Miracle-dz/UniFAR-FTUs huggingface dataset:
An Attention-Based Model for Predicting Contextual Informativeness and Curriculum Learning Applications

Both humans and machines learn the meaning of unknown words through contextual information in a sentence, but not all contexts are equally helpful for learning. We introduce an effective method for capturing the level of contextual informativeness with respect to a given target word. Our study makes three main contributions. First, we develop models for estimating contextual informativeness, focusing on the instructional aspect of sentences. Our attention-based approach using pre-trained embeddings demonstrates state-of-the-art performance on our single-context dataset and an existing multi-sentence context dataset. Second, we show how our model identifies key contextual elements in a sentence that are likely to contribute most to a reader's understanding of the target word. Third, we examine how our contextual informativeness model, originally developed for vocabulary learning applications for students, can be used for developing better training curricula for word embedding models in batch learning and few-shot machine learning settings. We believe our results open new possibilities for applications that support language learning for both human and machine learners
Giant component of the soft random geometric graph

Consider a 2-dimensional soft random geometric graph G ( λ, s, φ ), obtained by placing a Poisson( λs 2 ) number of vertices uniformly at random in a square of side s , with edges placed between each pair x, y of vertices with probability φ ( (cid:107) x − y (cid:107) ), where φ : R + → [0 , 1] is a ﬁnite-range connection function. This paper is concerned with the asymptotic behaviour of the graph G ( λ, s, φ ) in the large- s limit with ( λ, φ ) ﬁxed. We prove that the proportion of vertices in the largest component converges in probability to the percolation probability for the corresponding random connection model, which is a random graph deﬁned similarly for a Poisson process on the whole plane. We do not cover the case where λ equals the critical value λ c ( φ ).
The Maximum Spectral Radius of Non-Bipartite Graphs Forbidding Short Odd Cycles

It is well-known that eigenvalues of graphs can be used to describe structural properties and parameters of graphs. A theorem of Nosal and Nikiforov states that if $G$ is a triangle-free graph with $m$ edges, then $\lambda (G)\le \sqrt{m}$, equality holds if and only if $G$ is a complete bipartite graph. Recently, Lin, Ning and Wu [Combin. Probab. Comput. 30 (2021)] proved a generalization for non-bipartite triangle-free graphs. Moreover, Zhai and Shu [Discrete Math. 345 (2022)] presented a further improvement. In this paper, we present an alternative method for proving the improvement by Zhai and Shu. Furthermore, the method can allow us to give a refinement on the result of Zhai and Shu for non-bipartite graphs without short odd cycles.
Multi-task recommendation system for scientific papers with high-way networks

Finding and selecting the most relevant scientific papers from a large number of papers written in a research community is one of the key challenges for researchers these days. As we know, much information around research interest for scholars and academicians belongs to papers they read. Analysis and extracting contextual features from these papers could help us to suggest the most related paper to them. In this paper, we present a multi-task recommendation system (RS) that predicts a paper recommendation and generates its meta-data such as keywords. The system is implemented as a three-stage deep neural network encoder that tries to maps longer sequences of text to an embedding vector and learns simultaneously to predict the recommendation rate for a particular user and the paper's keywords. The motivation behind this approach is that the paper's topics expressed as keywords are a useful predictor of preferences of researchers. To achieve this goal, we use a system combination of RNNs, Highway and Convolutional Neural Networks to train end-to-end a context-aware collaborative matrix. Our application uses Highway networks to train the system very deep, combine the benefits of RNN and CNN to find the most important factor and make latent representation. Highway Networks allow us to enhance the traditional RNN and CNN pipeline by learning more sophisticated semantic structural representations. Using this method we can also overcome the cold start problem and learn latent features over large sequences of text.
Pixel2Mesh++: 3D Mesh Generation and Refinement From Multi-View Images

We study the problem of shape generation in 3D mesh representation from a small number of color images with or without camera poses. While many previous works learn to hallucinate the shape directly from priors, we adopt to further improve the shape quality by leveraging cross-view information with a graph convolution network. Instead of building a direct mapping function from images to 3D shape, our model learns to predict series of deformations to improve a coarse shape iteratively. Inspired by traditional multiple view geometry methods, our network samples nearby area around the initial mesh’s vertex locations and reasons an optimal deformation using perceptual feature statistics built from multiple input images. Extensive experiments show that our model produces accurate 3D shapes that are not only visually plausible from the input perspectives, but also well aligned to arbitrary viewpoints. With the help of physically driven architecture, our model also exhibits generalization capability across different semantic categories, and the number of input images. Model analysis experiments show that our model is robust to the quality of the initial mesh and the error of camera pose, and can be combined with a differentiable renderer for test-time optimization.
Magnetic phase transition induced ferroelectric polarization in 
BaFeF4
 with room-temperature weak ferromagnetism

Ba M F 4 ( M =Fe, Co, Ni and Mn) family are typical multiferroic materials, having antiferromagnetism at around liquid nitrogen temperature. In this work, polycrystalline BaFeF 4 has been prepared by solid state reaction. The slight deficiency of Fe leads to the coexistence of valence states of +2 and +3, facilitating the electrons to hop between the neighboring Fe 2+ and Fe 3+ ions through the middle F - ion, leading to the strong double exchange interaction with weak ferromagnetism above room temperature. A bifurcation at about 170 K between the zero-field-cooled and field-cooled temperature dependent magnetization curves indicates the onset of 2-dimensional antiferromagnetism, which is completed at about 125 K with the sudden drop of magnetization. Despite the fact of type-I multiferroic, its magnetoelectricity can be evidenced by the pyroelectric current, which shows a peak starting at about 170 K and finishing at about 125 K. The saturated ferroelectric polarization change of around 34 μC/m 2 is observed, which is switchable by the reversed poling electric field and decreases to about 30 μC/m 2 under a magnetic field of 90 kOe. This magnetoelectricity can be qualitatively reproduced by first-principles calculations. Our results represent substantial progress to search for high-temperature multiferroics in ferroelectric fluorides.
A Masked Image Reconstruction Network for Document-level Relation Extraction

Document-level relation extraction aims to extract relations among entities within a document. Compared with its sentence-level counterpart, Document-level relation extraction requires inference over multiple sentences to extract complex relational triples. Previous research normally complete reasoning through information propagation on the mention-level or entity-level document-graphs, regardless of the correlations between the relationships. In this paper, we propose a novel Document-level Relation Extraction model based on a Masked Image Reconstruction network (DRE-MIR), which models inference as a masked image reconstruction problem to capture the correlations between relationships. Specifically, we first leverage an encoder module to get the features of entities and construct the entity-pair matrix based on the features. After that, we look on the entity-pair matrix as an image and then randomly mask it and restore it through an inference module to capture the correlations between the relationships. We evaluate our model on three public document-level relation extraction datasets, i.e. DocRED, CDR, and GDA. Experimental results demonstrate that our model achieves state-of-the-art performance on these three datasets and has excellent robustness against the noises during the inference process.
Type IIB S-folds: flat deformations, holography and stability

We review recent progress in the study of S-folds in light of the gauge/gravity duality and the AdS swampland conjecture. S-folds correspond to non-geometric backgrounds of type IIB supergravity of the form AdS 4 × S 1 × M that involve a non-trivial SL ( 2 , Z ) (S-duality) monodromy for the type IIB ﬁelds when moving around the S 1 . We present four such solutions with M = S 5 that preserve N = 4 , 2 , 1 , 0 supersymmetries. Via the AdS/CFT correspondence, these solutions are conjectured to describe new strongly coupled three-dimensional CFT’s on a localised interface of SYM. We discuss the existence of ﬂat deformations in the gravity side dual to marginal deformations of the conjectured S-fold CFT’s. From a geometrical perspective, the ﬂat deformations induce a monodromy ℎ on M and replace S 1 × M by the so-called mapping torus 𝑇 (M) ℎ . Interestingly, the ﬂat deformations provide a controlled mechanism of supersymmetry breaking for N ≥ 2 S-folds. We present a class of such non-supersymmetric S-folds obtained by ﬂat-deforming the N = 4 S-fold and discuss their (non-)perturbative stability.
Manifestation of work-life balance in the Malaysian banking workforce: Transformational leadership the potent enabler

Baby Boomers are soon reaching their retirement and the workforce vacancies will inevitably occupied by the Xers and Millennials. The two younger cohorts are drastically different from their senior echelon where work-life balance is of utmost important to them. Transformational leadership is claimed as the potent enabler to the practice of work-life balance. Consequently, this study aimed to examine the extent of the relationship between the two. Questionnaires were distributed to two domestic banks. Findings reported the relationship between transformational leadership and work-life balance. No differences, however, are shown between demographic profiles of gender, marital status and age about work-life balance. © 2016. The Authors. Published for AMER ABRA by e-International Publishing House, Ltd., UK. This is an open access article under the CC BY-NC-ND license ( http://creative commons.org/licenses/by-nc-nd/4.0/). Peer–review under responsibility of AMER (Association of Malaysian Environment-Behaviour Researchers), ABRA (Association of Behavioural Researchers on Asians) and cE-Bs (Centre for Environment-Behaviour Studies), Faculty of Architecture, Planning & Surveying, Universiti Teknologi MARA, Malaysia. Keywords: Work-life balace; Transformational leadership; Banking Baby Boomers are soon reaching their retirement and the workforce vacancies will inevitably occupied by the Xers and Millennials. The two younger cohorts are drastically different from their senior echelon where work-life balance is of utmost important to them. Transformational leadership is claimed as the potent enabler to the practice of work-life balance. Consequently, this study aimed to examine the extent of the relationship between the two. Questionnaires were distributed to two domestic banks. Findings reported the relationship between transformational leadership and work-life balance. No differences, however, are shown between demographic profiles of gender, marital status and age about work-life balance. © 2016. The Authors. Published for AMER ABRA by e-International Publishing House, Ltd., UK. This is an open access article under the CC BY-NC-ND license ( http://creative commons.org/licenses/by-nc-nd/4.0/). Peer–review under responsibility of AMER (Association of Malaysian Environment-Behaviour Researchers), ABRA (Association of Behavioural Researchers on Asians) and cE-Bs (Centre for Environment-Behaviour Studies), Faculty of Architecture, Planning & Surveying, Universiti Teknologi MARA, Malaysia. Keywords: Work-life balace; Transformational leadership; Banking
Stability, Linear Convergence, and Robustness of the Wang-Elia Algorithm for Distributed Consensus Optimization

We revisit an algorithm for distributed consensus optimization proposed in 2010 by J. Wang and N. Elia. By means of a Lyapunov-based analysis, we prove input-to-state stability of the algorithm relative to a closed invariant set composed of optimal equilibria and with respect to perturbations affecting the algorithm’s dynamics. In the absence of perturbations, this result implies linear convergence of the local estimates and Lyapunov stability of the optimal steady state. Moreover, we unveil fundamental connections with the wellknown Gradient Tracking and with distributed integral control. Overall, our results suggest that a control theoretic approach can have a considerable impact on (distributed) optimization, especially when robustness is considered.
A Revealing Large-Scale Evaluation of Unsupervised Anomaly Detection Algorithms

Anomaly detection has many applications ranging from bank-fraud detection and cyber-threat detection to equipment maintenance and health monitoring. However, choosing a suitable algorithm for a given application remains a challenging design decision, often informed by the literature on anomaly detection algorithms. We extensively reviewed twelve of the most popular unsupervised anomaly detection methods. We observed that, so far, they have been compared using inconsistent protocols - the choice of the class of interest or the positive class, the split of training and test data, and the choice of hyperparameters - leading to ambiguous evaluations. This observation led us to define a coherent evaluation protocol which we then used to produce an updated and more precise picture of the relative performance of the twelve methods on five widely used tabular datasets. While our evaluation cannot pinpoint a method that outperforms all the others on all datasets, it identifies those that stand out and revise misconceived knowledge about their relative performances.
Linear-quadratic stochastic Volterra controls II: Optimal strategies and Riccati--Volterra equations

In this paper, we study linear-quadratic control problems for stochastic Volterra integral equations with singular and non-convolution-type coefficients. The weighting matrices in the cost functional are not assumed to be non-negative definite. From a new viewpoint, we formulate a framework of causal feedback strategies. The existence and the uniqueness of a causal feedback optimal strategy are characterized by means of the corresponding Riccati--Volterra equation.
Computationally Efficient and Statistically Optimal Robust Low-rank Matrix and Tensor Estimation

Low-rank matrix estimation under heavy-tailed noise is challenging, both computationally and statistically. Convex approaches have been proven statistically optimal but suffer from high computational costs, especially since robust loss functions are usually non-smooth. More recently, computationally fast non-convex approaches via sub-gradient descent are proposed, which, unfortunately, fail to deliver a statistically consistent estimator even under sub-Gaussian noise. In this paper, we introduce a novel Riemannian sub-gradient (RsGrad) algorithm which is not only computationally efficient with linear convergence but also is statistically optimal, be the noise Gaussian or heavy-tailed. Convergence theory is established for a general framework and specific applications to absolute loss, Huber loss, and quantile loss are investigated. Compared with existing non-convex methods, ours reveals a surprising phenomenon of dual-phase convergence. In phase one, RsGrad behaves as in a typical non-smooth optimization that requires gradually decaying stepsizes. However, phase one only delivers a statistically sub-optimal estimator which is already observed in the existing literature. Interestingly, during phase two, RsGrad converges linearly as if minimizing a smooth and strongly convex objective function and thus a constant stepsize suffices. Underlying the phase-two convergence is the smoothing effect of random noise to the non-smooth robust losses in an area close but not too close to the truth. Lastly, RsGrad is applicable for low-rank tensor estimation under heavy-tailed noise where a statistically optimal rate is attainable with the same phenomenon of dual-phase convergence, and a novel shrinkage-based second-order moment method is guaranteed to deliver a warm initialization. Numerical simulations confirm our theoretical discovery and showcase the superiority of RsGrad over prior methods.
Loblolly Pine Plantations in Southeastern US.

Intensively managed loblolly pine stands are often subjected to a variety of silvicultural treatments at time of planting or shortly thereafter. However, most loblolly pine growth-and-yield models predict growth after crown closure has occurred. In this article, we describe the development and implementation of a system of equations designed to simu- late growth of loblolly pine before the onset of intraspecific competition. Treatment re- sponse functions were also developed for various site pre'paration, herbaceous weed con- trol, and fertilization practices. These functions modify the baseline model predictions to simulate the effects of treatments on tree growth and stand development. This system was incorporated into the PTAEDAZ growth-and-yield simulator to make growth projections from time of planting through rotation age for intensively managed stands of loblolly pine in the southeastern United States. Growth simulations specifying either bedding or disking showed increases in volume growth for both treatments. The shear-and-pile site-preparation method reduced growth rates slightly. Volume growth responses to herbaceous weed control were positive, with magnitude of increase largely depending on duration of treat- ment. Increases in volume attributable to fertilization at planting were related to types of element applied. The largest predicted response was for phosphorus fertilization, whereas application of potassium was the least effective fertilization treatment. Site preparation, weed control, and fertilization treatment combinations produced additional volume gains when compared to single treatments, while maintaining predicted values that are within biological limits for loblolly pine growth. Levels of response to treatments are consistent with results from published studies on intensive management practices. The addition of the precompetitive growth system did not significantly affect the predictive behavior of the PTAEDA2 model for untreated stands. FOR. SCI. 50(6):823-835.
Assessment of energy and environmental performances of a bioclimatic dwelling in Algeria's North

Bioclimatic architecture strategies and solar active systems contribute strongly to the reduction of building energy demand and achieving thermal comfort for its occupants over the whole year. This paper deals with the study of the energy performance improvement of a pilot bioclimatic house located in Algiers (Algeria). First, a series of experimental measures are conducted during cold period to show the effect of passive and active solar gains on the improvement of the indoor air temperature of the house. Then, a dynamic model of a solar heating system coupled with a bioclimatic house has been developed using TRNSYS software and validated with experimental data. The validated model has been used to establish the energy balance of the pilot bioclimatic house without solar heating system and to compare them to those of a conventional house. Finally, the improvement of the energy balance of the pilot bioclimatic house has been done by passive and active ways. The passive one includes the increase of south facing windows size and the use of night cooling with the use of shading device in summer. The active one consists of the integration of a solar heating system. Furthermore, an environmental study has been performed. The experimental results show that the energy requirements of a pilot bioclimatic house are very low which is suitable for the use of solar heating system in building. The simulation results show that the application of bioclimatic strategies is a better way to provide thermal comfort in summer and decrease the space heating energy demand of the house with 48.70%. The active solar system will cover 67.74% of the energy demand for heating of the house. These energy savings generate a significant reduction in CO2 emissions. Practical application : This work will enable engineers and designers of modern buildings of buildings in a Mediterranean climate to improve building energy efficiency and reduce CO2 emissions by a conjunction of different passive heating and cooling techniques such as insulation, thermal mass, window shades, night ventilation, and the solar heating system. The paper provides designers an effective strategy in terms of energy savings and indoor thermal comfort while reducing CO2 emissions.
Social reproduction and pandemic neoliberalism: Planetary crises and the reorganisation of life, work and death

This article portrays the COVID-19 pandemic as a planetary crisis of capitalist life and analyses it through the feminist political economy lens of social reproduction. Celebrating the plurality and distinctiveness of social reproduction theorisations, the article deploys three approaches to map the contours of the present conjuncture; namely Social Reproduction Theory, Early Social Reproduction Analyses and Raced Social Reproduction approaches. These provide key complementary insights over the planetary crisis and reorganisation of life, work and death triggered by the pandemic. Through the compounded insights of social reproduction theorisations, the article argues that the pandemic does not represent a crisis of neoliberalism. Rather, it represents its outcome, and deepening of its logics, an argument which is substantiated by exploring the impact of COVID-19 on the reproductive architecture of neoliberal capitalism; on the world of work; and on racialised processes manufacturing different kinds of surplus subjects. In conclusion, the article discusses the political implications of this social reproduction-centred reading of the pandemic for a progressive post-pandemic politics to move beyond pandemic neoliberalism.
Revisiting Ideas of Power in Southeast Asia

ABSTRACT The five essays in this collection examine ideas of power in Southeast Asia and reflect on foundational studies by Lucien Hanks and Benedict Anderson on Thailand and Indonesia. The essays by Hanks and Anderson crossed anthropology and area studies. The cases explore the relevance of their work in Southeast Asia and comparatively, in relation to academic consensus and debate, social entanglements, political divergence, negotiation, pluralism, and reflexivity. The essays suggest that ‘the state’ is a problematic notion, and that the common conflation of society and the state hides a range of tensions between the rival principles of hierarchy and community. We examine power in relation to socialism, hierarchy, indigeneity, Buddhism, marginalisation, and nonstate identities, through fieldwork encounters as much as through historical and regional contrasts and comparisons on the ethnic frontiers of the modern nations of Indonesia, Laos, and Thailand.