Miracle-dz/UniFAR-FTUs huggingface dataset:
Backpressure Control with Estimated Queue Lengths for Urban Network Traffic

Backpressure (BP) control was originally used for packet routing in communications networks. Since its first application to network traffic control, it has undergone different modifications to tailor it to traffic problems with promising results. Most of these BP variants are based on an assumption of perfect knowledge of traffic conditions throughout the network at all times, specifically the queue lengths (more accurately, the traffic volumes). However, it has been well established that accurate queue length information at signalized intersections is never available except in fully connected environments. Although connected vehicle technologies are developing quickly, we are still far from a fully connected environment in the real world. This paper test the effectiveness of BP control when incomplete or imperfect knowledge about traffic conditions is available. We combine BP control with a speed/density field estimation module suitable for a partially connected environment. We refer to the proposed system as a BP with estimated queue lengths (BP-EQ). We test the robustness of BP-EQ to varying levels of connected vehicle penetration, and we compared BP-EQ with the original BP (i.e., assuming accurate knowledge of traffic conditions), a real-world adaptive signal controller, and optimized fixed timing control using microscopic traffic simulation with field calibrated data. Our results show that with a connected vehicle penetration rate as little as 10%, BP-EQ can outperform the adaptive controller and the fixed timing controller in terms of average delay, throughput, and maximum stopped queue lengths under high demand scenarios.
Many-Class Few-Shot Learning on Multi-Granularity Class Hierarchy

We study many-class few-shot (MCFS) problem in both supervised learning and meta-learning settings. Compared to the well-studied many-class many-shot and few-class few-shot problems, the MCFS problem commonly occurs in practical applications but has been rarely studied in previous literature. It brings new challenges of distinguishing between many classes given only a few training samples per class. In this article, we leverage the class hierarchy as a prior knowledge to train a coarse-to-fine classifier that can produce accurate predictions for MCFS problem in both settings. The propose model, “memory-augmented hierarchical-classification network (MahiNet)”, performs coarse-to-fine classification where each coarse class can cover multiple fine classes. Since it is challenging to directly distinguish a variety of fine classes given few-shot data per class, MahiNet starts from learning a classifier over coarse-classes with more training data whose labels are much cheaper to obtain. The coarse classifier reduces the searching range over the fine classes and thus alleviates the challenges from “many classes”. On architecture, MahiNet first deploys a convolutional neural network (CNN) to extract features. It then integrates a memory-augmented attention module and a multi-layer perceptron (MLP) together to produce the probabilities over coarse and fine classes. While the MLP extends the linear classifier, the attention module extends the KNN classifier, both together targeting the “few-shot” problem. We design several training strategies of MahiNet for supervised learning and meta-learning. In addition, we propose two novel benchmark datasets “mcfsImageNet” (as a subset of ImageNet) and “mcfsOmniglot” (re-splitted Omniglot) specially designed for MCFS problem. In experiments, we show that MahiNet outperforms several state-of-the-art models (e.g., prototypical networks and relation networks) on MCFS problems in both supervised learning and meta-learning.
Internal Surface Plasmon Excitation as the Root Cause of Laser-Induced Periodic Plasma Structure and Self-Organized Nanograting Formation in the Volume of Transparent Dielectric

A computer simulation of the dynamics of an optical discharge produced in the volume of a transparent dielectric (fused silica) by a focused femtosecond laser pulse was carried out taking into account the possibility of developing small-scale ionization-field instability. The presence of small foreign inclusions in the fused silica was taken into account with the model of a nanodispersed heterogeneous medium by using Maxwell Garnett formulas. The results of the calculations made it possible to reveal the previously unknown physical mechanism that determines the periodicity of the ordered plasma-field structure that is formed in each single breakdown pulse and is the root cause of the ordered volume nanograting formation in dielectric material exposed to a series of repeated pulses. Two main points are decisive in this mechanism: (i) the formation of a thin overcritical plasma layer at the breakdown wave front counter-propagated to the incident laser pulse and (ii) the excitation of the “internal surface plasmon” at this front, resulting in a rapid amplification of the corresponding spatial harmonic of random seed perturbations in the plasma and formation of a contrast structure with a period equal to the wavelength of the surface plasmon (0.7 of the wavelength in dielectric).
Improving Sequence Tagging for Vietnamese Text using Transformer-based Neural Models

This paper describes our study on using mutilingual BERT embeddings and some new neural models for improving sequence tagging tasks for the Vietnamese language. We propose new model architectures and evaluate them extensively on two named entity recognition datasets of VLSP 2016 and VLSP 2018, and on two part-of-speech tagging datasets of VLSP 2010 and VLSP 2013. Our proposed models outperform existing methods and achieve new state-of-the-art results. In particular, we have pushed the accuracy of part-of-speech tagging to 95.40% on the VLSP 2010 corpus, to 96.77% on the VLSP 2013 corpus; and the F1 score of named entity recognition to 94.07% on the VLSP 2016 corpus, to 90.31% on the VLSP 2018 corpus. Our code and pre-trained models viBERT and vELECTRA are released as open source to facilitate adoption and further research.
The gas, metal, and dust evolution in low-metallicity local and high-redshift galaxies

Context. The chemical enrichment in the interstellar medium (ISM) of galaxies is regulated by several physical processes: star birth and death, grain formation and destruction, and galactic inflows and outflows. Understanding such processes and their relative importance is essential to following galaxy evolution and the chemical enrichment through the cosmic epochs, and to interpreting current and future observations. Despite the importance of such topics, the contribution of different stellar sources to the chemical enrichment of galaxies, for example massive stars exploding as Type II supernovae (SNe) and low-mass stars, as well as the mechanisms driving the evolution of dust grains, such as for example grain growth in the ISM and destruction by SN shocks, remain controversial from both observational and theoretical viewpoints. Aims. In this work, we revise the current description of metal and dust evolution in the ISM of local low-metallicity dwarf galaxies and develop a new description of Lyman-break galaxies (LBGs) which are considered to be their high-redshift counterparts in terms of star formation, stellar mass, and metallicity. Our goal is to reproduce the observed properties of such galaxies, in particular (i) the peak in dust mass over total stellar mass (sMdust) observed within a few hundred million years; and (ii) the decrease in sMdust at a later time. Methods. We fitted spectral energy distribution of dwarf galaxies and LBGs with the “Code Investigating GALaxies Emission”, through which the total stellar mass, dust mass, and star formation rate are estimated. For some of the dwarf galaxies considered, the metal and gas content are available from the literature. We computed different prescriptions for metal and dust evolution in these systems (e.g. different initial mass functions for stars, dust condensation fractions, SN destruction, dust accretion in the ISM, and inflow and outflow efficiency), and we fitted the properties of the observed galaxies through the predictions of the models. Results. Only some combinations of models are able to reproduce the observed trend and simultaneously fit the observed properties of the galaxies considered. In particular, we show that (i) a top-heavy initial mass function that favours the formation of massive stars and a dust condensation fraction for Type II SNe of around 50% or more help to reproduce the peak of sMdust observed after ≈100 Myr from the beginning of the baryon cycle for both dwarf galaxies and LBGs; (ii) galactic outflows play a crucial role in reproducing the observed decline in sMdust with age and are more efficient than grain destruction from Type II SNe both in local galaxies and at high-redshift; (iii) a star formation efficiency (mass of gas converted into stars) of a few percent is required to explain the observed metallicity of local dwarf galaxies; and (iv) dust growth in the ISM is not necessary in order to reproduce the values of sMdust derived for the galaxies under study, and, if present, the effect of this process would be erased by galactic outflows.
Random Access in Persistent Strings

We consider compact representations of collections of similar strings that support random access queries. The collection of strings is given by a rooted tree where edges are labeled by an edit operation (inserting, deleting, or replacing a character) and a node represents the string obtained by applying the sequence of edit operations on the path from the root to the node. The goal is to compactly represent the entire collection while supporting fast random access to any part of a string in the collection. This problem captures natural scenarios such as representing the past history of a edited document or representing highly-repetitive collections. Given a tree with $n$ nodes, we show how to represent the corresponding collection in $O(n)$ space and optimal $O(\log n/ \log \log n)$ query time. This improves the previous time-space trade-offs for the problem. To obtain our results, we introduce new techniques and ideas, including a reduction to a new geometric line segment selection together with an efficient solution.
Fabric Image Representation Encoding Networks for Large-scale 3D Medical Image Analysis

Deep neural networks are parameterised by weights that encode feature representations, whose performance is dictated through generalisation by using large-scale feature-rich datasets. The lack of large-scale labelled 3D medical imaging datasets restrict constructing such generalised networks. In this work, a novel 3D segmentation network, Fabric Image Representation Networks (FIRENet), is proposed to extract and encode generalisable feature representations from multiple medical image datasets in a large-scale manner. FIRENet learns image specific feature representations by way of 3D fabric network architecture that contains exponential number of sub-architectures to handle various protocols and coverage of anatomical regions and structures. The fabric network uses Atrous Spatial Pyramid Pooling (ASPP) extended to 3D to extract local and image-level features at a fine selection of scales. The fabric is constructed with weighted edges allowing the learnt features to dynamically adapt to the training data at an architecture level. Conditional padding modules, which are integrated into the network to reinsert voxels discarded by feature pooling, allow the network to inherently process different-size images at their original resolutions. FIRENet was trained for feature learning via automated semantic segmentation of pelvic structures and obtained a state-of-the-art median DSC score of 0.867. FIRENet was also simultaneously trained on MR (Magnatic Resonance) images acquired from 3D examinations of musculoskeletal elements in the (hip, knee, shoulder) joints and a public OAI knee dataset to perform automated segmentation of bone across anatomy. Transfer learning was used to show that the features learnt through the pelvic segmentation helped achieve improved mean DSC scores of 0.962, 0.963, 0.945 and 0.986 for automated segmentation of bone across datasets.
BERTology Meets Biology: Interpreting Attention in Protein Language Models

Transformer architectures have proven to learn useful representations for protein classification and generation tasks. However, these representations present challenges in interpretability. Through the lens of attention, we analyze the inner workings of the Transformer and explore how the model discerns structural and functional properties of proteins. We show that attention (1) captures the folding structure of proteins, connecting amino acids that are far apart in the underlying sequence, but spatially close in the three-dimensional structure, (2) targets binding sites, a key functional component of proteins, and (3) focuses on progressively more complex biophysical properties with increasing layer depth. We also present a three-dimensional visualization of the interaction between attention and protein structure. Our findings align with known biological processes and provide a tool to aid discovery in protein engineering and synthetic biology. The code for visualization and analysis is available at https://github.com/salesforce/provis.
The structure‐mechanics relationship and the response to load of the acellular bone of neoteleost fish: a review

Summary Although fish species represent probably about half of all vertebrate species, the structure and material properties of fish skeletons are poorly understood. Since osteocytes (cells within the bone tissue) are believed to orchestrate the modeling and remodeling responses in mammalian bone, the unique lack of these cells in the bones of the majority of extant fishes (neoteleosts), raises several intriguing questions. In particular, how do their bones handle forces, especially those applied repeatedly and over long periods of time. This paper reviews the available information regarding the structure-function relationship of teleost bones – namely their known structural features, mechanical properties and response to load – with a particular focus on the acellular bone of neoteleosts. We present preliminary results of ongoing investigations in these areas and highlight topics (e.g. mechanisms of tissue fatigue, repair and resistance to high strain rates) we believe particularly ripe for and demanding further investigation.
The Expansion of Wronskian Hermite Polynomials in the Hermite Basis

We express Wronskian Hermite polynomials in the Hermite basis and obtain an explicit formula for the coefficients. From this we deduce an upper bound for the modulus of the roots in the case of partitions of length 2. We also derive a general upper bound for the modulus of the real and purely imaginary roots. These bounds are very useful in the study of irreducibility of Wronskian Hermite polynomials. Additionally, we generalize some of our results to a larger class of polynomials.
Large Deviation Principles for Stochastic Volatility Models with Reflection and Three Faces of the Stein and Stein Model

We introduce stochastic volatility models, in which the volatility is described by a time-dependent nonnegative function of a reflecting diffusion. The idea to use reflecting diffusions as building blocks of the volatility came into being because of a certain volatility misspecification in the classical Stein and Stein model. A version of this model that uses the reflecting Ornstein-Uhlenbeck process as the volatility process is a special example of a stochastic volatility model with reflection. The main results obtained in the present paper are sample path and small-noise large deviation principles for the log-price process in a stochastic volatility model with reflection under rather mild restrictions. We use these results to study the asymptotic behavior of binary barrier options and call prices in the small-noise regime.
COVID-19 detection using Residual Attention Network an Artificial Intelligence approach

Coronavirus Disease 2019 (COVID-19) is caused by the severe acute respiratory syndrome coronavirus 2 virus (SARS-CoV-2). The virus transmits rapidly, it has a basic reproductive number ($R_0$) of $2.2-2.7$. In March, 2020 the World Health Organization declared the COVID-19 outbreak a pandemic. Effective testing for COVID-19 is crucial to controlling the outbreak since infected patients can be quarantined. But the demand for testing outstrips the availability of test kits that use Reverse Transcription Polymerase Chain Reaction (RT-PCR). In this paper, we present a technique to detect COVID-19 using Artificial Intelligence. Our technique takes only a few seconds to detect the presence of the virus in a patient. We collected a dataset of chest X-ray images and trained several popular deep convolution neural network-based models (VGG, MobileNet, Xception, DenseNet, InceptionResNet) to classify chest X-rays. Unsatisfied with these models we then designed and built a Residual Attention Network that was able to detect COVID-19 with a testing accuracy of 98\% and a validation accuracy of 100\%. Feature maps of our model show which areas within a chest X-ray are important for classification. Our work can help to increase the adaptation of AI-assisted applications in clinical practice.
El endemismo sudafricano "Aiolocolax pulchella" Pocock ("Ceramiales?, "Rhodophyta"), parasito de "Falkenbergiella caespitosa" Pocock, en las costas atlánticas de Europa

PEREZ-CIRERA, J. L. , J. CREMADES & I. BARBARA (1989). The South African endemic Aiolocolax pulchella Pocock (Ceramiales? , Rhodophyta), a parasite on Falkenbergiella caespitosa Pocock, in the Atlantic coast ofEurope. Anales Jard. Bot. Madrid 46(1): 83-88 (in Spanish). Falkenbergiella caespitosa and its alloparasite Aiolocolax pulchella, two species restricted up to now to the South African coasts, have been recorded in the grassy communities on sandy substrate in the coasts of Galicia (NW Spain). Both species are described, and the possible reference of the records of Lophosiphonia scopulorum (Harvey) Womersley (for the southern Atlantic coasts of Europe) to Falkenbergiella caespitosa is discussed.
Radiography in high mass X-ray binaries

Context.In high mass X-ray binaries, an accreting compact object orbits a high mass star, which loses mass through a dense and inhomogeneous wind.Aims. Using the compact object as an X-ray backlight, the time variability of the absorbing column density in the wind can be exploited in order to shed light on the micro-structure of the wind and obtain unbiased stellar mass-loss rates for high mass stars.Methods. We developed a simplified representation of the stellar wind where all the matter is gathered in spherical “clumps” that are radially advected away from the star. This model enables us to explore the connections between the stochastic properties of the wind and the variability of the column density for a comprehensive set of parameters related to the orbit and to the wind micro-structure, such as the size of the clumps and their individual mass. In particular, we focus on the evolution with the orbital phase of the standard deviation of the column density and of the characteristic duration of enhanced absorption episodes. Using the porosity length, we derive analytical predictions and compare them to the standard deviations and coherence time scales that were obtained.Results. We identified the favorable systems and orbital phases to determine the wind micro-structure. The coherence time scale of the column density is shown to be the self-crossing time of a single clump in front of the compact object. We thus provide a procedure to get accurate measurements of the size and of the mass of the clumps, purely based on the observable time variability of the column density.Conclusions. The coherence time scale grants direct access to the size of the clumps, while their mass can be deduced separately from the amplitude of the variability. We further show how monitoring the variability at superior conjunctions can probe the onset of the clump-forming region above the stellar photosphere. If the high column density variations in some high mass X-ray binaries are due to unaccreted clumps which are passing by the line-of-sight, this would require high mass clumps to reproduce the observed peak-to-peak amplitude and coherence time scales. These clump properties are marginally compatible with the ones derived from radiative-hydrodynamics simulations. Alternatively, the following components could contribute to the variability of the column density: larger orbital scale structures produced by a mechanism that has yet to be identified or a dense environment in the immediate vicinity of the accretor, such as an accretion disk, an outflow, or a spherical shell surrounding the magnetosphere of the accreting neutron star.
Maximum Likelihood with Bias-Corrected Calibration is Hard-To-Beat at Label Shift Adaptation

Label shift refers to the phenomenon where the prior class probability p ( y ) changes between the training and test distributions, while the conditional probability p ( x | y ) stays ﬁxed. Label shift arises in settings like medical diagnosis, where a classiﬁer trained to predict disease given symptoms must be adapted to scenarios where the base-line prevalence of the disease is different. Given estimates of p ( y | x ) from a predictive model, Saerens et al. proposed an efﬁcient maximum likelihood algorithm to correct for label shift that does not require model retraining, but a limiting assumption of this algorithm is that p ( y | x ) is calibrated, which is not true of modern neural networks. Recently, Black Box Shift Learning (BBSL) and Regularized Learning under Label Shifts (RLLS) have emerged as state-of-the-art techniques to cope with label shift when a classi-ﬁer does not output calibrated probabilities, but both methods require model retraining with importance weights and neither has been benchmarked against maximum likelihood. Here we (1) show that combining maximum likelihood with a type of calibration we call bias-corrected calibration outperforms both BBSL and RLLS across diverse datasets and distribution shifts, (2) prove that the maximum likelihood objective is concave, and (3) introduce a principled strategy for estimating source-domain priors that improves robustness to poor calibration. This work demonstrates that the maximum likelihood with appropriate calibration is a formidable and efﬁcient baseline for label shift adaptation; notebooks reproducing experiments available https://github.com/
Ionized and hot molecular outflows in the inner 500 pc of NGC 1275

The role of feedback from Active Galactic Nuclei (AGN) in the evolution of galaxies is still not not fully understood, mostly due to the lack of observational constraints in the multi-phase gas kinematics on the ten to hundred parsec scales. We have used the Gemini Near-infrared Integral Field Spectrograph (NIFS) to map the molecular and ionized gas kinematics in the inner 900$\times$900 pc$^2$ of the Seyfert galaxy NGC1275 at a spatial resolution of $\sim$70 pc. From the fitting of the CO absorption bandheads in the K-band, we derive a stellar velocity dispersion of $265\pm26$ km s$^{-1}$, which implies a black hole mass of $M_{\rm SMBH}=1.1^{+0.9}_{-0.5}\times10^9$ M$_\odot$. We find hot ($T\gtrsim1000$ K) molecular and ionized outflows with velocities of up to 2 000 km s$^{-1}$ and mass outflow rates of $2.7\times10^{-2} {\rm M_\odot}$ yr$^{-1}$ and $1.6 {\rm M_\odot}$ yr$^{-1}$, respectively, in each of these gas phases. The kinetic power of the ionized outflows corresponds to only 0.05 per cent of the luminosity of the AGN of NGC 1275, indicating that they are not powerful enough to provide significant AGN feedback, but may be effective in redistributing the gas in the central region of the galaxy. The AGN driven outflows seem to be responsible for the shocks necessary to produce the observed H$_2$ and [Fe II] line emission.