial providers have started to supply public Cloud services, and corresponding performance evaluation is then inevitably required for Cloud provider selection or cost-benefit analysis. Unfortunately, inaccurate and confusing evaluation implementations can be often seen in the context of commercial Cloud Computing, which could severely interfere and spoil evaluation-related comprehension and communication. This paper introduces a taxonomy to help profile and standardize the details of performance evaluation of commercial Cloud services. Through a systematic literature review, we constructed the taxonomy along two dimensions by arranging the atomic elements of Cloud-related performance evaluation. As such, this proposed taxonomy can be employed both to analyze existing evaluation practices through decomposition into elements and to design new experiments through composing elements for evaluating performance of commercial Cloud services. Moreover, through smooth expansion, we can continually adapt this taxonomy to the more general area of evaluation of Cloud Computing.

preprint2013arXiv

Towards Technology Independent Strategies for SOA Implementations

Benefiting from the technology based strategies, Service-Oriented Architecture (SOA) has been able to achieve the general goals such as agility, flexibility, reusability and efficiency. Nevertheless, technical conditions alone cannot guarantee successful SOA implementations. As a valuable and necessary supplement, the space of technology independent strategies should also be explored. Through treating SOA system as an instance of organization and identifying the common ground on the similar process of SOA implementation and organization design, this paper uses existing work in organization theory area to inspire the research into technology independent strategies of SOA implementation. As a result, four preliminary strategies that can be applied to organizational area we identify to support SOA implementations. Furthermore, the novel methodology of investigating technology independent strategies for implementing SOA is revealed, which encourages interdisciplinary research across service-oriented computing and organization theory.

preprint2012arXiv

Impact of massive neutrinos on the Higgs self-coupling and electroweak vacuum stability

The presence of right-handed neutrinos in the type I seesaw mechanism may lead to significant corrections to the RG evolution of the Higgs self-coupling. Compared to the Standard Model case, the Higgs mass window can become narrower, and the cutoff scale become lower. Naively, these effects decrease with decreasing right-handed neutrino mass. However, we point out that the unknown Dirac Yukawa matrix may impact the vacuum stability constraints even in the low scale seesaw case not far away from the electroweak scale, hence much below the canonical seesaw scale of 10^15 GeV. This includes situations in which production of right-handed neutrinos at colliders is possible. We illustrate this within a particular parametrization of the Dirac Yukawas and with explicit low scale seesaw models. We also note the effect of massive neutrinos on the top quark Yukawa coupling, whose high energy value can be increased with respect to the Standard Model case.

preprint2012arXiv

Impacts of the Higgs mass on vacuum stability, running fermion masses and two-body Higgs decays

The latest results of the ATLAS and CMS experiments indicate 116 GeV \lesssim M_H \lesssim 131 GeV and 115 GeV \lesssim M_H \lesssim 127 GeV, respectively, for the mass of the Higgs boson in the standard model (SM) at the 95% confidence level. In particular, both experiments point to a preferred narrow mass range M_H \simeq (124 ... 126) GeV. We examine the impact of this preliminary result of M_H on the SM vacuum stability by using the two-loop renormalization-group equations (RGEs), and arrive at the cutoff scale Λ_VS \sim 4 \times 10^{12} GeV (for M_H = 125 GeV, M_t = 172.9 GeV and α_s(M_Z) = 0.1184) where the absolute stability of the SM vacuum is lost and some kind of new physics might take effect. We update the values of running lepton and quark masses at some typical energy scales, including the ones characterized by M_H, 1 TeV and Λ_VS, with the help of the two-loop RGEs. The branching ratios of some important two-body Higgs decay modes, such as H \to b\bar{b}, H \to τ^+ τ^-, H\to γγ, H\to W^+ W^- and H \to Z Z, are also recalculated by inputting the values of relevant particle masses at M_H.

preprint2012arXiv

Light Sterile Neutrino in the Minimal Extended Seesaw

Motivated by the recent observations on sterile neutrinos, we present a minimal extension of the canonical type-I seesaw by adding one extra singlet fermion. After the decoupling of right-handed neutrinos, an eV-scale mass eigenstate is obtained without the need of artificially inserting tiny mass scales or Yukawa couplings for sterile neutrinos. In particular, the active-sterile mixing is predicted to be of the order of 0.1. Moreover, we show a concrete flavor A_4 model, in which the required structures of the minimal extended seesaw are realized. We also comment on the feasibility of accommodating a keV sterile neutrino as an attractive candidate for warm dark matter.

preprint2012arXiv

Non-standard interactions versus non-unitary lepton flavor mixing at a neutrino factory

The impact of heavy mediators on neutrino oscillations is typically described by non-standard four-fermion interactions (NSIs) or non-unitarity (NU). We focus on leptonic dimension-six effective operators which do not produce charged lepton flavor violation. These operators lead to particular correlations among neutrino production, propagation, and detection non-standard effects. We point out that these NSIs and NU phenomenologically lead, in fact, to very similar effects for a neutrino factory, for completely different fundamental reasons. We discuss how the parameters and probabilities are related in this case, and compare the sensitivities. We demonstrate that the NSIs and NU can, in principle, be distinguished for large enough effects at the example of non-standard effects in the $μ$-$τ$-sector, which basically corresponds to differentiating between scalars and fermions as heavy mediators as leading order effect. However, we find that a near detector at superbeams could provide very synergistic information, since the correlation between source and matter NSIs is broken for hadronic neutrino production, while NU is a fundamental effect present at any experiment.

preprint2012arXiv

Renormalization group running of neutrino parameters in the inverse seesaw model

We perform a detailed study of the renormalization group equations in the inverse seesaw model. Especially, we derive compact analytical formulas for the running of the neutrino parameters in the standard model and the minimal supersymmetric standard model, and illustrate that, due to large Yukawa coupling corrections, significant running effects on the leptonic mixing angles can be naturally obtained in the proximity of the electroweak scale, perhaps even within the reach of the LHC. In general, if the mass spectrum of the light neutrinos is nearly degenerate, the running effects are enhanced to experimentally accessible levels, well suitable for the investigation of the underlying dynamics behind the neutrino mass generation and the lepton flavor structure. In addition, the effects of the seesaw thresholds are discussed, and a brief comparison to other seesaw models is carried out.

preprint2012arXiv

RG running in a minimal UED model in light of recent LHC Higgs mass bounds

We study how the recent ATLAS and CMS Higgs mass bounds affect the renormalization group running of the physical parameters in universal extra dimensions. Using the running of the Higgs self-coupling constant, we derive bounds on the cutoff scale of the extra-dimensional theory itself. We show that the running of physical parameters, such as the fermion masses and the CKM mixing matrix, is significantly restricted by these bounds. In particular, we find that the running of the gauge couplings cannot be sufficient to allow gauge unification at the cutoff scale.

preprint2012arXiv

Simple two Parameter Description of Lepton Mixing

We note that a simple two parameter description of lepton mixing is possible which reproduces the features that apparently emerge from global fits at the 1σlevel: if U_{e3} is non-zero it implies that the solar neutrino mixing parameter \sin^2 θ_{12} is less than 1/3 by order |U_{e3}|^2. If the CP phase δis around πit implies that the atmospheric neutrino mixing parameter \sin^2 θ_{23} is less than 1/2 by order |U_{e3}|. The mixing scheme can be described by a 23-rotation appearing to the right of a tri-bimaximal mixing matrix. We quantify the excellent agreement of the scheme with data statistically, and comment on model building aspects.

preprint2012arXiv

Sterile Neutrinos for Warm Dark Matter and the Reactor Anomaly in Flavor Symmetry Models

We construct a flavor symmetry model based on the tetrahedral group A_4 in which the right-handed neutrinos from the seesaw mechanism can be both keV warm dark matter particles and eV-scale sterile neutrinos. This is achieved by giving the right-handed neutrinos appropriate charges under the same Froggatt-Nielsen symmetry responsible for the hierarchy of the charged lepton masses. We discuss the effect of next-to-leading order corrections to deviate the zeroth order tri-bimaximal mixing. Those corrections have two sources: (i) higher order seesaw terms, which are important when the seesaw particles are eV-scale, and (ii) higher-dimensional effective operators suppressed by additional powers of the cut-off scale of the theory. Whereas the mixing angles of the active neutrinos typically receive corrections of the same order, the mixing of the sterile neutrinos with the active ones is rather stable as it is connected with a hierarchy of mass scales. We also modify an effective A_4 model to incorporate keV-scale sterile neutrinos.

preprint2011arXiv

Extended Empirical Fermion Mass Relation

It is known that the charged lepton masses obey to high precision an interesting empirical relation (Koide relation). In turn, the light neutrino masses cannot obey such a relation. We note that if neutrinos acquire their mass via the seesaw mechanism, the empirical mass relation could hold for the masses in the Dirac and/or heavy Majorana mass matrix. Examples for the phenomenological consequences are provided. We furthermore modify the mass relation for light neutrino masses including their Majorana phases, and show that it can be fulfilled in this case as well, with interesting predictions for neutrinoless double beta decay. Finally, we remark that while the relation does not hold for the up- and down-quarks, it may be valid for the u, d, s quarks, and for the c, b, t quarks.

preprint2011arXiv

Light Sterile Neutrinos: Models and Phenomenology

Motivated by recent hints in particle physics and cosmology, we study the realization of eV-scale sterile neutrinos within both the seesaw mechanism and flavor symmetry theories. We show that light sterile neutrinos can rather easily be accommodated in the popular A_4 flavor symmetry models. The exact tri-bimaximal mixing pattern is perturbed due to active-sterile mixing, which we discuss in detail for one example. In addition, we find an interesting extension of the type I seesaw, which can provide a natural origin for eV-scale sterile neutrinos as well as visible admixtures between sterile and active neutrinos. We also show that the presence of sterile neutrinos would significantly change the observables in neutrino experiments, specifically the oscillation probabilities in short-baseline experiments and the effective mass in neutrino-less double beta decay. The latter can prove particularly helpful to strengthen the case for eV-scale sterile neutrinos.

preprint2011arXiv

Minimal seesaw model with S_4 flavor symmetry

We discuss a neutrino mass model based on the S_4 flavor symmetry within the minimal seesaw framework, in which only two right-handed neutrinos are introduced and transform as 2 under S_4. Although the model contains less free parameters compared to the typical seesaw models, it provides a successful description of the observed neutrino parameters, and in particular, a nearly tri-bimaximal mixing pattern can be naturally accommodated. In addition, the heavy right-handed neutrino masses are found to be non-degenerate, while only the normal hierarchical mass spectrum is compatible with experiments for light neutrinos.

preprint2011arXiv

Non-standard antineutrino interactions at Daya Bay

We study the prospects of pinning down the effects of non-standard antineutrino interactions in the source and in the detector at the Daya Bay neutrino facility. It is well known that if the non-standard interactions in the detection process are of the same type as those in the production, their net effect can be subsumed into a mere shift in the measured value of the leptonic mixing angle theta_13. Relaxing this assumption, the ratio of the antineutrino spectra measured by the Daya Bay far and near detectors is distorted in a characteristic way, and good fits based on the standard oscillation hypothesis are no longer viable. We show that, under certain conditions, three years of Daya Bay running can be sufficient to provide a clear hint of non-standard neutrino physics.

preprint2011arXiv

Radiative corrections and explicit perturbations to the tetra-maximal neutrino mixing with large theta_13

The tetra-maximal neutrino mixing pattern predicts a relatively large reactor mixing angle theta_13 \approx 8.4^\circ, which is in good agreement with the latest best-fit value theta_13 = 9^\circ. However, its prediction for theta_12 \approx 30.4^\circ is inconsistent with current oscillation data at the 3sigma C.L. We show that explicit perturbations to the tetra-maximal mixing can naturally enhance theta_12 to its best-fit value theta_12 = 34^\circ. Furthermore, we demonstrate that if the tetra-maximal mixing is produced by a certain flavor symmetry at a high-energy scale Lambda = 10^14 GeV, significant radiative corrections in the minimal supersymmetric standard model can modify theta_12 to be compatible with experimental data at the electroweak scale Lambda_EW = 10^2 GeV. The predictions for theta_13 \approx 8.4^\circ and theta_23 = 45^\circ, as well as the CP-violating phases rho = sigma = -delta = 90^\ciic, are rather stable against radiative corrections.

preprint2011arXiv

Systematic search for successful lepton mixing patterns with nonzero theta_13

We perform a systematic search for simple but viable lepton mixing patterns. Our main criterion is that the mixing matrix can be parameterized by three rotation angles, which are simple fractions of pi. These simple rotation angles possess exact expressions for their sines and cosines, and often arise in the flavor symmetry models. All possible parameterizations of the mixing matrix are taken into account. In total, twenty successful mixing patterns are found to be consistent with the latest neutrino oscillation data (including the recent T2K results) in the CP conserving case, whereas fifteen mixing patterns are allowed in the maximal CP violating case. Potential radiative corrections to the constant mixing patterns are also calculated by solving the renormalization group equations.

preprint2011arXiv

Threshold effects on renormalization group running of neutrino parameters in the low-scale seesaw model

We show that, in the low-scale type-I seesaw model, renormalization group running of neutrino parameters may lead to significant modifications of the leptonic mixing angles in view of so-called seesaw threshold effects. Especially, we derive analytical formulas for radiative corrections to neutrino parameters in crossing the different seesaw thresholds, and show that there may exist enhancement factors efficiently boosting the renormalization group running of the leptonic mixing angles. We find that, as a result of the seesaw threshold corrections to the leptonic mixing angles, various flavor symmetric mixing patterns (e.g., bi-maximal and tri-bimaximal mixing patterns) can be easily accommodated at relatively low energy scales, which is well within the reach of running and forthcoming experiments (e.g., the LHC).

preprint2010arXiv

Dynamical Jumping Real-Time Fault-Tolerant Routing Protocol for Wireless Sensor Networks

In time-critical wireless sensor network (WSN) applications, a high degree of reliability is commonly required. A dynamical jumping real-time fault-tolerant routing protocol (DMRF) is proposed in this paper. Each node utilizes the remaining transmission time of the data packets and the state of the forwarding candidate node set to dynamically choose the next hop. Once node failure, network congestion or void region occurs, the transmission mode will switch to jumping transmission mode, which can reduce the transmission time delay, guaranteeing the data packets to be sent to the destination node within the specified time limit. By using feedback mechanism, each node dynamically adjusts the jumping probabilities to increase the ratio of successful transmission. Simulation results show that DMRF can not only efficiently reduce the effects of failure nodes, congestion and void region, but also yield higher ratio of successful transmission, smaller transmission delay and reduced number of control packets.

preprint2010arXiv

Higgs triplets at like-sign linear colliders and neutrino mixing

We study the phenomenology of the type-II seesaw model at a linear e^-e^- collider. We show that the process e^-e^- \rightarrow alpha^-beta^- (alpha, beta = e, mu, tau being charged leptons) mediated by a doubly charged scalar is very sensitive to the neutrino parameters, in particular the absolute neutrino mass scale and the Majorana CP-violating phases. We identify the regions in parameter space in which appreciable collider signatures in the channel with two like-sign muons in the final state are possible. This includes Higgs triplet masses beyond the reach of the LHC.

preprint2010arXiv

Non-unitarity of the leptonic mixing matrix in the TeV-scale type-I seesaw model

The non-unitarity effects in the leptonic flavor mixing are regarded as one of the generic features of the type-I seesaw model. Therefore, we explore these effects in the TeV-scale type-I seesaw model, and show that there exist non-trivial correlations among the non-unitarity parameters, stemming from the typical flavor structure of the low-scale seesaw model. In general, it follows from analytical discussions and numerical results that all the six non-unitarity parameters are related to three model parameters, while the widely studied parameters η_{eτ} and η_{μτ} cannot be phenomenologically significant

preprint2010arXiv

Signatures from an extra-dimensional seesaw model

We study the generation of small neutrino masses in an extra-dimensional model, where right-handed neutrinos are allowed to propagate in the extra dimension, while the Standard Model particles are confined to a brane. Motivated by the fact that extra-dimensional models are non-renormalizable, we truncate the Kaluza-Klein towers at a maximal extra-dimensional momentum. The structure of the bulk Majorana mass term, motivated by the Sherk-Schwarz mechanism, implies that the right-handed Kaluza-Klein neutrinos pair to form Dirac neutrinos, except for a number of unpaired Majorana neutrinos at the top of each tower. These heavy Majorana neutrinos are the only sources of lepton number breaking in the model, and similarly to the type-I seesaw mechanism, they naturally generate small masses for the left-handed neutrinos. The lower Kaluza-Klein modes mix with the light neutrinos, and the mixing effects are not suppressed with respect to the light-neutrino masses. Compared to conventional fermionic seesaw models, such mixing can be more significant. We study the signals of this model at the Large Hadron Collider, and find that the current low-energy bounds on the non-unitarity of the leptonic mixing matrix are strong enough to exclude an observation.

preprint2009arXiv

Non-unitarity effects in a realistic low-scale seesaw model

We analyze the structure of the non-unitary leptonic mixing matrix in the inverse seesaw model with heavy singlets accessible at the LHC. In this model, unlike in the usual TeV seesaw scenarios, thelow-scale right-handed neutrinos do not suffer from naturalness issues. Underlying correlations among various parameters governing the non-unitarity effects are established, which leads to a considerable improvement of the generic non-unitarity bounds. In view of this, we study the discovery potential of the non-unitarity effects at future experiments, focusing on the sensitivity limits at a neutrino factory.

preprint2009arXiv

The Minimal Seesaw Model at the TeV Scale

We point out that the minimal seesaw model can provide a natural framework to accommodate tiny neutrino masses, while its experimental testability and notable predictiveness are still maintained. This possibility is based on the observation that two heavy right-handed Majorana neutrinos in the minimal seesaw model may naturally emerge as a pseudo-Dirac fermion. In a specific scenario, we show that the tri-bimaximal neutrino mixing can be produced, and only the inverted neutrino mass hierarchy is allowed. The low-energy phenomena, including non-unitarity effects in neutrino oscillations, neutrinoless double-beta decays and rare lepton-flavor-violating decays of charged leptons l_alpha to l_beta + gamma, have been explored. The collider signatures of the heavy singlet neutrino are also briefly discussed.

preprint2006arXiv

Nearly Tri-bimaximal Neutrino Mixing and CP Violation from mu-tau Symmetry Breaking

Assuming the Majorana nature of massive neutrinos, we generalize the Friedberg-Lee neutrino mass model to include CP violation in the neutrino mass matrix $M^{}_ν$. We show that a favorable neutrino mixing pattern (with $θ^{}_{12} \approx 35.3^\circ$, $θ^{}_{23} = 45^\circ$, $θ^{}_{13} \neq 0^\circ$ and $δ= 90^\circ$) can naturally be derived from $M^{}_ν$, if it has an approximate or softly-broken $μ$-$τ$ symmetry. We point out a different way to obtain the nearly tri-bimaximal neutrino mixing with $δ= 0^\circ$ and non-vanishing Majorana phases. The most general case, in which all the free parameters of $M^{}_ν$ are complex and the resultant neutrino mixing matrix contains both Dirac and Majorana phases of CP violation, is also discussed.

preprint2005arXiv

Leptonic Unitarity Triangles in Matter

We present a geometric description of lepton flavor mixing and CP violation in matter by using the language of leptonic unitarity triangles. The exact analytical relations for both sides and inner angles are established between every unitarity triangle in vacuum and its effective counterpart in matter. The typical shape evolution of six triangles with the terrestrial matter density is illustrated for a realistic long-baseline neutrino oscillation experiment.

preprint2003arXiv

Lepton Mass Matrices with Four Texture Zeros

We propose two ans$\rm\ddot{a}$tze of lepton mass matrices with four texture zeros, and confront them with current experimental data on neutrino oscillations. The parameter space of each ansatz is carefully explored. We find that both ans$\rm\ddot{a}$tze can accommodate the normal hierarchy of neutrino masses and the bi-large pattern of lepton flavor mixing. Their predictions for the effective mass of the tritium beta decay and that of the neutrinoless double beta decay are too small to be detectable, but leptonic CP violation at the percent level is allowed. Some discussions are also given about the seesaw invariance of the four-zero texture of Dirac and Majorana neutrino mass matrices.

He Zhang

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69 published item(s)

BioHuman: Learning Biomechanical Human Representations from Video

OmniVCus: Feedforward Subject-driven Video Customization with Multimodal Control Conditions

A Programmable Spatiotemporal Quantum Parametric Mode Sorter

Metrics for Software Process Simulation Modeling

A Cross-Company Ethnographic Study on Software Teams for DevOps and Microservices: Organization, Benefits, and Issues

A Flexible Diffusion Model

A predictor-corrector deep learning algorithm for high dimensional stochastic partial differential equations

An Industrial Experience Report on Retro-inspection

Automorphisms and representations of quasi Laurent polynomial algebras

Boosting Robustness of Image Matting with Context Assembling and Strong Data Augmentation

Controllable Shadow Generation Using Pixel Height Maps

DoubleField: Bridging the Neural Surface and Radiance Fields for High-fidelity Human Reconstruction and Rendering

HVTR: Hybrid Volumetric-Textural Rendering for Human Avatars

Interactive Portrait Harmonization

Leveraging Cross Feedback of User and Item Embeddings with Attention for Variational Autoencoder based Collaborative Filtering

Representations of Quantum Coordinate Algebras at Generic $q$ and Wiring Diagrams

Threshold solutions for nonlocal reaction diffusion equations

A Driving Behavior Recognition Model with Bi-LSTM and Multi-Scale CNN

Single-Pixel Pattern Recognition with Coherent Nonlinear Optics

SummPip: Unsupervised Multi-Document Summarization with Sentence Graph Compression

Synergy between Machine/Deep Learning and Software Engineering: How Far Are We?

ThreshKnot: Thresholded ProbKnot for Improved RNA Secondary Structure Prediction

Traffic Agent Trajectory Prediction Using Social Convolution and Attention Mechanism

Wide-field, high-resolution lensless on-chip microscopy via near-field blind ptychographic modulation

LinearFold: linear-time approximate RNA folding by 5'-to-3' dynamic programming and beam search

Polarimetric Thermal to Visible Face Verification via Attribute Preserved Synthesis

Linear absorption coefficient of in-plane graphene on a silicon microring resonator

Towards Evidence-Based Ontology for Supporting Systematic Literature Review

Constraining Sterile Neutrinos Using Reactor Neutrino Experiments

Nonstandard interaction effects on neutrino parameters at medium-baseline reactor antineutrino experiments

Reducing θ_13 to 9 degrees

Renormalization Group Running of the Neutrino Mass Operator in Extra Dimensions

Status of the Zee-Babu model for neutrino mass and possible tests at a like-sign linear collider

A Factor Framework for Experimental Design for Performance Evaluation of Commercial Cloud Services

Building an Expert System for Evaluation of Commercial Cloud Services

Circumstantial-Evidence-Based Judgment for Software Effort Estimation

Effects of nonstandard neutrino interactions at PINGU

Exotic Charges, Multicomponent Dark Matter and Light Sterile Neutrinos

Meta-Analysis of Gene Level Association Tests

On a Catalogue of Metrics for Evaluating Commercial Cloud Services

Probing the leptonic Dirac CP-violating phase in neutrino oscillation experiments

Radiative corrections to the leptonic Dirac CP-violating phase

The Cloud's Cloudy Moment: A Systematic Survey of Public Cloud Service Outage

Towards a Taxonomy of Performance Evaluation of Commercial Cloud Services

Towards Technology Independent Strategies for SOA Implementations

Impact of massive neutrinos on the Higgs self-coupling and electroweak vacuum stability

Impacts of the Higgs mass on vacuum stability, running fermion masses and two-body Higgs decays

Light Sterile Neutrino in the Minimal Extended Seesaw

Non-standard interactions versus non-unitary lepton flavor mixing at a neutrino factory

Renormalization group running of neutrino parameters in the inverse seesaw model

RG running in a minimal UED model in light of recent LHC Higgs mass bounds

Simple two Parameter Description of Lepton Mixing

Sterile Neutrinos for Warm Dark Matter and the Reactor Anomaly in Flavor Symmetry Models

Extended Empirical Fermion Mass Relation

Light Sterile Neutrinos: Models and Phenomenology

Minimal seesaw model with S_4 flavor symmetry

Non-standard antineutrino interactions at Daya Bay

Radiative corrections and explicit perturbations to the tetra-maximal neutrino mixing with large theta_13

Systematic search for successful lepton mixing patterns with nonzero theta_13

Threshold effects on renormalization group running of neutrino parameters in the low-scale seesaw model

Dynamical Jumping Real-Time Fault-Tolerant Routing Protocol for Wireless Sensor Networks

Higgs triplets at like-sign linear colliders and neutrino mixing

Non-unitarity of the leptonic mixing matrix in the TeV-scale type-I seesaw model

Signatures from an extra-dimensional seesaw model

Non-unitarity effects in a realistic low-scale seesaw model

The Minimal Seesaw Model at the TeV Scale

Nearly Tri-bimaximal Neutrino Mixing and CP Violation from mu-tau Symmetry Breaking

Leptonic Unitarity Triangles in Matter

Lepton Mass Matrices with Four Texture Zeros