Miracle-dz/UniFAR-FTUs huggingface dataset:
An updated research focus on the employment of computer-aided drug discovery and repurposing techniques for the identification and evaluation of SARS-CoV-2 Main protease inhibitors: A protocol for a systematic review and meta-analysis

With the onset of the COVID-19 pandemic caused by the novel coronavirus (SARS-CoV-2), there has been a surge in the pursuit of potential therapeutic interventions for this deadly disease. Given the urgency of the situation, computational drug repurposing methods have emerged as a promising strategy for identifying effective treatments from a pool of approved drugs. This systematic review and meta-analaysis will assess the existing research on the use of computational approaches for drug repurposing in the context of COVID-19. SARS-CoV-2 Main Protease is a critical enzyme that plays a vital role in the replication cycle of the SARS-CoV-2 virus, and its inhibition is a promising strategy for the development of antiviral therapies. Different databases (PubMed, Cochrane Library, Cumulative Index to Nursing and Allied Health Literature (CINAHL), MEDLINE via EBSCOhost, Google Scholar, and WILEY online Library) will be utilized to identify and incorporate primary research articles in English and French that employed computational methodologies for drug repurposing in the context of COVID-19 and SARS-CoV-2 Main protease inhibition published between March 2020 to May 2023. According to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA-ScR), we will undertake a comprehensive search of relevant studies. Authors will also search peer-reviewed articles, grey literature sources, and reference lists to identify eligible studies. Title screening will be followed by independent abstract and full-text screening by two reviewers. Any study that focuses on the inhibition of the Mpro using computer aided methods will be included. The analysis of data will be carried out by utilizing two software tools - Review Manager software (version 5.3.5) and R software (version 3.6.1). To determine statistical heterogeneity, a standard chi-square test will be applied with a significance level of P < 0.10. Potential biases related to study size (such as publication bias) will be examined through the application of several techniques, including funnel plots, Egger's test, Begg's test, as well as Trim and Fill analysis. This study will provide evidence-based information and conduct a comprehensive analysis of the computer-aided drug discovery and repurposing of the SARS-CoV-2 Main protease inhibitors, thereby producing a high-quality synthesis of information. The study will also explore potential innovative therapeutic applications for preventing or treating the novel viral infection by the inhibition of the Main Protease. In addition, the review will highlight research gaps in the treatment of COVID-19 and provide suggestions for future research. The outcomes of this review will be shared through a peer-reviewed publication and presented at relevant conferences while ensuring proper dissemination to reach a wide audience.
Moisesdb: A dataset for source separation beyond 4-stems

In this paper, we introduce the MoisesDB dataset for musical source separation. It consists of 240 tracks from 45 artists, covering twelve musical genres. For each song, we provide its individual audio sources, organized in a two-level hierarchical taxonomy of stems. This will facilitate building and evaluating fine-grained source separation systems that go beyond the limitation of using four stems (drums, bass, other, and vocals) due to lack of data. To facilitate the adoption of this dataset, we publish an easy-to-use Python library to download, process and use MoisesDB. Alongside a thorough documentation and analysis of the dataset contents, this work provides baseline results for open-source separation models for varying separation granularities (four, five, and six stems), and discuss their results.
Optically resilient 3D micro-optics on the tips of optical fibers

In this paper we present a study aimed at investigating an optical resiliency of polymers that could be applied in 3D femtosecond laser lithography. These include popular in lithography SU8 and OrmoClear as well as hybrid organic-inorganic zirconium containing SZ2080. We show that latter material in its pure (non-photosensitized) form has the best optical resiliency out of all tested materials. Furthermore, its 3D structurability is investigated. Despite threshold-like quality degradation outside fabrication window, we show that this material is suitable for creating complex 3D structures on the tips of optical fibers. Overall it is demonstrated, that unique capability of 3DLL to structure pure materials can lead to very compact functional fiber-based devices that could withstand high (GW/cm2) light intensities.
Continuous-time channel prediction based on tensor neural ordinary differential equation

Channel prediction is critical to address the channel aging issue in mobile scenarios. Existing channel prediction techniques are mainly designed for discrete channel prediction, which can only predict the future channel in a fixed time slot per frame, while the other intra-frame channels are usually recovered by interpolation. However, these approaches suffer from a serious interpolation loss, especially for mobile millimeter-wave communications. To solve this challenging problem, we propose a tensor neural ordinary differential equation (TN-ODE) based continuous-time channel prediction scheme to realize the direct prediction of intra-frame channels. Specifically, inspired by the recently developed continuous mapping model named neural ODE in the field of machine learning, we first utilize the neural ODE model to predict future continuous-time channels. To improve the channel prediction accuracy and reduce computational complexity, we then propose the TN-ODE scheme to learn the structural characteristics of the high-dimensional channel by low-dimensional learnable transform. Simulation results show that the proposed scheme is able to achieve higher intra-frame channel prediction accuracy than existing schemes.
An Efficient Shapley Value Computation for the Naive Bayes Classifier

Variable selection or importance measurement of input variables to a machine learning model has become the focus of much research. It is no longer enough to have a good model, one also must explain its decisions. This is why there are so many intelligibility algorithms available today. Among them, Shapley value estimation algorithms are intelligibility methods based on cooperative game theory. In the case of the naive Bayes classifier, and to our knowledge, there is no ``analytical"formulation of Shapley values. This article proposes an exact analytic expression of Shapley values in the special case of the naive Bayes Classifier. We analytically compare this Shapley proposal, to another frequently used indicator, the Weight of Evidence (WoE) and provide an empirical comparison of our proposal with (i) the WoE and (ii) KernelShap results on real world datasets, discussing similar and dissimilar results. The results show that our Shapley proposal for the naive Bayes classifier provides informative results with low algorithmic complexity so that it can be used on very large datasets with extremely low computation time.
Sensor Selection using the Two-Target Cram\'er-Rao Bound for Angle of Arrival Estimation

Sensor selection is a useful method to help reduce data throughput, as well as computational, power, and hardware requirements, while still maintaining acceptable performance. Although minimizing the Cram\'er-Rao bound has been adopted previously for sparse sensing, it did not consider multiple targets and unknown source models. We propose to tackle the sensor selection problem for angle of arrival estimation using the worst-case Cram\'er-Rao bound of two uncorrelated sources. We cast the problem as a convex semi-definite program and retrieve the binary selection by randomized rounding. Through numerical examples related to a linear array, we illustrate the proposed method and show that it leads to the selection of elements at the edges plus the center of the linear array.
Deep Convolutional Neural Networks with Zero-Padding: Feature Extraction and Learning

This paper studies the performance of deep convolutional neural networks (DCNNs) with zero-padding in feature extraction and learning. After verifying the roles of zero-padding in enabling translation-equivalence, and pooling in its translation-invariance driven nature, we show that with similar number of free parameters, any deep fully connected networks (DFCNs) can be represented by DCNNs with zero-padding. This demonstrates that DCNNs with zero-padding is essentially better than DFCNs in feature extraction. Consequently, we derive universal consistency of DCNNs with zero-padding and show its translation-invariance in the learning process. All our theoretical results are verified by numerical experiments including both toy simulations and real-data running.
Deep Unrolling Networks with Recurrent Momentum Acceleration for Nonlinear Inverse Problems

Combining the strengths of model-based iterative algorithms and data-driven deep learning solutions, deep unrolling networks (DuNets) have become a popular tool to solve inverse imaging problems. While DuNets have been successfully applied to many linear inverse problems, nonlinear problems tend to impair the performance of the method. Inspired by momentum acceleration techniques that are often used in optimization algorithms, we propose a recurrent momentum acceleration (RMA) framework that uses a long short-term memory recurrent neural network (LSTM-RNN) to simulate the momentum acceleration process. The RMA module leverages the ability of the LSTM-RNN to learn and retain knowledge from the previous gradients. We apply RMA to two popular DuNets -- the learned proximal gradient descent (LPGD) and the learned primal-dual (LPD) methods, resulting in LPGD-RMA and LPD-RMA respectively. We provide experimental results on two nonlinear inverse problems: a nonlinear deconvolution problem, and an electrical impedance tomography problem with limited boundary measurements. In the first experiment we have observed that the improvement due to RMA largely increases with respect to the nonlinearity of the problem. The results of the second example further demonstrate that the RMA schemes can significantly improve the performance of DuNets in strongly ill-posed problems.
Constructing Markov State Models to elucidate the functional conformational changes of complex biomolecules

The function of complex biomolecular machines relies heavily on their conformational changes. Investigating these functional conformational changes is therefore essential for understanding the corresponding biological processes and promoting bioengineering applications and rational drug design. Constructing Markov State Models (MSMs) based on large‐scale molecular dynamics simulations has emerged as a powerful approach to model functional conformational changes of the biomolecular system with sufficient resolution in both time and space. However, the rapid development of theory and algorithms for constructing MSMs has made it difficult for nonexperts to understand and apply the MSM framework, necessitating a comprehensive guidance toward its theory and practical usage. In this study, we introduce the MSM theory of conformational dynamics based on the projection operator scheme. We further propose a general protocol of constructing MSM to investigate functional conformational changes, which integrates the state‐of‐the‐art techniques for building and optimizing initial pathways, performing adaptive sampling and constructing MSMs. We anticipate this protocol to be widely applied and useful in guiding nonexperts to study the functional conformational changes of large biomolecular systems via the MSM framework. We also discuss the current limitations of MSMs and some alternative methods to alleviate them. WIREs Comput Mol Sci 2018, 8:e1343. doi: 10.1002/wcms.1343
Towards Optimal Output-Sensitive Clique Listing or: Listing Cliques from Smaller Cliques

We study finding and listing $k$-cliques in a graph, for constant $k\geq 3$, a fundamental problem of both theoretical and practical importance. Our main contribution is a new output-sensitive algorithm for listing $k$-cliques in graphs, for arbitrary $k\geq 3$, coupled with lower bounds based on standard fine-grained assumptions, showing that our algorithm's running time is tight. Previously, the only known conditionally optimal output-sensitive algorithms were for the case of $3$-cliques by Bj\"{o}rklund, Pagh, Vassilevska W. and Zwick [ICALP'14]. Typical inputs to subgraph isomorphism or listing problems are measured by the number of nodes $n$ or the number of edges $m$. Our framework is very general in that it gives $k$-clique listing algorithms whose running times are measured in terms of the number of $\ell$-cliques $\Delta_\ell$ in the graph for any $1\leq \ell<k$. This generalizes the typical parameterization in terms of $n$ (the number of $1$-cliques) and $m$ (the number of $2$-cliques). If the matrix multiplication exponent $\omega$ is $2$, and if the size of the output, $\Delta_k$, is sufficiently large, then for every $\ell<k$, the running time of our algorithm for listing $k$-cliques is $$\tilde{O}\left(\Delta_\ell^{\frac{2}{\ell (k - \ell)}}\Delta_k^{1-\frac{2}{k(k-\ell)}}\right). $$ For sufficiently large $\Delta_k$, we prove that this runtime is in fact {\em optimal} for all $1 \leq \ell<k$ under the Exact $k$-Clique hypothesis. In the special cases of $k = 4$ and $5$, our algorithm in terms of $n$ is conditionally optimal for all values of $\Delta_k$ if $\omega = 2$. Moreover, our framework is powerful enough to provide an improvement upon the 19-year old runtimes for $4$ and $5$-clique detection in $m$-edge graphs, as a function of $m$ [Eisenbrand and Grandoni, TCS'04].
Learning Generalizable Tool Use with Non-Rigid Grasp-Pose Registration

Tool use, a hallmark feature of human intelligence, remains a challenging problem in robotics due the complex contacts and high-dimensional action space. In this work, we present a novel method to enable reinforcement learning of tool use behaviors. Our approach provides a scalable way to learn the operation of tools in a new category using only a single demonstration. To this end, we propose a new method for generalizing grasping configurations of multi-fingered robotic hands to novel objects. This is used to guide the policy search via favorable initializations and a shaped reward signal. The learned policies solve complex tool use tasks and generalize to unseen tools at test time. Visualizations and videos of the trained policies are available at https://maltemosbach.github. io/generalizable_tool_use.
Part-level Scene Reconstruction Affords Robot Interaction

Existing methods for reconstructing interactive scenes primarily focus on replacing reconstructed objects with CAD models retrieved from a limited database, resulting in significant discrepancies between the reconstructed and observed scenes. To address this issue, our work introduces a part-level reconstruction approach that reassembles objects using primitive shapes. This enables us to precisely replicate the observed physical scenes and simulate robot interactions with both rigid and articulated objects. By segmenting reconstructed objects into semantic parts and aligning primitive shapes to these parts, we assemble them as CAD models while estimating kinematic relations, including parent-child contact relations, joint types, and parameters. Specifically, we derive the optimal primitive alignment by solving a series of optimization problems, and estimate kinematic relations based on part semantics and geometry. Our experiments demonstrate that part-level scene reconstruction outperforms object-level reconstruction by accurately capturing finer details and improving precision. These reconstructed part-level interactive scenes provide valuable kinematic information for various robotic applications; we showcase the feasibility of certifying mobile manipulation planning in these interactive scenes before executing tasks in the physical world.
Quantum coherent and measurement feedback control based on atoms coupled with a semi-infinite waveguide

In this paper, we show that quantum feedback control may be applied to generate desired states for atomic and photonic systems based on a semi-infinite waveguide coupled with multiple two-level atoms. In this set-up, an initially excited atom can emit one photon into the waveguide, which can be reflected by the terminal mirror or other atoms to establish different feedback loops via the coherent interactions between the atom and photon. When there are at most two excitations in the waveguide quantum electrodynamics (waveguide QED) system, the evolution of quantum states can be interpreted using random graph theory. While this process is influenced by the environment, and we clarify that the environment-induced dynamics can be eliminated by measurement-based feedback control or coherent drives. Thus, in the open system atom-waveguide interactions, measurement-based feedback can modulate the final steady quantum state, while simultaneously, the homodyne detection noise in the measurement process can induce oscillations, which is treated by the coherent feedback designs.
Auditing Frameworks Need Resource Isolation: A Systematic Study on the Super Producer Threat to System Auditing and Its Mitigation

System auditing is a crucial technique for detecting APT attacks. However, attackers may try to compromise the system auditing frameworks to conceal their malicious activities. In this paper, we present a comprehensive and systematic study of the super producer threat in auditing frameworks, which enables attackers to either corrupt the auditing framework or paralyze the entire system. We analyze that the main cause of the super producer threat is the lack of data isolation in the centralized architecture of existing solutions. To address this threat, we propose a novel auditing framework, NODROP, which isolates provenance data generated by different processes with a threadlet-based architecture design. Our evaluation demonstrates that NODROP can ensure the integrity of the auditing frameworks while achieving an average 6.58% higher application overhead compared to vanilla Linux and 6.30% lower application overhead compared to a state-of-the-art commercial auditing framework, Sysdig across eight different hardware configurations.
Floer homology beyond borders

Bordered Floer homology is an invariant for 3-manifolds with boundary, defined by the authors in 2008. It extends the Heegaard Floer homology of closed 3-manifolds, defined in earlier work of Zolt\'an Szab\'o and the second author. In addition to its conceptual interest, bordered Floer homology also provides powerful computational tools. This survey outlines the theory, focusing on recent developments and applications.
On the canonical bundle of complex solvmanifolds and applications to hypercomplex geometry

In this article we study solvmanifolds $\Gamma\backslash G$ equipped with invariant complex structures such that its canonical bundle is trivial. We show that the trivializing section of this bundle can be either invariant or non-invariant by the action of $G$. First we characterize the existence of invariant trivializing sections in terms of a 1-form $\psi$ canonically associated to $(\mathfrak{g},J)$, where $\mathfrak{g}$ is the Lie algebra of $G$, and we use this characterization to recover some known results in the literature as well as to produce new examples of complex solvmanifolds with trivial canonical bundle. Later we consider the non-invariant case and we provide an algebraic obstruction, also in terms of $\psi$, for a complex solvmanifold to have trivial (or more generally holomorphically torsion) canonical bundle. In addition, this obstruction leads us to a way of building explicit non-invariant sections which we illustrate with some examples. Finally, we apply our results to hypercomplex manifolds in order to provide a negative answer to a question posed by M. Verbitsky.
Prospects for future experimental tests of gravity with black hole imaging: Spherical symmetry

Astrophysical black holes (BHs) are universally expected to be described by the Kerr metric, a stationary, vacuum solution of general relativity (GR). Indeed, by imaging M87$^\star$ and Sgr A$^\star$ and measuring the size of their shadows, we have substantiated this hypothesis through successful null tests. Here we discuss the potential of upcoming improved imaging observations in constraining deviations of the spacetime geometry from that of a Schwarzschild BH (the nonspinning, vacuum GR solution), with a focus on the photon ring. The photon ring comprises a series of time-delayed, self-similarly nested higher-order images of the accretion flow, and is located close to the boundary of the shadow. In spherical spacetimes, these images are indexed by the number of half-loops executed around the BH by the photons that arrive in them. The delay time offers an independent shadow size estimate, enabling tests of shadow achromaticity, as predicted by GR. The image self-similarity relies on the lensing Lyapunov exponent, which is linked to photon orbit instability near the unstable circular orbit. Notably, this critical exponent, specific to the spacetime, is sensitive to the $rr-$component of the metric, and also offers insights into curvature, beyond the capabilities of currently available shadow size measurements. The Lyapunov time, a characteristic instability timescale, provides yet another probe of metric and curvature. The ratio of the Lyapunov and the delay times also yields the lensing Lyapunov exponent, providing alternative measurement pathways. Remarkably, the width of the first-order image can also serve as a discriminator of the spacetime. Each of these observables, potentially accessible in the near future, offers spacetime constraints that are orthogonal to those of the shadow size, enabling precision tests of GR.
On relative complexity of quantum and classical branching programs

Abstract We give a definition of a quantum branching program. A well-known Boolean function MODp is considered. We prove that any deterministic branching program and any probabilistic ordered stable branching program which (1/2 + ε)-compute the function MODp are of width no less than p. We construct a stable quantum branching program of width O(log p) which computes the function MODp with one-sided error ε > 0.
Ad-hoc polymorphic delimited continuations

We designed and implemented a framework for creating extensible domain-specific languages that consists of library-defined keywords. First-class language features in other programming languages can be implemented as libraries with the help of our framework. The core concept in our framework is the type class Dsl, which can be considered as both the ad-hoc polymorphic version of a delimited continuation and a more generic version of Monad. Thus it can be also used as a statically typed extensible effect system that is more efficient and more concise than existing Monad-based effect systems.