Anti-charmed pentaquark from B decays

We explore the possibility of observing the anti-charmed pentaquark state from the $\Theta_c \bar{n} \pi^+$ decay of $B$ meson produced at $B$-factory experiments. We first show that the observed branching ratio of the $B^+$ to $\Lambda^-_c p \pi^+$, as well as its open histograms, can be remarkably well explained by assuming that the decay proceeds first through the $\pi^+ \bar{D}^0$ (or $\bar{D}^{*0}$) decay, whose branching ratios are known, and then through the subsequent decay of the virtual $\bar{D}^0$ or $\bar{D}^{*0}$ mesons to $\Lambda_c^- p$, whose strength are calculated using previously fit hadronic parameters. We then note that the $\Theta_c$ can be similarly produced when the virtual $\bar{D}^0$ or $\bar{D}^{*0}$ decay into an anti-nucleon and a $\Theta_c$. Combining the present theoretical estimates for the ratio $g_{D N \Lambda_c} / g_{D N \Theta_c} \sim 13$ and $g_{D^* N \Theta_c} \sim {1/3} g_{D N \Theta_c}$, we find that the anti-charmed pentaquark $\Theta_c$, which was predicted to be bound by several model calculations, can be produced via $B^+ \to \Theta_c \bar{n} \pi^+$, and be observed from the $B$-factory experiments through the weak decay of $\Theta_c \to p K^+ \pi^- \pi^-$.Comment: 4 pages, 4 figures, Revised version to be published in Physical Review Letter

Dynamic Graph Generation Network: Generating Relational Knowledge from Diagrams

In this work, we introduce a new algorithm for analyzing a diagram, which contains visual and textual information in an abstract and integrated way. Whereas diagrams contain richer information compared with individual image-based or language-based data, proper solutions for automatically understanding them have not been proposed due to their innate characteristics of multi-modality and arbitrariness of layouts. To tackle this problem, we propose a unified diagram-parsing network for generating knowledge from diagrams based on an object detector and a recurrent neural network designed for a graphical structure. Specifically, we propose a dynamic graph-generation network that is based on dynamic memory and graph theory. We explore the dynamics of information in a diagram with activation of gates in gated recurrent unit (GRU) cells. On publicly available diagram datasets, our model demonstrates a state-of-the-art result that outperforms other baselines. Moreover, further experiments on question answering shows potentials of the proposed method for various applications

Advancing Adversarial Training by Injecting Booster Signal

Recent works have demonstrated that deep neural networks (DNNs) are highly vulnerable to adversarial attacks. To defend against adversarial attacks, many defense strategies have been proposed, among which adversarial training has been demonstrated to be the most effective strategy. However, it has been known that adversarial training sometimes hurts natural accuracy. Then, many works focus on optimizing model parameters to handle the problem. Different from the previous approaches, in this paper, we propose a new approach to improve the adversarial robustness by using an external signal rather than model parameters. In the proposed method, a well-optimized universal external signal called a booster signal is injected into the outside of the image which does not overlap with the original content. Then, it boosts both adversarial robustness and natural accuracy. The booster signal is optimized in parallel to model parameters step by step collaboratively. Experimental results show that the booster signal can improve both the natural and robust accuracies over the recent state-of-the-art adversarial training methods. Also, optimizing the booster signal is general and flexible enough to be adopted on any existing adversarial training methods.Comment: Accepted at IEEE Transactions on Neural Networks and Learning System

Probing sterile neutrino in BB (DD) meson decays at Belle II (BESIII)

We present, how a systematic study of $B \to D\ell N$ ($D \to K \ell N$) decays with $\ell=\mu,\tau$, at Belle II (BESIII) can provide unambiguous signature of a heavy neutrino $N$ and/or constrain its mixing with active neutrinos $\nu_\ell$, which is parameterized by $|U_{\ell N}|^2$. Our constraint on $|U_{\mu N}|^2$ that can be achieved from the full Belle II data is comparable with what can be obtained from the much larger data set of the upgraded LHCb. Additionally, our method offers better constraint on $|U_{\mu N}|^2$ for mass of sterile neutrino $m_N < 2$ GeV. We can also probe the Dirac and Majorana nature of $N$ by observing the sequential decay of $N$, including suppression from observation of a displaced vertex as well as helicity flip, for Majorana $N$.Comment: 9 pages, 6 figures. This is a pre-print of an article published in European Physical Journal C. The final authenticated version is available online at https://doi.org/10.1140/epjc/s10052-020-8310-

Compose and Conquer: Diffusion-Based 3D Depth Aware Composable Image Synthesis

Addressing the limitations of text as a source of accurate layout representation in text-conditional diffusion models, many works incorporate additional signals to condition certain attributes within a generated image. Although successful, previous works do not account for the specific localization of said attributes extended into the three dimensional plane. In this context, we present a conditional diffusion model that integrates control over three-dimensional object placement with disentangled representations of global stylistic semantics from multiple exemplar images. Specifically, we first introduce \textit{depth disentanglement training} to leverage the relative depth of objects as an estimator, allowing the model to identify the absolute positions of unseen objects through the use of synthetic image triplets. We also introduce \textit{soft guidance}, a method for imposing global semantics onto targeted regions without the use of any additional localization cues. Our integrated framework, \textsc{Compose and Conquer (CnC)}, unifies these techniques to localize multiple conditions in a disentangled manner. We demonstrate that our approach allows perception of objects at varying depths while offering a versatile framework for composing localized objects with different global semantics. Code: https://github.com/tomtom1103/compose-and-conquer/Comment: ICLR 202

Seeing Through the Conversation: Audio-Visual Speech Separation based on Diffusion Model

The objective of this work is to extract target speaker's voice from a mixture of voices using visual cues. Existing works on audio-visual speech separation have demonstrated their performance with promising intelligibility, but maintaining naturalness remains a challenge. To address this issue, we propose AVDiffuSS, an audio-visual speech separation model based on a diffusion mechanism known for its capability in generating natural samples. For an effective fusion of the two modalities for diffusion, we also propose a cross-attention-based feature fusion mechanism. This mechanism is specifically tailored for the speech domain to integrate the phonetic information from audio-visual correspondence in speech generation. In this way, the fusion process maintains the high temporal resolution of the features, without excessive computational requirements. We demonstrate that the proposed framework achieves state-of-the-art results on two benchmarks, including VoxCeleb2 and LRS3, producing speech with notably better naturalness.Comment: Project page with demo: https://mm.kaist.ac.kr/projects/avdiffuss

Physics-Informed Convolutional Transformer for Predicting Volatility Surface

Predicting volatility is important for asset predicting, option pricing and hedging strategies because it cannot be directly observed in the financial market. The Black-Scholes option pricing model is one of the most widely used models by market participants. Notwithstanding, the Black-Scholes model is based on heavily criticized theoretical premises, one of which is the constant volatility assumption. The dynamics of the volatility surface is difficult to estimate. In this paper, we establish a novel architecture based on physics-informed neural networks and convolutional transformers. The performance of the new architecture is directly compared to other well-known deep-learning architectures, such as standard physics-informed neural networks, convolutional long-short term memory (ConvLSTM), and self-attention ConvLSTM. Numerical evidence indicates that the proposed physics-informed convolutional transformer network achieves a superior performance than other methods.Comment: Submitted to Quantitative Financ

VLANet: Video-Language Alignment Network for Weakly-Supervised Video Moment Retrieval

Video Moment Retrieval (VMR) is a task to localize the temporal moment in untrimmed video specified by natural language query. For VMR, several methods that require full supervision for training have been proposed. Unfortunately, acquiring a large number of training videos with labeled temporal boundaries for each query is a labor-intensive process. This paper explores methods for performing VMR in a weakly-supervised manner (wVMR): training is performed without temporal moment labels but only with the text query that describes a segment of the video. Existing methods on wVMR generate multi-scale proposals and apply query-guided attention mechanisms to highlight the most relevant proposal. To leverage the weak supervision, contrastive learning is used which predicts higher scores for the correct video-query pairs than for the incorrect pairs. It has been observed that a large number of candidate proposals, coarse query representation, and one-way attention mechanism lead to blurry attention maps which limit the localization performance. To handle this issue, Video-Language Alignment Network (VLANet) is proposed that learns sharper attention by pruning out spurious candidate proposals and applying a multi-directional attention mechanism with fine-grained query representation. The Surrogate Proposal Selection module selects a proposal based on the proximity to the query in the joint embedding space, and thus substantially reduces candidate proposals which leads to lower computation load and sharper attention. Next, the Cascaded Cross-modal Attention module considers dense feature interactions and multi-directional attention flow to learn the multi-modal alignment. VLANet is trained end-to-end using contrastive loss which enforces semantically similar videos and queries to gather. The experiments show that the method achieves state-of-the-art performance on Charades-STA and DiDeMo datasets.Comment: 16 pages, 6 figures, European Conference on Computer Vision, 202