Miracle-dz/UniFAR-FTUs huggingface dataset:
Verbal autopsy analysis of childhood deaths in rural Gambia

Background In low-resource settings, it is challenging to ascertain the burden and causes of under-5 mortality as many deaths occur outside health facilities. Verbal autopsy (VA) is an important tool that provides data on causes of death in communities with limited access to health care. We aimed to determine the causes of childhood deaths by VA in rural Gambia. Methodology We used WHO standard questionnaires to conduct VAs for deaths under-5 years of age in the Basse and Fuladu West Health and Demographic Surveillance Systems in rural Gambia between September 01, 2019, and December 31, 2021. Two physicians assigned a cause of death and discordant diagnoses were resolved by consensus. Causes of death were classified using the International Classification of Disease 10th edition codes. Results VAs were conducted for 89% (647/727) of deaths. Of these deaths, 49.5% (n=319) occurred at home, 50.1% (n=324) in females, 37.1% (n=240) in neonates, and 27.1% (n=175) in infants aged 1-11 months. Outside the neonatal period, pneumonia (27.0%, n=110), diarrhoeal diseases (23.3%, n=95), and sepsis (21.6%, n=88) were the commonest primary causes of death. In the neonatal period, unspecified perinatal causes of death (29.6%, n=71), birth asphyxia (23.8%, n=57) and prematurity/low birth weight (17.1%, n=41) were the commonest causes. Severe malnutrition (28.6%, n=185), unspecified perinatal deaths (10.7%, n=69), pneumonia (10.2%, n=66), and prematurity/low birth weight (10.2%, n=66) were the commonest underlying causes of death. Conclusion According to VA analysis, half of deaths amongst children under-5 in rural Gambia occur at home. Pneumonia, diarrhoea, and sepsis, and the underlying cause of severe malnutrition, as well as birth asphyxia in neonates, remain the predominant causes of child mortality in rural Gambia. Improved health care and health-seeking behaviour may reduce childhood deaths in rural Gambia.
Bi-Directional Iterative Prompt-Tuning for Event Argument Extraction

Recently, prompt-tuning has attracted growing interests in event argument extraction (EAE). However, the existing prompt-tuning methods have not achieved satisfactory performance due to the lack of consideration of entity information. In this paper, we propose a bi-directional iterative prompt-tuning method for EAE, where the EAE task is treated as a cloze-style task to take full advantage of entity information and pre-trained language models (PLMs). Furthermore, our method explores event argument interactions by introducing the argument roles of contextual entities into prompt construction. Since template and verbalizer are two crucial components in a cloze-style prompt, we propose to utilize the role label semantic knowledge to construct a semantic verbalizer and design three kind of templates for the EAE task. Experiments on the ACE 2005 English dataset with standard and low-resource settings show that the proposed method significantly outperforms the peer state-of-the-art methods.
Evaluating the Impact of Loss Function Variation in Deep Learning for Classification

The loss function is arguably among the most important hyperparameters for a neural network. Many loss functions have been designed to date, making a correct choice nontrivial. However, elaborate justifications regarding the choice of the loss function are not made in related work. This is, as we see it, an indication of a dogmatic mindset in the deep learning community which lacks empirical foundation. In this work, we consider deep neural networks in a supervised classification setting and analyze the impact the choice of loss function has onto the training result. While certain loss functions perform suboptimally, our work empirically shows that under-represented losses such as the KL Divergence can outperform the State-of-the-Art choices significantly, highlighting the need to include the loss function as a tuned hyperparameter rather than a fixed choice.
Decontamination of the scientific literature

Research misconduct and frauds pollute the scientific literature. Honest errors and malevolent data fabrication, image manipulation, journal hijacking, and plagiarism passed peer review unnoticed. Problematic papers deceive readers, authors citing them, and AI-powered literature-based discovery. Flagship publishers accepted hundreds flawed papers despite claiming to enforce peer review. This application ambitions to decontaminate the scientific literature using curative and preventive actions.
Hierarchical Speaker Representation for Target Speaker Extraction

Target speaker extraction aims to isolate a specific speaker's voice from a composite of multiple sound sources, guided by an enrollment utterance or called anchor. Current methods predominantly derive speaker embeddings from the anchor and integrate them into the separation network to separate the voice of the target speaker. However, the representation of the speaker embedding is too simplistic, often being merely a 1*1024 vector. This dense information makes it difficult for the separation network to harness effectively. To address this limitation, we introduce a pioneering methodology called Hierarchical Representation (HR) that seamlessly fuses anchor data across granular and overarching 5 layers of the separation network, enhancing the precision of target extraction. HR amplifies the efficacy of anchors to improve target speaker isolation. On the Libri-2talker dataset, HR substantially outperforms state-of-the-art time-frequency domain techniques. Further demonstrating HR's capabilities, we achieved first place in the prestigious ICASSP 2023 Deep Noise Suppression Challenge. The proposed HR methodology shows great promise for advancing target speaker extraction through enhanced anchor utilization.
Improving Lipschitz-Constrained Neural Networks by Learning Activation Functions

Lipschitz-constrained neural networks have several advantages over unconstrained ones and can be applied to a variety of problems, making them a topic of attention in the deep learning community. Unfortunately, it has been shown both theoretically and empirically that they perform poorly when equipped with ReLU activation functions. By contrast, neural networks with learnable 1-Lipschitz linear splines are known to be more expressive. In this paper, we show that such networks correspond to global optima of a constrained functional optimization problem that consists of the training of a neural network composed of 1-Lipschitz linear layers and 1-Lipschitz freeform activation functions with second-order total-variation regularization. Further, we propose an efficient method to train these neural networks. Our numerical experiments show that our trained networks compare favorably with existing 1-Lipschitz neural architectures.
Time scales of dynamic stall development on a vertical-axis wind turbine blade

Abstract Vertical-axis wind turbines are excellent candidates to diversify wind energy technology, but their aerodynamic complexity limits industrial deployment. To improve the efficiency and lifespan of vertical-axis wind turbines, we desire data-driven models and control strategies that take into account the timing and duration of subsequent events in the unsteady flow development. Here, we aim to characterise the chain of events that leads to dynamic stall on a vertical-axis wind turbine blade and to quantify the influence of the turbine operation conditions on the duration of the individual flow development stages. We present time-resolved flow and unsteady load measurements of a wind turbine model undergoing dynamic stall for a wide range of tip-speed ratios. Proper orthogonal decomposition is used to identify dominant flow structures and to distinguish six characteristic stall stages: the attached flow, shear-layer growth, vortex formation, upwind stall, downwind stall and flow reattachment stage. The timing and duration of the individual stages are best characterised by the non-dimensional convective time. Dynamic stall stages are also identified based on aerodynamic force measurements. Most of the aerodynamic work is done during the shear-layer growth and the vortex formation stage which underlines the importance of managing dynamic stall on vertical-axis wind turbines.
List Agreement Expansion from Coboundary Expansion

One of the key components in PCP constructions are agreement tests. In agreement test the tester is given access to subsets of fixed size of some set, each equipped with an assignment. The tester is then tasked with testing whether these local assignments agree with some global assignment over the entire set. One natural generalization of this concept is the case where, instead of a single assignment to each local view, the tester is given access to $l$ different assignments for every subset. The tester is then tasked with testing whether there exist $l$ global functions that agree with all of the assignments of all of the local views. In this work we present sufficient condition for a set system to exhibit this generalized definition of list agreement expansion. This is, to our knowledge, the first work to consider this natural generalization of agreement testing. Despite initially appearing very similar to agreement expansion, list agreement expansion seem to require a different set of techniques. This is due to the fact that the natural extension of agreement testing does not suffice when testing for list agreement, as list agreement crucially relies on a global structure. It follows that if a local assignments satisfy list agreement they must not only agree locally but also exhibit some additional structure. In order to test for the existence of this additional structure we use a connection between covering spaces of a high dimensional complex and its coboundaries. We use this connection as a form of ``decoupling''. Moreover, we show that any set system that exhibits list agreement expansion also supports direct sum testing. This is the first scheme for direct sum testing that works regardless of the parity of the sizes of the local sets. Prior to our work the schemes for direct sum testing were based on the parity of the sizes of the local tests.
Brain modeling for control: A review

Neurostimulation technologies have seen a recent surge in interest from the neuroscience and controls communities alike due to their proven potential to treat conditions such as epilepsy, Parkinson’s Disease, and depression. The provided stimulation can be of different types, such as electric, magnetic, and optogenetic, and is generally applied to a specific region of the brain in order to drive the local and/or global neural dynamics to a desired state of (in)activity. For most neurostimulation techniques, however, an underlying theoretical understanding of their efficacy is still lacking. From a control-theoretic perspective, it is important to understand how each stimulus modality interacts with the inherent complex network dynamics of the brain in order to assess the controllability of the system and develop neurophysiologically relevant computational models that can be used to design the stimulation profile systematically and in closed loop. In this paper, we review the computational modeling studies of 1) deep brain stimulation, 2) transcranial magnetic stimulation, 3) direct current stimulation, 4) transcranial electrical stimulation, and 5) optogenetics as five of the most popular and commonly used neurostimulation technologies in research and clinical settings. For each technology, we split the reviewed studies into 1) theory-driven biophysical models capturing the low-level physics of the interactions between the stimulation source and neuronal tissue, 2) data-driven stimulus-response models which capture the end-to-end effects of stimulation on various biomarkers of interest, and 3) data-driven dynamical system models that extract the precise dynamics of the brain’s response to neurostimulation from neural data. While our focus is particularly on the latter category due to their greater utility in control design, we review key works in the former two categories as the basis and context in which dynamical system models have been and will be developed. In all cases, we highlight the strength and weaknesses of the reviewed works and conclude the review with discussions on outstanding challenges and critical avenues for future work.
Towards Data-and Knowledge-Driven Artificial Intelligence: A Survey on Neuro-Symbolic Computing

Neural-symbolic computing (NeSy), which pursues the integration of the symbolic and statistical paradigms of cognition, has been an active research area of Artificial Intelligence (AI) for many years. As NeSy shows promise of reconciling the advantages of reasoning and interpretability of symbolic representation and robust learning in neural networks, it may serve as a catalyst for the next generation of AI. In the present paper, we provide a systematic overview of the recent developments and important contributions of NeSy research. Firstly, we introduce study history of this area, covering early work and foundations. We further discuss background concepts and identify key driving factors behind the development of NeSy. Afterward, we categorize recent landmark approaches along several main characteristics that underline this research paradigm, including neural-symbolic integration, knowledge representation, knowledge embedding, and functionality. Next, we briefly discuss the successful application of modern NeSy approaches in several domains. Then, we benchmark several NeSy methods on three representative application tasks. Finally, we identify the open problems together with potential future research directions. This survey is expected to help new researchers enter this rapidly evolving field and accelerate the progress towards data-and knowledge-driven AI.
Black hole mirages: Electron lensing and Berry curvature effects in inhomogeneously tilted Weyl semimetals

We study electronic transport in Weyl semimetals with spatially varying nodal tilt profiles. We find that the flow of electrons can be guided precisely by judiciously chosen tilt profiles. In a broad regime of parameters, we show that electron flow is described well by semiclassical equations of motion similar to the ones governing gravitational attraction. This analogy provides a physically transparent tool for designing tiltronic devices like electronic lenses. The analogy to gravity circumvents the notoriously difficult full-fledged description of inhomogeneous solids. A comparison to microscopic lattice simulations shows that it is only valid for trajectories sufficiently far from analogue black holes. We finally comment on the Berry curvature-driven transverse motion and relate the latter to spin precession physics.
Hidden topological transitions in emergent magnetic monopole lattices

Topological defects, called magnetic hedgehogs, realize emergent magnetic monopoles, which are not allowed in the ordinary electromagnetism described by Maxwell's equations. Such monopoles were experimentally discovered in magnets in two different forms: tetrahedral $4Q$ and cubic $3Q$ hedgehog lattices. The spin textures are modulated by the chemical composition, an applied magnetic field, and temperature, leading to quantum transport and optical phenomena through movement and pair annihilation of magnetic monopoles, but the theoretical understanding remains elusive, especially in the regions where different types of hedgehog lattices are competing. Here we propose a theoretical model that can stabilize both tetrahedral and cubic hedgehog lattices, and perform a thorough investigation of the phase diagram while changing the interaction parameters, magnetic field, and temperature, by using a recently developed method that delivers exact solutions in the thermodynamic limit. We find that the model exhibits various types of topological transitions with changes of the density of monopoles and antimonopoles, some of which are accompanied by anomalies in the thermodynamic quantities, while the others are hidden with less or no anomaly. We also find another hidden topological transition with pair annihilation of two-dimensional vortices in the three-dimensional system. These results not only provide useful information for understanding the existing experimental data, but also challenge the identification of hidden topological transitions and the exploration of emergent electromagnetism in magnetic monopole lattices.
Data-driven Output Regulation via Gaussian Processes and Luenberger Internal Models

This paper deals with the problem of adaptive output regulation for multivariable nonlinear systems by presenting a learning-based adaptive internal model-based design strategy. The approach builds on the recently proposed adaptive internal model design techniques based on the theory of nonlinear Luenberger observers, and the adaptation side is approached as a probabilistic regression problem. In particular, Gaussian process priors are employed to cope with the learning problem. Unlike the previous approaches in the field, here only coarse assumptions about the friend structure are required, making the proposed approach suitable for applications where the exosystem is highly uncertain. The paper presents performance bounds on the attained regulation error and numerical simulations showing how the proposed method outperforms previous approaches.
Vox-Fusion: Dense Tracking and Mapping with Voxel-based Neural Implicit Representation

In this work, we present a dense tracking and mapping system named Vox-Fusion, which seamlessly fuses neural implicit representations with traditional volumetric fusion methods. Our approach is inspired by the recently developed implicit mapping and positioning system and further extends the idea so that it can be freely applied to practical scenarios. Specifically, we leverage a voxel-based neural implicit surface representation to encode and optimize the scene inside each voxel. Furthermore, we adopt an octree-based structure to divide the scene and support dynamic expansion, enabling our system to track and map arbitrary scenes without knowing the environment like in previous works. Moreover, we proposed a high-performance multi-process framework to speed up the method, thus supporting some applications that require real-time performance. The evaluation results show that our methods can achieve better accuracy and completeness than previous methods. We also show that our Vox-Fusion can be used in augmented reality and virtual reality applications. Our source code is publicly available at https://github.com/zju3dv/Vox-Fusion.
Understanding Adverse Biological Effect Predictions Using Knowledge Graphs

Extrapolation of adverse biological (toxic) eﬀects of chemicals is an important contribution to expand available hazard data in (eco)toxicology without the use of animals in laboratory experiments. In this work, we extrapolate ef-fects based on a knowledge graph (KG) consisting of the most relevant eﬀect data as domain-speciﬁc background knowledge. An eﬀect prediction model, with and without background knowledge, was used to predict mean adverse biological eﬀect concentration of chemicals as a prototypical type of stressors. The background knowledge improves the model prediction performance by up to 40% in terms of R 2 ( i . e ., coeﬃcient of determination). We use the KG and KG embeddings to provide quantitative and qualitative insights into the predictions. These insights are expected to improve the conﬁdence in ef-fect prediction. Larger scale implementation of such extrapolation models should be expected to support hazard and risk assessment, by simplifying and reducing testing needs.
Large unidirectional spin Hall and Rashba−Edelstein magnetoresistance in topological insulator/magnetic insulator heterostructures

Thanks to its unique symmetry, the unidirectional spin Hall and Rashba-Edelstein magnetoresistance (USRMR) is of great fundamental and practical interest, particularly in the context of reading magnetization states in two-terminal spin-orbit torque switching memory and logic devices. Recent studies show that topological insulators could improve USRMR amplitude. However, the topological insulator device configurations studied so far in this context, namely ferromagnetic metal/topological insulator bilayers and magnetically doped topological insulators, suffer from current shunting by the metallic layer and low Curie temperature, respectively. Here, we report large USRMR in a new material category - magnetic insulator/topological insulator bi-layered heterostructures. Such structures exhibit USRMR that is about an order of magnitude larger than the highest values reported so far in all-metal Ta/Co bilayers. We also demonstrate current-induced magnetization switching aided by an Oersted field, and electrical read out by the USRMR, as a prototype memory device.
Solving Math Word Problems via Cooperative Reasoning induced Language Models

Large-scale pre-trained language models (PLMs) bring new opportunities to challenging problems, especially those that need high-level intelligence, such as the math word problem (MWPs). However, directly applying existing PLMs to MWPs can fail as the generation process lacks sufficient supervision and thus lacks fast adaptivity as humans. We notice that human reasoning has a dual reasoning framework that consists of an immediate reaction system (system 1) and a delicate reasoning system (system 2), where the entire reasoning is determined by their interaction. This inspires us to develop a cooperative reasoning-induced PLM for solving MWPs, called Cooperative Reasoning (CoRe), resulting in a human-like reasoning architecture with system 1 as the generator and system 2 as the verifier. In our approach, the generator is responsible for generating reasoning paths, and the verifiers are used to supervise the evaluation in order to obtain reliable feedback for the generator. We evaluate our CoRe framework on several mathematical reasoning datasets and achieve decent improvement over state-of-the-art methods, up to 9.6% increase over best baselines.
The limited role of galaxy mergers in driving stellar mass growth over cosmic time

© The Author(s) 2017. This is an Open Access article, distributed under the terms of the Creative Commons Attribution licence (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted re-use, distribution, and reproduction in any medium, provided the original work is properly cited. Published by Oxford University Press on behalf of the Royal Astronomical Society.
Localized Randomized Smoothing for Collective Robustness Certification

Models for image segmentation, node classification and many other tasks map a single input to multiple labels. By perturbing this single shared input (e.g. the image) an adversary can manipulate several predictions (e.g. misclassify several pixels). Collective robustness certification is the task of provably bounding the number of robust predictions under this threat model. The only dedicated method that goes beyond certifying each output independently is limited to strictly local models, where each prediction is associated with a small receptive field. We propose a more general collective robustness certificate for all types of models. We further show that this approach is beneficial for the larger class of softly local models, where each output is dependent on the entire input but assigns different levels of importance to different input regions (e.g. based on their proximity in the image). The certificate is based on our novel localized randomized smoothing approach, where the random perturbation strength for different input regions is proportional to their importance for the outputs. Localized smoothing Pareto-dominates existing certificates on both image segmentation and node classification tasks, simultaneously offering higher accuracy and stronger certificates.