Miracle-dz/UniFAR-FTUs huggingface dataset:
Single Model Deep Learning on Imbalanced Small Datasets for Skin Lesion Classification

Deep convolutional neural network (DCNN) models have been widely explored for skin disease diagnosis and some of them have achieved the diagnostic outcomes comparable or even superior to those of dermatologists. However, broad implementation of DCNN in skin disease detection is hindered by small size and data imbalance of the publically accessible skin lesion datasets. This paper proposes a novel single-model based strategy for classification of skin lesions on small and imbalanced datasets. First, various DCNNs are trained on different small and imbalanced datasets to verify that the models with moderate complexity outperform the larger models. Second, regularization DropOut and DropBlock are added to reduce overfitting and a Modified RandAugment augmentation strategy is proposed to deal with the defects of sample underrepresentation in the small dataset. Finally, a novel Multi-Weighted New Loss (MWNL) function and an end-to-end cumulative learning strategy (CLS) are introduced to overcome the challenge of uneven sample size and classification difficulty and to reduce the impact of abnormal samples on training. By combining Modified RandAugment, MWNL and CLS, our single DCNN model method achieved the classification accuracy comparable or superior to those of multiple ensembling models on different dermoscopic image datasets. Our study shows that this method is able to achieve a high classification performance at a low cost of computational resources and inference time, potentially suitable to implement in mobile devices for automated screening of skin lesions and many other malignancies in low resource settings.
Silver(I) complexes with quinazoline and phthalazine: synthesis, structural characterization and evaluation of biological activities

New silver(I) complexes with quinazoline (qz) and phthalazine (phtz), [Ag(NO3)(qz)]n (1) and {[Ag(CH3CN)]2(μ-phtz)2}[BF4]2 (2), have been synthesized and structurally characterized by using different spectroscopic and single-crystal X-ray diffraction techniques. The obtained results revealed that the reaction of AgNO3 with qz at room temperature in a 2:1 molar ratio led to the formation of the polynuclear complex 1. However, the reaction of AgBF4 with phtz under the same experimental conditions resulted in the formation of the dinuclear complex 2. The solution behaviour and air/light stability of these silver(I) complexes have been investigated. The complexes 1 and 2, along with the silver(I) salts used for their synthesis, were evaluated by in vitro antimicrobial studies against a panel of microbial strains that lead to many skin and soft tissue, respiratory, wound, and nosocomial infections. The obtained results indicate that all tested silver(I) compounds have good antibacterial activity with MIC values in the range from 1.5 to 15.6 μg mL−1 against the investigated strains. On the other hand, their antifungal activity against Candida albicans was moderate. In order to determine the therapeutic potential of 1 and 2, their antiproliferative effect on the normal human lung fibroblast cell line MRC5, hemolytic effect on red blood cells and embryotoxicity on zebrafish (Danio rerio) have also been evaluated.
Event-Driven LSTM For Forex Price Prediction

The majority of studies in the field of AI guided financial trading focus on applying machine learning algorithms to continuous historical price and technical analysis data. However, due to the non-stationary and highly volatile nature of Forex markets most algorithms fail in real practice. In this paper, we developed novel event-driven features which indicate a change of trend in direction. We then built long deep learning models to predict a retracement point providing a perfect entry point to gain maximum profit. We used a simple recurrent neural network (RNN) as our baseline model and compared that with short-term memory (LSTM), bidirectional long short-term memory (BiLSTM) and gated recurrent unit (GRU). Our experiment results show that the proposed event-driven feature selection together with the proposed models can form a robust prediction system which supports accurate trading strategies with minimal risk. Our best model on 15-minutes interval data for the EUR/GBP currency achieved RMSE $1.5{\mathrm {x}}10^{-3}$ with a MAPE 0.12% outperforming previous studies.
Approximately Solving Mean Field Games via Entropy-Regularized Deep Reinforcement Learning

The recent mean field game (MFG) formalism facilitates otherwise intractable computation of approximate Nash equilibria in many-agent settings. In this paper, we consider discrete-time finite MFGs subject to finite-horizon objectives. We show that all discrete-time finite MFGs with non-constant fixed point operators fail to be contractive as typically assumed in existing MFG literature, barring convergence via fixed point iteration. Instead, we incorporate entropy-regularization and Boltzmann policies into the fixed point iteration. As a result, we obtain provable convergence to approximate fixed points where existing methods fail, and reach the original goal of approximate Nash equilibria. All proposed methods are evaluated with respect to their exploitability, on both instructive examples with tractable exact solutions and high-dimensional problems where exact methods become intractable. In high-dimensional scenarios, we apply established deep reinforcement learning methods and empirically combine fictitious play with our approximations.
A Lyapunov Theory for Finite-Sample Guarantees of Asynchronous Q-Learning and TD-Learning Variants

This paper develops an unified framework to study finite-sample convergence guarantees of a large class of value-based asynchronous reinforcement learning (RL) algorithms. We do this by first reformulating the RL algorithms as \textit{Markovian Stochastic Approximation} (SA) algorithms to solve fixed-point equations. We then develop a Lyapunov analysis and derive mean-square error bounds on the convergence of the Markovian SA. Based on this result, we establish finite-sample mean-square convergence bounds for asynchronous RL algorithms such as $Q$-learning, $n$-step TD, TD$(\lambda)$, and off-policy TD algorithms including V-trace. As a by-product, by analyzing the convergence bounds of $n$-step TD and TD$(\lambda)$, we provide theoretical insights into the bias-variance trade-off, i.e., efficiency of bootstrapping in RL. This was first posed as an open problem in (Sutton, 1999).
Coupled spin–orbital fluctuations in a three orbital model for 4d and 5d oxides with electron fillings n = 3, 4, 5—application to NaOsO3, Ca2RuO4 and Sr2IrO4

A unified approach is presented for investigating coupled spin–orbital fluctuations within a realistic three-orbital model for strongly spin–orbit coupled systems with electron fillings n = 3, 4, 5 in the t 2g sector of d yz , d xz , d xy orbitals. A generalized fluctuation propagator is constructed which is consistent with the generalized self-consistent Hartree–Fock approximation where all Coulomb interaction contributions involving orbital diagonal and off-diagonal spin and charge condensates are included. Besides the low-energy magnon, intermediate-energy orbiton and spin–orbiton, and high-energy spin–orbit exciton modes, the generalized spectral function also shows other high-energy excitations such as the Hund’s coupling induced gapped magnon modes. We relate the characteristic features of the coupled spin–orbital excitations to the complex magnetic behavior resulting from the interplay between electronic bands, spin–orbit coupling, Coulomb interactions, and structural distortion effects, as realized in the compounds NaOsO3, Ca2RuO4, and Sr2IrO4.
Soil acidity and aluminium toxicity as affected by surface liming and cover oat residues under a no‐till system

A 3‐year field trial examined in a long‐term no‐till system the effects of surface‐applied lime and cover black oat (Avena strigosa Schreb) residues on soil chemical attributes, root growth and grain yield of corn (Zea mays L.) and soybean (Glycine max L. Merrill) on a loamy, kaolinitic, thermic Typic Hapludox in Paraná State, Brazil. The treatments consisted of dolomitic lime broadcast on the soil surface at 0 or 12 t/ha, with and without cover of black oat residues. Corn and soybeans were grown without rainfall limitation. Applying lime on the surface improved soil acidity and decreased aluminium (Al) toxicity to a 10‐cm depth 1 year after application. Surface liming increased pH and the content of exchangeable Ca2+ to a 20‐cm depth, and decreased Al toxicity to a 40‐ to 60‐cm depth, 3 years after application, indicating that the surface‐applied lime moved deeper. Cover black oat residues did not favour the mobility of surface‐applied lime to alleviate subsoil acidity and an increase in the Al3+ saturation level at the soil surface was found in unlimed plots with black oat residues. Root growth and grain yields of corn and soybean were not influenced by surface liming with or without cover black oat residue. Despite the soil acidity level, root length of corn and soybean ranged from 55 to 60% at 0‐ to 10‐cm depth. The results suggest that Al toxicity is low in no‐till systems during cropping seasons with adequate and well‐distributed rainfall, but this effect is not related to the presence of cover oat residues.
A novel family of rotating black hole mimickers

The recent opening of gravitational wave astronomy has shifted the debate about black hole mimickers from a purely theoretical arena to a phenomenological one. In this respect, missing a definitive quantum gravity theory, the possibility to have simple, meta-geometries describing in a compact way alternative phenomenologically viable scenarios is potentially very appealing. A recently proposed metric by Simpson and Visser is exactly an example of such meta-geometry describing, for different values of a single parameter, different non-rotating black hole mimickers. Here, we employ the Newman-Janis procedure to construct a rotating generalisation of such geometry. We obtain a stationary, axially symmetric metric that depends on mass, spin and an additional real parameter ℓ. According to the value of such parameter, the metric may represent a rotating traversable wormhole, a rotating regular black hole with one or two horizons, or three more limiting cases. By studying the internal and external rich structure of such solutions, we show that the obtained metric describes a family of interesting and simple regular geometries providing viable Kerr black hole mimickers for future phenomenological studies.
Online Learning with Simple Predictors and a Combinatorial Characterization of Minimax in 0/1 Games

Which classes can be learned properly in the online model? -- that is, by an algorithm that at each round uses a predictor from the concept class. While there are simple and natural cases where improper learning is necessary, it is natural to ask how complex must the improper predictors be in such cases. Can one always achieve nearly optimal mistake/regret bounds using"simple"predictors? In this work, we give a complete characterization of when this is possible, thus settling an open problem which has been studied since the pioneering works of Angluin (1987) and Littlestone (1988). More precisely, given any concept class C and any hypothesis class H, we provide nearly tight bounds (up to a log factor) on the optimal mistake bounds for online learning C using predictors from H. Our bound yields an exponential improvement over the previously best known bound by Chase and Freitag (2020). As applications, we give constructive proofs showing that (i) in the realizable setting, a near-optimal mistake bound (up to a constant factor) can be attained by a sparse majority-vote of proper predictors, and (ii) in the agnostic setting, a near-optimal regret bound (up to a log factor) can be attained by a randomized proper algorithm. A technical ingredient of our proof which may be of independent interest is a generalization of the celebrated Minimax Theorem (von Neumann, 1928) for binary zero-sum games. A simple game which fails to satisfy Minimax is"Guess the Larger Number", where each player picks a number and the larger number wins. The payoff matrix is infinite triangular. We show this is the only obstruction: if a game does not contain triangular submatrices of unbounded sizes then the Minimax Theorem holds. This generalizes von Neumann's Minimax Theorem by removing requirements of finiteness (or compactness), and captures precisely the games of interest in online learning.
O-Methyloximes of Testosterone and of 17α-Methyltestosterone: TLC and QSPR Study of RF Values

The Z (1b and 2b, respectively) and E diastereomers (1c and 2c, respectively) of the O-methyloximes of testosterone (1a) and 17a-methyltestosterone (2a) have been synthesized and separated by TLC. 1H NMR assignments confirmed the corresponding structures. The TLC RF values of these six compounds could be rationalized by a quantitative structure-property relationship (QSPR) using one topological/topographical index.
Adaptive Random Bandwidth for Inference in CAViaR Models

This paper investigates the size performance of Wald tests for CAViaR models (Engle and Manganelli, 2004). We find that the usual estimation strategy on test statistics yields inaccuracies. Indeed, we show that existing density estimation methods cannot adapt to the time-variation in the conditional probability densities of CAViaR models. Consequently, we develop a method called adaptive random bandwidth which can approximate time-varying conditional probability densities robustly for inference testing on CAViaR models based on the asymptotic normality of the model parameter estimator. This proposed method also avoids the problem of choosing an optimal bandwidth in estimating probability densities, and can be extended to multivariate quantile regressions straightforward.
Internal-wave billiards in trapezoids and similar tables

We call internal-wave billiard the dynamical system of a point particle that moves freely inside a planar domain (the table) and is reflected by its boundary according to this nonstandard rule: the angles that the incident and reflected velocities form with a fixed direction (representing gravity) are the same. These systems are point particle approximations for the motion of internal gravity waves in closed containers, hence the name. For a class of tables similar to rectangular trapezoids, but with the slanted leg replaced by a general curve with downward concavity, we prove that the dynamics has only three asymptotic regimes: (1) there exist a global attractor and a global repellor, which are periodic and might coincide; (2) there exists a beam of periodic trajectories, whose boundary (if any) comprises an attractor and a repellor for all the other trajectories; (3) all trajectories are dense (that is, the system is minimal). Furthermore, in the prominent case where the table is an actual trapezoid, we study the sets in parameter space relative to the three regimes. We prove in particular that the set for (1) has positive measure (giving a rigorous proof of the existence of Arnol’d tongues for internal-wave billiards), whereas the sets for (2) and (3) are non-empty but have measure zero.
Enjoy Your Editing: Controllable GANs for Image Editing via Latent Space Navigation

Controllable semantic image editing enables a user to change entire image attributes with few clicks, e.g., gradually making a summer scene look like it was taken in winter. Classic approaches for this task use a Generative Adversarial Net (GAN) to learn a latent space and suitable latent-space transformations. However, current approaches often suffer from attribute edits that are entangled, global image identity changes, and diminished photo-realism. To address these concerns, we learn multiple attribute transformations simultaneously, we integrate attribute regression into the training of transformation functions, apply a content loss and an adversarial loss that encourage the maintenance of image identity and photo-realism. We propose quantitative evaluation strategies for measuring controllable editing performance, unlike prior work which primarily focuses on qualitative evaluation. Our model permits better control for both single- and multiple-attribute editing, while also preserving image identity and realism during transformation. We provide empirical results for both real and synthetic images, highlighting that our model achieves state-of-the-art performance for targeted image manipulation.
CTC-based Compression for Direct Speech Translation

Previous studies demonstrated that a dynamic phone-informed compression of the input audio is beneficial for speech translation (ST). However, they required a dedicated model for phone recognition and did not test this solution for direct ST, in which a single model translates the input audio into the target language without intermediate representations. In this work, we propose the first method able to perform a dynamic compression of the input in direct ST models. In particular, we exploit the Connectionist Temporal Classification (CTC) to compress the input sequence according to its phonetic characteristics. Our experiments demonstrate that our solution brings a 1.3-1.5 BLEU improvement over a strong baseline on two language pairs (English-Italian and English-German), contextually reducing the memory footprint by more than 10%.
Permute & Add Network Codes via Group Algebras

A class of network codes have been proposed in the literature where the symbols transmitted on network edges are binary vectors and the coding operation performed in network nodes consists of the application of (possibly several) permutations on each incoming vector and XOR-ing the results to obtain the outgoing vector. These network codes, which we will refer to as permute-and-add network codes, involve simpler operations and are known to provide lower complexity solutions than scalar linear codes. The complexity of these codes is determined by their degree which is the number of permutations applied on each incoming vector to compute an outgoing vector. Constructions of permute-and-add network codes for multicast networks are known. In this paper, we provide a new framework based on group algebras to design permute-and-add network codes for arbitrary (not necessarily multicast) networks. Our framework allows the use of any finite group of permutations (including circular shifts, proposed in prior work) and admits a trade-off between coding rate and the degree of the code. Further, our technique permits elegant recovery and generalizations of the key results on permute-and-add network codes known in the literature.
Neural Data Augmentation via Example Extrapolation

In many applications of machine learning, certain categories of examples may be underrepresented in the training data, causing systems to underperform on such"few-shot"cases at test time. A common remedy is to perform data augmentation, such as by duplicating underrepresented examples, or heuristically synthesizing new examples. But these remedies often fail to cover the full diversity and complexity of real examples. We propose a data augmentation approach that performs neural Example Extrapolation (Ex2). Given a handful of exemplars sampled from some distribution, Ex2 synthesizes new examples that also belong to the same distribution. The Ex2 model is learned by simulating the example generation procedure on data-rich slices of the data, and it is applied to underrepresented, few-shot slices. We apply Ex2 to a range of language understanding tasks and significantly improve over state-of-the-art methods on multiple few-shot learning benchmarks, including for relation extraction (FewRel) and intent classification + slot filling (SNIPS).
A thermophysical and dynamical study of the Hildas, (1162) Larissa, and (1911) Schubart

The Hilda asteroids are among the least studied populations in the asteroid belt, despite their potential importance as markers of Jupiter’s migration in the early Solar system. We present new mid-infrared observations of two notable Hildas, (1162) Larissa and (1911) Schubart, obtained using the Faint Object infraRed CAmera for the SOFIA Telescope (FORCAST), and use these to characterise their thermal inertia and physical properties. For (1162) Larissa, we obtain an effective diameter of 46.5+2.3 −1.7 km, an albedo of 0.12 ± 0.02, and a thermal inertia of 15+10 −8 Jm −2s1/2K−1. In addition, our Larissa thermal measurements are well matched with an ellipsoidal shape with an axis ratio a/b=1.2 for the most-likely spin properties. Our modelling of (1911) Schubart is not as refined, but the thermal data point towards a high-obliquity spin-pole, with a best-fit a/b=1.3 ellipsoidal shape. This spin-shape solution is yielding a diameter of 72+3 −4 km, an albedo of 0.039± 0.02, and a thermal inertia below 30 Jm −2s1/2K−1 (or 10+20 −5 Jm −2s1/2K−1). As with (1162) Larissa, our results suggest that (1911) Schubart is aspherical, and likely elongated in shape. Detailed dynamical simulations of the two Hildas reveal that both exhibit strong dynamical stability, behaviour that suggests that they are primordial, rather than captured objects. The differences in their albedos, along with their divergent taxonomical classification, suggests that despite their common origin, the two have experienced markedly different histories.
Canonical scale separation in two-dimensional incompressible hydrodynamics

Abstract The rules that govern a two-dimensional inviscid incompressible fluid are simple. Yet, to characterise the long-time behaviour is a knotty problem. The fluid fulfils Euler's equations: a nonlinear Hamiltonian system with an infinite number of conservation laws. In both experiments and numerical simulations, coherent vortex structures emerge after an initial stage. These formations dominate the large-scale dynamics, but small scales also emerge and persist. The resulting scale separation resembles Kraichnan's theory of forward and backward cascades of enstrophy and energy. Previous attempts to model the double cascade use filtering techniques that enforce separation from the outset. Here, we show that Euler's equations possess an intrinsic, canonical splitting of the vorticity function. The splitting is remarkable in four ways: (i) it is defined solely by the Poisson bracket and the Hamiltonian; (ii) it characterises steady flows; (iii) it innately separates scales, enabling the dynamics behind Kraichnan's qualitative description; and (iv) it accounts for ‘broken line’ energy spectra observed in both experiments and numerical simulations. The splitting originates from Zeitlin's truncated model of Euler's equations in combination with a standard quantum tool: the spectral decomposition of Hermitian matrices. In addition to theoretical insight, the scale separation dynamics enables stochastic model reduction, where multiplicative noise models small scales.
On Generative Spoken Language Modeling from Raw Audio

Abstract We introduce Generative Spoken Language Modeling, the task of learning the acoustic and linguistic characteristics of a language from raw audio (no text, no labels), and a set of metrics to automatically evaluate the learned representations at acoustic and linguistic levels for both encoding and generation. We set up baseline systems consisting of a discrete speech encoder (returning pseudo-text units), a generative language model (trained on pseudo- text), and a speech decoder (generating a waveform from pseudo-text) all trained without supervision and validate the proposed metrics with human evaluation. Across 3 speech encoders (CPC, wav2vec 2.0, HuBERT), we find that the number of discrete units (50, 100, or 200) matters in a task-dependent and encoder- dependent way, and that some combinations approach text-based systems.1