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Identifying and discovering drug-target interactions(DTIs) are vital steps in drug discovery and development. They play a crucial role in assisting scientists in finding new drugs and accelerating the drug development process. Recently, knowledge graph and knowledge graph embedding (KGE) models have made rapid advancements and demonstrated impressive performance in drug discovery. However, such models lack authenticity and accuracy in drug target identification, leading to an increased misjudgment rate and reduced drug development efficiency. To address these issues, we focus on the problem of drug-target interactions, with knowledge mapping as the core technology. Specifically, a causal intervention-based confidence measure is employed to assess the triplet score to improve the accuracy of the drug-target interaction prediction model. Experimental results demonstrate that the developed confidence measurement method based on causal intervention can significantly enhance the accuracy of DTI link prediction, particularly for high-precision models. The predicted results are more valuable in guiding the design and development of subsequent drug development experiments, thereby significantly improving the efficiency of drug development.

There are confusions about angular momentum propagation in scattering or decay processes involving the transition between particle systems that appear to transform differently under Lorentz transformations. This paper provides an analysis of the transformation properties of the states and interactions for a few typical processes within the standard model of particle physics, and performs explicit calculations showing how angular momentum transfers in these processes. We shall show that angular momentum conservation is completely consistent with the calculation in quantum field theory.

The orthogonal time frequency space (OTFS) modulation as a promising signal representation attracts growingcinterest for integrated sensing and communication (ISAC), yet its merits over orthogonal frequency division multiplexing (OFDM) remain controversial. This paper devotes to a comprehensive comparison of OTFS and OFDM for sensing from the perspective of Cramer-Rao lower bounds (CRLB) analysis. To this end, we develop the cyclic prefix (CP)-Free and CP-added model for OFDM, while for OTFS, we consider the Zak transform based and the Two-Step conversion based models, respectively. Then we rephrase these four models into a unified matrix format to derive the CRLB of the delays and doppler shifts for multipath scenario. Numerical results demonstrate the superiority of OTFS modulation for sensing, and the effect of physical parameters for performance achievement.

Interpreting and explaining the behavior of deep neural networks is critical for many tasks. Explainable AI provides a way to address this challenge, mostly by providing per-pixel relevance to the decision. Yet, interpreting such explanations may require expert knowledge. Some recent attempts toward interpretability adopt a concept-based framework, giving a higher-level relationship between some concepts and model decisions. This paper proposes Bottleneck Concept Learner (BotCL), which represents an image solely by the presence/absence of concepts learned through training over the target task without explicit supervision over the concepts. It uses self-supervision and tailored regularizers so that learned concepts can be human-understandable. Using some image classification tasks as our testbed, we demonstrate BotCL's potential to rebuild neural networks for better interpretability. Code is available at https://github.com/wbw520/BotCL and a simple demo is available at https://botcl.liangzhili.com/., Comment: Accepted in CVPR 2023

Attaining the equilibrium state of a catalyst-adsorbate system is key to fundamentally assessing its effective properties, such as adsorption energy. Machine learning methods with finer supervision strategies have been applied to boost and guide the relaxation process of an atomic system and better predict its properties at the equilibrium state. In this paper, we present a novel graph neural network (GNN) supervision and prediction strategy DR-Label. The method enhances the supervision signal, reduces the multiplicity of solutions in edge representation, and encourages the model to provide node predictions that are graph structural variation robust. DR-Label first Deconstructs finer-grained equilibrium state information to the model by projecting the node-level supervision signal to each edge. Reversely, the model Reconstructs a more robust equilibrium state prediction by transforming edge-level predictions to node-level with a sphere-fitting algorithm. The DR-Label strategy was applied to three radically distinct models, each of which displayed consistent performance enhancements. Based on the DR-Label strategy, we further proposed DRFormer, which achieved a new state-of-the-art performance on the Open Catalyst 2020 (OC20) dataset and the Cu-based single-atom-alloyed CO adsorption (SAA) dataset. We expect that our work will highlight crucial steps for the development of a more accurate model in equilibrium state property prediction of a catalysis system., Comment: 11 pages, 3 figures

Offline safe RL is of great practical relevance for deploying agents in real-world applications. However, acquiring constraint-satisfying policies from the fixed dataset is non-trivial for conventional approaches. Even worse, the learned constraints are stationary and may become invalid when the online safety requirement changes. In this paper, we present a novel offline safe RL approach referred to as SaFormer, which tackles the above issues via conditional sequence modeling. In contrast to existing sequence models, we propose cost-related tokens to restrict the action space and a posterior safety verification to enforce the constraint explicitly. Specifically, SaFormer performs a two-stage auto-regression conditioned by the maximum remaining cost to generate feasible candidates. It then filters out unsafe attempts and executes the optimal action with the highest expected return. Extensive experiments demonstrate the efficacy of SaFormer featuring (1) competitive returns with tightened constraint satisfaction; (2) adaptability to the in-range cost values of the offline data without retraining; (3) generalizability for constraints beyond the current dataset.

Hybrid digital/analog architecture and low-resolution analog-to-digital/digital-to-analog converters (ADCs /DACs) are two low-cost implementations for large-scale millimeter wave (mmWave) systems. In this paper, we investigate the problem of constant-envelope transmit beamforming for large-scale multiple-input multiple-output (MIMO) radar system, where the transmit array adopts a hybrid digital/analog architecture with a small number of RF chains and the receive array adopts a fully digital architecture with low-resolution ADCs. We derive the relative entropy between the probability density functions associated with the two test hypotheses under low-resolution ADCs. We formulate our optimization problem by maximizing the relative entropy, subject to the constant envelope and orthogonality constraints. To suboptimally solve the resultant problem, a two-stage framework is developed. In the first stage, we optimize the transmit power at the directions of the target and clutter. In the second stage, an efficient iterative algorithm based on majorization-minimization is presented to obtain the constant-envelope beamformer according to the attained transmit power. Specifically, we apply a quadratic function as the minorizer, leading to a low-complexity solution at each iteration. In addition, to further facilitate low-cost implementation of the constant-envelope beamformer, we consider the problem of one-bit beamforming design and propose an efficient iterative method based on the Nesterov-like gradient method to solve it. Numerical simulations are provided to demonstrate the effectiveness of the proposed schemes.

This work focuses on the use of reconfigurable intelligent surface (RIS) in dual-function radar-communication (DFRC) systems to improve communication capacity and sensing precision, and enhance coverage for both functions. In contrast to most of the existing RIS aided DFRC works where the RIS is modeled as a diagonal phase shift matrix and can only reflect signals to half space, we propose a novel beyond diagonal RIS (BD-RIS) aided DFRC system. Specifically, the proposed BD-RIS supports the hybrid reflecting and transmitting mode, and is compatible with flexible architectures, enabling the system to realize full-space coverage and to achieve enhanced performance. To achieve the expected benefits, we jointly optimize the transmit waveform, the BD-RIS matrices, and sensing receive filters, by maximizing the minimum signal-to-clutter-plus-noise ratio for fair target detection, subject to the constraints of the communication quality of service, different BD-RIS architectures and power budget. To solve the non-convex and non-smooth max-min problem, a general solution based on the alternating direction method of multipliers is provided. Numerical simulations validate the efficacy of the proposed algorithm and show the superiority of the BD-RIS aided DFRC system in terms of both communication and sensing compared to conventional RIS aided DFRC., Comment: IEEE Transactions on Communications, 2024

Germanium selenide (GeSe) is a unique two-dimensional (2D) material showing various polymorphs stable at ambient condition. Recently, a new phase with a layered hexagonal lattice ({\gamma}-GeSe) was synthesized with ambient stability and extraordinary electronic conductivity even higher than graphite while its monolayer is semiconducting. In this work, via using first-principles derived force constants and Boltzmann transport theory we explore the lattice thermal conductivity ({\kappa}_l) of the monolayer {\gamma}-GeSe, together with a comparison with monolayer {\alpha}-GeSe and {\beta}-GeSe. The {\kappa}_l of {\gamma}-phase is relatively low (5.50 W/mK), comparable with those of {\alpha}- and {\beta}- phases. The acoustic branches in {\alpha}-GeSe are well separated from the optical branches, limiting scattering channels in the phase space, while for \b{eta}-GeSe and {\gamma}-GeSe the acoustic branches are resonant with the low-frequency optical branches facilitating more phonon-phonon scattering. For {\gamma}-GeSe, the cumulative {\kappa}_l is isotropic and phononic representative mean free path (rMFP) is the shortest (17.07 nm) amongst the three polymorphs, indicating that the {\kappa}_l of the {\gamma} phase is less likely to be affected by the size of the sample, while for {\alpha}-GeSe the cumulative {\kappa}_l grows slowly with mean free path and the rMFP is longer (up to 20.56 and 35.94 nm along zigzag and armchair direction, respectively), showing a stronger size-dependence of {\kappa}_l. Our work suggests that GeSe polymorphs with overall low thermal conductivity are promising contenders for thermoelectric and thermal management applications.

Two-dimensional (2D) van der Waals (vdW) materials have extraordinary thermal properties due to the effect of quantum confinement, making them promising for thermoelectric energy conversion and thermal management in microelectronic devices. In this review, the mechanism of phonon anharmonicity originating from three- and four-phonon interactions is derived. The phonon anharmonicity of 2D vdW materials, involving the Gr\"uneisen parameter, phonon lifetime, and thermal conductivity, is summarized and derived in detail. The size-dependent thermal conductivity of representative 2D vdW materials is discussed experimentally and theoretically. This review will present fundamental and advanced knowledge on how to evaluate the phonon anharmonicity in 2D vdW materials, which will aid the design of new structures and materials for applications related to energy transfer and conversion.

Graph neural networks (GNNs) have drawn more and more attention from material scientists and demonstrated a high capacity to establish connections between the structure and properties. However, with only unrelaxed structures provided as input, few GNN models can predict the thermodynamic properties of relaxed configurations with an acceptable level of error. In this work, we develop a multi-task (MT) architecture based on DimeNet++ and mixture density networks to improve the performance of such task. Taking CO adsorption on Cu-based single-atom alloy catalysts as an illustration, we show that our method can reliably estimate CO adsorption energy with a mean absolute error of 0.087 eV from the initial CO adsorption structures without costly first-principles calculations. Further, compared to other state-of-the-art GNN methods, our model exhibits improved generalization ability when predicting catalytic performance of out-of-domain configurations, built with either unseen substrate surfaces or doping species. We show that the proposed MT GNN strategy can facilitate catalyst discovery., Comment: 22 pages, 3 figures, 2 tables

This paper investigates dynamic hybrid beamforming (HBF) for a dual-function radar-communication (DFRC) system, where the DFRC base station (BS) simultaneously serves multiple single-antenna users and senses a target in the presence of multiple clutters. Particularly, we apply a HBF architecture with dynamic subarrays and double phase shifters in the DFRC BS. Aiming at maximizing the radar mutual information, we consider jointly designing the dynamic HBF of the DFRC system, subject to the constraints of communication quality of service (QoS), transmit power, and analog beamformer. To solve the complicated non-convex optimization, an efficient alternating optimization algorithm based on the majorization-minimization methods is developed. Simulation results verify the advancement of the considered HBF architecture and the effectiveness of the proposed design method.

As a promising technology in beyond-5G (B5G) and 6G, dual-function radar-communication (DFRC) aims to ensure both radar sensing and communication on a single integrated platform with unified signaling schemes. To achieve accurate sensing and reliable communication, large-scale arrays are anticipated to be implemented in such systems, which brings out the prominent issues on hardware cost and power consumption. To address these issues, hybrid beamforming (HBF), beyond its successful deployment in communication-only systems, could be a promising approach in the emerging DFRC ones. In this article, we investigate the development of the HBF techniques on the DFRC system in a self-contained manner. Specifically, we first introduce the basics of the HBF based DFRC system, where the system model and different receive modes are discussed with focus. Then we illustrate the corresponding design principles, which span from the performance metrics and optimization formulations to the design approaches and our preliminary results. Finally, potential extension and key research opportunities, such as the combination with the reconfigurable intelligent surface, are discussed concisely., Comment: This manuscript has not been fully modified

Precision measurement of quark Yukawa couplings is a crucial aspect of Higgs property study. Proposed as a future upgrade of the Large Hadron Collider (LHC), the Large Hadron electron Collider (LHeC) provides opportunities to probe quark Yukawa couplings with a high precision because of relatively low rate from QCD background as compared with that of the Higgs processes at the LHC. For this purpose, it is important to have a precision prediction of the Higgs production rate at the LHeC. The leading production channel of the Higgs boson at the LHeC is via weak boson fusion (WBF) which has the unique kinematic feature of forward tagging jets. Since there is no color exchange between the forward-backward objects in WBF processes, electroweak radiative corrections become particularly important. In this paper, we focus on the electroweak radiative corrections at next-to-leading order for Higgs boson production in the WBF channel at the LHeC. In the two renormalization schemes we use, the loop corrections are respectively at the level of 9% and 18% relative to the leading order result, and the next-to-leading calculation in both schemes agree up to a truncation error expected from the perturbative method. The size of the EW correction exceeds that from QCD radiations and is shown to be important for the study of Higgs phenomenology.

Lead-free tin-based perovskites are highly appealing for the next generation of solar cells due to their intriguing optoelectronic properties. However, the tendency of Sn2+ oxidation to Sn4+ in the tin-based perovskites induces serious film degradation and performance deterioration. Herein, we demonstrate, through the density functional theory based first-principle calculations in a surface slab model, that the surface defects of the Sn-based perovskite FASnI3 (FA = NH2CHNH2+) could be effectively passivated by the Lewis base molecules. The passivation performance of Lewis base molecules in tin-based perovskite is tightly correlated with their molecular hardness. We reveal that the degree of hardness of Lewis adsorbate governs the stabilization via dual effects: first, changing the stubborn spatial distribution of tin vacancy (VSn) by triggering charge redistribution; second, saturating the dangling states while simultaneously reducing the amounts of deep band gap states. Specifically, the hard Lewis base molecules like edamine (N-donor group) and Isatin-Cl (Cl-donor group) would show a better healing effect than other candidates on the defects-contained tin-based perovskite surface with a somehow hard Lewis acid nature. Our research provides a general strategy for additive engineering and fabricating stable and high-efficiency lead-free Sn-based perovskite solar cells.

Existence of van der Waals gaps renders two-dimensional (2D) materials ideal passages of lithium for being used as anode materials. However, the requirement of good conductivity significantly limits the choice of 2D candidates. So far only graphite is satisfying due to its relatively high conductivity. Recently, a new polymorph of layered germanium selenide (Gamma-GeSe) was proven to be semimetal in its bulk phase with a higher conductivity than graphite while its monolayer behaves semiconducting. In this work, by using first-principles calculations, we examined the possibility of using this new group-IV monochalcogenide, Gamma-GeSe, as anode in the Li-ion battery (LIBs). Our studies revealed that Li atom would form an ionic adsorption with adjacent selenium atoms at the hollow site and exist in cationic state (lost 0.89 e to Gamma-GeSe). Results of climbing image-nudged elastic band show the diffusion barrier of Li is 0.21 eV in the monolayer limit, which can activate a relatively fast diffusion even at room temperature on the Gamma-GeSe surface. The calculated theoretical average voltages range from 0.071 to 0.015 V at different stoichiometry of LixGeSe with minor volume variation, suggesting its potential application as anode of LIBs. The predicted moderate binding energy, a low open circuit voltage (comparable to graphite) and a fast motion of Li suggests that Gamma-GeSe nanosheet can be chemically exfoliated via Li intercalation and a promising candidate as the anode material for LIBs.

The thermal conductivities of pristine and defective tungsten diselenide (WSe2) are investigated by using equilibrium molecular dynamics method. The thermal conductivity of WSe2 increases dramatically with size below a characteristic with of ~ 5 nm and levels off for broader samples and reaches a constant value of ~2 W/mK. By introducing atomic vacancies, we discovered that the thermal conductivity of WSe2 is significantly reduced. In particular, the W vacancy has a greater impact on thermal conductivity reduction than Se vacancies: the thermal conductivity of pristine WSe2 reduced by ~60% and ~70% with the adding of ~1% of Se and W vacancies, respectively. The reduction of thermal conductivity is found to be related with the decrease of mean free path (MFP) of phonons in the defective WSe2. The MFP of WSe2 decreases from ~4.2 nm for prefect WSe2 to ~2.2 nm with the adding of 0.9% Se vacancies. More sophisticated types of point defects, such as vacancy clusters and anti-site defects, are explored in addition to single vacancies, and are found to dramatically renormalize the phonons. The reconstruction of the bonds leads to localized phonons in the forbidden gap in the phonon density of states which leads to the drop of thermal conduction. This work demonstrates the influence of different defects on thermal conductivity of single-layer WSe2, providing insight into the process of defect-induced phonon transport as well as ways to improve heat dissipation in WSe2-based electronic devices.

The thermal conductivities k of wurtzite InxGa1-xN are investigated using equilibrium molecular dynamics (MD) method. The k of InxGa1-xN rapidly declines from InN (k_InN = 141 W/mK) or GaN (k_GaN = 500 W/mK) to InxGa1-xN, and reaches a minimum (k_min = 19 W/mK) when x is around 0.5 at 300 K. The mean free path (MFP) of InxGa1-xN, ranging from 2 to 5 nm and following the same trend with the k, is extrapolated in our simulation and a parabolic relationship between x and MFP is established. We find that the k of InxGa1-xN decreases with increasing temperatures. The evolution of k of InxGa1-xN is also examined by projecting the momentum-energy relationship of phonons from MD trajectories. The phonon dispersion and phonon density of states for InxGa1-xN reflect a slightly more flattened dispersive phononic curve of the alloying system. Despite an overestimated k than experimental values, our calculated k at 300 K agrees well with the results obtained by solving Boltzmann transport equation and also has the same stoichiometric trend with the experimental data. Our study provides the coherent analysis of the effect of thickness, temperature and stoichiometric content on the thermal transport of InxGa1-xN which is helpful for the thermal management of InxGa1-xN based devices.

Precise congestion prediction from a placement solution plays a crucial role in circuit placement. This work proposes the lattice hypergraph (LH-graph), a novel graph formulation for circuits, which preserves netlist data during the whole learning process, and enables the congestion information propagated geometrically and topologically. Based on the formulation, we further developed a heterogeneous graph neural network architecture LHNN, jointing the routing demand regression to support the congestion spot classification. LHNN constantly achieves more than 35% improvements compared with U-nets and Pix2Pix on the F1 score. We expect our work shall highlight essential procedures using machine learning for congestion prediction., Comment: Accepted as a conference paper in DAC 2022; 6 pages, 4 figures