Miracle-dz/UniFAR-FTUs huggingface dataset:
DNA Cleavage by Copper Complexes of 2‐ and 3‐Clip‐Phen Derivatives

The DNA cleavage activity of copper complexes of phenanthroline has been increased by the use of “2-Clip-Phen” and “3-Clip-Phen” ligands containing two phenanthroline entities linked by a serinol bridge. In order to optimize DNA cleavage activity, two series of new Clip-Phen ligands (L) with two phenanthroline residues linked through their C-2 or C-3 carbon atoms by different bridges have been synthesized. The physicochemical properties and the DNA cleavage activities of the corresponding (Clip-Phen)copper complexes were investigated in the presence of a reductant and air. X-ray analyses of single crystals showed that the cupric complexes of Clip-Phen were able adopt dimeric double-helical geometries with L2Cu2 stoichiometries in the solid state. These complexes adopt different geometries and stoichiometries in solution, with monomeric LCu species predominant. The compounds with a bridge connected at C-2 are less active as DNA cleavage agents than complexes with the bridge at the C-3 positions. Copper complexes with a bridge containing three methylene units appear to give the best results in DNA cleavage experiments. This activity is increased in the case of 3-Clip-Phen·CuCl2 by the presence of the primary amine function of the serinol bridge. This amine function is probably coordinated to the copper atom in the case of 2-Clip-Phen·CuCl2, decreasing the reactivity toward DNA. (© Wiley-VCH Verlag GmbH & Co KGaA, 69451 Weinheim, Germany, 2003)
Size dependence in flux-flow Hall effect using time-dependent Ginzburg-Landau equations

Vineet Punyamoorty,1 Aditya Malusare,2 Shamashis Sengupta,3 Sumiran Pujari,4 and Kasturi Saha1 Department of Electrical Engineering, Indian Institute of Technology Bombay, Mumbai 400076, India Department of Physics and Astronomy, Purdue University, West Lafayette, IN 47907 Université Paris-Saclay, CNRS/IN2P3, IJCLab, 91405 Orsay, France Department of Physics, Indian Institute of Technology Bombay, Mumbai 400076, India (Dated: 2 February 2021)
Broadband dipolar recoupling in the nuclear magnetic resonance of rotating solids: A compensated C7 pulse sequence

We introduce an improved variant of the C7 pulse-sequence for efficient recoupling of spin-1/2 pair dipolar interactions in magic-angle spinning solid-state NMR spectroscopy. The tolerance of C7 toward isotropic as well as anisotropic chemical shift offsets and rf inhomogeneity is improved considerably by replacing the original basic element Cφ44=(2π)φ(2π)φ+π with the cyclically permuted element Cφ143=(π/2)φ(2π)φ+π(3π/2)φ. The improved performance of this permutationally offset stabilized variant of C7 is analyzed by average Hamiltonian theory to fifth order, numerical simulations, and demonstrated by experiments on powder samples of doubly 13C-labeled barium oxalate hemihydrate and diammonium fumarate.
Sampling in Combinatorial Spaces with SurVAE Flow Augmented MCMC

Hybrid Monte Carlo is a powerful Markov Chain Monte Carlo method for sampling from complex continuous distributions. However, a major limitation of HMC is its inability to be applied to discrete domains due to the lack of gradient signal. In this work, we introduce a new approach based on augmenting Monte Carlo methods with SurVAE Flows to sample from discrete distributions using a combination of neural transport methods like normalizing flows and variational dequantization, and the Metropolis-Hastings rule. Our method first learns a continuous embedding of the discrete space using a surjective map and subsequently learns a bijective transformation from the continuous space to an approximately Gaussian distributed latent variable. Sampling proceeds by simulating MCMC chains in the latent space and mapping these samples to the target discrete space via the learned transformations. We demonstrate the efficacy of our algorithm on a range of examples from statistics, computational physics and machine learning, and observe improvements compared to alternative algorithms.
A Possible Artificial Intelligence Ecosystem Avatar: the Moorea case (IDEA)

High-throughput data collection techniques and largescale (cloud) computing are transforming our understanding of ecosystems at all scales by allowing the integration of multimodal data such as physics, chemistry, biology, ecology, fishing, economics and other social sciences in a common computational framework. We focus in this paper on a large scale data assimilation and prediction backbone based on Deep Stacking Networks (DSN) in the frame of the IDEA (Island Digital Ecosystem Avatars) project (Moorea Island), based on the subdivision of the island in watersheds and lagoon units. We also describe several kinds of raw data that can train and constrain such an ecosystem avatar model, as well as second level data such as ecological or physical indexes / indicators.
Dual-embedding based Neural Collaborative Filtering for Recommender Systems

Among various recommender techniques, collaborative filtering (CF) is the most successful one. And a key problem in CF is how to represent users and items. Previous works usually represent a user (an item) as a vector of latent factors (aka. \textit{embedding}) and then model the interactions between users and items based on the representations. Despite its effectiveness, we argue that it's insufficient to yield satisfactory embeddings for collaborative filtering. Inspired by the idea of SVD++ that represents users based on themselves and their interacted items, we propose a general collaborative filtering framework named DNCF, short for Dual-embedding based Neural Collaborative Filtering, to utilize historical interactions to enhance the representation. In addition to learning the primitive embedding for a user (an item), we introduce an additional embedding from the perspective of the interacted items (users) to augment the user (item) representation. Extensive experiments on four publicly datasets demonstrated the effectiveness of our proposed DNCF framework by comparing its performance with several traditional matrix factorization models and other state-of-the-art deep learning based recommender models.
Resonant spin amplification in Faraday geometry

P. Schering,1 E. Evers,2 V. Nedelea,2 D. S. Smirnov,3 E. A. Zhukov,2, 3 D. R. Yakovlev,2, 3 M. Bayer,2, 3 G. S. Uhrig,1 and A. Greilich2 Condensed Matter Theory, Technische Universität Dortmund, 44221 Dortmund, Germany Experimentelle Physik 2, Technische Universität Dortmund, 44221 Dortmund, Germany Ioffe Institute, Russian Academy of Sciences, 194021 St. Petersburg, Russia (Dated: February 5, 2021)
Normalizing flows for microscopic calculations of the nuclear equation of state

Normalizing flows are a class of machine learning models used to construct a complex distribution through a bijective mapping of a simple base distribution. We demonstrate that normalizing flows are particularly well suited as a Monte Carlo integration framework for nuclear many-body calculations that require the repeated evaluation of high-dimensional integrals across smoothly varying integrands and integration regions. As an example, we consider the finite-temperature nuclear equation of state. An important advantage of normalizing flows is the ability to build highly expressive models of the target integrand, which we demonstrate enables precise evaluations of the nuclear free energy and its derivatives. Furthermore, we show that a normalizing flow model trained on one target integrand transfers remarkably well to related integrals, such that the nuclear equation of state at varying density and temperature can be mapped with computational efficiency and precision. This work will support future efforts to build microscopic equations of state for numerical simulations of supernovae and neutron star mergers that employ state-of-the-art nuclear forces and many-body methods.
A Living Review of Machine Learning for Particle Physics

Modern machine learning techniques, including deep learning, are rapidly being applied, adapted, and developed for high energy physics. Given the fast pace of this research, we have created a living review with the goal of providing a nearly comprehensive list of citations for those developing and applying these approaches to experimental, phenomenological, or theoretical analyses. As a living document, it will be updated as often as possible to incorporate the latest developments. A list of proper (unchanging) reviews can be found within. Papers are grouped into a small set of topics to be as useful as possible. Suggestions and contributions are most welcome, and we provide instructions for participating.
Liquid Chromatographic Method for the Analysis of Brimonidine in Ophthalmic Formulations

A reverse phase LC method was developed for the determination of Brimonidine in eye drops. Chromatography was carried on an Inertsil ODS 3V column (C18) using a mixture of Octane 1- sulfonic acid sodium salt (0.02M) (pH 3.5 ± 0.05) and acetonitrile (64:36 v/v) as mobile phase at a flow rate of 1 mL/min with UV detection at 254 nm. The drug was eluted at 4.636 min. The detector response was linear in the concentration range of 0.4–72 μg/mL. The limit of detection and limit of quantification were found to be 0.0561 and 0.1848 μg/mL respectively. The proposed method was validated as per the ICH guidelines and can be applied for the routine analysis of Brimonidine in eye drops.
Audio Adversarial Examples: Attacks Using Vocal Masks

We construct audio adversarial examples on automatic Speech-To-Text systems . Given any audio waveform, we produce an another by overlaying an audio vocal mask generated from the original audio. We apply our audio adversarial attack to five SOTA STT systems: DeepSpeech, Julius, Kaldi, wav2letter@anywhere and CMUSphinx. In addition, we engaged human annotators to transcribe the adversarial audio. Our experiments show that these adversarial examples fool State-Of-The-Art Speech-To-Text systems, yet humans are able to consistently pick out the speech. The feasibility of this attack introduces a new domain to study machine and human perception of speech.
Hamiltonicity of graphs perturbed by a random geometric graph

We study Hamiltonicity in graphs obtained as the union of a deterministic n $n$ ‐vertex graph H $H$ with linear degrees and a d $d$ ‐dimensional random geometric graph G d ( n , r ) ${G}^{d}(n,r)$ , for any d ≥ 1 $d\ge 1$ . We obtain an asymptotically optimal bound on the minimum r $r$ for which a.a. s. H ∪ G d ( n , r ) $H\cup {G}^{d}(n,r)$ is Hamiltonian. Our proof provides a linear time algorithm to find a Hamilton cycle in such graphs.
Fractionally log-concave and sector-stable polynomials: counting planar matchings and more

We show fully polynomial time randomized approximation schemes (FPRAS) for counting matchings of a given size, or more generally sampling/counting monomer-dimer systems in planar, not-necessarily-bipartite, graphs. While perfect matchings on planar graphs can be counted exactly in polynomial time, counting non-perfect matchings was shown by Jerrum (J Stat Phys 1987) to be #P-hard, who also raised the question of whether efficient approximate counting is possible. We answer this affirmatively by showing that the multi-site Glauber dynamics on the set of monomers in a monomer-dimer system always mixes rapidly, and that this dynamics can be implemented efficiently on downward-closed families of graphs where counting perfect matchings is tractable. As further applications of our results, we show how to sample efficiently using multi-site Glauber dynamics from partition-constrained strongly Rayleigh distributions, and nonsymmetric determinantal point processes. In order to analyze mixing properties of the multi-site Glauber dynamics, we establish two notions for generating polynomials of discrete set-valued distributions: sector-stability and fractional log-concavity. These notions generalize well-studied properties like real-stability and log-concavity, but unlike them robustly degrade under useful transformations applied to the distribution. We relate these notions to pairwise correlations in the underlying distribution and the notion of spectral independence introduced by Anari et al. (FOCS 2020), providing a new tool for establishing spectral independence based on geometry of polynomials. As a byproduct of our techniques, we show that polynomials avoiding roots in a sector of the complex plane must satisfy what we call fractional log-concavity; this generalizes a classic result established by Gårding (J Math Mech 1959) who showed homogeneous polynomials that have no roots in a half-plane must be log-concave over the positive orthant.
Disambiguation of Weak Supervision leading to Exponential Convergence rates

Machine learning approached through supervised learning requires expensive annotation of data. This motivates weakly supervised learning, where data are annotated with incomplete yet discriminative information. In this paper, we focus on partial labelling, an instance of weak supervision where, from a given input, we are given a set of potential targets. We review a disambiguation principle to recover full supervision from weak supervision, and propose an empirical disambiguation algorithm. We prove exponential convergence rates of our algorithm under classical learnability assumptions, and we illustrate the usefulness of our method on practical examples.
A Faster Algorithm for Finding Closest Pairs in Hamming Metric

We study the Closest Pair Problem in Hamming metric, which asks to find the pair with the smallest Hamming distance in a collection of binary vectors. We give a new randomized algorithm for the problem on uniformly random input outperforming previous approaches whenever the dimension of input points is small compared to the dataset size. For moderate to large dimensions, our algorithm matches the time complexity of the previously best-known locality sensitive hashing based algorithms. Technically our algorithm follows similar design principles as Dubiner (IEEE Trans. Inf. Theory 2010) and May-Ozerov (Eurocrypt 2015). Besides improving the time complexity in the aforementioned areas, we significantly simplify the analysis of these previous works. We give a modular analysis, which allows us to investigate the performance of the algorithm also on non-uniform input distributions. Furthermore, we give a proof of concept implementation of our algorithm which performs well in comparison to a quadratic search baseline. This is the first step towards answering an open question raised by May and Ozerov regarding the practicability of algorithms following these design principles.
Reduced-order Model for Fluid Flows via Neural Ordinary Differential Equations

Reduced order models play an important role in the design, optimization and control of dynamical systems. In recent years, there has been an increasing interest in the application of data-driven techniques for model reduction that can decrease the computational burden of numerical solutions, while preserving the most important features of complex physical problems. In this paper, we use the proper orthogonal decomposition to reduce the dimensionality of the model and introduce a novel generative neural ODE (NODE) architecture to forecast the behavior of the temporal coefficients. With this methodology, we replace the classical Galerkin projection with an architecture characterized by the use of a continuous latent space. We exemplify the methodology on the dynamics of the Von Karman vortex street of the flow past a cylinder generated by a Large-eddy Simulation (LES)-based code. We compare the NODE methodology with an LSTM baseline to assess the extrapolation capabilities of the generative model and present some qualitative evaluations of the flow reconstructions.
High-Rate Quantum Private Information Retrieval with Weakly Self-Dual Star Product Codes

In the classical private information retrieval (PIR) setup, a user wants to retrieve a file from a database or a distributed storage system (DSS) without revealing the file identity to the servers holding the data. In the quantum PIR (QPIR) setting, a user privately retrieves a classical file by receiving quantum information from the servers. The QPIR problem has been treated by Song et al. in the case of replicated servers, both with and without collusion. QPIR over [n, k] maximum distance separable (MDS) coded servers was recently considered by Allaix et al., but the collusion was essentially restricted to t = n - $k$ servers in the sense that a smaller $t$ would not improve the retrieval rate. In this paper, the QPIR setting is extended to allow for retrieval with high rate for any number of colluding servers $t$ with 1 ≤ $t$ ≤ $n$ - k. Similarly to the previous cases, the rates achieved are better than those known or conjectured in the classical counterparts, as well as those of the previously proposed coded and colluding QPIR schemes. This is enabled by considering the stabilizer formalism and weakly self-dual generalized Reed-Solomon (GRS) star product codes.
SelfNorm and CrossNorm for Out-of-Distribution Robustness

Normalization techniques are crucial in stabilizing and accelerating the training of deep neural networks. However, they are mainly designed for the independent and identically distributed (IID) data, not satisfying many real-world out-of-distribution (OOD) situations. Unlike most previous works, this paper presents two normalization methods, SelfNorm and CrossNorm, to promote OOD generalization. SelfNorm uses attention to recalibrate statistics (channel-wise mean and variance), while CrossNorm exchanges the statistics between feature maps. SelfNorm and CrossNorm can complement each other in OOD generalization, though exploring different directions in statistics usage. Extensive experiments on different domains (vision and language), tasks (classiﬁcation and segmentation), and settings (supervised and semi-supervised) show their effectiveness.
Decoding Reed-Solomon codes by solving a bilinear system with a Gröbner basis approach

Decoding a Reed-Solomon code can be modeled by a bilinear system which can be solved by Gröbner basis techniques. We will show that in this particular case, these techniques are much more efficient than for generic bilinear systems with the same number of unknowns and equations (where these techniques have exponential complexity). Here we show that they are able to solve the problem in polynomial time up to the Sudan radius. Moreover, beyond this radius these techniques recover automatically polynomial identities that are at the heart of improvements of the power decoding approach for reaching the Johnson decoding radius. They also allow to derive new polynomial identities that can be used to derive new algebraic decoding algorithms for Reed-Solomon codes. We provide numerical evidence that this sometimes allows to correct efficiently slightly more errors than the Johnson radius. Note A full version containing the proofs is accessible on arxiv at: https://arxiv.org/abs/2102.02544
Deep learning-based synthetic-CT generation in radiotherapy and PET: a review

Recently, deep learning (DL)-based methods for the generation of synthetic computed tomography (sCT) have received significant research attention as an alternative to classical ones. We present here a systematic review of these methods by grouping them into three categories, according to their clinical applications: I) to replace CT in magnetic resonance (MR)-based treatment planning, II) facilitate cone-beam computed tomography (CBCT)-based image-guided adaptive radiotherapy, and III) derive attenuation maps for the correction of positron emission tomography (PET). Appropriate database searching was performed on journal articles published between January 2014 and December 2020. The DL methods' key characteristics were extracted from each eligible study, and a comprehensive comparison among network architectures and metrics was reported. A detailed review of each category was given, highlighting essential contributions, identifying specific challenges, and summarising the achievements. Lastly, the statistics of all the cited works from various aspects were analysed, revealing the popularity and future trends and the potential of DL-based sCT generation. The current status of DL-based sCT generation was evaluated, assessing the clinical readiness of the presented methods.
The Analysis from Nonlinear Distance Metric to Kernel-based Drug Prescription Prediction System

Distance metrics and their nonlinear variant play a crucial role in machine learning based real-world problem solving. We demonstrated how Euclidean and cosine distance measures differ not only theoretically but also in real-world medical application, namely, outcome prediction of drug prescription. Euclidean distance exhibits favorable properties in the local geometry problem. To this regard, Euclidean distance can be applied under short-term disease with low-variation outcome observation. Moreover, when presenting to highly variant chronic disease, it is preferable to use cosine distance. These different geometric properties lead to different submanifolds in the original embedded space, and hence, to different optimizing nonlinear kernel embedding frameworks. We first established the geometric properties that we needed in these frameworks. From these properties interpreted their differences in certain perspectives. Our evaluation on real-world, large-scale electronic health records and embedding space visualization empirically validated our approach.
A Heuristic for Dynamic Output Predictive Control Design for Uncertain Nonlinear Systems

In this paper, a simple heuristic is proposed for the design of uncertainty aware predictive controllers for nonlinear models involving uncertain parameters. The method relies on Machine Learning-based approximation of ideal deterministic MPC solutions with perfectly known parameters. An efficient construction of the learning data set from these off-line solutions is proposed in which each solution provides many samples in the learning data. This enables a drastic reduction of the required number of Non Linear Programming problems to be solved off-line while explicitly exploiting the statistics of the parameters dispersion. The learning data is then used to design a fast on-line output dynamic feedback that explicitly incorporate information of the statistics of the parameters dispersion. An example is provided to illustrate the efficiency and the relevance of the proposed framework. It is in particular shown that the proposed solution recovers up to 78\% of the expected advantage of having a perfect knowledge of the parameters compared to nominal design.
Causality, crossing and analyticity in conformal field theories

Analyticity and crossing properties of four-point function are investigated in conformal field theories in the frameworks of Wightman axioms. A Hermitian scalar conformal field, satisfying the Wightman axioms, is considered. The crucial role of microcausality in deriving analyticity domains is discussed and domains of analyticity are presented. A pair of permuted Wightman functions are envisaged. The crossing property is derived by appealing to the technique of analytic completion for the pair of permuted Wightman functions. The operator product expansion of a pair of scalar fields is studied and analyticity property of the matrix elements of composite fields, appearing in the operator product expansion, is investigated. An integral representation is presented for the commutator of composite fields where microcausality is a key ingredient. Three fundamental theorems of axiomatic local field theories; namely, PCT theorem, the theorem proving equivalence between PCT theorem and weak local commutativity and the edge-of-the-wedge theorem are invoked to derive a conformal bootstrap equation rigorously.