Miracle-dz/UniFAR-FTUs huggingface dataset:
Deep Learning Methods for Small Molecule Drug Discovery: A Survey

With the development of computer-assisted techniques, research communities, including biochemistry and deep learning, have been devoted into the drug discovery field for over a decade. Various applications of deep learning have drawn great attention in drug discovery, such as molecule generation, molecular property prediction, retrosynthesis prediction, and reaction prediction. While most of the existing surveys only focus on one of the applications, limiting the view of researchers in the community, in this article, we present a comprehensive review on the aforementioned four aspects and discuss the relationships among different applications. The latest literature and classical benchmarks are presented for better understanding the development of a variety of approaches. We commence by summarizing the molecule representation format in these works, followed by an introduction of recent proposed approaches for each of the four tasks. Furthermore, we review a variety of commonly used datasets and evaluation metrics and compare the performance of deep-learning-based models. Finally, we conclude by identifying remaining challenges and discussing the future trend for deep learning methods in drug discovery.
Empowering Networks With Scale and Rotation Equivariance Using A Similarity Convolution

The translational equivariant nature of Convolutional Neural Networks (CNNs) is a reason for its great success in computer vision. However, networks do not enjoy more general equivariance properties such as rotation or scaling, ultimately limiting their generalization performance. To address this limitation, we devise a method that endows CNNs with simultaneous equivariance with respect to translation, rotation, and scaling. Our approach defines a convolution-like operation and ensures equivariance based on our proposed scalable Fourier-Argand representation. The method maintains similar efficiency as a traditional network and hardly introduces any additional learnable parameters, since it does not face the computational issue that often occurs in group-convolution operators. We validate the efficacy of our approach in the image classification task, demonstrating its robustness and the generalization ability to both scaled and rotated inputs.
A Study on Accuracy, Miscalibration, and Popularity Bias in Recommendations

Recent research has suggested different metrics to measure the inconsistency of recommendation performance, including the accuracy difference between user groups, miscalibration, and popularity lift. However, a study that relates miscalibration and popularity lift to recommendation accuracy across different user groups is still missing. Additionally, it is unclear if particular genres contribute to the emergence of inconsistency in recommendation performance across user groups. In this paper, we present an analysis of these three aspects of five well-known recommendation algorithms for user groups that differ in their preference for popular content. Additionally, we study how different genres affect the inconsistency of recommendation performance, and how this is aligned with the popularity of the genres. Using data from LastFm, MovieLens, and MyAnimeList, we present two key findings. First, we find that users with little interest in popular content receive the worst recommendation accuracy, and that this is aligned with miscalibration and popularity lift. Second, our experiments show that particular genres contribute to a different extent to the inconsistency of recommendation performance, especially in terms of miscalibration in the case of the MyAnimeList dataset.
Semi-Supervised Parametric Real-World Image Harmonization

Learning-based image harmonization techniques are usually trained to undo synthetic random global transformations applied to a masked foreground in a single ground truth photo. This simulated data does not model many of the important appearance mismatches (illumination, object boundaries, etc.) between foreground and background in real composites, leading to models that do not generalize well and cannot model complex local changes. We propose a new semi-supervised training strategy that addresses this problem and lets us learn complex local appearance harmonization from unpaired real composites, where foreground and background come from different images. Our model is fully parametric. It uses RGB curves to correct the global colors and tone and a shading map to model local variations. Our method outperforms previous work on established benchmarks and real composites, as shown in a user study, and processes high-resolution images interactively. Code, and project page available at: https://kewang0622.github. io/sprih/
Insights into air cushion dynamics during drop impact on heated substrate at low impact energy

We study the air layer dynamics beneath a drop impinging a heated surface at low impact energy using high-speed reflection interferometry imaging and theoretical analysis. The air film has been subdivided into two distinct disjoint regions, the central dimple and the peripheral disc. We decipher that a gaussian profile can approximate the dynamic shape evolution of the central air dimple. We further observe that the dimple geometry is a function of impact energy and its dependence on surface temperature is relatively weak. The air layer rupture time and rupture radius increases with increase in substrate temperature. We characterize the air layer profile as a 2D Knudsen field and show that a unified treatment, including continuum and non-continuum mechanics, is required to comprehend the air layer dynamics coherently. The airflow dynamics in the central dimple region falls within the purview of continuum stokes regime. In contrast, the peripheral air disc falls within the non-continuum (gas kinetic effects) slip flow and transition regime characterized by a high Knudsen number. However, the initial average air disc expansion dynamics could be understood in terms of stokes approximation. In non-continuum regimes of the peripheral air disc, we discover intriguing asymmetric interface perturbations. The asymmetric wetting of the substrate initiates at the edge of the peripheral disc region.These perturbative structures cause asymmetric wetting/contact between the droplet and the substrate. Due to the asymptotic effects of capillary and van der Waals interaction in the disc region, the sub-micron spatial structures can exist at short time scales.
Plasma induced surface modification of sapphire and its influence on graphene grown by PECVD

The catalyst-free synthesis of graphene on dielectrics prevents the damage induced by the transfer process. Although challenging, to master this synthesis would boost the integration of graphene on consumer electronics since defects hinder its optoelectronic properties. In this work, the influence of the different surface terminations of c-plane sapphire substrates on the synthesis of graphene via plasma-enhanced chemical vapour deposition (PECVD) is studied. The different terminations of the sapphire surface are controlled by a plasma etching process. A design of experiments (DoE) procedure was carried out to evaluate the major effects governing the etching process of four different parameters: i.e. discharge power, time, pressure and gas employed. In the characterization of the substrate, two sapphire surface terminations were identified and characterized by means of contact angle measurements, being a hydrophilic (hydrophobic) surface the fingerprint of an Al- (OH-) terminated surface, respectively. The defects within the synthesized graphene were analysed by Raman spectroscopy. Notably, we found that the ID/IG ratio decreases for graphene grown on OH-terminated surfaces. Furthermore, two different regimes related to the nature of graphene defects were identified and depending on the sapphire terminated surface are bound either to vacancy or boundary like defects. Finally, studying the density of defects and the crystallite area, as well as their relationship with the sapphire surface termination paves the way for increasing the crystallinity of the synthesized graphene.
Covalency, correlations, and interlayer interactions governing the magnetic and electronic structure of 
<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mrow><mml:msub><mml:mi>Mn</mml:mi><mml:mn>3</mml:mn></mml:msub><mml:msub><mml:mi>Si</mml:mi><mml:mn>2</mml:mn></mml:msub><mml:msub><m

Mn$_3$Si$_2$Te$_6$ is a rare example of a layered ferrimagnet. It has recently been shown to host a colossal angular magnetoresistance as the spin orientation is rotated from the in- to out-of-plane direction, proposed to be underpinned by a topological nodal-line degeneracy in its electronic structure. Nonetheless, the origins of its ferrimagnetic structure remain controversial, while its experimental electronic structure, and the role of correlations in shaping this, are little explored to date. Here, we combine x-ray and photoemission-based spectroscopies with first-principles calculations, to probe the elemental-selective electronic structure and magnetic order in Mn$_3$Si$_2$Te$_6$. Through these, we identify a marked Mn-Te hybridisation, which weakens the electronic correlations and enhances the magnetic anisotropy. We demonstrate how this strengthens the magnetic frustration in Mn$_3$Si$_2$Te$_6$, which is key to stabilising its ferrimagnetic order, and find a crucial role of both exchange interactions extending beyond nearest-neighbours and anti-symmetric exchange in dictating its ordering temperature. Together, our results demonstrate a powerful methodology of using experimental electronic structure probes to constrain the parameter space for first-principles calculations of magnetic materials, and through this approach, reveal a pivotal role played by covalency in stabilising the ferrimagnetic order in Mn$_3$Si$_2$Te$_6$.
Finite element modeling of thermopiezomagnetic smart structures

Linearconstitutiveequationsofa thermopiezomagneticmedium involving mechanical, electrical, magnetic, and thermal e elds are presented with the aid of a thermodynamic potential. A thermopiezomagnetic medium can be formed by bonding together a piezoelectric and magnetostrictive composite. Two energy functionals are dee ned. It is shown via Hamilton’ s principle that these functionals yield the equations of motion for the mechanical e eld, Maxwell’ s equilibrium equations for the electrical and magnetic e elds, and the generalized heat equation for the thermal e eld. Finite element equations for the thermopiezomagnetic media are obtained by using the linear constitutive equations in Hamilton’ s principle together with the e nite element approximations. The e nite element equations are utilized on an example two-layer smartstructure, which consistsof a piezoceramic (barium titanate ) layer at the bottom and a magnetoceramic (cobalt ferrite ) layer at the top. An electrostatic e eld applied to the piezoceramic layer causes strain in the structure. This strain then produces magnetic e eld in the magnetoceramic layer.
S-NeRF: Neural Radiance Fields for Street Views

Neural Radiance Fields (NeRFs) aim to synthesize novel views of objects and scenes, given the object-centric camera views with large overlaps. However, we conjugate that this paradigm does not fit the nature of the street views that are collected by many self-driving cars from the large-scale unbounded scenes. Also, the onboard cameras perceive scenes without much overlapping. Thus, existing NeRFs often produce blurs, 'floaters' and other artifacts on street-view synthesis. In this paper, we propose a new street-view NeRF (S-NeRF) that considers novel view synthesis of both the large-scale background scenes and the foreground moving vehicles jointly. Specifically, we improve the scene parameterization function and the camera poses for learning better neural representations from street views. We also use the the noisy and sparse LiDAR points to boost the training and learn a robust geometry and reprojection based confidence to address the depth outliers. Moreover, we extend our S-NeRF for reconstructing moving vehicles that is impracticable for conventional NeRFs. Thorough experiments on the large-scale driving datasets (e.g., nuScenes and Waymo) demonstrate that our method beats the state-of-the-art rivals by reducing 7% to 40% of the mean-squared error in the street-view synthesis and a 45% PSNR gain for the moving vehicles rendering.
Towards a Privacy-Preserving Dispute Resolution Protocol on Ethereum

We present a new dispute resolution protocol that can be built on the Ethereum blockchain. Unlike existing applications like Kleros, privacy is ensured by design through the use of the zero-knowledge protocols Semaphore and MACI (Minimal Anti-Collusion Infrastructure), which provide, among other things, resistance to Sybil-like attacks and corruption. Differently from Kleros, dispute resolution is guaranteed despite the users having the final say. Moreover, the proposed model does not use a native token on the platform, but aims to reward stakeholders through a social incentive mechanism based on soulbound tokens, introduced by Weyl, Ohlhaver, and Buterin in 2022. Users with these tokens will be considered trustworthy and will have the ability to govern the platform. As far as we know, this is one of the first blockchain projects that seeks to introduce social governance rather than one based on economic incentives.
An Overview: Steady-State Quantum Entanglement via Reservoir Engineering

We present a short overview of quantum entanglement generation and preservation in a steady state. In addition to the focus on quantum entanglement stabilization, we briefly discuss the same objective for steady-state quantum coherence. The overview classifies the approaches into two main categories: hybrid drive and dissipation methods and purely dissipative schemes. Furthermore, purely dissipative schemes are discussed under two subclasses of equilibrium and nonequilibrium environments. The significance of the dissipative route to sustained quantum entanglement and challenges against it are pointed out. Besides the value of steady-state entanglement for existing quantum technologies, quantum computation, communication, sensing, and simulation, its unique opportunities for emerging and future quantum technology applications, particularly quantum heat engines and quantum energy processing, are discussed.
A Unified Momentum-based Paradigm of Decentralized SGD for Non-Convex Models and Heterogeneous Data

Emerging distributed applications recently boosted the development of decentralized machine learning, especially in IoT and edge computing fields. In real-world scenarios, the common problems of non-convexity and data heterogeneity result in inefficiency, performance degradation, and development stagnation. The bulk of studies concentrates on one of the issues mentioned above without having a more general framework that has been proven optimal. To this end, we propose a unified paradigm called UMP, which comprises two algorithms, D-SUM and GT-DSUM, based on the momentum technique with decentralized stochastic gradient descent(SGD). The former provides a convergence guarantee for general non-convex objectives. At the same time, the latter is extended by introducing gradient tracking, which estimates the global optimization direction to mitigate data heterogeneity(i.e., distribution drift). We can cover most momentum-based variants based on the classical heavy ball or Nesterov's acceleration with different parameters in UMP. In theory, we rigorously provide the convergence analysis of these two approaches for non-convex objectives and conduct extensive experiments, demonstrating a significant improvement in model accuracy by up to 57.6% compared to other methods in practice.
Robustness of quantum algorithms against coherent control errors

Coherent control errors, for which ideal Hamiltonians are perturbed by unknown multiplicative noise terms, are a major obstacle for reliable quantum computing. In this paper, we present a framework for analyzing the robustness of quantum algorithms against coherent control errors using Lipschitz bounds. We derive worst-case fidelity bounds which show that the resilience against coherent control errors is mainly influenced by the norms of the Hamiltonians generating the individual gates. These bounds are explicitly computable even for large circuits, and they can be used to guarantee fault-tolerance via threshold theorems. Moreover, we apply our theoretical framework to derive a novel guideline for robust quantum algorithm design and transpilation, which amounts to reducing the norms of the Hamiltonians. Using the $3$-qubit Quantum Fourier Transform as an example application, we demonstrate that this guideline targets robustness more effectively than existing ones based on circuit depth or gate count. Furthermore, we apply our framework to study the effect of parameter regularization in variational quantum algorithms. The practicality of the theoretical results is demonstrated via implementations in simulation and on a quantum computer.
On the Integration of Physics-Based Machine Learning with Hierarchical Bayesian Modeling Techniques

Machine Learning (ML) has widely been used for modeling and predicting physical systems. These techniques offer high expressive power and good generalizability for interpolation within observed data sets. However, the disadvantage of black-box models is that they underperform under blind conditions since no physical knowledge is incorporated. Physics-based ML aims to address this problem by retaining the mathematical flexibility of ML techniques while incorporating physics. In accord, this paper proposes to embed mechanics-based models into the mean function of a Gaussian Process (GP) model and characterize potential discrepancies through kernel machines. A specific class of kernel function is promoted, which has a connection with the gradient of the physics-based model with respect to the input and parameters and shares similarity with the exact Autocovariance function of linear dynamical systems. The spectral properties of the kernel function enable considering dominant periodic processes originating from physics misspecification. Nevertheless, the stationarity of the kernel function is a difficult hurdle in the sequential processing of long data sets, resolved through hierarchical Bayesian techniques. This implementation is also advantageous to mitigate computational costs, alleviating the scalability of GPs when dealing with sequential data. Using numerical and experimental examples, potential applications of the proposed method to structural dynamics inverse problems are demonstrated.
Decentralized Model Dissemination Empowered Federated Learning in mmWave Aerial-Terrestrial Integrated Networks

It is anticipated that aerial-terrestrial integrated networks incorporating unmanned aerial vehicles (UAVs) mounted relays will offer improved coverage and connectivity in the beyond 5G era. Meanwhile, federated learning (FL) is a promising distributed machine learning technique for building inference models over wireless networks due to its ability to maintain user privacy and reduce communication overhead. However, off-the-shelf FL models aggregate global parameters at a central parameter server (CPS), increasing energy consumption and latency, as well as inefficiently utilizing radio resource blocks (RRBs) for distributed user devices (UDs). This paper presents a resource-efficient FL framework, called FedMoD (\textbf{fed}erated learning with \textbf{mo}del \textbf{d}issemination), for millimeter-wave (mmWave) aerial-terrestrial integrated networks with the following two unique characteristics. Firstly, FedMoD presents a novel decentralized model dissemination algorithm that makes use of UAVs as local model aggregators through UAV-to-UAV and device-to-device (D2D) communications. As a result, FedMoD (i) increases the number of participant UDs in developing FL model and (ii) achieves global model aggregation without involving CPS. Secondly, FedMoD reduces the energy consumption of FL using radio resource management (RRM) under the constraints of over-the-air learning latency. In order to achieve this, by leveraging graph theory, FedMoD optimizes the scheduling of line-of-sight (LOS) UDs to suitable UAVs/RRBs over mmWave links and non-LOS UDs to available LOS UDs via overlay D2D communications. Extensive simulations reveal that decentralized FedMoD offers same convergence rate performance as compared to conventional FL frameworks.
Anisotropic Diffusion Limited Aggregation: From Self-Similarity to Self-Affinity

Diffusion-limited particle aggregation is simulated with a modified growth rule. By means of a parameter the growth anisotropy along the axes of symmetry can be varied continually from isotropic to strongly anisotropic. For weak anisotropy one initially observes an isotropic structure with a fractal dimension D = 1.7 which crosses over to an anisotropic structure with two different scaling exponents Daxial = 1.5 and Ddiagonal = 2.0 along the axes, respectively, along the diagonals, on a square lattice. One can understand the growth in the anisotropic regime in terms of classical aggregation, where the fluctuations are neglected.
Ground states and periodic orbits for expanding Thurston maps

Expanding Thurston maps form a class of branched covering maps on the topological $2$-sphere $S^{2}$ which are topological models of some non-uniformly expanding rational maps without any smoothness or holomorphicity assumption initially investigated by W. P. Thurston, M. Bonk, D. Meyer, P. Ha\"issinsky, and K. M. Pilgrim. The measures of maximal entropy and the absolutely continuous invariant measures for these maps have been studied by these authors, and equilibrium states by the first-named author. In this paper, we initiate the investigation on two new classes of invariant measures (namely, the maximizing measures and ground states), and establish the Liv\v{s}ic theorem, a local Anosov closing lemma, and give a positive answer to the Typically Periodic Optimization Conjecture from ergodic optimization for these maps. As an application, we establish these results for Misiurewicz--Thurston rational maps (i.e., postcritically-finite rational maps without periodic critical points) on the Riemann sphere including the Latt\`es maps with respect to the spherical metric. Our strategy relies on the visual metrics developed by the above authors. In particular, we verify, in a first non-uniformly expanding setting, the Typically Periodic Optimization Conjecture, establishing that for a generic H\"{o}lder continuous potential, there exists a unique maximizing measure, moreover, this measure is supported on a periodic orbit, it satisfies the locking property, and it is the unique ground state. The expanding Thurston maps we consider include those that are not topologically conjugate to rational maps, in particular, they can have periodic critical points.