Miracle-dz/UniFAR-FTUs huggingface dataset:
Spectral Properties of Dyes with Interfragmental Charge Transfer: Solvatochromism and Solvatofluorochromism of 2-(3-Coumaroyl)-benzopyrylium Perchlorates

The paper deals with the spectral properties of dyes with interfragmental charge transfer, in particular, derivatives of 2-(3-coumaroyl)benzopyrylium (CBP) perchlorates. The positions of long-wavelength absorption and emission bands, quantum yields, and lifetimes of fluorescence were measured, mostly in aprotic solvents. Regression analysis of the relationship between the spectral characteristics of CBP and the main solvent parameters (polarity, polarisability, nucleophilicity, and electrophilicity) was carried out. It was found that the characteristics of CBP depend mainly on two solvent parameters—polarity and/or nucleophilicity. An increase in these parameters results in a hypsochromic shift of absorption bands and a decrease of lifetimes and fluorescence intensity. The positions of the emission bands can demonstrate either hypsochromism or bathochromism, depending on the nature of the substituents. The solvatofluorochromic effects are not as strong as the solvatochromic ones. We believe that the spectral behaviour of CBP can be explained by cation depolarisation and by a decrease in the stability of nucleophilic complexes with solvent molecules that is associated with interfragmental charge transfer following excitation, relaxation, and radiative deactivation of the excited state.
Robust estimation of tree structured models

Consider the problem of learning undirected graphical models on trees from corrupted data. Recently Katiyar et al. showed that it is possible to recover trees from noisy binary data up to a small equivalence class of possible trees. Their other paper on the Gaussian case follows a similar pattern. By framing this as a special phylogenetic recovery problem we largely generalize these two settings. Using the framework of linear latent tree models we discuss tree identifiability for binary data under a continuous corruption model. For the Ising and the Gaussian tree model we also provide a characterisation of when the Chow-Liu algorithm consistently learns the underlying tree from the noisy data.
Do the propensity and drivers of academics' engagement in research collaboration with industry vary over time?

This study is about public-private research collaboration. In particular, we want to measure how the propensity of academics to collaborate with their colleagues from private firms varies over time and whether the typical profile of such academics change. Furthermore, we investigate the change of the weights of main drivers underlying the academics' propensity to collaborate with industry. In order to achieve such goals, we apply an inferential model on a dataset of professors working in Italian universities in two subsequent periods, 2010-2013 and 2014-2017. Results can be useful for supporting the definition of policies aimed at fostering public-private research collaborations, and should be taken into account when assessing their effectiveness afterwards.
RoBIC: A Benchmark Suite For Assessing Classifiers Robustness

Many defenses have emerged with the development of adversarial attacks. Models must be objectively evaluated accordingly. This paper systematically tackles this concern by proposing a new parameter-free benchmark we coin ROBIC. ROBIC fairly evaluates the robustness of image classifiers using a new half-distortion measure. It gauges the robustness of the network against white and black box attacks, independently of its accuracy. ROBIC is faster than the other available benchmarks. We present the significant differences in the robustness of 16 recent models as assessed by ROBIC.We make this benchmark publicly available for use and contribution at https://gitlab.inria. fr/t;maho/robustness_benchmark.
Adversarial Robustness: What fools you makes you stronger

We prove an exponential separation for the sample complexity between the standard PAC-learning model and a version of the Equivalence-Query-learning model. We then show that this separation has interesting implications for adversarial robustness. We explore a vision of designing an adaptive defense that in the presence of an attacker computes a model that is provably robust. In particular, we show how to realize this vision in a simplified setting. In order to do so, we introduce a notion of a strong adversary: he is not limited by the type of perturbations he can apply but when presented with a classifier can repetitively generate different adversarial examples. We explain why this notion is interesting to study and use it to prove the following. There exists an efficient adversarial-learning-like scheme such that for every strong adversary $\mathbf{A}$ it outputs a classifier that (a) cannot be strongly attacked by $\mathbf{A}$, or (b) has error at most $\epsilon$. In both cases our scheme uses exponentially (in $\epsilon$) fewer samples than what the PAC bound requires.
On the Existence of Optimal Transport Gradient for Learning Generative Models

The use of optimal transport cost for learning generative models has become popular with Wasserstein Generative Adversarial Networks (WGAN). Training of WGAN relies on a theoretical background: the calculation of the gradient of the optimal transport cost with respect to the generative model parameters. We first demonstrate that such gradient may not be defined, which can result in numerical instabilities during gradient-based optimization. We address this issue by stating a valid differentiation theorem in the case of entropic regularized transport and specify conditions under which existence is ensured. By exploiting the discrete nature of empirical data, we formulate the gradient in a semi-discrete setting and propose an algorithm for the optimization of the generative model parameters. Finally, we illustrate numerically the advantage of the proposed framework.
Towards Tangible Cultural Heritage Experiences—Enriching VR-based Object Inspection with Haptic Feedback

VR/AR technology is a key enabler for new ways of immersively experiencing cultural heritage artifacts based on their virtual counterparts obtained from a digitization process. In this article, we focus on enriching VR-based object inspection by additional haptic feedback, thereby creating tangible cultural heritage experiences. For this purpose, we present an approach for interactive and collaborative VR-based object inspection and annotation. Our system supports high-quality 3D models with accurate reflectance characteristics while additionally providing haptic feedback regarding shape features of the object based on a 3D printed replica. The digital object model in terms of a printable representation of the geometry as well as reflectance characteristics are stored in a compact and streamable representation on a central server, which streams the data to remotely connected users/clients. The latter can jointly perform an interactive inspection of the object in VR with additional haptic feedback through the 3D printed replica. Evaluations regarding system performance, visual quality of the considered models, as well as insights from a user study indicate an improved interaction, assessment, and experience of the considered objects.
Kinetic model for electron-ion transport in warm dense matter.

We present a model for electron-ion transport in warm dense matter that incorporates Coulomb coupling effects into the quantum Boltzmann equation of Uehling and Uhlenbeck through the use of a statistical potential of mean force. Although the model presented here can be derived rigorously in the classical limit [S. D. Baalrud and J. Daligault, Phys. Plasmas 26, 082106 (2019)PHPAEN1070-664X10.1063/1.5095655], its quantum generalization is complicated by the uncertainty principle. Here we apply an existing model for the potential of mean force based on the quantum Ornstein-Zernike equation coupled with an average-atom model [C. E. Starrett, High Energy Density Phys. 25, 8 (2017)1574-181810.1016/j.hedp. 2017.09. 003]. This potential contains correlations due to both Coulomb coupling and exchange, and the collision kernel of the kinetic theory enforces Pauli blocking while allowing for electron diffraction and large-angle collisions. We use the Uehling-Uhlenbeck equation to predict the momentum and temperature relaxation times and electrical conductivity of solid density aluminum plasma based on electron-ion collisions. We present results for density and temperature conditions that span the transition from classical weakly-coupled plasma to degenerate moderately-coupled plasma. Our findings agree well with recent quantum molecular dynamics simulations.
NumaPerf: Predictive and Full NUMA Profiling

Parallel applications are extremely challenging to achieve the optimal performance on the NUMA architecture, which necessitates the assistance of profiling tools. However, existing NUMA-profiling tools share some similar shortcomings, such as portability, effectiveness, and helpfulness issues. This paper proposes a novel profiling tool - NumaPerf - that overcomes these issues. NumaPerf aims to identify potential performance issues for any NUMA architecture, instead of only on the current hardware. To achieve this, NumaPerf focuses on memory sharing patterns between threads, instead of real remote accesses. NumaPerf further detects potential thread migrations and load imbalance issues that could significantly affect the performance but are omitted by existing profilers. NumaPerf also separates cache coherence issues that may require different fix strategies. Based on our extensive evaluation, NumaPerf is able to identify more performance issues than any existing tool, while fixing these bugs leads to up to 5.94x performance speedup.
Information Prediction using Knowledge Graphs for Contextual Malware Threat Intelligence.

Large amounts of threat intelligence information about mal-ware attacks are available in disparate, typically unstructured, formats. Knowledge graphs can capture this information and its context using RDF triples represented by entities and relations. Sparse or inaccurate threat information, however, leads to challenges such as incomplete or erroneous triples. Named entity recognition (NER) and relation extraction (RE) models used to populate the knowledge graph cannot fully guaran-tee accurate information retrieval, further exacerbating this problem. This paper proposes an end-to-end approach to generate a Malware Knowledge Graph called MalKG, the first open-source automated knowledge graph for malware threat intelligence. MalKG dataset called MT40K1 contains approximately 40,000 triples generated from 27,354 unique entities and 34 relations. We demonstrate the application of MalKGin predicting missing malware threat intelligence information in the knowledge graph. For ground truth, we manually curate a knowledge graph called MT3K, with 3,027 triples generated from 5,741 unique entities and 22 relations. For entity prediction via a state-of-the-art entity prediction model(TuckER), our approach achieves 80.4 for the hits@10 metric (predicts the top 10 options for missing entities in the knowledge graph), and 0.75 for the MRR (mean reciprocal rank). We also propose a framework to automate the extraction of thousands of entities and relations into RDF triples, both manually and automatically, at the sentence level from1,100 malware threat intelligence reports and from the com-mon vulnerabilities and exposures (CVE) database.
DHLink: A Microservice Platform supporting Rapid Application Development and Secure Real-time Data Sharing in Digital Health

Digital health applications that leverage multiple sources of patient data for insights to patients' behaviours or disease symptoms as well as remote patient monitoring, nudging and treatments are becoming increasingly popular in various medical practices and research. One common issue among these applications is that they are generally based on project-specific solutions and developed from scratch. Such application development fashion results in large amounts of repetitive effort, for example, in building study specific websites and mobile frontends, deploying customised infrastructures, and collecting data that may have already been collected in other studies and projects. What is worse, the data collected, and functions built cannot be easily reused by other applications. In this paper, we present an event-driven microservice platform, namely DHLink, to address this issue. DHLink securely links existing digital health applications of different projects, facilitates real-time data sharing, and supports rapid application development by reusing data and functions of existing digital health applications. In addition, comes with DHLink, a set of highly generic and reusable microservices is provided, which allows developers to rapidly create a typical above-mentioned digital health application by only developing the core algorithms. Two use cases outlined in this paper have shown the use of DHLink and the set of microservices for application collaboration and new application development to be efficient and practical.
Detecting Localized Adversarial Examples: A Generic Approach using Critical Region Analysis

Deep neural networks (DNNs) have been applied in a wide range of applications, e.g., face recognition and image classification; however, they are vulnerable to adversarial examples. By adding a small amount of imperceptible perturbations, an attacker can easily manipulate the outputs of a DNN. Particularly, the localized adversarial examples only perturb a small and contiguous region of the target object, so that they are robust and effective in both digital and physical worlds. Although the localized adversarial examples have more severe real-world impacts than traditional pixel attacks, they have not been well addressed in the literature. In this paper, we propose a generic defense system called TaintRadar to accurately detect localized adversarial examples via analyzing critical regions that have been manipulated by attackers. The main idea is that when removing critical regions from input images, the ranking changes of adversarial labels will be larger than those of benign labels. Compared with existing defense solutions, TaintRadar can effectively capture sophisticated localized partial attacks, e.g., the eye-glasses attack, while not requiring additional training or fine-tuning of the original model’s structure. Comprehensive experiments have been conducted in both digital and physical worlds to verify the effectiveness and robustness of our defense.
Sparse Factorization-Based Detection of Off-the-Grid Moving Targets Using FMCW Radars

In this paper, we investigate the application of continuous sparse signal reconstruction algorithms for the estimation of the ranges and speeds of multiple moving targets using an FMCW radar. Conventionally, to be reconstructed, continuous sparse signals are approximated by a discrete representation. This discretization of the signal’s parameter domain leads to mismatches with the actual signal. While increasing the grid density mitigates these errors, it dramatically increases the algorithmic complexity of the reconstruction. To overcome this issue, we propose a fast greedy algorithm for off-the-grid detection of multiple moving targets. This algorithm extends existing continuous greedy algorithms to the framework of factorized sparse representations of the signals. This factorized representation is obtained from simplifications of the radar signal model which, up to a model mismatch, strongly reduces the dimensionality of the problem. Monte-Carlo simulations of a K-band radar system validate the ability of our method to produce more accurate estimations with less computation time than the on-the-grid methods and than methods based on non-factorized representations.
Star-topology registers: NMR and quantum information perspectives

Quantum control of large spin registers is crucial for many applications ranging from spectroscopy to quantum information. A key factor that determines the efficiency of a register for implementing a given information processing task is its network topology. One particular type, called star-topology, involves a central qubit uniformly interacting with a set of ancillary qubits. A particular advantage of the star-topology quantum registers is in the efficient preparation of large entangled states, called NOON states, and their generalized variants. Thanks to the robust generation of such correlated states, spectral simplicity, ease of polarization transfer from ancillary qubits to the central qubit, as well as the availability of large spin-clusters, the star-topology registers have been utilized for several interesting applications over the last few years. Here we review some recent progress with the star-topology registers, particularly via nuclear magnetic resonance methods.
Nested Sinkhorn Divergence To Compute The Nested Distance

The nested distance builds on the Wasserstein distance to quantify the difference of stochastic processes, including also the information modelled by filtrations. The Sinkhorn divergence is a relaxation of the Wasserstein distance, which can be computed considerably faster. For this reason we employ the Sinkhorn divergence and take advantage of the related (fixed point) iteration algorithm. Furthermore, we investigate the transition of the entropy throughout the stages of the stochastic process and provide an entropy-regularized nested distance formulation, including a characterization of its dual. Numerical experiments affirm the computational advantage and supremacy.
Nonstochastic Bandits with Infinitely Many Experts

We study the problem of nonstochastic bandits with expert advice, extending the setting from finitely many experts to any countably infinite set: A learner aims to maximize the total reward by taking actions sequentially based on bandit feedback while benchmarking against a set of experts. We propose a variant of Exp4.P that, for finitely many experts, enables inference of correct expert rankings while preserving the order of the regret upper bound. We then incorporate the variant into a meta-algorithm that works on infinitely many experts. We prove a high-probability upper bound of $\tilde {\mathcal{O}}\left( {{i^{\ast}}K + \sqrt {KT} } \right)$ on the regret, up to polylog factors, where i* is the unknown position of the best expert, K is the number of actions, and T is the time horizon. We also provide an example of structured experts and discuss how to expedite learning in such case. Our meta-learning algorithm achieves optimal regret up to polylog factors when ${i^{\ast}} = \tilde O\left( {\sqrt {T/K} } \right)$. If a prior distribution is assumed to exist for i*, the probability of optimality increases with T, the rate of which can be fast.
Biomedical Question Answering: A Comprehensive Review

Question Answering (QA) is a benchmark Natural Language Processing (NLP) task where models predict the answer for a given question using related documents, images, knowledge bases and question-answer pairs. Automatic QA has been successfully applied in various domains like search engines and chat-bots. However, for speciﬁc domains like biomedicine, QA systems are still rarely used in real-life settings. Biomedical QA (BQA), as an emerging QA task, enables innovative applications to effectively perceive, access and understand complex biomedical knowledge. In this work, we provide a critical review of recent efforts in BQA. We comprehensively investigate prior BQA approaches, which are classiﬁed into 6 major methodologies (open-domain, knowledge base, information retrieval, machine reading comprehension, question entailment and visual QA), 4 topics of contents (scientiﬁc, clinical, consumer health and examination) and 5 types of formats (yes/no, extraction, generation, multi-choice and retrieval). In the end, we highlight several key challenges of BQA and explore potential directions for future works.
Geometric signatures of tissue surface tension in a three-dimensional model of confluent tissue

In dense biological tissues, cell types performing different roles remain segregated by maintaining sharp interfaces. To better understand the mechanisms for such sharp compartmentalization, we study the effect of an imposed heterotypic tension at the interface between two distinct cell types in a fully 3D Voronoi model for confluent tissues. We find that cells rapidly sort and self-organize to generate a tissue-scale interface between cell types, and cells adjacent to this interface exhibit signature geometric features including nematic-like ordering, bimodal facet areas, and registration, or alignment, of cell centers on either side of the two-tissue interface. The magnitude of these features scales directly with the magnitude of the imposed tension, suggesting that biologists can estimate the magnitude of tissue surface tension between two tissue types simply by segmenting a 3D tissue. To uncover the underlying physical mechanisms driving these geometric features, we develop two minimal, ordered models using two different underlying lattices that identify an energetic competition between bulk cell shapes and tissue interface area. When the interface area dominates, changes to neighbor topology are costly and occur less frequently, which generates the observed geometric features.
Towards Certifying (cid:96) ∞ Robustness using Neural Networks with (cid:96) ∞ -dist Neurons

It is well-known that standard neural networks, even with a high classiﬁcation accuracy, are vulnerable to small (cid:96) ∞ -norm bounded adversarial perturbations. Although many attempts have been made, most previous works either can only provide empirical veriﬁcation of the defense to a particular attack method, or can only develop a certi-ﬁed guarantee of the model robustness in limited scenarios. In this paper, we seek for a new approach to develop a theoretically principled neural network that inherently resists (cid:96) ∞ perturbations. In particular, we design a novel neuron that uses (cid:96) ∞ -distance as its basic operation (which we call (cid:96) ∞ -dist neuron), and show that any neural network constructed with (cid:96) ∞ -dist neurons (called (cid:96) ∞ -dist net) is naturally a 1-Lipschitz function with respect to (cid:96) ∞ -norm. This directly provides a rigorous guarantee of the certiﬁed robustness based on the margin of prediction outputs. We then prove that such networks have enough expressive power to approximate any 1-Lipschitz function with robust generalization guarantee. We further provide a holistic training strategy that can greatly alleviate optimization difﬁculties. Experimental results show that using (cid:96) ∞ -dist nets as basic building blocks, we consistently achieve state-of-the-art performance on commonly used datasets: 93.09% certiﬁed accuracy on MNIST ( (cid:15) = 0 . 3 ), 35.42% on CIFAR-10 ( (cid:15) = 8 / 255 ) and 16.31% on TinyImageNet ( (cid:15) = 1 / 255 ).
Adversarially Robust Classifier with Covariate Shift Adaptation

Existing adversarially trained models typically perform inference on test examples independently from each other. This mode of testing is unable to handle covariate shift in the test samples. Due to this, the performance of these models often degrades significantly. In this paper, we show that simple adaptive batch normalization (BN) technique that involves re-estimating the batch-normalization parameters during inference, can significantly improve the robustness of these models for any random perturbations, including the Gaussian noise. This simple finding enables us to transform adversarially trained models into randomized smoothing classifiers to produce certified robustness to $\ell_2$ noise. We show that we can achieve $\ell_2$ certified robustness even for adversarially trained models using $\ell_{\infty}$-bounded adversarial examples. We further demonstrate that adaptive BN technique significantly improves robustness against common corruptions, while often enhancing performance against adversarial attacks. This enables us to achieve both adversarial and corruption robustness for the same classifier.