Recent theoretical studies have suggested that the suddenly recoiled atom struck by dark matter (DM) particle is much more likely to excite or lose its electrons than expected. Such Migdal effect ...opens a new avenue for exploring the sub-GeV DM particles. There have been various attempts to describe the Migdal effect in liquid and semiconductor targets. In this paper we incorporate the treatment of the bremsstrahlung process and the electronic many-body effects to give a full description of the Migdal effect in bulk semiconductor targets diamond and silicon. Compared with the results obtained with the atom-centered localized Wannier functions (WFs) under the framework of the tight-binding (TB) approximation, the method proposed in this study yields much larger event rates in the low energy regime, due to a ω−4 scaling. We also find that the effect of the bremsstrahlung photon mediating the Coulomb interaction between recoiled ion and the electron-hole pair is equivalent to that of the exchange of a single phonon.
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The physics of the electronic excitation in semiconductors induced by sub-GeV dark matter (DM) have been extensively discussed in literature, under the framework of the standard plane wave ...(PW) and pseudopotential calculation scheme. In this paper, we investigate the implication of the all-electron (AE) reconstruction on estimation of the DM-induced electronic transition event rates. As a benchmark study, we first calculate the wavefunctions in silicon and germanium bulk crystals based on both the AE and pseudo (PS) schemes within the projector augmented wave (PAW) framework, and then make comparisons between the calculated excitation event rates obtained from these two approaches. It turns out that in process where large momentum transfer is kinetically allowed, the two calculated event rates can differ by a factor of a few. Such discrepancies are found to stem from the high-momentum components neglected in the PS scheme. It is thus implied that the correction from the AE wavefunction in the core region is necessary for an accurate estimate of the DM-induced transition event rate in semiconductors.
Recent studies have theoretically investigated the atomic excitation and ionization induced by the dark matter–nucleus scattering, and it was found that the suddenly recoiled atom is much more likely ...to excite or lose its electrons than expected. This phenomenon is called the "Migdal effect." In this paper, we extend the established strategy to describe the Migdal effect in isolated atoms to the case in semiconductors under the framework of the tight-binding approximation. Since the localized aspects of electrons are respected in the form of the Wannier functions, the extension of the existing Migdal approach for isolated atoms is much more natural, while the extensive nature of electrons in solids is reflected in the hopping integrals. We take a diamond target as a concrete proof of principle for the methodology, and calculate relevant energy spectra and the projected sensitivity of such a diamond detector. It turns out that our method as a preliminary attempt is theoretically self-consistent and practically effective.
Pedestrian misalignment, which mainly arises from detector errors and pose variations, is a critical problem for a robust person re-identification (re-ID) system. With poor alignment, the feature ...learning and matching process might be largely compromised. To address this problem, this paper introduces pose-invariant embedding (PIE) as a pedestrian descriptor. First, in order to align pedestrians to a standard pose, the PoseBox structure is introduced, which is generated through pose estimation followed by affine transformations. Second, to reduce the impact of pose estimation errors and information loss during the PoseBox construction, we design a PoseBox fusion (PBF) CNN architecture that takes the original image, the PoseBox, and the pose estimation confidence as input. The proposed PIE descriptor is thus defined as the fully connected layer of the PBF network for the retrieval task. Experiments are conducted on the Market-1501, CUHK03-NP, and DukeMTMC-reID datasets. We show that PoseBox alone yields decent re-ID accuracy and that when integrated in the PBF network, the learned PIE descriptor produces competitive performance compared with state-of-the-art approaches.
In the early days, content-based image retrieval (CBIR) was studied with global features. Since 2003, image retrieval based on local descriptors (de facto SIFT) has been extensively studied for over ...a decade due to the advantage of SIFT in dealing with image transformations. Recently, image representations based on the convolutional neural network (CNN) have attracted increasing interest in the community and demonstrated impressive performance. Given this time of rapid evolution, this article provides a comprehensive survey of instance retrieval over the last decade. Two broad categories, SIFT-based and CNN-based methods, are presented. For the former, according to the codebook size, we organize the literature into using large/medium-sized/small codebooks. For the latter, we discuss three lines of methods, i.e., using pre-trained or fine-tuned CNN models, and hybrid methods. The first two perform a single-pass of an image to the network, while the last category employs a patch-based feature extraction scheme. This survey presents milestones in modern instance retrieval, reviews a broad selection of previous works in different categories, and provides insights on the connection between SIFT and CNN-based methods. After analyzing and comparing retrieval performance of different categories on several datasets, we discuss promising directions towards generic and specialized instance retrieval.
Graphite anodes are prone to dangerous Li plating during fast charging, but the difficulty to identify the rate‐limiting step has made a challenging to eliminate Li plating thoroughly. Thus, the ...inherent thinking on inhibiting Li plating needs to be compromised. Herein, an elastic solid electrolyte interphase (SEI) with uniform Li‐ion flux is constructed on graphite anode by introducing a triglyme (G3)‐LiNO3 synergistic additive (GLN) to commercial carbonate electrolyte, for realizing a dendrite‐free and highly‐reversible Li plating under high rates. The cross‐linked oligomeric ether and Li3N particles derived from the GLN greatly improve the stability of the SEI before and after Li plating and facilitate the uniform Li deposition. When 51 % of lithiation capacity is contributed from Li plating, the graphite anode in the electrolyte with 5 vol.% GLN achieved an average 99.6 % Li plating reversibility over 100 cycles. In addition, the 1.2‐Ah LiFePO4 | graphite pouch cell with GLN‐added electrolyte stably operated over 150 cycles at 3 C, firmly demonstrating the promise of GLN in commercial Li‐ion batteries for fast‐charging applications.
This paper proposes a strategy of Li plating regulation rather than Li plating inhibition. In this situation, Li plating on graphite has a dendrite‐free morphology and a high reversibility. By effectively utilizing Li plating instead of suppressing it, it is possible to achieve a compatible pathway for fast charging of graphite anodes.
Person re-identification (re-ID) is a cross-camera retrieval task that suffers from image style variations caused by different cameras. The art implicitly addresses this problem by learning a ...camera-invariant descriptor subspace. In this paper, we explicitly consider this challenge by introducing camera style (CamStyle). CamStyle can serve as a data augmentation approach that reduces the risk of deep network overfitting and that smooths the CamStyle disparities. Specifically, with a style transfer model, labeled training images can be style transferred to each camera, and along with the original training samples, form the augmented training set. This method, while increasing data diversity against overfitting, also incurs a considerable level of noise. In the effort to alleviate the impact of noise, the label smooth regularization (LSR) is adopted. The vanilla version of our method (without LSR) performs reasonably well on few camera systems in which overfitting often occurs. With LSR, we demonstrate consistent improvement in all systems regardless of the extent of overfitting. We also report competitive accuracy compared with the state of the art on Market-1501 and DukeMTMC-re-ID. Importantly, CamStyle can be employed to the challenging problems of one view learning and unsupervised domain adaptation (UDA) in person re-identification (re-ID), both of which have critical research and application significance. The former only has labeled data in one camera view and the latter only has labeled data in the source domain. Experimental results show that CamStyle significantly improves the performance of the baseline in the two problems. Specially, for UDA, CamStyle achieves state-of-the-art accuracy based on a baseline deep re-ID model on Market-1501 and DukeMTMC-reID. Our code is available at: https://github.com/zhunzhong07/CamStyle.
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