Miracle-dz/UniFAR-FTUs huggingface dataset:
Writer Identification and Writer Retrieval Based on NetVLAD with Re-ranking

This paper addresses writer identification and retrieval which is a challenging problem in the document analysis field. In this work, a novel pipeline is proposed for the problem by employing a unified neural network architecture consisting of the ResNet-20 as a feature extractor and an integrated NetVLAD layer, inspired by the vectors of locally aggregated descriptors (VLAD), in the head of the latter part. Having defined this architecture, triplet semi-hard loss function is used to directly learn an embedding for individual input image patches. Generalised max-pooling is used for the aggregation of embedded descriptors of each handwritten image. In the evaluation part, for identification and retrieval, re-ranking has been done based on query expansion and $k$-reciprocal nearest neighbours, and it is shown that the pipeline can benefit tremendously from this step. Experimental evaluation shows that our writer identification and writer retrieval pipeline is superior compared to the state-of-the-art pipelines, as our results on the publicly available ICDAR13 and CVL datasets set new standards by achieving 96.5\% and 98.4\% mAP, respectively.
Unsupervised deep learning for individualized brain functional network identification

A novel unsupervised deep learning method is developed to identify individual-specific large scale brain functional networks (FNs) from resting-state fMRI (rsfMRI) in an end-to-end learning fashion. Our method leverages deep Encoder-Decoder networks and conventional brain decomposition models to identify individual-specific FNs in an unsupervised learning framework and facilitate fast inference for new individuals with one forward pass of the deep network. Particularly, convolutional neural networks (CNNs) with an Encoder-Decoder architecture are adopted to identify individual-specific FNs from rsfMRI data by optimizing their data fitting and sparsity regularization terms that are commonly used in brain decomposition models. Moreover, a time-invariant representation learning module is designed to learn features invariant to temporal orders of time points of rsfMRI data. The proposed method has been validated based on a large rsfMRI dataset and experimental results have demonstrated that our method could obtain individual-specific FNs which are consistent with well-established FNs and are informative for predicting brain age, indicating that the individual-specific FNs identified truly captured the underlying variability of individualized functional neuroanatomy.
Power Amplifiers in CMOS Technology: A contribution to power amplifier theory and techniques

In order to meet the demands from the market on cheaper, miniaturized mobile communications devices realization of RF power amplifiers(PAs) in the mainstream CMOS technology is essential. In general, CMOS PAs require high supply-voltage to decrease the matching network losses and for high output power whereas the mainstream CMOS technology is optimized for low-voltage operation. This calls for innovative solutions. Three new approaches are presented: • Exploration of tuning methods of switching PAs for finding the optimum operation of RF PAs under given reliability, output power and efficiency conditions: Analytical design equations for the single ended Class-E PA are presented. The obtained results link all known Class-E PA design equations as well as presenting new design equations. Some of the new Class-E PAs require higher load resistance for the same output power and reliability conditions which is advantages for low-breakdown voltage technologies such as CMOS. Moreover, important advantages of sub-optimum Class-E PAs (e.g. lower peak switch voltage, larger switch (transistor) size) for CMOS are presented. • Exploration of high voltage circuit techniques to enable the RF CMOS PAs withstand high voltages: Extended-drain RF power devices achieved more than 70% PAE for 1, 2 and 3.4W output power at 2GHz in standard 65nm CMOS are presented. The power devices operate in sub-optimum Class-E mode, taking the advantage of higher output power with a slight PAE penalty in comparison to the conventional Class-E under the same input drive, load impedance and reliability conditions. A scalable layout approach in the design of power devices is introduced. The output power of the power devices scales with the device sizes without any degradation in the PAE, demonstrating that power-efficiency can be preserved for high power devices in CMOS technology. • Exploration of degradation detection and elimination methods to increase the reliable life-time of RF CMOS PAs: An approach is introduced to extend the lifetime of high voltage analog circuits in CMOS technologies based on redundancy, like that known for DRAMS. A large power transistor is segmented into N smaller ones in parallel. If a sub-transistor is broken, it is removed automatically from the compound transistor.
Few-Shot Segmentation Without Meta-Learning: A Good Transductive Inference Is All You Need?

We show that the way inference is performed in few-shot segmentation tasks has a substantial effect on performances—an aspect often overlooked in the literature in favor of the meta-learning paradigm. We introduce a transductive inference for a given query image, leveraging the statistics of its unlabeled pixels, by optimizing a new loss containing three complementary terms: i) the cross-entropy on the labeled support pixels; ii) the Shannon entropy of the posteriors on the unlabeled query-image pixels; and iii) a global KL-divergence regularizer based on the proportion of the predicted foreground. As our inference uses a simple linear classifier of the extracted features, its computational load is comparable to inductive inference and can be used on top of any base training. Foregoing episodic training and using only standard cross-entropy training on the base classes, our inference yields competitive performances on standard benchmarks in the 1-shot scenarios. As the number of available shots increases, the gap in performances widens: on PASCAL-5i, our method brings about 5% and 6% improvements over the state-of-the-art, in the 5- and 10-shot scenarios, respectively. Furthermore, we introduce a new setting that includes domain shifts, where the base and novel classes are drawn from different datasets. Our method achieves the best performances in this more realistic setting. Our code is freely available online: https://github.com/mboudiaf/RePRI-for-Few-Shot-Segmentation.
Modelling forest transpiration from different perspectives

Forest transpiration models have been developed in different disciplines such as plant physiology, ecology, meteorology, hydrology and soil science. In the present study, three different types of model perspectives for transpiration control are used: leaf cooling, CO2 assimilation and the combined energy and water balance. All three process-orientated models are calibrated on measurements in a Douglas fir stand in the Netherlands. The performances of these models are equally good, although they have different complexities, different numbers of calibration parameters (ranging from 1 to 6) and the models are calibrated on different measurements (eddy correlation at canopy level or CO2 measurements at leaf level). The resemblance of the model results is caused by the calibration procedure and by the high impact of radiation in all three cases. Significant discrepancies become apparent when differences between model responses are examined and when specific (short) periods are selected when input variables are uncoupled. The main differences between the models are caused by another formulation of leaf area index and vapour pressure deficit (VPD). Considerable differences in simulated transpiration occur in the afternoon as a result of the diurnal hysteresis between VPD and radiation. Copyright © 2000 John Wiley & Sons, Ltd.
Recent Theoretical Advances in Non-Convex Optimization

Motivated by recent increased interest in optimization algorithms for non-convex optimization in application to training deep neural networks and other optimization problems in data analysis, we give an overview of recent theoretical results on global performance guarantees of optimization algorithms for non-convex optimization. We start with classical arguments showing that general non-convex problems could not be solved efficiently in a reasonable time. Then we give a list of problems that can be solved efficiently to find the global minimizer by exploiting the structure of the problem as much as it is possible. Another way to deal with non-convexity is to relax the goal from finding the global minimum to finding a stationary point or a local minimum. For this setting, we first present known results for the convergence rates of deterministic first-order methods, which are then followed by a general theoretical analysis of optimal stochastic and randomized gradient schemes, and an overview of the stochastic first-order methods. After that, we discuss quite general classes of non-convex problems, such as minimization of $\alpha$-weakly-quasi-convex functions and functions that satisfy Polyak--Lojasiewicz condition, which still allow obtaining theoretical convergence guarantees of first-order methods. Then we consider higher-order and zeroth-order/derivative-free methods and their convergence rates for non-convex optimization problems.
Type-Centric Kotlin Compiler Fuzzing: Preserving Test Program Correctness by Preserving Types

Kotlin is a relatively new programming language from JetBrains: its development started in 2010 with release 1.0 done in early 2016. The Kotlin compiler, while slowly and steadily becoming more and more mature, still crashes from time to time on the more tricky input programs, not least because of the complexity of its features and their interactions. This makes it a great target for fuzzing, even the basic forms of which can find a significant number of Kotlin compiler crashes.There is a problem with fuzzing, however, closely related to the cause of the crashes: generating a random, non-trivial and semantically valid Kotlin program is hard. In this paper, we talk about type-centric compiler fuzzing in the form of type-centric enumeration, an approach inspired by skeletal program enumeration [1] and based on a combination of generative and mutation-based fuzzing, which solves this problem by focusing on program types. After creating the skeleton program, we fill the typed holes with fragments of suitable type, created via generation and enhanced by semantic-aware mutation.We implemented this approach in our Kotlin compiler fuzzing framework called Backend Bug Finder (BBF) and did an extensive evaluation, not only testing the real-world feasibility of our approach, but also comparing it to other compiler fuzzing techniques. The results show our approach to be significantly better compared to other fuzzing approaches at generating semantically valid Kotlin programs, while creating more interesting crash-inducing inputs at the same time. We managed to find more than 50 previously unknown compiler crashes, of which 18 were considered important after their triage by the compiler team.
Hypocoercivity and global hypoellipticity for the kinetic Fokker-Planck equation in $ H^k $ spaces

The purpose of this paper is to extend the hypocoercivity results for the kinetic Fokker-Planck equation in $H^1$ space in Villani's memoir \cite{Villani} to higher order Sobolev spaces. As in the $L^2$ and $H^1$ setting, there is lack of coercivity in $H^k$ for the associated operator. To remedy this issue, we shall modify the usual $H^k$ norm with certain well-chosen mixed terms and with suitable coefficients which are constructed by induction on $k$. In parallel, a similar strategy but with coefficients depending on time (c.f. \cite{Herau}), usually referred as H\'erau's method, can be employed to prove global hypoellipticity in $H^k$. The exponents in our regularity estimates are optimal in short time. Moreover, as in our recent work \cite{GLWZ}, the general results here can be applied in the mean-field setting to get estimates independent of the dimension; in particular, an application to the Curie-Weiss model is presented.
Highly efficient lattice Boltzmann multiphase simulations of immiscible fluids at high-density ratios on CPUs and GPUs through code generation

A high-performance implementation of a multiphase lattice Boltzmann method based on the conservative Allen-Cahn model supporting high-density ratios and high Reynolds numbers is presented. Meta-programming techniques are used to generate optimized code for CPUs and GPUs automatically. The coupled model is specified in a high-level symbolic description and optimized through automatic transformations. The memory footprint of the resulting algorithm is reduced through the fusion of compute kernels. A roofline analysis demonstrates the excellent efficiency of the generated code on a single GPU. The resulting single GPU code has been integrated into the multiphysics framework waLBerla to run massively parallel simulations on large domains. Communication hiding and GPUDirect-enabled MPI yield near-perfect scaling behavior. Scaling experiments are conducted on the Piz Daint supercomputer with up to 2048 GPUs, simulating several hundred fully resolved bubbles. Further, validation of the implementation is shown in a physically relevant scenario—a three-dimensional rising air bubble in water.
Changes in soil chemistry accompanying acidification over more than 100 years under woodland and grass at Rothamsted Experimental Station, UK

We have examined the effect that acid deposition and other sources of acidity have had over the last 110–140 years on soil under woodland (Broadbalk and Geescroft Wildernesses) and grassland (Park Grass) comprising some of the Classical Experiments at Rothamsted Experimental Station. Changes in soil chemistry have been followed by analysing some of the unique archive of stored samples for pH, water‐soluble and exchangeable base cations, aluminium, iron and manganese, exchangeable acidity, cation exchange capacity (CEC) and soluble anions. Proton balances and historical data show the importance of acid deposition to acidification and concomitant changes in the chemistry of the soil. The pH of the surface soil of Geescroft Wilderness has fallen from 6.2 to 3.8 since 1883. The decrease in the pH of the unlimed, unfertilized plot on Park Grass was less over a similar period (from pH 5.2 to 4.2), illustrating the significant effect of the woodland canopy on the interception of acidifying pollutants. The effect of increasing acidity on the soil chemistry of Geescroft Wilderness is seen in its decreasing base saturation and CEC, with base cations moving down the soil profile. Clay minerals are being irreversibly weathered, and Mn and Al progressively mobilized, so that today Al occupies 70% of the exchange complex in the surface soil. Even with present reductions in sulphur deposition critical loads for sulphur, nitrogen and acidity are still exceeded. Such semi‐natural ecosystems are unsustainable under the current climate of pollution.
Ultrafine particle deposition in a human tracheobronchial cast

The deposition of 0.20, 0.15, and 0.04 μm diameter particles was measured in a human central airway cast using a variable larynx with cyclic inspiratory flow. Data were compared with theoretical predictions for deposition from laminar flow for the first seven airway generations. With the exception of tracheal deposition, which on average exceeded predictions by a factor of 9, the measured deposition was about twice that predicted. The enhanced deposition is attributable to secondary swirling flows. Less enhancement is observed at higher inspiratory flow rates as turbulence increases. The surface density of particles deposited at bifurcations was approximately 20% greater than along the airway lengths. This increased deposition at bifurcations should be considered when calculating tissue dose for particles which act before the initial deposit is removed by clearance processes.
Diversity and dynamics of mycorrhizal associations in tropical rain forests with different disturbance regimes in South Cameroon

The present study documents the occurrence of mycorrhizal associations in the rain forests of south Cameroon. All species investigated are mycorrhizal. Most timber species form arbuscular mycorrhiza, but some timber species, which usually occur in clumps, form ectomycorrhiza. Species diversity of ectomycorrhizal fungi in the undisturbed rain forest is substantial, with more than 125 species having been recorded. Inoculum potential of arbuscular mycorrhizal and ectomycorrhizal fungi is high in the undisturbed rain forest. The shifting cultivation cycle increases inoculum potential of arbuscular mycorrhizal fungi, but lowers inoculum potential of ectomycorrhizal fungi to various extent. On sites of forestry practices (skid trails, landings) inoculum potential of arbuscular mycorrhizal and ectomycorrhizal fungi is very substantially reduced and recovery rates are low. Mycorrhizal colonisation and seedling growth are positively correlated with mycorrhiza inoculum potential. Inoculum potential of arbuscular mycorrhizal fungi and performance of seedlings of arbuscular mycorrhizal trees can be boosted after inoculum addition. Both inoculum quantity and inoculum quality are important criteria for inoculation practices. Ectomycorrhizal inoculum potential cannot be increased through inoculum addition and management of the intact ectomycorrhizal network is necessary for maintenance of the ectomycorrhizal tree species. Key words : Arbuscular mycorrhiza, ectomycorrhiza, disturbance, rain forest, diversity, inoculum potential, Cameroon, forestry practices
Job Quality in Economy for the Common Good Firms in Austria and Germany

The Economy for the Common Good (ECG) pursues an alternative economic model built on values oriented to the common welfare such as human dignity, solidarity, ecological sustainability, social justice, transparency and democratic participation. The principles endorsed by the ECG coincide with those of the social economy as it promotes a system in which the motivation of economic competition is replaced by cooperation so that the collective interest and common good is put above companies’ profits. (High) quality of work plays an important role in the ECG. The main points of its work-related values can be summarised by the willingness to elude discrimination and employment precariousness; the encouragement of information and worker participation; and the promotion of beneficial psychosocial factors at work. However, there is scarce knowledge on the actual labour conditions of workers employed in such type of firms. Thus, in this paper, our goal is to describe the quality of jobs in companies following the Economy for the Common Good in Austria and Germany, the countries where this economic model is most widespread. Using data published in the Common Good Balances reports available on the ECG website1 , we extracted data informing about job quality at an organisational level of 59 firms with at least 5 employees in Austria and Germany. Although the breadth and depth of the information collected in the Common Good Balances reports is variable, results suggest a widespread presence of elements of good quality of work: limited use of precarious employment arrangements (yet, thorough information about fixedterm contracts is lacking), provision of training and a decent degree of decisionmaking autonomy. Direct participation practices are more prevailing than representative participation forms.
Generation of Fock-State Superpositions and Binomial-Code Holonomic Gates via Dressed Intermediate States in the Ultrastrong Light-Matter Coupling Regime.

By using the dressed-state properties of an ultrastrong coupling system, we propose to generate and manipulate, with high fidelities, arbitrary superposition of Fock states. These generated states can form bosonic codes (e.g., binomial codes) to implement nonadiabatic holonomic quantum computation, making the computation fast, robust, and fault-tolerant. The holonomic gates are induced by geometric phases, which possess a built-in noise-resilience feature against local noises. No high-order processes or nonlinear driving are needed in this work, so that the quantum gates can be implemented in tens of nanoseconds. Such a fast evolution makes our protocols robust against decoherence caused by the decays and dephasings of both the cavity and the atom. Moreover, we design the control fields by a systematic-error-sensitivity nullification method, thus our protocols can be mostly insensitive to systematic errors caused by pulse imperfections.
Anisotropies in Compressible MHD Turbulence: Probing Magnetic Fields and Measuring Magnetization

Probing magnetic fields in the interstellar medium (ISM) is notoriously challenging. Motivated by the modern theories of magnetohydrodynamic (MHD) turbulence and turbulence anisotropy, we introduce the Structure Function Analysis (SFA) as a new approach to measure the magnetic-field orientation and estimate the magnetization. We analyze the statistics of turbulent velocities in three-dimensional compressible MHD simulations through the second-order structure functions in both local and global reference frames. In the sub-Alfvénic turbulence with the magnetic energy larger than the turbulent energy, the SFA of turbulent velocities measured in the directions perpendicular and parallel to the magnetic field can be significantly different. Their ratio has a power-law dependence on the Alfvén Mach number M A, which is inversely proportional to the magnetic-field strength. We demonstrate that the anisotropic structure functions of turbulent velocities can be used to estimate both the orientation and strength of magnetic fields. With turbulent velocities measured using different tracers, our approach can be generally applied to probing the magnetic fields in the multiphase ISM.
Sums of four and more unit fractions and approximate parametrizations

We prove new upper bounds on the number of representations of rational numbers mn as a sum of four unit fractions, giving five different regions, depending on the size of m in terms of n . In particular, we improve the most relevant cases, when m is small, and when m is close to n . The improvements stem from not only studying complete parametrizations of the set of solutions, but simplifying this set appropriately. Certain subsets of all parameters define the set of all solutions, up to applications of divisor functions, which has little impact on the upper bound of the number of solutions. These ‘approximate parametrizations’ were the key point to enable computer programmes to filter through a large number of equations and inequalities. Furthermore, this result leads to new upper bounds for the number of representations of rational numbers as sums of more than four unit fractions.
THE IMPACT OF TEMPERATURE ON FURAN RESIN AND BINDER STRUCTURE

Exposure to high temperature causes a series of structural changes in binders used to prepare moulding and core sands. These changes may consist in the formation of new bonds, changes to their character or a transformation of the structure which may be accompanied, for example, by the destruction (disappearance) of some chemical bonds. They have a significant impact on the mechanical and technological properties of sands, but also on their environmental noxiousness or the ability to reclaim the sand. This article presents the results of infrared structural examinations of two furan resins and two binders produced based on those resins. Both pure resins and binders made of them were exposed to temperature. Changes taking place in their structure in the 25-200 °C temperature range were determined using infrared spectroscopy.