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Accurate and robust prediction of drug-target interactions (DTIs) plays a vital role in drug discovery. Despite extensive efforts have been invested in predicting novel DTIs, existing approaches still suffer from insufficient labeled data and cold start problems. More importantly, there is currently a lack of studies focusing on elucidating the mechanism of action (MoA) between drugs and targets. Distinguishing the activation and inhibition mechanisms is critical and challenging in drug development. Here, we introduce a unified framework called DTIAM, which aims to predict interactions, binding affinities, and activation/inhibition mechanisms between drugs and targets. DTIAM learns drug and target representations from large amounts of label-free data through self-supervised pre-training, which accurately extracts the substructure and contextual information of drugs and targets, and thus benefits the downstream prediction based on these representations. DTIAM achieves substantial performance improvement over other state-of-the-art methods in all tasks, particularly in the cold start scenario. Moreover, independent validation demonstrates the strong generalization ability of DTIAM. All these results suggested that DTIAM can provide a practically useful tool for predicting novel DTIs and further distinguishing the MoA of candidate drugs. DTIAM, for the first time, provides a unified framework for accurate and robust prediction of drug-target interactions, binding affinities, and activation/inhibition mechanisms.

We consider the Cauchy problem of the Euler-Poincar\'e equations in $\mathbb{R}^d$ with a varying dispersion parameter $\alpha$. Based on the convex entropy structure and the modified commutator estimates, we have proved that the Euler-Poincar\'e equations have a uniform existence time with respect to $\alpha$ in Sobolev spaces $H^s.$ Combined with the Bona-Simth method, we obtain convergence of the solutions to the Euler-Poincar\'e equations as $\alpha\to 0$ in the same space where the initial data are located.

Water Cherenov detector is a vital part in most of neutrino or cosmic ray research. As detectors grow in size, the water attenuation length (WAL) becomes increasingly essential for detector performance. It is essential to measure or monitor the WAL. While many experiments have measured WAL in the lab or detector, only the Super-Kamiokande experiment has achieved values exceeding 50 meters in the detector with a moving light source. However, it is impractical for many experiments to place a moving light source inside the detector, necessitating an alternative method for investigating long WAL. A novel system has been proposed to address the challenge of investigating long WAL. This system focuses on ample water Cherenkov detectors and features a fixed light source and photomultiplier tubes (PMTs) at varying distances, eliminating the need for moving parts. The static setup demands high precision for accurate measurement of long WAL. Each component, including LED, diffuse ball, PMTs, and fibers, is introduced to explain uncertainty control. Based on lab tests, the system's uncertainty has been controlled within 5\%. Additionally, camera technology is also used during the evaluation of the system uncertainty, which has the potential to replace PMTs in the future for this measurement. Monte Carlo simulations have shown that the system can achieve a 5\% measurement uncertainty at WAL of 80 meters and 8\% at WAL of 100 meters. This system can be used in experiments with large Cherenkov detectors such as JUNO water veto and Hyper-K.

The scalars $f_0$ closest to 1.5 GeV contain the mesons $f_0(1370)$, $f_0(1500)$ and $f_0(1710)$, and the latter two ones are usually viewed as the potential candidates for the scalar glueballs. In this work, by including the important contributions from the vertex corrections, we study the decays $B^0 \to J/\psi f_0$ within the improved perturbative QCD approach and analyze the possible scalar glueball hunting. Together with the two mixing models, namely, $f_0(1500) (f_0(1710))$ being the primary scalar glueball in model I (II), and two classification scenarios, namely, $f_0$ being the $q\bar q$ excited (ground) states in scenario 1 (2), the branching fractions associated with their ratios for $B^0 \to J/\psi f_0$ are evaluated comprehensively. The predictions with still large uncertainties in the considered two mixing models are roughly consistent with currently limited data, which indicates that both more rich data and more precise predictions are urgently demanded to figure out the scalar glueball clearly in the future. Moreover, several interesting ratios between the branching fractions of $B^0 \to J/\psi f_0(\to \pi^+ \pi^-/K^+ K^-)$ and $B^0 \to J/\psi \rho^0/\phi (\to \pi^+ \pi^-/ K^+ K^-)$ that could help us to understand the nature of scalar $f_0$ are defined and predicted theoretically. These ratios should be examined in future experiments., Comment: 28 pages, 3 figures, 6 tables; revised version according to referee's comments; contents improved and references added

End-to-end task-oriented dialogue (EToD) can directly generate responses in an end-to-end fashion without modular training, which attracts escalating popularity. The advancement of deep neural networks, especially the successful use of large pre-trained models, has further led to significant progress in EToD research in recent years. In this paper, we present a thorough review and provide a unified perspective to summarize existing approaches as well as recent trends to advance the development of EToD research. The contributions of this paper can be summarized: (1) \textbf{\textit{First survey}}: to our knowledge, we take the first step to present a thorough survey of this research field; (2) \textbf{\textit{New taxonomy}}: we first introduce a unified perspective for EToD, including (i) \textit{Modularly EToD} and (ii) \textit{Fully EToD}; (3) \textbf{\textit{New Frontiers}}: we discuss some potential frontier areas as well as the corresponding challenges, hoping to spur breakthrough research in EToD field; (4) \textbf{\textit{Abundant resources}}: we build a public website\footnote{We collect the related papers, baseline projects, and leaderboards for the community at \url{https://etods.net/}.}, where EToD researchers could directly access the recent progress. We hope this work can serve as a thorough reference for the EToD research community., Comment: Accepted at EMNLP2023

In this paper, we investigate a self-sensing intelligent reflecting surface (IRS) aided millimeter wave (mmWave) integrated sensing and communication (ISAC) system. Unlike the conventional purely passive IRS, the self-sensing IRS can effectively reduce the path loss of sensing-related links, thus rendering it advantageous in ISAC systems. Aiming to jointly sense the target/scatterer/user positions as well as estimate the sensing and communication (SAC) channels in the considered system, we propose a two-phase transmission scheme, where the coarse and refined sensing/channel estimation (CE) results are respectively obtained in the first phase (using scanning-based IRS reflection coefficients) and second phase (using optimized IRS reflection coefficients). For each phase, an angle-based sensing turbo variational Bayesian inference (AS-TVBI) algorithm, which combines the VBI, messaging passing and expectation-maximization (EM) methods, is developed to solve the considered joint location sensing and CE problem. The proposed algorithm effectively exploits the partial overlapping structured (POS) sparsity and 2-dimensional (2D) block sparsity inherent in the SAC channels to enhance the overall performance. Based on the estimation results from the first phase, we formulate a Cram\'{e}r-Rao bound (CRB) minimization problem for optimizing IRS reflection coefficients, and through proper reformulations, a low-complexity manifold-based optimization algorithm is proposed to solve this problem. Simulation results are provided to verify the superiority of the proposed transmission scheme and associated algorithms.

Quantitative susceptibility mapping (QSM) is a post-processing technique for deriving tissue magnetic susceptibility distribution from MRI phase measurements. Deep learning (DL) algorithms hold great potential for solving the ill-posed QSM reconstruction problem. However, a significant challenge facing current DL-QSM approaches is their limited adaptability to magnetic dipole field orientation variations during training and testing. In this work, we propose a novel Orientation-Adaptive Latent Feature Editing (OA-LFE) module to learn the encoding of acquisition orientation vectors and seamlessly integrate them into the latent features of deep networks. Importantly, it can be directly Plug-and-Play (PnP) into various existing DL-QSM architectures, enabling reconstructions of QSM from arbitrary magnetic dipole orientations. Its effectiveness is demonstrated by combining the OA-LFE module into our previously proposed phase-to-susceptibility single-step instant QSM (iQSM) network, which was initially tailored for pure-axial acquisitions. The proposed OA-LFE-empowered iQSM, which we refer to as iQSM+, is trained in a self-supervised manner on a specially-designed simulation brain dataset. Comprehensive experiments are conducted on simulated and in vivo human brain datasets, encompassing subjects ranging from healthy individuals to those with pathological conditions. These experiments involve various MRI platforms (3T and 7T) and aim to compare our proposed iQSM+ against several established QSM reconstruction frameworks, including the original iQSM. The iQSM+ yields QSM images with significantly improved accuracies and mitigates artifacts, surpassing other state-of-the-art DL-QSM algorithms., Comment: 13pages, 9figures

The full disk, full of gas and dust, determines the upper limit of planet masses, and its lifetime is critical for planet formation, especially for giant planets. In this work, we studied the evolutionary timescales of the full disks of T Tauri stars (TTSs) and their relations to accretion. Combined with Gaia EDR3, 2MASS, and WISE data, 1077 disk-bearing TTS candidates were found in LAMOST DR8, and stellar parameters were obtained. Among them, 783 are newly classified by spectra as classical T Tauri stars (CTTSs; 169) or weak-lined T Tauri stars (WTTSs). Based on EW and FWHM of Ha, 157 TTSs in accretion were identified, with ~ 82% also having full disks. For TTSs with M<0.35 Mo, about 80% seem to already lose their full disks at ~ 0.1 Myr, which may explain their lower mass, while the remaining 20% with full disks evolve at similar rates of non-full disks within 5 Myr, possibly suffice to form giant planets. The fraction of accreting TTSs to disk-bearing TTSs is stable at ~10% and can last $\sim$ 5-10 Myr, suggesting that full disks and accretion evolve with similar rates as non-full disks. For TTSs with M>0.35Mo, almost all full disks can survive more than 0.1 Myr, most for 1 Myr and some even for 20 Myr, which implies planets are more likely to be formed in their disks than those of M<0.35 Mo, and thus M dwarfs with M>0.35Mo can have more planets. The fraction of full-disk TTSs to disk-bearing TTSs decreases with age following the relation $f\propto t^{-0.35}$, and similar relations existed in the fraction of accreting TTSs and the fraction of full-disk CTTSs, suggesting faster full disks and accretion evolution than non-full disks. For full disk stars, the ratio of accretion of lower-mass stars is systematically lower than that of higher-mass stars, confirming the dependence of accretion on stellar mass., Comment: 19 pages, 12 figures, 1 table

Satellite-based greenhouse gases (GHG) sensing technologies play a critical role in the study of global carbon emissions and climate change. However, none of the existing satellite-based GHG sensing technologies can achieve the measurement of broad bandwidth, high temporal-spatial resolution, and high sensitivity at the same time. Recently, dual-comb spectroscopy (DCS) has been proposed as a superior candidate technology for GHG sensing because it can measure broadband spectra with high temporal-spatial resolution and high sensitivity. The main barrier to DCS's display on satellites is its short measurement distance in open air achieved thus far. Prior research has not been able to implement DCS over 20 km of open-air path. Here, by developing a bistatic setup using time-frequency dissemination and high-power optical frequency combs, we have implemented DCS over a 113 km turbulent horizontal open-air path. Our experiment successfully measured GHG with 7 nm spectral bandwidth and a 10 kHz frequency and achieved a CO2 sensing precision of <2 ppm in 5 minutes and <0.6 ppm in 36 minutes. Our results represent a significant step towards advancing the implementation of DCS as a satellite-based technology and improving technologies for GHG monitoring, Comment: 24 pages, 6 figures

In this paper, we focus on zero-filter limit problem for the Camassa-Holm equation in the more general Besov spaces. We prove that the solution of the Camassa-Holm equation converges strongly in $L^\infty(0,T;B^s_{2,r}(\R))$ to the inviscid Burgers equation as the filter parameter $\alpha$ tends to zero with the given initial data $u_0\in B^s_{2,r}(\R)$. Moreover, we also show that the zero-filter limit for the Camassa-Holm equation does not converges uniformly with respect to the initial data in $B^s_{2,r}(\R)$.

Anchor-based detectors have been continuously developed for object detection. However, the individual anchor box makes it difficult to predict the boundary's offset accurately. Instead of taking each bounding box as a closed individual, we consider using multiple boxes together to get prediction boxes. To this end, this paper proposes the \textbf{Box Decouple-Couple(BDC) strategy} in the inference, which no longer discards the overlapping boxes, but decouples the corner points of these boxes. Then, according to each corner's score, we couple the corner points to select the most accurate corner pairs. To meet the BDC strategy, a simple but novel model is designed named the \textbf{Anchor-Intermediate Detector(AID)}, which contains two head networks, i.e., an anchor-based head and an anchor-free \textbf{Corner-aware head}. The corner-aware head is able to score the corners of each bounding box to facilitate the coupling between corner points. Extensive experiments on MS COCO show that the proposed anchor-intermediate detector respectively outperforms their baseline RetinaNet and GFL method by $\sim$2.4 and $\sim$1.2 AP on the MS COCO test-dev dataset without any bells and whistles. Code is available at: https://github.com/YilongLv/AID., Comment: Submitted 29 September, 2023; originally announced October 2023. Accepted by ICCV2023