Miracle-dz/UniFAR-FTUs huggingface dataset:
Predicting the Travel Distance of Patients to Access Healthcare Using Deep Neural Networks

Objective: Improving geographical access remains a key issue in determining the sufficiency of regional medical resources during health policy design. However, patient choices can be the result of the complex interactivity of various factors. The aim of this study is to propose a deep neural network approach to model the complex decision of patient choice in travel distance to access care, which is an important indicator for policymaking in allocating resources. Method: We used the 4-year nationwide insurance data of Taiwan and accumulated the possible features discussed in earlier literature. This study proposes the use of a convolutional neural network (CNN)-based framework to make predictions. The model performance was tested against other machine learning methods. The proposed framework was further interpreted using Integrated Gradients (IG) to analyze the feature weights. Results: We successfully demonstrated the effectiveness of using a CNN-based framework to predict the travel distance of patients, achieving an accuracy of 0.968, AUC of 0.969, sensitivity of 0.960, and specificity of 0.989. The CNN-based framework outperformed all other methods. In this research, the IG weights are potentially explainable; however, the relationship does not correspond to known indicators in public health. Conclusions: Our results demonstrate the feasibility of the deep learning-based travel distance prediction model. It has the potential to guide policymaking in resource allocation. Clinical and Translational Impact Statement— Deep learning technology is feasible in investigating the distance that patients would travel while accessing care. It is a tool that integrates complex interactive variables with highly imbalanced data distributions.
Generation of Non-Deterministic Synthetic Face Datasets Guided by Identity Priors

Enabling highly secure applications (such as border crossing) with face recognition requires extensive biometric performance tests through large scale data. However, using real face images raises concerns about privacy as the laws do not allow the images to be used for other purposes than originally intended. Using representative and subsets of face data can also lead to unwanted demographic biases and cause an imbalance in datasets. One possible solution to overcome these issues is to replace real face images with synthetically generated samples. While generating synthetic images has benefited from recent advancements in computer vision, generating multiple samples of the same synthetic identity resembling real-world variations is still unaddressed, i.e., mated samples. This work proposes a non-deterministic method for generating mated face images by exploiting the well-structured latent space of StyleGAN. Mated samples are generated by manipulating latent vectors, and more precisely, we exploit Principal Component Analysis (PCA) to define semantically meaningful directions in the latent space and control the similarity between the original and the mated samples using a pre-trained face recognition system. We create a new dataset of synthetic face images (SymFace) consisting of 77,034 samples including 25,919 synthetic IDs. Through our analysis using well-established face image quality metrics, we demonstrate the differences in the biometric quality of synthetic samples mimicking characteristics of real biometric data. The analysis and results thereof indicate the use of synthetic samples created using the proposed approach as a viable alternative to replacing real biometric data.
Improving Dynamic Regret in Distributed Online Mirror Descent Using Primal and Dual Information

We consider the problem of distributed online optimization, with a group of learners connected via a dynamic communication graph. The goal of the learners is to track the global minimizer of a sum of time-varying loss functions in a distributed manner. We propose a novel algorithm, termed Distributed Online Mirror Descent with Multiple Averaging Decision and Gradient Consensus (DOMD-MADGC), which is based on mirror descent but incorporates multiple consensus averaging iterations over local gradients as well as local decisions. The key idea is to allow the local learners to collect a sufficient amount of global information, which enables them to more accurately approximation the time-varying global loss, so that they can closely track the dynamic global minimizer over time. We show that the dynamic regret of DOMD-MADGC is upper bounded by the path length, which is defined as the cumulative distance between successive minimizers. The resulting bound improves upon the bounds of existing distributed online algorithms and removes the explicit dependence on $T$.
CCasGNN: Collaborative Cascade Prediction Based on Graph Neural Networks

Cascade prediction aims at modeling information diffusion in the network. Most previous methods concentrate on mining either structural or sequential features from the network and the propagation path. Recent efforts devoted to combining network structure and sequence features by graph neural networks and recurrent neural networks. Nevertheless, the limitation of spectral or spatial methods restricts the improvement of prediction performance. Moreover, recurrent neural networks are time-consuming and computation-expensive, which causes the inefficiency of prediction. Here, we propose a novel method CCasGNN considering the individual profile, structural features, and sequence information. The method benefits from using a collaborative framework of GAT and GCN and stacking positional encoding into the layers of graph neural networks, which is different from all existing ones and demonstrates good performance. The experiments conducted on two real-world datasets confirm that our method significantly improves the prediction accuracy compared to state-of-the-art approaches. Whats more, the ablation study investigates the contribution of each component in our method.
Randomness and Frustration in a S = 1/2 Square-Lattice Heisenberg Antiferromagnet

We explore the interplay between randomness and magnetic frustration in the series of $S = 1/2$ Heisenberg square-lattice compounds Sr$_2$CuTe$_{1-x}$W$_x$O$_6$. Substituting W for Te alters the magnetic interactions dramatically, from strongly nearest-neighbor to next-nearest-neighbor antiferromagnetic coupling. We perform neutron scattering measurements to probe the magnetic ground state and excitations over a range of $x$. We propose a bond-disorder model that reproduces ground states with only short-ranged spin correlations in the mixed compounds. The calculated neutron diffraction patterns and powder spectra agree well with the measured data and allow detailed predictions for future measurements. We conclude that quenched randomness plays the major role in defining the physics of Sr$_2$CuTe$_{1-x}$W$_x$O$_6$ with frustration being less significant.
Federated Causal Discovery

Causal discovery aims to learn a causal graph from observational data. To date, most causal discovery methods require data to be stored in a central server. However, data owners gradually refuse to share their personalized data to avoid privacy leakage, making this task more troublesome by cutting o the rst step. A puzzle arises: how do we infer causal relations from decentralized data? In this paper, with the additive noise model assumption of data, we take the rst step in developing a gradient-based learning framework named DAG-Shared Federated Causal Discovery (DS-FCD), which can learn the causal graph without directly touching local data and naturally handle the data heterogeneity. DS-FCD bene ts from a two-level structure of each local model. The rst level learns the causal graph and communicates with the server to get model information from other clients, while the second level approximates causal mechanisms and personally updates from its own data to accommodate the data heterogeneity. Moreover, DS-FCD formulates the overall learning task as a continuous optimization problem by taking advantage of an equality acyclicity constraint, which can be naturally solved by gradient descent methods. Extensive experiments on both synthetic and real-world datasets verify the e cacy of the proposed method. ∗Work was done during an internship at JD Explore Academy. 1 ar X iv :2 11 2. 03 55 5v 1 [ cs . L G ] 7 D ec 2 02 1
Medium voltage 12-pulse converter: ac side compensation using a shunt active power filter in a novel front end transformer configuration

In this study, a shunt active power filter (APF), with predictive current control is coupled to the 12-pulse converter in two different configurations where the same performance is achieved. The proposed configuration (the second configuration) utilises a three-winding transformer (star/tapped-star/delta) with an APF connected to the secondary taps. This transformer configuration reduces the filter side voltage avoiding the need of a high bandwidth step-down transformer. This reduces the voltage rating of the APF switches, consequently, increases the switching frequency limit. Moreover, the APF-injected compensation current is minimised, as it utilises the natural mitigation of the current harmonic components generated by each 6-pulse converter, because of the phase shift property of the front-end transformer. The APF is controlled to compensate system reactive power, distortion and the oscillating power at the converter ac side. The compensated sinusoidal input current has acceptable total harmonic distortion (THD) and near unity power factor. The converter operates under a specific power locus that compromises between a symmetrical firing power locus and asymmetrical firing loci. This locus provides minimum input current THD when the reactive power is less than 0.5 pu. The simulation results for compensating a 3.3-kV, medium voltage, MV, 12-pulse converter system are experimentally verified using a scaled prototype 12-pulse converter with a shunt APF.
Persistent Homology Analysis for Materials Research and Persistent Homology Software: HomCloud

1 Cyber-Physical Engineering Informatics Research Core (Cypher), Okayama University, 3-1-1 Tsushima-naka, Kita-ku, Okayama 700-8530 Japan 2 National Institute of Advanced Industrial Science and Technology(AIST), 1-1-1, Umezono, Tsukuba, Ibaraki 305-8568, Japan 3 Kyoto University Institute for Advanced Study. WPI-ASHBi. Kyoto University, Yoshida Ushinomiya-cho, Sakyo-ku, Kyoto 606-8501, Japan 4 Center for Advanced Intelligence Project, RIKEN. 1-4-1 Nihonbashi, Chuo-ku, Tokyo
Dilated convolution with learnable spacings

Recent works indicate that convolutional neural networks (CNN) need large receptive fields (RF) to compete with visual transformers and their attention mechanism. In CNNs, RFs can simply be enlarged by increasing the convolution kernel sizes. Yet the number of trainable parameters, which scales quadratically with the kernel's size in the 2D case, rapidly becomes prohibitive, and the training is notoriously difficult. This paper presents a new method to increase the RF size without increasing the number of parameters. The dilated convolution (DC) has already been proposed for the same purpose. DC can be seen as a convolution with a kernel that contains only a few non-zero elements placed on a regular grid. Here we present a new version of the DC in which the spacings between the non-zero elements, or equivalently their positions, are no longer fixed but learnable via backpropagation thanks to an interpolation technique. We call this method"Dilated Convolution with Learnable Spacings"(DCLS) and generalize it to the n-dimensional convolution case. However, our main focus here will be on the 2D case. We first tried our approach on ResNet50: we drop-in replaced the standard convolutions with DCLS ones, which increased the accuracy of ImageNet1k classification at iso-parameters, but at the expense of the throughput. Next, we used the recent ConvNeXt state-of-the-art convolutional architecture and drop-in replaced the depthwise convolutions with DCLS ones. This not only increased the accuracy of ImageNet1k classification but also of typical downstream and robustness tasks, again at iso-parameters but this time with negligible cost on throughput, as ConvNeXt uses separable convolutions. Conversely, classic DC led to poor performance with both ResNet50 and ConvNeXt. The code of the method is available at: https://github.com/K-H-Ismail/Dilated-Convolution-with-Learnable-Spacings-PyTorch.
Spiking Control Systems

Spikes and rhythms organize control and communication in the animal world, in contrast to the bits and clocks of digital technology. As continuous-time signals that can be counted, spikes have a mixed nature. This article reviews ongoing efforts to develop a control theory of spiking systems. The central thesis is that the mixed nature of spiking results from a mixed feedback principle, and a control theory of mixed feedback can be grounded in the operator theoretic concept of maximal monotonicity. As a nonlinear generalization of passivity, maximal monotonicity acknowledges at once the physics of electrical circuits, the algorithmic tractability of convex optimization, and the feedback control theory of incremental passivity. We discuss the relevance of a theory of spiking control systems in the emerging age of event-based technology.
PLACE Dropout: A Progressive Layer-wise and Channel-wise Dropout for Domain Generalization

Domain generalization (DG) aims to learn a generic model from multiple observed source domains that generalizes well to arbitrary unseen target domains without further training. The major challenge in DG is that the model inevitably faces a severe overfitting issue due to the domain gap between source and target domains. To mitigate this problem, some dropout-based methods have been proposed to resist overfitting by discarding part of the representation of the intermediate layers. However, we observe that most of these methods only conduct the dropout operation in some specific layers, leading to an insufficient regularization effect on the model. We argue that applying dropout at multiple layers can produce stronger regularization effects, which could alleviate the overfitting problem on source domains more adequately than previous layer-specific dropout methods. In this article, we develop a novel layer-wise and channel-wise dropout for DG, which randomly selects one layer and then randomly selects its channels to conduct dropout. Particularly, the proposed method can generate a variety of data variants to better deal with the overfitting issue. We also provide theoretical analysis for our dropout method and prove that it can effectively reduce the generalization error bound. Besides, we leverage the progressive scheme to increase the dropout ratio with the training progress, which can gradually boost the difficulty of training the model to enhance its robustness. Extensive experiments on three standard benchmark datasets have demonstrated that our method outperforms several state-of-the-art DG methods. Our code is available at https://github.com/lingeringlight/PLACEdropout.
Modelling of Whiplash Trauma Parametric study of rear-end impacts using FEM and CFD

This work is a combination of Finite Element (FE) modelling and Computational Fluid Dynamics (CFD) to investigate the effect of acceleration pulses, head restraint and seating posture on facet joint loads and pressure transient magnitudes on the neck injury outcome during whiplash motion. A parametric study is carried out with the FE human body model THUMS (Total HUman Model for Safety) for a variety of crash pulses and crash conditions that have a known relative risk of long term neck injuries in rear-end impacts. The injury criterion NIC (Neck Injury Criteria) is addressed for the studies made. The THUMS model is used to generate the motion data of the spinal canal, which is used as an input to simulate the behaviour of the spinal canal to analyse the pressure transients in the network of blood vessels during the whiplash motion performing CFD simulations using the OpenFOAM CFD toolbox. Using the output of FE simulations, a moving mesh technique is used to achieve the motion of the mesh points in the CFD simulations. The results give an insight into proposed injury mechanisms and injury risk assessment criteria concerning long term neck injuries. The effect of head restraint and its position with respect to the driver’s head in mitigating the above injuries is discussed for different crash scenarios.
Toric reflection groups

Several finite complex reflection groups have a braid group which is isomorphic to a torus knot group. The reflection group is obtained from the torus knot group by declaring meridians to have order $k$ for some $k\geq 2$, and meridians are mapped to reflections. We study all possible quotients of torus knot groups obtained by requiring meridians to have finite order. Using the theory of $J$-groups of Achar and Aubert, we show that these groups behave like (in general infinite) complex reflection groups of rank two. The large family of"toric reflection groups"which we obtain includes, among others, all finite complex reflection groups of rank two with a single conjugacy class of reflecting hyperplanes, as well as Coxeter's truncations of the $3$-strand braid group. We classify these toric reflection groups and explain why the corresponding torus knot group can be naturally considered as its braid group. In particular, this yields a new infinite family of reflection-like groups admitting a braid group which is a Garside group. Moreover, we show that a toric reflection group has cyclic center by showing that the quotient by the center is isomorphic to the alternating subgroup of a Coxeter group of rank three. To this end we use the fact that the center of the alternating subgroup of an irreducible, infinite Coxeter group of rank at least three is trivial. Several ingredients of the proofs are purely Coxeter-theoretic, and might be of independent interest.
Derivation of A Fast Algorithm of Modified

fast convergences for system identification of FIR systems at a reasonable computational requirement, whose order is equal to that of the LMS algorithm. Nevertheless, its per­ formance is still unsatisfactory because the standard fast Kalman filter fails to trac k the time -varying impulse rc� sponses. The failure of tracking in the the fast Kalman filter is due to the absence of system noise in the state­ space model to be used. However, according to the deria­ tion of the fast Kalman filter, it is essen tially difficult to theoretically introduce the term of system noise in to the algorithm. In this paper, a new fast filtering algorithm, which is called a fast Hoo filter, is derived in the sense of Hoo op­ timization, which possesses the capability of successfully trac king the time-varying impulse responses on real-time with a reasonable computational cost. Furthermore, a con­ dition for the fast Hoo filter to exist is given, which makes it easy to use the filter in practical situations.
Self-Supervised Camera Self-Calibration from Video

Camera calibration is integral to robotics and computer vision algorithms that seek to infer geometric properties of the scene from visual input streams. In practice, calibration is a laborious procedure requiring specialized data collection and careful tuning. This process must be repeated whenever the parameters of the camera change, which can be a frequent occurrence for mobile robots and autonomous vehicles. In contrast, self-supervised depth and ego-motion estimation approaches can bypass explicit calibration by in-ferring per-frame projection models that optimize a view-synthesis objective. In this paper, we extend this approach to explicitly calibrate a wide range of cameras from raw videos in the wild. We propose a learning algorithm to regress per-sequence calibration parameters using an efficient family of general camera models. Our procedure achieves self-calibration results with sub-pixel reprojection error, outperforming other learning-based methods. We validate our approach on a wide variety of camera geometries, including perspective, fisheye, and catadioptric. Finally, we show that our approach leads to improvements in the downstream task of depth estimation, achieving state-of-the-art results on the EuRoC dataset with greater computational efficiency than contemporary methods. The project page: https://sites.google. com/ttic.edu/self-sup-self-calib
Variable selection for discrete survival model with frailty in presence of left truncation and right censoring: Studying association of environmental toxicants on time‐to‐pregnancy

Understanding the association between mixtures of environmental toxicants and time‐to‐pregnancy (TTP) is an important scientific question as sufficient evidence has emerged about the impact of individual toxicants on reproductive health and that individuals are exposed to a whole host of toxicants rather than an individual toxicant. Assessing mixtures of chemical effects on TTP poses significant statistical challenges, namely (i) TTP being a discrete survival outcome, typically subject to left truncation and right censoring, (ii) chemical exposures being strongly correlated, (iii) appropriate transformation to account for some lipid‐binding chemicals, (iv) non‐linear effects of some chemicals, and (v) high percentage of concentration below the limit of detection (LOD) for some chemicals. We propose a discrete frailty modeling framework (named Discnet) that allows selection of correlated covariates while appropriately addressing the methodological issues mentioned above. Discnet is shown to have better and stable false negative and false positive rates compared to alternative methods in various simulation settings. We did a detailed analysis of the pre‐conception endocrine disrupting chemicals and TTP from the LIFE study and found that older females, female exposure to cotinine (smoking), DDT conferred a delay in getting pregnant, which was consistent across various approaches to account for LOD as well as non‐linear associations.
Wild ToFu: Improving Range and Quality of Indirect Time-of-Flight Depth with RGB Fusion in Challenging Environments

Indirect Time-of-Flight (I-ToF) imaging is a widespread way of depth estimation for mobile devices due to its small size and affordable price. Previous works have mainly focused on quality improvement for I-ToF imaging especially curing the effect of Multi Path Interference (MPI). These investigations are typically done in specifically constrained scenarios at close distance, indoors and under little ambient light. Surprisingly little work has investigated I-ToF quality improvement in real-life scenarios where strong ambient light and far distances pose difficulties due to an extreme amount of induced shot noise and signal sparsity, caused by the attenuation with limited sensor power and light scattering. In this work, we propose a new learning based end-to-end depth prediction network which takes noisy raw I-ToF signals as well as an RGB image and fuses their latent representation based on a multi step approach involving both implicit and explicit alignment to predict a high quality long range depth map aligned to the RGB viewpoint. We test our approach on challenging real-world scenes and show more than 40% RMSE improvement on the final depth map compared to the baseline approach [33].