Miracle-dz/UniFAR-FTUs huggingface dataset:
Numerics for Diﬀerent Bases in Certifying Nonnegativity of Polynomials

Polynomial optimization is widely applicatible in solving both practical and theoretical compu-tation problem. One particularly successful way to solve the polynomial optimization certifying the nonnegativity of a polynomial using the sum of squares (SOS) certiﬁcate. In this thesis, we study how diﬀerent choices of the polynomial basis can aﬀect the numerical stability of the linear system generated when using Semideﬁnite programming (SDP) to test SOS. We run numerical experiments comparing the performance of monomial basis and diﬀerent types of orthogonal basis, and we discuss the diﬀerent trade-oﬀs between the choice two types of polynomial basis.
Experimental study on the skewed integral bridge by using crushed concrete geobags as scour protection

Department of Civil Engineering, University of Malaya, 50603 Kuala Lumpur, Malaysia Email: Shatirahakib@yahoo.co.uk Department of Civil Engineering, University of Malaya, 50603 Kuala Lumpur, Malaysia Department of Civil Engineering, University of Malaya, 50603 Kuala Lumpur, Malaysia Department of Civil Engineering, University of Malaya, 50603 Kuala Lumpur, Malaysia Department of Civil Engineering, University of Malaya, 50603 Kuala Lumpur, Malaysia
Metaphor Detection in Poems in Misurata Arabic Sub-Dialect : An Metaphor Detection in Poems in Misurata Arabic Sub-Dialect : An LSTM Model LSTM Model

Natural Language Processing (NLP) in Arabic is witnessing an increasing interest in investigating different topics in the field. One of the topics that have drawn attention is the automatic processing of Arabic figurative language. The focus in previous projects is on detecting and interpreting metaphors in comments from social media as well as phrases and/or headlines from news articles. The current project focuses on metaphor detection in poems written in the Misurata Arabic sub-dialect spoken in Misurata, located in the North African region. The dataset is initially annotated by a group of linguists, and their annotation is treated as the seed data used in the project. Moreover, the verses in the dataset are annotated by layman native speakers of the sub-dialect who are not acquainted with the rhetorical principles of this kind of poetry. The model applied in the project is built on the Long Short-Term Memory (LSTM) architecture. The aim is to compare the performance of the model to the performance of human annotators who are not experts in the Arabic figurative language used in poetry. The results show that the model outperforms the output provided by the human annotators and scores a higher score of 79%. In addition, the model scores an 80.7 % accuracy score in predicting metaphors from unseen blind data. Since Arabic sub-dialects are acquired as a native language, it becomes important to develop NLP models that can be trained on these informal varieties of Arabic in order to fulfill many tasks such as auto-correction, machine translation, dialogue systems, and sentiment analysis among others.
An integrated product service system modelling methodology with a case study of clothing industry

Product-service system (PSS) is a business model designed to help enterprises achieve servitisation. PSS not only considers service-oriented logic to increase product value but also supports enterprises with waste reduction. An increasing number of studies have emphasised PSS development. However, much of the literature remains focused on separate development phases, such as idea generation or a specific product or industry application. An integrative system modelling methodology designed to help companies formulate a complete scenario is absent. This study provides a methodology for developing a systematic and complete PSS strategy. It combines failure modes and effects and importance-performance analysis to identify potential customer needs. A service-product development matrix and a product-service life cycle analysis are developed. Following the service concepts generation, grey relational analysis and scenario analysis are utilised to evaluate the alternatives. A case study involving the clothing industry was conducted to test the proposed approach. Results indicate the methodology can help service providers identify existing problems and develop service alternatives systematically. These alternatives can then be evaluated and the scenario which achieves the highest profitability, is the most environmental friendly, and is the most social friendly can be identified.
Structural grid shell design with Islamic pattern topologies

S.B. Civil Engineering, Massachusetts Institute of Technology (2015) Submitted to the Department of Architecture in partial fulfillment of the requirements for the degree of Master of Science in Building Technology at the MASSACHUSETTS INSTITUTE OF TECHNOLOGY June 2017 © Noor K. Khouri, 2017. All rights reserved. The author hereby grants to MIT permission to reproduce and to distribute publicly paper and electronic copies of this thesis document in whole or in part in any medium now known or hereafter created.
Clusters Facing Competition: The Importance of External Linkages

Introduction, Elisa Giuliani, Roberta Rabellotti, Meine Pieter van Dijk Upgrading in global value chains: lessons from Latin American clusters, Carlo Pietrobelli and Roberta Rabellotti Making value chain governance work for the implementation of quality, labour and environmental standards: upgrading challenges in the footwear industry, Lizbeth Navas-Aleman and Luiza Bazan Cooperation and upgrading in 'small' global chains: Vietnamese and Danish garment firms, Henrik Schaumburg-Muller Challenges to Vietnamese firms in the global garment and textile value chain, John Thoburn, Khalid Nadvi, Chris Edwards and Markus Eberhardt Multinational subsidiaries and manufacturing clusters in Guangdong, China, Filip De Beule, Daniel van den Bulcke and Luodan Xu Lead firms and clusters in the North and in the South: a comparison of the aerospace industry in Montreal and Sao Jose dos Campos, Andrea Goldstein Technological learning in a Chilean wine cluster and its linkages with the national system of innovation, Elisa Giuliani How do local institutions contribute to fostering competitiveness of industrial clusters? the upgrading process in the Italian eyewear system, Francesca Gambarotto and Stefano Solari A life cycle model for the creation of national venture industries: comparing the U.S. and Israeli experiences, Gil Avnimelech, Martin Kenney and Morris Teubal Cluster governance in an emerging city-wide ICT cluster in Nanjing, China, Meine Pieter van Dijk and Quanscheng Wang Industrial clusters in Mexico, Alejandro Davila Flores Firms interaction and industrial development: a simulation model, Tommaso Ciarli and Marco Valente Clusters facing competition: some concluding remarks, Elisa Giuliani and Roberta Rabellotti Index.
Black-box Generalization of Machine Teaching

Hypothesis-pruning maximizes the hypothesis updates for active learning to find those desired unlabeled data. An inherent assumption is that this learning manner can derive those updates into the optimal hypothesis. However, its convergence may not be guaranteed well if those incremental updates are negative and disordered. In this paper, we introduce a black-box teaching hypothesis $h^\mathcal{T}$ employing a tighter slack term $\left(1+\mathcal{F}^{\mathcal{T}}(\widehat{h}_t)\right)\Delta_t$ to replace the typical $2\Delta_t$ for pruning. Theoretically, we prove that, under the guidance of this teaching hypothesis, the learner can converge into a tighter generalization error and label complexity bound than those non-educated learners who do not receive any guidance from a teacher:1) the generalization error upper bound can be reduced from $R(h^*)+4\Delta_{T-1}$ to approximately $R(h^{\mathcal{T}})+2\Delta_{T-1}$, and 2) the label complexity upper bound can be decreased from $4 \theta\left(TR(h^{*})+2O(\sqrt{T})\right)$ to approximately $2\theta\left(2TR(h^{\mathcal{T}})+3 O(\sqrt{T})\right)$. To be strict with our assumption, self-improvement of teaching is firstly proposed when $h^\mathcal{T}$ loosely approximates $h^*$. Against learning, we further consider two teaching scenarios: teaching a white-box and black-box learner. Experiments verify this idea and show better generalization performance than the fundamental active learning strategies, such as IWAL, IWAL-D, etc.
Multiclass-SGCN: Sparse Graph-Based Trajectory Prediction with Agent Class Embedding

Trajectory prediction of road users in real-world scenarios is challenging because their movement patterns are stochastic and complex. Previous pedestrian-oriented works have been successful in modelling the complex interactions among pedestrians, but fail in predicting trajectories when other types of road users are involved (e.g., cars, cyclists, etc.), because they ignore user types. Although a few recent works construct densely connected graphs with user label information, they suffer from superfluous spatial interactions and temporal dependencies. To address these issues, we propose Multiclass-SGCN, a sparse graph convolution network based approach for multi-class trajectory prediction that takes into consideration velocity and agent label information and uses a novel interaction mask to adaptively decide the spatial and temporal connections of agents based on their interaction scores. The proposed approach significantly outperformed state-of-the-art approaches on the Stanford Drone Dataset, providing more realistic and plausible trajectory predictions.
Brain-like combination of feedforward and recurrent network components achieves prototype extraction and robust pattern recognition

Associative memory has been a prominent candidate for the computation performed by the massively recurrent neocortical networks. Attractor networks implementing associative memory have offered mechanistic explanation for many cognitive phenomena. However, attractor memory models are typically trained using orthogonal or random patterns to avoid interference between memories, which makes them unfeasible for naturally occurring complex correlated stimuli like images. We approach this problem by combining a recurrent attractor network with a feedforward network that learns distributed representations using an unsupervised Hebbian-Bayesian learning rule. The resulting network model incorporates many known biological properties: unsupervised learning, Hebbian plasticity, sparse distributed activations, sparse connectivity, columnar and laminar cortical architecture, etc. We evaluate the synergistic effects of the feedforward and recurrent network components in complex pattern recognition tasks on the MNIST handwritten digits dataset. We demonstrate that the recurrent attractor component implements associative memory when trained on the feedforward-driven internal (hidden) representations. The associative memory is also shown to perform prototype extraction from the training data and make the representations robust to severely distorted input. We argue that several aspects of the proposed integration of feedforward and recurrent computations are particularly attractive from a machine learning perspective.
Understanding Physical Effects for Effective Tool-Use

We present a robot learning and planning framework that produces an effective tool-use strategy with the least joint efforts, capable of handling objects different from training. Leveraging a Finite Element Method (FEM)-based simulator that reproduces fine-grained, continuous visual and physical effects given observed tool-use events, the essential physical properties contributing to the effects are identified through the proposed Iterative Deepening Symbolic Regression (IDSR) algorithm. We further devise an optimal control-based motion planning scheme to integrate robot- and tool-specific kinematics and dynamics to produce an effective trajectory that enacts the learned properties. In simulation, we demonstrate that the proposed framework can produce more effective tool-use strategies, drastically different from the observed ones in two exemplar tasks.
TTS-by-TTS 2: Data-selective augmentation for neural speech synthesis using ranking support vector machine with variational autoencoder

Recent advances in synthetic speech quality have enabled us to train text-to-speech (TTS) systems by using synthetic corpora. However, merely increasing the amount of synthetic data is not always advantageous for improving training efficiency. Our aim in this study is to selectively choose synthetic data that are beneficial to the training process. In the proposed method, we first adopt a variational autoencoder whose posterior distribution is utilized to extract latent features representing acoustic similarity between the recorded and synthetic corpora. By using those learned features, we then train a ranking support vector machine (RankSVM) that is well known for effectively ranking relative attributes among binary classes. By setting the recorded and synthetic ones as two opposite classes, RankSVM is used to determine how the synthesized speech is acoustically similar to the recorded data. Then, synthetic TTS data, whose distribution is close to the recorded data, are selected from large-scale synthetic corpora. By using these data for retraining the TTS model, the synthetic quality can be significantly improved. Objective and subjective evaluation results show the superiority of the proposed method over the conventional methods.
NeRF, meet differential geometry!

Neural radiance ﬁelds, or NeRF, represent a breakthrough in the ﬁeld of novel view synthesis and 3D modeling of complex scenes from multi-view image collections. Numerous recent works have been focusing on making the models more robust, by means of regularization, so as to be able to train with possibly inconsistent and/or very sparse data. In this work, we scratch the surface of how differential geometry can provide regularization tools for robustly training NeRF-like models, which are modiﬁed so as to represent continuous and inﬁnitely differentiable functions. In particular, we show how these tools yield a direct mathematical formalism of previously proposed NeRF variants aimed at improving the performance in challenging conditions (i.e. RegNeRF). Based on this, we show how the same formalism can be used to natively encourage the regularity of surfaces (by means of Gaussian and Mean Curvatures) making it possible, for example, to learn surfaces from a very limited number of views.
Momentum Diminishes the Effect of Spectral Bias in Physics-Informed Neural Networks

Physics-informed neural network (PINN) algorithms have shown promising results in solving a wide range of problems involving partial differential equations (PDEs). However, they often fail to converge to desirable solutions when the target function contains high-frequency features, due to a phenomenon known as spectral bias. In the present work, we exploit neural tangent kernels (NTKs) to investigate the training dynamics of PINNs evolving under stochastic gradient descent with momentum (SGDM). This demonstrates SGDM significantly reduces the effect of spectral bias. We have also examined why training a model via the Adam optimizer can accelerate the convergence while reducing the spectral bias. Moreover, our numerical experiments have confirmed that wide-enough networks using SGDM still converge to desirable solutions, even in the presence of high-frequency features. In fact, we show that the width of a network plays a critical role in convergence.
Space and time transformations with a minimal length

Phenomenological studies of quantum gravity have proposed a modification of the commutator between position and momentum in quantum mechanics to introduce a minimal uncertainty in position in quantum mechanics. In the present work, we show the influence of space and time transformations in shaping quantities such as momentum, energy, and their relations with the generators of transformations. Thus, such an influence determines, among other aspects, the time evolution of a quantum system. In the exemplary case of Galilean transformations, the Schrödinger equation is identical to the ordinary case.
Neural Surface Reconstruction of Dynamic Scenes with Monocular RGB-D Camera

We propose Neural-DynamicReconstruction (NDR), a template-free method to recover high-fidelity geometry and motions of a dynamic scene from a monocular RGB-D camera. In NDR, we adopt the neural implicit function for surface representation and rendering such that the captured color and depth can be fully utilized to jointly optimize the surface and deformations. To represent and constrain the non-rigid deformations, we propose a novel neural invertible deforming network such that the cycle consistency between arbitrary two frames is automatically satisfied. Considering that the surface topology of dynamic scene might change over time, we employ a topology-aware strategy to construct the topology-variant correspondence for the fused frames. NDR also further refines the camera poses in a global optimization manner. Experiments on public datasets and our collected dataset demonstrate that NDR outperforms existing monocular dynamic reconstruction methods.
Tailoring the phase transition from topological superconductor to trivial superconductor induced by magnetic textures of a spin chain on a 
p
-wave superconductor

We theoretically investigate the phase transition from a non-trivial topological $p$-wave superconductor to a trivial $s$-wave like superconducting phase through a gapless phase, driven by different magnetic textures as an one-dimensional spin-chain impurity, e.g. Bloch-type, in-plane and out-of-plane N\'eel-type spin-chains etc. In our proposal, the chain of magnetic impurities is placed on a spin-triplet $p$-wave superconductor where we obtain numerically as well as analytically an effective $s$-wave like pairing due to spin rotation, resulting in gradual destruction of the Majorana zero modes present in the topological superconducting phase. In particular, when the impurity spins are antiferromagnetically aligned i.e. spiral wave vector $G_{s}=\pi$, the system becomes an effective $s$-wave superconductor without Majorana zero modes in the local density of states. The Shiba bands, on the other hand, formed due to the overlapping of Yu-Shiba-Rusinov states play a crucial role in this topological to trivial superconductor phase transition, confirmed by the sign change in the minigap within the Shiba bands. We also characterize this topological phase transition via gap closing and winding number analysis. Moreover, interference of the Shiba bands exhibiting oscillatory behavior within the superconducting gap, $-\Delta_{p}$ to $\Delta_{p}$, as a function of $G_{s}$, also reflects an important evidence for the formation of an effective $s$-wave pairing. Such oscillation is absent in the $p$-wave regime.
Topological data analysis and machine learning

ABSTRACT Topological data analysis refers to approaches for systematically and reliably computing abstract ‘shapes’ of complex data sets. There are various applications of topological data analysis in life and data sciences, with growing interest among physicists. We present a concise review of applications of topological data analysis to physics and machine learning problems in physics including the unsupervised detection of phase transitions. We finish with a preview of anticipated directions for future research. Graphical abstract
Goodput Maximization With Quantized Feedback in the Finite Blocklength Regime for Quasi-Static Channels

In this paper, we study a quantized feedback scheme to maximize the goodput of a finite blocklength communication scenario over a quasi-static fading channel. It is assumed that the receiver has perfect channel state information (CSI) and sends back the CSI to the transmitter over a resolution-limited error-free feedback channel. With this partial CSI, the transmitter is supposed to select the optimum transmission rate, such that it maximizes the overall goodput of the communication system. This problem has been studied for the asymptotic blocklength regime, however, no solution has so far been presented for finite blocklength. Here, we study this problem in two cases: with and without constraint on reliability. We first formulate the optimization problems and analytically solve them. Iterative algorithms that successfully exploit the system parameters for both cases are presented. It is shown that although the achievable maximum goodput decreases with shorter blocklengths and higher reliability requirements, significant improvement can be achieved even with coarsely quantized feedback schemes.
Learning Underrepresented Classes from Decentralized Partially Labeled Medical Images

Using decentralized data for federated training is one promising emerging research direction for alleviating data scarcity in the medical domain. However, in contrast to large-scale fully labeled data commonly seen in general object recognition tasks, the local medical datasets are more likely to only have images annotated for a subset of classes of interest due to high annotation costs. In this paper, we consider a practical yet under-explored problem, where underrepresented classes only have few labeled instances available and only exist in a few clients of the federated system. We show that standard federated learning approaches fail to learn robust multi-label classifiers with extreme class imbalance and address it by proposing a novel federated learning framework, FedFew. FedFew consists of three stages, where the first stage leverages federated self-supervised learning to learn class-agnostic representations. In the second stage, the decentralized partially labeled data are exploited to learn an energy-based multi-label classifier for the common classes. Finally, the underrepresented classes are detected based on the energy and a prototype-based nearest-neighbor model is proposed for few-shot matching. We evaluate FedFew on multi-label thoracic disease classification tasks and demonstrate that it outperforms the federated baselines by a large margin.
Custom Pretrainings and Adapted 3D-ConvNeXt Architecture for COVID Detection and Severity Prediction

Since COVID strongly aﬀects the respiratory system, lung CT scans can be used for the analysis of a patients health. We introduce an neural network for the prediction of the severity of lung damage and the detection of infection using three-dimensional CT-scans. Therefore, we adapt the recent ConvNeXt model to process three-dimensional data. Furthermore, we introduce diﬀerent pretraining methods speciﬁ-cally adjusted to improve the models ability to handle three-dimensional CT-data. In order to test the performance of our model, we participate in the 2nd COV19D Competition for severity prediction and infection detection.
Anomaly Detection in Echocardiograms with Dynamic Variational Trajectory Models

We propose a novel anomaly detection method for echocardiogram videos. The introduced method takes advantage of the periodic nature of the heart cycle to learn three variants of a variational latent trajectory model (TVAE). While the first two variants (TVAE-C and TVAE-R) model strict periodic movements of the heart, the third (TVAE-S) is more general and allows shifts in the spatial representation throughout the video. All models are trained on the healthy samples of a novel in-house dataset of infant echocardiogram videos consisting of multiple chamber views to learn a normative prior of the healthy population. During inference, maximum a posteriori (MAP) based anomaly detection is performed to detect out-of-distribution samples in our dataset. The proposed method reliably identifies severe congenital heart defects, such as Ebstein's Anomaly or Shone-complex. Moreover, it achieves superior performance over MAP-based anomaly detection with standard variational autoencoders when detecting pulmonary hypertension and right ventricular dilation. Finally, we demonstrate that the proposed method enables interpretable explanations of its output through heatmaps highlighting the regions corresponding to anomalous heart structures.