arXiv subjects

Tao Han

Publications and source records attributed to Tao Han.

At least 163 records · Page 9Linked to original sources

arXiv2021

Unveiling Hidden Physics at the LHC

The field of particle physics is at the crossroads. The discovery of a Higgs-like boson completed the Standard Model (SM), but the lacking observation of convincing resonances Beyond the SM (BSM) offers no guidance for the future of particle physics. On the other hand, the motivation for New Physics has not diminished and is, in fact, reinforced by several striking anomalous results in many experiments. Here we summarise the status of the most significant anomalies, including the most recent results for the flavour anomalies, the multi-lepton anomalies at the LHC, the Higgs-like excess at around 96 GeV, and anomalies in neutrino physics, astrophysics, cosmology, and cosmic rays. While the LHC promises up to 4/ab of integrated luminosity and far-reaching physics programmes to unveil BSM physics, we consider the possibility that the latter could be tested with present data, but that systemic shortcomings of the experiments and their search strategies may preclude their discovery for several reasons, including: final states consisting in soft particles only, associated production processes, QCD-like final states, close-by SM resonances, and SUSY scenarios where no missing energy is produced. New search strategies could help to unveil the hidden BSM signatures, devised by making use of the CERN open data as a new testing ground. We discuss the CERN open data with its policies, challenges, and potential usefulness for the community. We showcase the example of the CMS collaboration, which is the only collaboration regularly releasing some of its data. We find it important to stress that individuals using public data for their own research does not imply competition with experimental efforts, but rather provides unique opportunities to give guidance for further BSM searches by the collaborations. Wide access to open data is paramount to fully exploit the LHCs potential.

hep-ph↗

arXiv2021

VisDrone-CC2020: The Vision Meets Drone Crowd Counting Challenge Results

Crowd counting on the drone platform is an interesting topic in computer vision, which brings new challenges such as small object inference, background clutter and wide viewpoint. However, there are few algorithms focusing on crowd counting on the drone-captured data due to the lack of comprehensive datasets. To this end, we collect a large-scale dataset and organize the Vision Meets Drone Crowd Counting Challenge (VisDrone-CC2020) in conjunction with the 16th European Conference on Computer Vision (ECCV 2020) to promote the developments in the related fields. The collected dataset is formed by $3,360$ images, including $2,460$ images for training, and $900$ images for testing. Specifically, we manually annotate persons with points in each video frame. There are $14$ algorithms from $15$ institutes submitted to the VisDrone-CC2020 Challenge. We provide a detailed analysis of the evaluation results and conclude the challenge. More information can be found at the website: \url{http://www.aiskyeye.com/}.

cs.CV↗

arXiv2021

Supervised Contrastive Learning for Accented Speech Recognition

Neural network based speech recognition systems suffer from performance degradation due to accented speech, especially unfamiliar accents. In this paper, we study the supervised contrastive learning framework for accented speech recognition. To build different views (similar "positive" data samples) for contrastive learning, three data augmentation techniques including noise injection, spectrogram augmentation and TTS-same-sentence generation are further investigated. From the experiments on the Common Voice dataset, we have shown that contrastive learning helps to build data-augmentation invariant and pronunciation invariant representations, which significantly outperforms traditional joint training methods in both zero-shot and full-shot settings. Experiments show that contrastive learning can improve accuracy by 3.66% (zero-shot) and 3.78% (full-shot) on average, comparing to the joint training method.

cs.SD↗

arXiv2021

WIMPs at High Energy Muon Colliders

The Weakly Interacting Massive Particle (WIMP) paradigm is one of the most compelling scenarios for particle dark matter (DM). We show in this paper that a high energy muon collider can make decisive statements about the WIMP DM, and this should serve as one of its main physics driver cases. We demonstrate this by employing the DM as the lightest member of an electroweak (EW) multiplet, which is a simple, yet one of the most challenging WIMP scenarios given its minimal collider signature and high thermal target mass scale of 1 TeV$-$23 TeV. We perform a first study of the reach of high energy muon colliders, focusing on the simple, inclusive and conservative signals with large missing mass, through the mono-photon, VBF di-muon and a novel mono-muon channel. Using these inclusive signals, it is possible to cover the thermal targets of doublet and triplet with a 10 TeV muon collider. Higher energies, 14 TeV$-$75 TeV, would ensure a $5 σ$ reach above the thermal targets for the higher EW multiplets. We also estimate the reach of a search for disappearing tracks, demonstrating the potential significant enhancement of the sensitivity.

hep-ph↗

arXiv2021

Electroweak Phase Transition with an SU(2) Dark Sector

We consider a non-Abelian dark SU(2)$_{\rm D}$ model where the dark sector couples to the Standard Model (SM) through a Higgs portal. We investigate two different scenarios of the dark sector scalars with $Z_2$ symmetry, with Higgs portal interactions that can introduce mixing between the SM Higgs boson and the SM singlet scalars in the dark sector. We utilize the existing collider results of the Higgs signal rate, direct heavy Higgs searches, and electroweak precision observables to constrain the model parameters. The $\text{SU(2)}_{\text{D}}$ partially breaks into $\text{U(1)}_{\text{D}}$ gauge group by the scalar sector. The resulting two stable massive dark gauge bosons and pseudo-Goldstone bosons can be viable cold dark matter candidates, while the massless gauge boson from the unbroken $\text{U(1)}_{\text{D}}$ subgroup is a dark radiation and can introduce long-range attractive dark matter (DM) self-interaction, which can alleviate the small-scale structure issues. We study in detail the pattern of strong first-order phase transition and gravitational wave (GW) production triggered by the dark sector symmetry breaking, and further evaluate the signal-to-noise ratio for several proposed space interferometer missions. We conclude that the rich physics in the dark sector may be observable with the current and future measurements at colliders, DM experiments, and GW interferometers.

hep-ph↗

arXiv2021

Scalar and tensor neutrino interactions

We constrain general Dirac neutrino interactions based on the Standard Model Effective Field Theory framework extended with right-handed neutrinos $N$ (SMNEFT) using deep inelastic and coherent elastic neutrino scattering, nuclear beta decay, and meson decay data, and high energy electron-proton and proton-proton collider data. We compute the one-loop anomalous dimensions of the low-energy effective field theory (LEFT) below the electroweak scale and of SMNEFT above the electroweak scale. The tree-level matching between LEFT and SMNEFT is performed at the electroweak scale. Currently, the most stringent limits on scalar and tensor interactions arise from pseudoscalar meson decays and the LHC measurements at the per mille level. In the future, the upcoming High-Luminosity LHC (HL-LHC) has the potential to reach the $10^{-4}$ level and LHeC can play an important role under certain theoretical assumptions.

hep-ph↗

arXiv2021

References of References: How Far is the Knowledge Ancestry

Scientometrics studies have extended from direct citations to high-order citations, as simple citation count is found to tell only part of the story regarding scientific impact. This extension is deemed to be beneficial in scenarios like research evaluation, science history modeling, and information retrieval. In contrast to citations of citations (forward citation generations), references of references (backward citation generations) as another side of high-order citations, is relatively less explored. We adopt a series of metrics for measuring the unfolding of backward citations of a focal paper, tracing back to its knowledge ancestors generation by generation. Two sub-fields in Physics are subject to such analysis on a large-scale citation network. Preliminary results show that (1) most papers in our dataset can be traced to their knowledge ancestry; (2) the size distribution of backward citation generations presents a decreasing-and-then-increasing shape; and (3) citations more than one generation away are still relevant to the focal paper, from either a forward or backward perspective; yet, backward citation generations are higher in topic relevance to the paper of interest. Furthermore, the backward citation generations shed lights for literature recommendation, science evaluation, and sociology of science studies.

cs.DL↗

arXiv2021

Neuron Linear Transformation: Modeling the Domain Shift for Crowd Counting

Cross-domain crowd counting (CDCC) is a hot topic due to its importance in public safety. The purpose of CDCC is to alleviate the domain shift between the source and target domain. Recently, typical methods attempt to extract domain-invariant features via image translation and adversarial learning. When it comes to specific tasks, we find that the domain shifts are reflected on model parameters' differences. To describe the domain gap directly at the parameter-level, we propose a Neuron Linear Transformation (NLT) method, exploiting domain factor and bias weights to learn the domain shift. Specifically, for a specific neuron of a source model, NLT exploits few labeled target data to learn domain shift parameters. Finally, the target neuron is generated via a linear transformation. Extensive experiments and analysis on six real-world datasets validate that NLT achieves top performance compared with other domain adaptation methods. An ablation study also shows that the NLT is robust and more effective than supervised and fine-tune training. Code is available at: \url{https://github.com/taohan10200/NLT}.

cs.CV↗

arXiv2020

LiveMap: Real-Time Dynamic Map in Automotive Edge Computing

Autonomous driving needs various line-of-sight sensors to perceive surroundings that could be impaired under diverse environment uncertainties such as visual occlusion and extreme weather. To improve driving safety, we explore to wirelessly share perception information among connected vehicles within automotive edge computing networks. Sharing massive perception data in real time, however, is challenging under dynamic networking conditions and varying computation workloads. In this paper, we propose LiveMap, a real-time dynamic map, that detects, matches, and tracks objects on the road with crowdsourcing data from connected vehicles in sub-second. We develop the data plane of LiveMap that efficiently processes individual vehicle data with object detection, projection, feature extraction, object matching, and effectively integrates objects from multiple vehicles with object combination. We design the control plane of LiveMap that allows adaptive offloading of vehicle computations, and develop an intelligent vehicle scheduling and offloading algorithm to reduce the offloading latency of vehicles based on deep reinforcement learning (DRL) techniques. We implement LiveMap on a small-scale testbed and develop a large-scale network simulator. We evaluate the performance of LiveMap with both experiments and simulations, and the results show LiveMap reduces 34.1% average latency than the baseline solution.

cs.NI↗

arXiv2020

Off-shell Higgs Couplings in $H^*\to ZZ\to \ell\ellνν$

We explore the new physics reach for the off-shell Higgs boson measurement in the ${pp \to H^* \rightarrow Z(\ell^{+}\ell^{-})Z(ν\barν)}$ channel at the high-luminosity LHC. The new physics sensitivity is parametrized in terms of the Higgs boson width, effective field theory framework, and a non-local Higgs-top coupling form factor. Adopting Machine-learning techniques, we demonstrate that the combination of a large signal rate and a precise phenomenological probe for the process energy scale, due to the transverse $ZZ$ mass, leads to significant sensitivities beyond the existing results in the literature for the new physics scenarios considered.

hep-ph↗

arXiv2020

Type-I 2HDM under the Higgs and Electroweak Precision Measurements

We explore the extent to which future precision measurements of the Standard Model (SM) observables at the proposed $Z$-factories and Higgs factories may have impacts on new physics beyond the Standard Model, as illustrated by studying the Type-I Two-Higgs-doublet model (Type-I 2HDM). We include the contributions from the heavy Higgs bosons at the tree-level and at the one-loop level in a full model-parameter space. While only small $\tanβ$ region is strongly constrained at tree level, the large $\tanβ$ region gets constrained at loop level due to $\tanβ$ enhanced tri-Higgs couplings. We perform a multiple variable global fit with non-alignment and non-degenerate masses. We find that the allowed parameter ranges could be tightly constrained by the future Higgs precision measurements, especially for small and large values of $\tanβ$. Indirect limits on the masses of heavy Higgs bosons can be obtained, which can be complementary to the direct searches of the heavy Higgs bosons at hadron colliders. We also find that the expected accuracies at the $Z$-pole and at a Higgs factory are quite complementary in constraining mass splittings of heavy Higgs bosons. The typical results are $|\cos(β-α)| < 0.05, |Δm_Φ| < 200\ {\rm GeV}$, and $\tanβ\gtrsim 0.3$. The reaches from CEPC, FCCee and ILC are also compared, for both Higgs and $Z$-pole precision measurements. Comparing to the Type-II 2HDM, the 95\% C.L. allowed range of $\cos(β-α)$ is larger, especially for large values of $\tanβ$.

hep-ph↗

arXiv2020

Answer-checking in Context: A Multi-modal FullyAttention Network for Visual Question Answering

Visual Question Answering (VQA) is challenging due to the complex cross-modal relations. It has received extensive attention from the research community. From the human perspective, to answer a visual question, one needs to read the question and then refer to the image to generate an answer. This answer will then be checked against the question and image again for the final confirmation. In this paper, we mimic this process and propose a fully attention based VQA architecture. Moreover, an answer-checking module is proposed to perform a unified attention on the jointly answer, question and image representation to update the answer. This mimics the human answer checking process to consider the answer in the context. With answer-checking modules and transferred BERT layers, our model achieves the state-of-the-art accuracy 71.57\% using fewer parameters on VQA-v2.0 test-standard split.

cs.CV↗

arXiv2020

Unsupervised Semantic Aggregation and Deformable Template Matching for Semi-Supervised Learning

Unlabeled data learning has attracted considerable attention recently. However, it is still elusive to extract the expected high-level semantic feature with mere unsupervised learning. In the meantime, semi-supervised learning (SSL) demonstrates a promising future in leveraging few samples. In this paper, we combine both to propose an Unsupervised Semantic Aggregation and Deformable Template Matching (USADTM) framework for SSL, which strives to improve the classification performance with few labeled data and then reduce the cost in data annotating. Specifically, unsupervised semantic aggregation based on Triplet Mutual Information (T-MI) loss is explored to generate semantic labels for unlabeled data. Then the semantic labels are aligned to the actual class by the supervision of labeled data. Furthermore, a feature pool that stores the labeled samples is dynamically updated to assign proxy labels for unlabeled data, which are used as targets for cross-entropy minimization. Extensive experiments and analysis across four standard semi-supervised learning benchmarks validate that USADTM achieves top performance (e.g., 90.46$\%$ accuracy on CIFAR-10 with 40 labels and 95.20$\%$ accuracy with 250 labels). The code is released at https://github.com/taohan10200/USADTM.

cs.CV↗

arXiv2020

Finding the Evidence: Localization-aware Answer Prediction for Text Visual Question Answering

Image text carries essential information to understand the scene and perform reasoning. Text-based visual question answering (text VQA) task focuses on visual questions that require reading text in images. Existing text VQA systems generate an answer by selecting from optical character recognition (OCR) texts or a fixed vocabulary. Positional information of text is underused and there is a lack of evidence for the generated answer. As such, this paper proposes a localization-aware answer prediction network (LaAP-Net) to address this challenge. Our LaAP-Net not only generates the answer to the question but also predicts a bounding box as evidence of the generated answer. Moreover, a context-enriched OCR representation (COR) for multimodal fusion is proposed to facilitate the localization task. Our proposed LaAP-Net outperforms existing approaches on three benchmark datasets for the text VQA task by a noticeable margin.

cs.CV↗

arXiv2020

A Flow Base Bi-path Network for Cross-scene Video Crowd Understanding in Aerial View

Drones shooting can be applied in dynamic traffic monitoring, object detecting and tracking, and other vision tasks. The variability of the shooting location adds some intractable challenges to these missions, such as varying scale, unstable exposure, and scene migration. In this paper, we strive to tackle the above challenges and automatically understand the crowd from the visual data collected from drones. First, to alleviate the background noise generated in cross-scene testing, a double-stream crowd counting model is proposed, which extracts optical flow and frame difference information as an additional branch. Besides, to improve the model's generalization ability at different scales and time, we randomly combine a variety of data transformation methods to simulate some unseen environments. To tackle the crowd density estimation problem under extreme dark environments, we introduce synthetic data generated by game Grand Theft Auto V(GTAV). Experiment results show the effectiveness of the virtual data. Our method wins the challenge with a mean absolute error (MAE) of 12.70. Moreover, a comprehensive ablation study is conducted to explore each component's contribution.

cs.CV↗

arXiv2020

Electroweak Couplings of the Higgs Boson at a Multi-TeV Muon Collider

We estimate the expected precision at a multi-TeV muon collider for measuring the Higgs boson couplings with electroweak gauge bosons, $HVV$ and $HHVV\ (V=W^\pm,Z)$, as well as the trilinear Higgs self-coupling $HHH$. At very high energies both single and double Higgs productions rely on the vector-boson fusion (VBF) topology. The outgoing remnant particles have a strong tendency to stay in the very forward region, leading to the configuration of the "inclusive process" and making it difficult to isolate $ZZ$ fusion events from the $WW$ fusion. In the single Higgs channel, we perform a maximum likelihood analysis on $HWW$ and $HZZ$ couplings using two categories: the inclusive Higgs production and the 1-muon exclusive signal. In the double Higgs channel, we consider the inclusive production and study the interplay of the trilinear $HHH$ and the quartic $VVHH$ couplings, by utilizing kinematic information in the invariant mass spectrum. We find that at a centre-of-mass energy of 10 TeV (30 TeV) with an integrated luminosity of 10 ab$^{-1}$ (90 ab$^{-1}$), one may reach a 95\% confidence level sensitivity of 0.073\% (0.023\%) for $WWH$ coupling, 0.61\% (0.21\%) for $ZZH$ coupling, 0.62\% (0.20\%) for $WWHH$ coupling, and 5.6\% (2.0\%) for $HHH$ coupling. For dim-6 operators contributing to the processes, these sensitivities could probe the new physics scale $Λ$ in the order of $1-10$ ($2-20$) TeV at a 10 TeV (30 TeV) muon collider.

hep-ph↗

arXiv2020

DeepSlicing: Deep Reinforcement Learning Assisted Resource Allocation for Network Slicing

Network slicing enables multiple virtual networks run on the same physical infrastructure to support various use cases in 5G and beyond. These use cases, however, have very diverse network resource demands, e.g., communication and computation, and various performance metrics such as latency and throughput. To effectively allocate network resources to slices, we propose DeepSlicing that integrates the alternating direction method of multipliers (ADMM) and deep reinforcement learning (DRL). DeepSlicing decomposes the network slicing problem into a master problem and several slave problems. The master problem is solved based on convex optimization and the slave problem is handled by DRL method which learns the optimal resource allocation policy. The performance of the proposed algorithm is validated through network simulations.

eess.SP↗

arXiv2020

Comparative Studies of 2HDMs under the Higgs Boson Precision Measurements

We perform comparative studies for four types of the two Higgs Doublet Models (2HDMs) under the precision measurements of the Standard Model (SM) Higgs observables at the proposed Higgs factories. We explore the discovery potential based on the hypothetical deviations in the precision data for the 2HDMs up to one-loop level. We find $5σ$ observability from the $χ^2$ fitting in a significant theory parameter space at future Higgs factories. For the Type-I 2HDM, regions with $\cos(β-α)\lesssim -0.1$ or $\cos(β-α)\gtrsim 0.08$ are discoverable at more than $5σ$ level. For the other three types of 2HDMs, the $5σ$ region is even bigger: $|\cos(β-α)|\gtrsim 0.02$ for $\tanβ\sim 1$. At small and large values of $\tanβ$, the region in $\cos(β-α)$ is further tightened. We examine the extent to which the different 2HDM theories may be distinguishable from one to the other at the $95\%$ Confidence Level with four benchmark points as case studies. We show that a large part of the parameter space of the other types of 2HDMs can be distinguished from the benchmark points of the target model. The impacts of loop corrections are found to be significant in certain parameter regions.

hep-ph↗