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Yi Jin

Publications and source records attributed to Yi Jin.

At least 55 records · Page 3Linked to original sources

Displaced Heavy Neutral Lepton from New Higgs Doublet

Heavy neutral leptons $N$ are introduced to explain the tiny neutrino masses via the seesaw mechanism. For proper small mixing parameter $V_{\ell N}$, the heavy neutral leptons $N$ become long-lived, which leads to the displaced vertex signature at colliders. In this paper, we consider the displaced heavy neutral lepton from the neutrinophilic Higgs doublet $Φ_ν$ decay. The new Higgs doublet with MeV scale VEV can naturally explain the tiny neutrino masses with TeV scale $N$. Different from current experimental searches via the $W^\pm\to \ell^\pm N$ decay, the new decays as $H^\pm\to \ell^\pm N$ are not suppressed by the small mixing parameter $V_{\ell N}$. Therefore, a larger parameter space is expected to be detected at colliders. We then investigate the promising region at the 14 TeV HL-LHC and the 3 TeV CLIC. According to our simulation, the DV signature could probe $|V_{\ell N}|^2\gtrsim10^{-19}$ with $m_N<m_{H^+}$, which covers the seesaw predicted value $|V_{\ell N}|^2\sim m_ν/m_N$. We could probe $m_{H^+}\lesssim1200$ GeV at the 14 TeV HL-LHC and $m_{H^+}\lesssim1490$ GeV at the 3 TeV CLIC.

hep-ph↗

Observation of Heat Pumping Effect by Radiative Shuttling

Heat shuttling phenomenon is characterized by the presence of a non-zero heat flow between two bodies without net thermal bias on average. It was initially predicted in the context of nonlinear heat conduction within atomic lattices coupled to two time-oscillating thermostats. Recent theoretical works revealed an analog of this effect for heat exchanges mediated by thermal photons between two solids having a temperature dependent emissivity. In this paper, we present the experimental proof of this effect using systems made with composite materials based on phase change materials. By periodically modulating the temperature of one of two solids we report that the system akin to heat pumping with a controllable heat flow direction. Additionally, we demonstrate the effectiveness of a simultaneous modulation of two temperatures to control both the strength and direction of heat shuttling by exploiting the phase delay between these temperatures. These results show that this effect is promising for an active thermal management of solid-state technology, to cool down solids, to insulate them from their background or to amplify heat exchanges.

physics.app-ph↗

Domain Adaptive Attention Learning for Unsupervised Person Re-Identification

Person re-identification (Re-ID) across multiple datasets is a challenging task due to two main reasons: the presence of large cross-dataset distinctions and the absence of annotated target instances. To address these two issues, this paper proposes a domain adaptive attention learning approach to reliably transfer discriminative representation from the labeled source domain to the unlabeled target domain. In this approach, a domain adaptive attention model is learned to separate the feature map into domain-shared part and domain-specific part. In this manner, the domain-shared part is used to capture transferable cues that can compensate cross-dataset distinctions and give positive contributions to the target task, while the domain-specific part aims to model the noisy information to avoid the negative transfer caused by domain diversity. A soft label loss is further employed to take full use of unlabeled target data by estimating pseudo labels. Extensive experiments on the Market-1501, DukeMTMC-reID and MSMT17 benchmarks demonstrate the proposed approach outperforms the state-of-the-arts.

cs.CV↗

Transient Measurement of Near-field Thermal Radiation between Macroscopic Objects

The involvement of evanescent waves in the near-field regime could greatly enhance the spontaneous thermal radiation, offering a unique opportunity to study nanoscale photon-phonon interaction. However, accurately characterizing this subtle phenomenon is very challenging. This paper proposes a transient all-optical method for rapidly characterizing near-field radiative heat transfer (NFRHT) between macroscopic objects, using the first law of thermodynamics. Significantly, a full measurement at a fixed gap distance is completed within tens of seconds. By simplifying the configuration, the transient all-optical method achieves high measurement accuracy and reliable reproducibility. The proposed method can effectively analyze the NFRHT in various material systems, including SiO2, SiC, and Si, which involve different phonon or plasmon polaritons. Experimental observations demonstrate significant super-Planckian radiation, which arises from the near-field coupling of bounded surface modes. Furthermore, the method achieves excellent agreement with theory, with a minimal discrepancy of less than 2.7% across a wide temperature range. This wireless method could accurately characterize the NFRHT for objects with different sizes or optical properties, enabling the exploration of both fundamental interests and practical applications.

physics.optics↗

Radiative Thermal Transistor

Developing thermal analogues of field-effect transistor could open the door to a low-power and even zero-power communication technology working with heat rather than electricity. These solid-sate devices could also find many applications in the field of active thermal management in numerous technologies (microelectronic, building science, energy harvesting,conversion,...). Recent theoretical works has suggested that a photonic transistor made with three terminals can in principle be used to switch, modulate, and even amplify heat flux through exchange of thermal photons. Here, we report an experimental demonstration of thermal transistor effect using a non-contact system composed by a temperature-controlled metal-insulator-based material interacting in far-field regime with two blackbodies held at two different temperatures. We demonstrate that, with a tiny change in the temperature of the active layer, the heat flux received by the cold blackbody can be drastically modified. An amplification parameter of heat flux over 20 is reported.

physics.app-ph↗

Real-Time 4K Super-Resolution of Compressed AVIF Images. AIS 2024 Challenge Survey

This paper introduces a novel benchmark as part of the AIS 2024 Real-Time Image Super-Resolution (RTSR) Challenge, which aims to upscale compressed images from 540p to 4K resolution (4x factor) in real-time on commercial GPUs. For this, we use a diverse test set containing a variety of 4K images ranging from digital art to gaming and photography. The images are compressed using the modern AVIF codec, instead of JPEG. All the proposed methods improve PSNR fidelity over Lanczos interpolation, and process images under 10ms. Out of the 160 participants, 25 teams submitted their code and models. The solutions present novel designs tailored for memory-efficiency and runtime on edge devices. This survey describes the best solutions for real-time SR of compressed high-resolution images.

cs.CV↗

Stronger, Fewer, & Superior: Harnessing Vision Foundation Models for Domain Generalized Semantic Segmentation

In this paper, we first assess and harness various Vision Foundation Models (VFMs) in the context of Domain Generalized Semantic Segmentation (DGSS). Driven by the motivation that Leveraging Stronger pre-trained models and Fewer trainable parameters for Superior generalizability, we introduce a robust fine-tuning approach, namely Rein, to parameter-efficiently harness VFMs for DGSS. Built upon a set of trainable tokens, each linked to distinct instances, Rein precisely refines and forwards the feature maps from each layer to the next layer within the backbone. This process produces diverse refinements for different categories within a single image. With fewer trainable parameters, Rein efficiently fine-tunes VFMs for DGSS tasks, surprisingly surpassing full parameter fine-tuning. Extensive experiments across various settings demonstrate that Rein significantly outperforms state-of-the-art methods. Remarkably, with just an extra 1% of trainable parameters within the frozen backbone, Rein achieves a mIoU of 78.4% on the Cityscapes, without accessing any real urban-scene datasets.Code is available at https://github.com/w1oves/Rein.git.

cs.CV↗

Seed Optimization with Frozen Generator for Superior Zero-shot Low-light Enhancement

In this work, we observe that the generators, which are pre-trained on massive natural images, inherently hold the promising potential for superior low-light image enhancement against varying scenarios.Specifically, we embed a pre-trained generator to Retinex model to produce reflectance maps with enhanced detail and vividness, thereby recovering features degraded by low-light conditions.Taking one step further, we introduce a novel optimization strategy, which backpropagates the gradients to the input seeds rather than the parameters of the low-light enhancement model, thus intactly retaining the generative knowledge learned from natural images and achieving faster convergence speed. Benefiting from the pre-trained knowledge and seed-optimization strategy, the low-light enhancement model can significantly regularize the realness and fidelity of the enhanced result, thus rapidly generating high-quality images without training on any low-light dataset. Extensive experiments on various benchmarks demonstrate the superiority of the proposed method over numerous state-of-the-art methods qualitatively and quantitatively.

cs.CV↗

The Cascaded Forward Algorithm for Neural Network Training

Backpropagation algorithm has been widely used as a mainstream learning procedure for neural networks in the past decade, and has played a significant role in the development of deep learning. However, there exist some limitations associated with this algorithm, such as getting stuck in local minima and experiencing vanishing/exploding gradients, which have led to questions about its biological plausibility. To address these limitations, alternative algorithms to backpropagation have been preliminarily explored, with the Forward-Forward (FF) algorithm being one of the most well-known. In this paper we propose a new learning framework for neural networks, namely Cascaded Forward (CaFo) algorithm, which does not rely on BP optimization as that in FF. Unlike FF, our framework directly outputs label distributions at each cascaded block, which does not require generation of additional negative samples and thus leads to a more efficient process at both training and testing. Moreover, in our framework each block can be trained independently, so it can be easily deployed into parallel acceleration systems. The proposed method is evaluated on four public image classification benchmarks, and the experimental results illustrate significant improvement in prediction accuracy in comparison with the baseline.

cs.CV↗

Transmission line condition prediction based on semi-supervised learning

Transmission line state assessment and prediction are of great significance for the rational formulation of operation and maintenance strategy and improvement of operation and maintenance level. Aiming at the problem that existing models cannot take into account the robustness and data demand, this paper proposes a state prediction method based on semi-supervised learning. Firstly, for the expanded feature vector, the regular matrix is used to fill in the missing data, and the sparse coding problem is solved by representation learning. Then, with the help of a small number of labelled samples to initially determine the category centers of line segments in different defective states. Finally, the estimated parameters of the model are corrected using unlabeled samples. Example analysis shows that this method can improve the recognition accuracy and use data more efficiently than the existing models.

eess.SP↗

Probing Dirac Neutrino Properties with Dilepton Signature

The neutrinophilic two Higgs doublet model is one of the simplest models to explain the origin of tiny Dirac neutrino masses. This model introduces a new Higgs doublet with eV scale VEV to naturally generate the tiny neutrino masses. Depending on the same Yukawa coupling, the neutrino oscillation patterns can be probed with the dilepton signature from the decay of charged scalar $H^\pm$. For example, the normal hierarchy predicts BR$(H^+\to e^+ν)\ll$ BR$(H^+\to μ^+ν)\approx$ BR$(H^+\to τ^+ν)\simeq0.5$ when the lightest neutrino mass is below 0.01 eV, while the inverted hierarchy predicts BR$(H^+\to e^+ν)/2\simeq$ BR$(H^+\to μ^+ν)\simeq$ BR$(H^+\to τ^+ν)\simeq0.25$. By precise measurement of BR$(H^+\to \ell^+ν)$, we are hopefully to probe the lightest neutrino mass and the atmospheric mixing angle $θ_{23}$. Through the detailed simulation of the dilepton signature and corresponding backgrounds, we find that the 3 TeV CLIC could discover $M_{H^+}\lesssim1220$ GeV for NH and $M_{H^+}\lesssim1280$ GeV for IH. Meanwhile, the future 100 TeV FCC-hh collider could probe $M_{H^+}\lesssim1810$ GeV for NH and $M_{H^+}\lesssim2060$ GeV for IH.

hep-ph↗

An Empirical Analysis of Just-in-Time Compilation in Modern Databases

JIT (Just-in-Time) technology has garnered significant attention for improving the efficiency of database execution. It offers higher performance by eliminating interpretation overhead compared to traditional execution engines. LLVM serves as the primary JIT architecture, which was implemented in PostgreSQL since version 11. However, recent advancements in WASM-based databases, such as Mutable, present an alternative JIT approach. This approach minimizes the extensive engineering efforts associated with the execution engine and focuses on optimizing supported operators for lower latency and higher throughput. In this paper, we perform comprehensive experiments on these two representative open-source databases to gain deeper insights into the effectiveness of different JIT architectures.

cs.DB↗

Measurement of Near-field Thermal Radiation between Multilayered Metamaterials

The near-field radiative heat transfer (NFRHT) between one-dimensional metamaterials comprising phonon dielectric multilayers was experimented. Large sized (1cm x 1cm) near-field samples were fabricated using SiC, SiO2 and Ge layers at a certain gap distance, and the effect of layer stacking order and phonon resonance quality on the NFRHT was examined. The measured results show good agreement with those obtained theoretically employing the transmission matrix method. Super-Planckian blackbody radiation was observed between the emitters and receivers with identical structures. Measurements demonstrate failure of the effective medium theory (EMT) in predicting the near-field heat flux especially in the presence of bounded surface modes, such as the epsilon-near-zero (ENZ) mode. Analyses also indicate that, in certain cases, the EMT can provide reasonable physical insight into the underlying coupling process from the perspective of homogenized media. The conditions to apply the EMT in the near-field regime was also touched upon.

physics.optics↗

Collaborative Perception in Autonomous Driving: Methods, Datasets and Challenges

Collaborative perception is essential to address occlusion and sensor failure issues in autonomous driving. In recent years, theoretical and experimental investigations of novel works for collaborative perception have increased tremendously. So far, however, few reviews have focused on systematical collaboration modules and large-scale collaborative perception datasets. This work reviews recent achievements in this field to bridge this gap and motivate future research. We start with a brief overview of collaboration schemes. After that, we systematically summarize the collaborative perception methods for ideal scenarios and real-world issues. The former focuses on collaboration modules and efficiency, and the latter is devoted to addressing the problems in actual application. Furthermore, we present large-scale public datasets and summarize quantitative results on these benchmarks. Finally, we highlight gaps and overlook challenges between current academic research and real-world applications. The project page is https://github.com/CatOneTwo/Collaborative-Perception-in-Autonomous-Driving

cs.CV↗

Sterile Neutrino Portal Dark Matter from Semi-Production

In this paper, we study the feeble sterile neutrino portal dark matter under the $Z_3$ symmetry. The dark sector consists of one fermion singlet $χ$ and one scalar singlet $χ$, which transforms as $χ\to e^{i2π/3}χ, ϕ\to e^{i2π/3}ϕ$ under the $Z_3$ symmetry. Regarding fermion singlet $χ$ as the dark matter candidate, the new interaction terms $y_χϕ\bar{χ^c}χ$ and $μϕ^3/2$ could induce various new production channels. For instance, when $m_ϕ>2m_χ$, the pair decay $ϕ\toχχ$ could be the dominant channel, rather than the delayed decay $ϕ\toχν$. Another appealing scenario is when the dark sector is initially produced through the scattering process as $NN\toχχ, NN\toϕϕ,hν\toχϕ$, then the semi-production processes $N χ\toϕϕ, Nϕ\toϕχ, Nχ\toχχ$ could lead to the exponential growth of dark sector abundances. The phenomenology of sterile neutrino and the cosmological impact of the dark scalar are also considered in the $Z_3$ symmetric model.

hep-ph↗

Disentangle then Parse:Night-time Semantic Segmentation with Illumination Disentanglement

Most prior semantic segmentation methods have been developed for day-time scenes, while typically underperforming in night-time scenes due to insufficient and complicated lighting conditions. In this work, we tackle this challenge by proposing a novel night-time semantic segmentation paradigm, i.e., disentangle then parse (DTP). DTP explicitly disentangles night-time images into light-invariant reflectance and light-specific illumination components and then recognizes semantics based on their adaptive fusion. Concretely, the proposed DTP comprises two key components: 1) Instead of processing lighting-entangled features as in prior works, our Semantic-Oriented Disentanglement (SOD) framework enables the extraction of reflectance component without being impeded by lighting, allowing the network to consistently recognize the semantics under cover of varying and complicated lighting conditions. 2) Based on the observation that the illumination component can serve as a cue for some semantically confused regions, we further introduce an Illumination-Aware Parser (IAParser) to explicitly learn the correlation between semantics and lighting, and aggregate the illumination features to yield more precise predictions. Extensive experiments on the night-time segmentation task with various settings demonstrate that DTP significantly outperforms state-of-the-art methods. Furthermore, with negligible additional parameters, DTP can be directly used to benefit existing day-time methods for night-time segmentation.

cs.CV↗

Bridging the Gap: Multi-Level Cross-Modality Joint Alignment for Visible-Infrared Person Re-Identification

Visible-Infrared person Re-IDentification (VI-ReID) is a challenging cross-modality image retrieval task that aims to match pedestrians' images across visible and infrared cameras. To solve the modality gap, existing mainstream methods adopt a learning paradigm converting the image retrieval task into an image classification task with cross-entropy loss and auxiliary metric learning losses. These losses follow the strategy of adjusting the distribution of extracted embeddings to reduce the intra-class distance and increase the inter-class distance. However, such objectives do not precisely correspond to the final test setting of the retrieval task, resulting in a new gap at the optimization level. By rethinking these keys of VI-ReID, we propose a simple and effective method, the Multi-level Cross-modality Joint Alignment (MCJA), bridging both modality and objective-level gap. For the former, we design the Modality Alignment Augmentation, which consists of three novel strategies, the weighted grayscale, cross-channel cutmix, and spectrum jitter augmentation, effectively reducing modality discrepancy in the image space. For the latter, we introduce a new Cross-Modality Retrieval loss. It is the first work to constrain from the perspective of the ranking list, aligning with the goal of the testing stage. Moreover, based on the global feature only, our method exhibits good performance and can serve as a strong baseline method for the VI-ReID community.

cs.CV↗

Sterile Neutrino Portal Dark Matter with $Z_3$ Symmetry

In this paper, we consider the sterile neutrino portal dark matter with $Z_3$ symmetry. This model further extends the canonical type-I seesaw with a fermion singlet $χ$ and a scalar singlet $ϕ$. Under the $Z_3$ symmetry, the dark sector transforms as $χ\to e^{i2π/3}χ, ϕ\to e^{i2π/3}ϕ$, while the standard model particles and the sterile neutrino $N$ transform trivially. Besides the interactions as $y_{N} ϕ\barχN$ and $λ_{Hϕ} (H^†H) (ϕ^†ϕ)$ allowed in the $Z_2$ symmetry, the $Z_3$ symmetry also introduces two new terms, i.e., $y_χϕ\overline{χ^{c}} χ$ and $μϕ^3/2$. These new interactions induce additional semi-annihilation processes as $χχ\to Nχ$ and $ϕϕ\to hϕ$ for WIMP dark matter. We then perform a comprehensive analysis of the phenomenology of this $Z_3$ symmetric model. Viable parameter space is explored under the constraints from dark matter relic density, Higgs invisible decay, indirect and direct detection for both fermion and scalar dark matter. We find that the semi-annihilation channels $χχ\to Nχ$ and $ϕϕ\to Nχ$ can lead to quite different phenomena from the $Z_2$ symmetric model, which provides a viable pathway to distinguish these two kinds of model.

hep-ph↗