Miracle-dz/UniFAR-FTUs huggingface dataset:
Improving Low resource Reading Comprehension via Cross lingual Transposition Rethinking

Extractive Question Answering (QA) has made tremendous advances enabled by the availability of large scale high quality QA training data. Although such rapid progress and widespread application, extractive QA datasets in languages other than English remain scarce. Moreover, collecting such a sufficient amount of training data for each language is costly and even impossible. To address this issue, we propose a Cross Lingual Transposition ReThinking (XLTT) model by modelling existing high quality extractive reading comprehension datasets in a multilingual environment. To be specific, we present multilingual adaptive attention (MAA) to combine intra attention and inter attention to learn more general generalizable semantic and lexical knowledge from each pair of language families. Furthermore, to make full use of existing datasets, we adopt a new training framework to train our model by calculating task level similarities between each existing dataset and target dataset. The experimental results show that our XLTT model surpasses six baselines on two multilingual ERC benchmarks, especially more effective for low resource languages with 3.9 and 4.1 average improvement in F1 and EM, respectively.
Topological synchronization: explosive transition and rhythmic phase

Topological signals deﬁned on nodes, links and higher dimensional simplices deﬁne the dynamical state of a network or of a simplicial complex. As such, topological signals are attracting increasing attention in network theory, dynamical systems, signal processing and machine learning. Topological signals deﬁned on the nodes are typically studied in network dynamics, while topological signals deﬁned on links are much less explored. Here we investigate topological synchronization describing locally coupled topological signals deﬁned on the nodes and on the links of a network. The dynamics of signals deﬁned on the nodes is aﬀected by a phase lag depending on the dynamical state of nearby links and vice versa, the dynamics of topological signals deﬁned on the links is aﬀected by a phase lag depending on the dynamical state of nearby nodes. We show that topological synchronization on a fully connected network is explosive and leads to a discontinuous forward transition and a continuous backward transition. The analytical investigation of the phase diagram provides an analytical expression for the critical threshold of the discontinuous explosive synchronization. The model also displays an exotic coherent synchronized phase, also called rhythmic phase, characterized by having non-stationary order parameters which can shed light on topological mechanisms for the emergence of brain rhythms.
DaCy: A Unified Framework for Danish NLP

Danish natural language processing (NLP) has in recent years obtained considerable improvements with the addition of multiple new datasets and models. However, at present, there is no coherent framework for applying state-of-the-art models for Danish. We present DaCy: a unified framework for Danish NLP built on SpaCy. DaCy uses efficient multitask models which obtain state-of-the-art performance on named entity recognition, part-of-speech tagging, and dependency parsing. DaCy contains tools for easy integration of existing models such as for polarity, emotion, or subjectivity detection. In addition, we conduct a series of tests for biases and robustness of Danish NLP pipelines through augmentation of the test set of DaNE. DaCy large compares favorably and is especially robust to long input lengths and spelling variations and errors. All models except DaCy large display significant biases related to ethnicity while only Polyglot shows a significant gender bias. We argue that for languages with limited benchmark sets, data augmentation can be particularly useful for obtaining more realistic and fine-grained performance estimates. We provide a series of augmenters as a first step towards a more thorough evaluation of language models for low and medium resource languages and encourage further development.
A Sublinear Bound on the Page Number of Upward Planar Graphs

The page number of a directed acyclic graph $G$ is the minimum $k$ for which there is a topological ordering of $G$ and a $k$-coloring of the edges such that no two edges of the same color cross, i.e., have alternating endpoints along the topological ordering. We address the long-standing open problem asking for the largest page number among all upward planar graphs. We improve the best known lower bound to $5$ and present the first asymptotic improvement over the trivial $O(n)$ upper bound, where $n$ denotes the number of vertices in $G$. Specifically, we first prove that the page number of every upward planar graph is bounded in terms of its width, as well as its height. We then combine both approaches to show that every $n$-vertex upward planar graph has page number $O(n^{2/3} \log(n)^{2/3})$.
Prb-GAN: A Probabilistic Framework for GAN Modelling

Generative adversarial networks (GANs) are very popular to generate realistic images, but they often suffer from the training instability issues and the phenomenon of mode loss. In order to attain greater diversity in GAN synthesized data, it is critical to solving the problem of mode loss. Our work explores probabilistic approaches to GAN modelling that could allow us to tackle these issues. We present Prb-GANs, a new variation that uses dropout to create a distribution over the network parameters with the posterior learnt using variational inference. We describe theoretically and validate experimentally using simple and complex datasets the benefits of such an approach. We look into further improvements using the concept of uncertainty measures. Through a set of further modifications to the loss functions for each network of the GAN, we are able to get results that show the improvement of GAN performance. Our methods are extremely simple and require very little modification to existing GAN architecture.
Magnetic photocurrents in multifold Weyl fermions

We examine the magneto-optical response of chiral multifold fermions. Specifically, we show that they are ideal candidates for observing the Helical Magnetic Effect (HME) previously predicted for simple Weyl fermions. Unlike Weyl fermions, the HME is present in multifold fermions even in the simplest case where the low-energy dispersion is linear and spherically symmetric. In this ideal case, we derive an analytical expression for the HME and find it is proportional to the circular photogalvanic effect; for realistic parameters and accounting for the geometry of the setup, the HME photocurrent could be roughly the same order of magnitude as the circular photogalvanic effect observed in multifold fermions. Additional non-linear and symmetry-breaking terms will ruin the quantization but not hurt the observation of the HME.
Radiative corrections to 
τ→π(K)ντ[γ]
: A reliable new physics test

M. A. Arroyo-Ureña, G. Hernández-Tomé, G. López-Castro, P. Roig, and I. Rosell 1 Departamento de F́ısica, Centro de Investigación y de Estudios Avanzados del Instituto Politécnico Nacional, Apartado Postal 14-740, 07000 Ciudad de México, México and 2 Departamento de Matemáticas, F́ısica y Ciencias Tecnológicas, Universidad Cardenal Herrera-CEU, CEU Universities, 46115 Alfara del Patriarca, València, Spain
Unconditionally stable exponential time differencing schemes for the mass‐conserving Allen–Cahn equation with nonlocal and local effects

It is well known that the classic Allen–Cahn equation satisfies the maximum bound principle (MBP), that is, the absolute value of its solution is uniformly bounded for all time by certain constant under suitable initial and boundary conditions. In this paper, we consider numerical solutions of the modified Allen–Cahn equation with a Lagrange multiplier of nonlocal and local effects, which not only shares the same MBP as the original Allen–Cahn equation but also conserves the mass exactly. We reformulate the model equation with a linear stabilizing technique, then construct first‐ and second‐order exponential time differencing schemes for its time integration. We prove the unconditional MBP preservation and mass conservation of the proposed schemes in the time discrete sense and derive their error estimates under some regularity assumptions. Various numerical experiments in two and three dimensions are also conducted to verify the theoretical results.
Learn from Anywhere: Rethinking Generalized Zero-Shot Learning with Limited Supervision

A common problem with most zero and few-shot learning approaches is they suffer from bias towards seen classes resulting in sub-optimal performance. Existing efforts aim to utilize unlabeled images from unseen classes (i.e transductive zero-shot) during training to enable generalization. However, this limits their use in practical scenarios where data from target unseen classes is unavailable or infeasible to collect. In this work, we present a practical setting of inductive zero and few-shot learning, where unlabeled images from other out-of-data classes, that do not belong to seen or unseen categories, can be used to improve generalization in any-shot learning. We leverage a formulation based on product-of-experts and introduce a new AUD module that enables us to use unlabeled samples from out-of-data classes which are usually easily available and practically entail no annotation cost. In addition, we also demonstrate the applicability of our model to address a more practical and challenging, Generalized Zero-shot under a limited supervision setting, where even base seen classes do not have sufficient annotated samples.
The fundamentals of the operation of polymer electrolyte membrane fuel cells under dry and flooded conditions with an efficient approach to the management of liquid water

Polymer electrolyte membrane (PEM) fuel cells are power conversion devices that have the potential to become an important part of a distributed energy portfolio independent of fossil fuels. In order to improve their performance and make their mainstream use possible, the fundamental physics of PEM fuel cell operation must be thoroughly understood. Included in this is the effect of the water balance inside the fuel cell. Either too little water or an excess leading to liquid water accumulation can cause fuel cell failure. Several factors affect the water balance, some inherent in fuel cells and others determined by the system design. The tool used to study fuel cell operation was the Segmented Anode Parallel Channel (SAPC) fuel cell. Transparent flow channels and a segmented anode allowed for direct visualization of liquid water and measurement of the current distribution along the flow channel. Under dry conditions, hydration fronts were observed to propagate along the flow channel as the fuel cell ignited or extinguished. Under wet conditions, liquid water accumulation observed in the flow channel created areas of local reactant starvation. Each segment of the SAPC fuel cell was modeled as a differential element, coupled together electrically and through the flux of the reaction components. The design of the SAPC fuel cell was very simple. The fuel cell was allowed to operate autonomously without potentiostatic or galvanostatic controllers that hide much of the physics of fuel cell operation. The setup and experimental results elucidated the importance of flow pattern, temperature, load resistance, flow channel orientation with respect to gravity, gas diffusion layer material, flow rates, and flow field construction. Each of these factors changed how much water was removed as a vapor or how severely
A Weakly-Supervised Depth Estimation Network Using Attention Mechanism

Monocular depth estimation (MDE) is a fundamental task in many applications such as scene understanding and reconstruction. However, most of the existing methods rely on accurately labeled datasets. A weakly-supervised framework based on attention nested U-net (ANU) named as ANUW is introduced in this paper for cases with wrong labels. The ANUW is trained end-to-end to convert an input single RGB image into a depth image. It consists of a dense residual network structure, an adaptive weight channel attention (AWCA) module, a patch second non-local (PSNL) module and a soft label generation method. The dense residual network is the main body of the network to encode and decode the input. The AWCA module can adaptively adjust the channel weights to extract important features. The PSNL module implements the spatial attention mechanism through a second-order non-local method. The proposed soft label generation method uses the prior knowledge of the dataset to produce soft labels to replace false ones. The proposed ANUW is trained on a defective monocular depth dataset and the trained model is tested on three public datasets, and the results demonstrate the superiority of ANUW in comparison with the state-of-the-art MDE methods.
The Effect of Wind and Air Temperature on Metabolism and Evaporative Water Loss Rates of Dark-Eyed Juncos, Junco hyemalis: A Standard Operative Temperature Scale

Because wind significantly increases heat loss from birds, its effects must be incorporated in studies of behavior, habitat selection, and ecological energetics. We studied dark-eyed juncos (Junco hyemalis) exposed to air temperatures (Ta) from −10 ° C to 40° C and wind speeds of 0.1, 0.64, 1.96, and 3 m/s. We measured O₂ consumption, CO₂ production, and evaporative water loss (EWL). Metabolic rate M increased with decreasing temperature and increasing wind speed for Ta < 22.5° C. As Ta dropped from 22.5° C to 15° C, M increased rapidly. The rate of increase slowed as Ta dropped below 15° C. Metabolic rate was independent of Ta and wind from 22.5° C to 37.5° C and appeared to increase at 40° C. The ratio of EWL to M increased with temperature, but the increase was less marked than that of the standard avian curve. Overall thermal conductance decreased as Ta decreased from 37° to 0° C. We divided the data set and used one part to define and the other to test an operative temperature scale (Tes) for juncos. The Tes scale eliminated wind effects on M, and data for all wind speeds fell on a common curve. A generalized passerine Ts scale was somewhat less effective. The variation of M among individuals had a standard deviation of about 0.065 W. This is equal to the difference between metabolic rates in sheltered versus unsheltered roosts. We found evidence that 15%–20% of the individuals may have increased metabolic rate in response to fan noise or wind force at thermal neutral temperatures.
Organo-Clays As Sorbents of Hydrophobic Organic Contaminants: Sorptive Characteristics and Approaches to Enhancing Sorption Capacity

When clay minerals, notably smectites, intercalate organic cations, their interlayer surfaces change from hydrophilic to hydrophobic. The resultant intercalates, known as organo-clays (OCs), have a large affinity for hydrophobic organic contaminants (HOCs). Organo-clays are used as sorbents of HOCs in wastewater treatment and as sorptive barriers in landfill liners. The structural and sorptive characteristics of OCs with respect to HOCs have been studied extensively, and a large volume of literature has accumulated over the past few decades. The interactions of OCs with HOCs and the various approaches to improving the sorption capacity of OCs are reviewed here, with particular reference to the application of novel analytical techniques, such as molecular modeling, to characterizing the OC—HOC interaction.
Structured Directional Pruning via Perturbation Orthogonal Projection

Structured pruning is an effective compression technique to reduce the computation of neural networks, which is usually achieved by adding perturbations to reduce network parameters at the cost of slightly increasing training loss. A more reasonable approach is to find a sparse minimizer along the flat minimum valley found by optimizers, i.e. stochastic gradient descent, which keeps the training loss constant. To achieve this goal, we propose the structured directional pruning based on orthogonal projecting the perturbations onto the flat minimum valley. We also propose a fast solver sDprun and further prove that it achieves directional pruning asymptotically after sufficient training. Experiments using VGG-Net and ResNet on CIFAR-10 and CIFAR-100 datasets show that our method obtains the state-of-the-art pruned accuracy (i.e. 93.97% on VGG16, CIFAR-10 task) without retraining. Experiments using DNN, VGG-Net and WRN28X10 on MNIST, CIFAR-10 and CIFAR-100 datasets demonstrate our method performs structured directional pruning, reaching the same minimum valley as the optimizer.
Experimental and theoretical characterization of a non-equilibrium steady state of a periodically driven qubit

Periodically driven dynamics of open quantum systems is very interesting because typically non-equilibrium steady state is reached, which is characterized by a non-vanishing current. In this work, we study time discrete and periodically driven dynamics experimentally for a single photon that its coupled to its environment. We develop a comprehensive theory which explains the experimental observations and offers an analytical characterization of the non-equilibrium steady states of the system. We demonstrate that the periodic driving and the properties of the environment can be engineered in such a way that there is asymptotically non-vanishing bidirectional information flow between the open system and the environment.
Lifting of the Action of Symplectomorphism Group on the Bi-Lagrangian Structure

We consider a smooth manifold M endowed of a bi-Lagrangian structure (ω,F1,F2). That is, ω is a symplectic form and (F1,F2) is a pair of transversal Lagrangian foliations on M . Such structure has an important geometric object called Hess Connection. In this work, we show that a class of bi-Lagrangian structures on M can be lifted as a bi-Lagrangian structure on its cotangent bundle T ∗M . We define a dynamic on symplectomorphism group and the set of bi-Lagrangian structures (that is an action of symplectomorphism group on the set of biLagrangian structures). This dynamic is compatible with Hess connections. We lift on T ∗M a dynamic consisting of the action of symplectomorphism group on the set of lifted bi-Lagrangian structures. This lifting coincides with the initial dynamic on T ∗M at least on some bi-Lagrangian structures of T ∗M .
Playing Angry Birds with a Domain-Independent PDDL+ Planner

This demo paper presents the first system for playing the popular Angry Birds game using a domain-independent planner. Our system models Angry Birds levels using PDDL+, a planning language for mixed discrete/continuous domains. It uses a domain-independent PDDL+ planner to generate plans and executes them. In this demo paper, we present the system's PDDL+ model for this domain, identify key design decisions that reduce the problem complexity, and compare the performance of our system to model-specific methods for this domain. The results show that our system's performance is on par with other domain-specific systems for Angry Birds, suggesting the applicability of domain-independent planning to this benchmark AI challenge.
TTAN: Two-Stage Temporal Alignment Network for Few-shot Action Recognition

Few-shot action recognition aims to recognize novel action classes (query) using just a few samples (support). The majority of current approaches follow the metric learning paradigm, which learns to compare the similarity between videos. Recently, it has been observed that directly measuring this similarity is not ideal since different action instances may show distinctive temporal distribution, resulting in severe misalignment issues across query and support videos. In this paper, we arrest this problem from two distinct aspects – action duration misalignment and motion evolution misalignment. We address them sequentially through a Two-stage Temporal Alignment Network (TTAN). The first stage performs temporal transformation with the predicted affine warp parameters, while the second stage utilizes a cross-attention mechanism to coordinate the features of the support and query to a consistent evolution. Besides, we devise a novel multi-shot fusion strategy, which takes the misalignment among support samples into consideration. Ablation studies and visualizations demonstrate the role played by both stages in addressing the misalignment. Extensive experiments on benchmark datasets show the potential of the proposed method in achieving state-of-the-art performance for few-shot action recognition.
Ammonia elimination from protonated nucleobases

Chapter 1: The dediazoniations of the diazonium ion 1 of 4-aminopyrimidine and of the tautomeric cytosinediazonium ions 2 and 3 are facile and result in the formations of cations 4, 5, and 6. The pyrimidine ring-opening of 4, 5 and 6 form their acyclic isomers 7 – 9, respectively. The stability of (E)and (Z)-isomers is studied. Chapter 2: The results are discussed of mass-spectrometric studies of the nucleobases adenine 1h (1, R = H), guanine 2h, and cytosine 3h. The protonated nucleobases are generated by electrospray ionization of adenosine 1r (1, R = ribose), guanosine 2r, and deoxycytidine 3d (3, R = deoxyri-bose) and their fragmentations are studied with tandem mass spectrometry. Possible NH3 elimination fragmentation paths for all the ions are given. Chapter 3: The conformational and isomer preferences of cyanoamine 1 and carbodiimide 2, their conjugate acids and the formation of isoguanosine are discussed. Possible NH3 elimination paths from the protonated cyanoamine 1 and carbodiimide 2 are studied.
Choosing Imputation Models

Abstract Imputing missing values is an important preprocessing step in data analysis, but the literature offers little guidance on how to choose between imputation models. This letter suggests adopting the imputation model that generates a density of imputed values most similar to those of the observed values for an incomplete variable after balancing all other covariates. We recommend stable balancing weights as a practical approach to balance covariates whose distribution is expected to differ if the values are not missing completely at random. After balancing, discrepancy statistics can be used to compare the density of imputed and observed values. We illustrate the application of the suggested approach using simulated and real-world survey data from the American National Election Study, comparing popular imputation approaches including random forests, hot-deck, predictive mean matching, and multivariate normal imputation. An R package implementing the suggested approach accompanies this letter.
Rayleigh–Bénard–Marangoni Cellular Convection: Expressions for Heat and Mass Transfer Rates

Cellular convection caused by Rayleigh and Marangoni instability can greatly enhance the mass and heat transfer rate across the interface between fluids. Expressions for the liquid-phase mass and heat transfer rate enhanced by cellular convection were established, employing the theory of second order and first order non-equilibrium phase transitions for roll convection and hexagonal convection, respectively. These expressions can be used for cases where initially unperturbed concentration or temperature profiles can be described by a film model. The validity of the expressions was tested using heat transfer data measured by previous authors.