Miracle-dz/UniFAR-FTUs huggingface dataset:
Paper Conservation Using Aqueous Solutions of Calcium Hydroxide / Methyl Cellulose. 3. The influence on the degradation of papers

SUMMARIES Bleached softwood bisulphite and alum-rosin sized rag paper samples were subjected to conservation treatments with calcium hydroxide and with calcium hydroxide – methyl cellulose solutions. Accelerated ageing at 90ºC and 50% RH was done in order to evaluate the influence of these treatments on several paper qualities: degree of polymerisation, wet zero-span tensile index, tensile strength, ISO-brightness, pH of the cold extract and alkaline reserve. The depolymerisation of the cellulose of the papers at the initial stages was explained by the first-order kinetics of the Ekenstam model. The influence of the conservation treatment on the life expectancy of the treated papers was evaluated. It was concluded that the main reason for tensile strength decrease in bleached softwood bisulphite paper treated with methyl cellulose was the degradation of this compound during accelerated ageing. Des échantilons de papier de pâte au bisulfite blanchie et de papier chiffon encollé à la résine d'alun ont subi un traitement de conservation au moyen de solutions aqueuses d'hydroxyde de calcium et d'hydroxyde de calcium avec de la cellulose de méthyle. Le vieillissement artificiel leur a été appliqué à 90°C et 50% d'humidité relative afin d'évaluer l'influence de ces traitements sur plusieurs propriétés du papier: le degré de polymérisation, la charge de rupture zéro en milieu humide, la résistance à la traction, le degré de blancheur, le pH de l'extrait froid et la réserve alcaline. La dépolymérisation de la cellulose du papier aux premiers stades a été décrite comme une cinétique de premier ordre selon le modèle de Ekenstam. Ensuite on a évalué l'influence des traitements de conservation sur l'espérance de vie des papiers traités. On est arrivé à la conclusion que le facteur principal responsable de la dégradation de la résistance à la traction du papier de pâte au bisulfite blanchie traité à la cellulose de méthyle réside dans la dégradation de cette substance au cours du vieillissement artificiel. Papier aus gebleichtem Bisulfit- Zellstoff und harzgleimtes Hadernpapier wurden mit wäßrigen Lösungen von Calciumhydroxid und Calciumhydroxid – Methylcelulose behandelt und bei 90ºC 50% beschleunigt gealtert, um den Einfluß dieser konservatorischen Behandlungen auf verschiedene Eigenschaften des Papiers zu ermitteln: Polymerisationsgrad, Naß-Nullbruchkraft, Bruchkraft, Weißgrad, pH des Kaltextrakts und alkalische Reserve. Die Depolymerisierung der Cellulose der Papiere im Anfangsstadium wird als Kinetik erster Ordnung nach dem Ekenstam-Modell beschrieben. Sodann wurde der Einfluß der genannten Behandlungen auf die Lebenserwartung der Papiere ermittelt. Als Hauptursache für den Rückgang der Zugfestigkeit des MC-behandelten Papiers aus gebleichtem Bisulfit-Zellstoff ist der Abbau dieser Substanz bei der beschleunigten Alterung anzunehmen.
Federated Nonconvex Sparse Learning

Nonconvex sparse learning plays an essential role in many areas, such as signal processing and deep network compression. Iterative hard thresholding (IHT) methods are the state-of-the-art for nonconvex sparse learning due to their capability of recovering true support and scalability with large datasets. Theoretical analysis of IHT is currently based on centralized IID data. In realistic large-scale situations, however, data are distributed, hardly IID, and private to local edge computing devices. It is thus necessary to examine the property of IHT in federated settings, which update in parallel on local devices and communicate with a central server only once in a while without sharing local data. In this paper, we propose two IHT methods: Federated Hard Thresholding (Fed-HT) and Federated Iterative Hard Thresholding (FedIter-HT). We prove that both algorithms enjoy a linear convergence rate and have strong guarantees to recover the optimal sparse estimator, similar to traditional IHT methods, but now with decentralized non-IID data. Empirical results demonstrate that the Fed-HT and FedIter-HT outperform their competitor - a distributed IHT, in terms of decreasing the objective values with lower requirements on communication rounds and bandwidth.
VisualSparta: Sparse Transformer Fragment-level Matching for Large-scale Text-to-Image Search

Text-to-image retrieval is an essential task in multi-modal information retrieval, i.e. retrieving relevant images from a large and un-labelled image dataset given textual queries. In this paper, we propose VisualSparta, a novel text-to-image retrieval model that shows substantial improvement over existing models on both accuracy and efﬁciency. We show that VisualSparta is capable of outperforming all previous scalable methods in MSCOCO and Flickr30K. It also shows substantial retrieving speed advantages, i.e. for an index with 1 million images, VisualSparta gets over 391x speed up compared to standard vector search. Experiments show that this speed advantage even gets bigger for larger datasets because VisualSparta can be efﬁciently implemented as an inverted index. To the best of our knowledge, VisualSparta is the ﬁrst transformer-based text-to-image retrieval model that can achieve real-time searching for very large dataset, with signiﬁcant accuracy improvement compared to previous state-of-the-art methods.
Algorithmic Complexities in Backpropagation and Tropical Neural Networks

In this note, we propose a novel technique to reduce the algorithmic complexity of neural network training by using matrices of tropical real numbers instead of matrices of real numbers. Since the tropical arithmetics replaces multiplication with addition, and addition with max, we theoretically achieve several order of magnitude better constant factors in time complexities in the training phase. The fact that we replace the field of real numbers with the tropical semiring of real numbers and yet achieve the same classification results via neural networks come from deep results in topology and analysis, which we verify in our note. We then explore artificial neural networks in terms of tropical arithmetics and tropical algebraic geometry, and introduce the multi-layered tropical neural networks as universal approximators. After giving a tropical reformulation of the backpropagation algorithm, we verify the algorithmic complexity is substantially lower than the usual backpropagation as the tropical arithmetic is free of the complexity of usual multiplication.
Photofunctional thin film devices composed of polymer nanosheet assemblies

We have developed so-called polymer Langmuir–Blodgett (LB) films (polymer nanosheets) based on acrylamide polymer and its copolymers, and have reported a series of various functional polymer nanosheet assemblies. To fabricate photofunctional nanodevices, we have succeeded in the introduction of various photoactive chromophores such as redox species, aromatic hydrocarbons, optically nonlinear groups, and metal nanoparticles into polymer nanosheet assemblies. In this article, we focus on our recent topics of research, namely photo-electric conversion, optical logic gates based on photoinduced electron transfer, and plasmon coupling with metal nanoparticles in hybrid polymer nanosheet assemblies.
An Integration Routine Based on Profile Fitting with Optimized Fitting Area for the Evaluation of Weak and/or Overlapped Two-Dimensional Laue or Monochromatic Patterns

New software for the integration of weak and/or spatially overlapped diffraction patterns is presented. The program, named PROW (profile fitting for overlapped and weak data), uses box integration with an optimized summation area for strong nonoverlapped spots, and profile fitting with an optimized fitting area for weak and/or overlapped spots. The sizes of the integration areas are dynamically adjusted to find the best compromise between minimal bias resulting from statistical fluctuations of the X-ray background and maximal use of the information content in each diffraction spot. Deconvolution of spatially overlapped spots is performed by a standard least-squares procedure. The program is compatible with the Daresbury Laue software suite and with the DENZO package. It has been tested on several data-sets recorded on image-plate and CCD (charge-coupled device) detectors. Intensities extracted from very weak Laue data-sets containing as many as 60% overlapped spots were shown to be of sufficient quality to lead to an accurate structure refinement. Difficult monochromatic data-sets were also processed successfully, leading to a generally better compromise between reliability factors, redundancy and completeness than previously achieved. Concomitant improvement in atomic model refinement was also observed.
Macropolyhedral boron-containing cluster chemistry: nineteen-vertex [(PPh3)NiS2B16H12(PPh3)] and eighteen-vertex S2B16H14(PPh3)

Reaction of S2B16H16 and [NiBr2(PPh3)2] in the presence of base has yielded nineteen-vertex [(PPh3)NiS2B16H12(PPh3)], of which the basic cluster structure consists of a nido-shaped nine-vertex {1-NiB8} subcluster and a closo-shaped twelve-vertex {1,4-NiSB10} subcluster fused with the {1′,3′,4′ : 1,2,3}{NiB2} triangular face in common; a related co-product is non-metallated eighteen-vertex S2B16H14(PPh3), of which the basic cluster structure consists of a nido-shaped {B8} eight-vertex subcluster and a nido-shaped {SB9} eleven-vertex subcluster fused with the {3′,8′ : 6,7} diboron edge in common.
Structural phase transitions and specific-heat coefficients of alkaline-earth metals

The linear-muffin-tin-orbital method within the atomic-sphere approximation has been used to calculate the structural energy differences and specific-heat coefficients of the alkaline-earth metals. It has been shown that while non-local exchange corrections to the local-density approximation play almost no role for the structural energy differences, specific-heat coefficient values, especially for Sr, are correctly predicted only when these corrections are taken into account.
Using Stochastic Petri net modeling for self-adapting Publish/Subscribe IoT systems

The Internet of Things has received increasing attention during past years. It allows various embedded devices to connect through the internet or a wireless network. This environment brings out a broad range of diverse applications, offering different services. However, these applications use different technologies, making their execution complex. Moreover, the IoT environment is characterized by an unstable communication infrastructure, including frequent disconnections and limited resources (battery, memory, and CPU). Consequently, most of the time, users suffer from degraded performance of daily-used applications. Therefore, it is crucial to be able to analyze IoT application performance to ensure improved quality of service. So, we present a Stochastic Petri Net modeling and analysis approach of an IoT platform based on Publish/subscribe communications. The efficiency of our approach is illustrated through a set of experimental results.
23.1: Invited Paper: Luminescence Quenching by Charge Carriers in Organic Light‐Emitting Diodes

We report photoluminescence quenching studies on model DCJTB‐doped Alq3 electroluminescent devices. The spectroscopy versus doping density suggests that solvatochromic effects are minor but that energy transfer plays a large role. Quenching of DCJTB fluorescence is due to Förster energy transfer to DCJTB cations, and measurements of the bleaching recovery dynamics show that charge extraction from ionized DCJTB sites is slow and consistent with biexponential kinetics. Temperature‐dependent studies find effective activation barriers for detrapping that are too small to be consistent with simple HOMO offsets between host and dopant.
Design of the robust fractional Bayesian filter with randomly delayed measurements

Facing the state estimation problem for nonlinear fractional-order dynamical systems with measurement stochastic delays, a robust fractional Bayesian filtering strategy is presented in this paper. The measurement equation is first reconstructed using two variables that portray the stochastic delay. One random variable follows a Bernoulli distribution to describe whether the measurement delay occurs at a certain time. The other obeys a geometric distribution to characterize the number of delay steps. This way, it is no longer bound to a fixed number of delay steps and is more in line with engineering practice. The robust fractional Bayesian filter is then implemented based on Bayesian theory in three iterative steps of state prediction, measurement correction and estimation update. In addition an explicit implementation based on first-order Stirling polynomial interpolation is provided. Finally, the effectiveness of the proposed method is verified by numerical simulation, and two important probability parameters in the algorithm are fully analyzed.
Mechanistic Aspects of the Bismuth‐Catalysed Oxidation of Epoxides to α‐Diketones

The mechanism of the direct oxidation of 1,2-disubstituted epoxides to α-diketones catalysed by BiIII derivatives was examined. The oxidation was carried out in DMSO under dioxygen. Experimental evidence for the various steps of the reaction was obtained from spectroscopic data, isolation of intermediate species, reactions with Bi0, reaction under dinitrogen rather than under dioxygen, measurement of the oxygen consumption, cyclic voltammetry, and oxidations performed in the presence of methyl phenyl sulfoxide. The direct epoxide-to-diketone oxidation involves two main reaction steps: a first DMSO-mediated oxidative ring-opening of the oxirane ring, and a second Bi-catalysed oxidation to the α-diketone by dioxygen. A catalytic cycle is proposed combining the different results. A new alternative preparation of Bi(OTf)3 by oxidative dissolution of Bi0 is also described. (© Wiley-VCH Verlag GmbH & Co. KGaA, 69451 Weinheim, Germany, 2004)
Conditioning on the pre-test versus gain score modeling: revisiting the controversy in a multilevel setting

We consider estimating the effect of a treatment on the progress of subjects tested both before and after treatment assignment. A vast literature compares the competing approaches of modeling the post-test score conditionally on the pre-test score versus modeling the difference, namely the gain score. Our contribution resides in analyzing the merits and drawbacks of the two approaches in a multilevel setting. This is relevant in many fields, for example education with students nested into schools. The multilevel structure raises peculiar issues related to the contextual effects and the distinction between individual-level and cluster-level treatment. We derive approximate analytical results and compare the two approaches by a simulation study. For an individual-level treatment our findings are in line with the literature, whereas for a cluster-level treatment we point out the key role of the cluster mean of the pre-test score, which favors the conditioning approach in settings with large clusters.
Phase defect lines during cardiac arrhythmias: from theory to experiment

During heart rhythm disorders, complex spatio-temporal patterns of electrical activation can self-organize in vortices. Previously, it was assumed that at the center of such vortex, there is a point singularity in the activation phase. We hypothesize that there is another possibility, namely a region of discontinuous phase that can exist during cardiac arrhythmias instead of a point singularity. In this paper, we provide some theoretical insights into the existence of such phase defect lines (PDL) and validate their presence in ventricular tachycardia in the whole ex-vivo heart using optical mapping experiments. We discuss the implications of phase defect lines and surfaces for the theory of arrhythmias, cardiac modeling and practical cardiology. Significance statement During cardiac arrhythmias, electromechanical vortices have been observed. Previously, the cores of such rotors were considered as point-like phase singularities; these are actively targeted in arrhythmia therapy such as catheter ablation. Our novel analysis of experimental data of ventricular tachycardia, however, shows that at the vortex core, wave block rather than a phase singularity occurs, such that the fundamental entity is a phase defect line (PDL). The location of experimentally reconstructed phase defect lines coincides with the location where multiple short-lived phase singularities were found. This observation could help to understand why stable rotors are seen in simulations, while clinical experiments usually reveal multiple short-lived rotors. Both theoretical consequences and clinical implications of the PDL concept should be investigated.
The Challenges in Modeling Human Performance in 3D Space with Fitts’ Law

With the rapid growth in virtual reality technologies, object interaction is becoming increasingly more immersive, elucidating human perception and leading to promising directions towards evaluating human performance under different settings. This spike in technological growth exponentially increased the need for a human performance metric in 3D space. Fitts’ law is perhaps the most widely used human prediction model in HCI history attempting to capture human movement in lower dimensions. Despite the collective effort towards deriving an advanced extension of a 3D human performance model based on Fitts’ law, a standardized metric is still missing. Moreover, most of the extensions to date assume or limit their findings to certain settings, effectively disregarding important variables that are fundamental to 3D object interaction. In this review, we investigate and analyze the most prominent extensions of Fitts’ law and compare their characteristics pinpointing to potentially important aspects for deriving a higher-dimensional performance model. Lastly, we mention the complexities, frontiers as well as potential challenges that may lay ahead.
Research on Backstepping control of flexible joint manipulator with state constraint

“Large computation” is a problem of the traditional backstepping method, leading to the poor control effect of the system. To solve this problem, a backstepping controller based on active disturbance rejection control (ADRC) is designed for the nonlinear system of flexible joint manipulator with state constraints. The uncertainties of the model are approximated with the nonlinear extended state observer (ESO) to further improve the control effect of the system. The Lyapunov function is designed to restrict all states of the system. Results show that the designed controller can effectively achieve the state constraint performance and improve the position tracking accuracy of the system significantly.
Characterizing (Un)moderated Textual Data in Social Systems

Despite the valuable social interactions that online media promote, these systems provide space for speech that would be potentially detrimental to different groups of people. The moderation of content imposed by many social media has motivated the emergence of a new social system for free speech named Gab, which lacks moderation of content. This article characterizes and compares moderated textual data from Twitter with a set of unmoderated data from Gab. In particular, we analyze distinguishing characteristics of moderated and unmoderated content in terms of linguistic features, evaluate hate speech and its different forms in both environments. Our work shows that unmoderated content presents different psycholinguistic features, more negative sentiment and higher toxicity. Our findings support that unmoderated environments may have proportionally more online hate speech. We hope our analysis and findings contribute to the debate about hate speech and benefit systems aiming at deploying hate speech detection approaches.
Fast Ensemble Learning Using Adversarially-Generated Restricted Boltzmann Machines

Machine Learning has been applied in a wide range of tasks throughout the last years, ranging from image classification to autonomous driving and natural language processing. Restricted Boltzmann Machine (RBM) has received recent attention and relies on an energy-based structure to model data probability distributions. Notwithstanding, such a technique is susceptible to adversarial manipulation, i.e., slightly or profoundly modified data. An alternative to overcome the adversarial problem lies in the Generative Adversarial Networks (GAN), capable of modeling data distributions and generating adversarial data that resemble the original ones. Therefore, this work proposes to artificially generate RBMs using Adversarial Learning, where pre-trained weight matrices serve as the GAN inputs. Furthermore, it proposes to sample copious amounts of matrices and combine them into ensembles, alleviating the burden of training new models’. Experimental results demonstrate the suitability of the proposed approach under image reconstruction and image classification tasks, and describe how artificial-based ensembles are alternatives to pre-training vast amounts of RBMs.
Consensus-Guided Correspondence Denoising

Correspondence selection between two groups of feature points aims to correctly recognize the consistent matches (inliers) from the initial noisy matches. The selection is generally challenging since the initial matches are generally extremely unbalanced, where outliers can easily dominate. Moreover, random distributions of outliers lead to the limited robustness of previous works when applied to different scenarios. To address this issue, we propose to denoise correspondences with a local-to-global consensus learning framework to robustly identify correspondence. A novel “pruning” block is introduced to distill reliable candidates from initial matches according to their consensus scores estimated by dynamic graphs from local to global regions. The proposed correspondence denoising is progressively achieved by stacking multiple pruning blocks sequentially. Our method outperforms state-of-the-arts on robust line ﬁtting, wide-baseline image matching and image localization benchmarks by noticeable margins and shows promising generalization capability on different distributions of initial matches.
Jamming Attacks and Anti-Jamming Strategies in Wireless Networks: A Comprehensive Survey

Wireless networks are a key component of the telecommunications infrastructure in our society, and wireless services become increasingly important as the applications of wireless devices have penetrated every aspect of our lives. Although wireless technologies have significantly advanced in the past decades, most wireless networks are still vulnerable to radio jamming attacks due to the openness nature of wireless channels, and the progress in the design of jamming-resistant wireless networking systems remains limited. This stagnation can be attributed to the lack of practical physical-layer wireless technologies that can efficiently decode data packets in the presence of jamming attacks. This article surveys existing jamming attacks and anti-jamming strategies in wireless local area networks (WLANs), cellular networks, cognitive radio networks (CRNs), ZigBee networks, Bluetooth networks, vehicular networks, LoRa networks, RFID networks, GPS system, millimeter-wave (mmWave) and learning-assisted wireless systems, with the objective of offering a comprehensive knowledge landscape of existing jamming and anti-jamming strategies and therefore stimulating more research efforts to secure wireless networks against jamming attacks. Different from prior survey papers, this article conducts a comprehensive, in-depth review on jamming and anti-jamming strategies, casting insights on the design of jamming-resilient wireless networking systems. An outlook on promising anti-jamming techniques is offered at the end of this article to delineate important research directions.