Miracle-dz/UniFAR-FTUs huggingface dataset:
HRCenterNet: An Anchorless Approach to Chinese Character Segmentation in Historical Documents

The information provided by historical documents has always been indispensable in the transmission of human civilization, but it has also made these books susceptible to damage due to various factors. Thanks to recent technology, the automatic digitization of these documents are one of the quickest and most effective means of preservation. The main steps of automatic text digitization can be divided into two stages, mainly: character segmentation and character recognition, where the recognition results depend largely on the accuracy of segmentation. Therefore, in this study, we will only focus on the character segmentation of historical Chinese documents. In this research, we propose a model named HRCenterNet, which is combined with an anchorless object detection method and parallelized architecture. The MTHv2 dataset consists of over 3000 Chinese historical document images and over 1 million individual Chinese characters; with these enormous data, the segmentation capability of our model achieves IoU 0.81 on average with the best speed-accuracy trade-off compared to the others. Our source code is available at https://github.com/Tverous/HRCenterNet.
The Representation Power of Neural Networks: Breaking the Curse of Dimensionality

In this paper, we analyze the number of neurons and training parameters that a neural networks needs to approximate multivariate functions of bounded second mixed derivatives -- Korobov functions. We prove upper bounds on these quantities for shallow and deep neural networks, breaking the curse of dimensionality. Our bounds hold for general activation functions, including ReLU. We further prove that these bounds nearly match the minimal number of parameters any continuous function approximator needs to approximate Korobov functions, showing that neural networks are near-optimal function approximators.
Stochastic Damped L-BFGS with Controlled Norm of the Hessian Approximation

We propose a new stochastic variance-reduced damped L-BFGS algorithm, where we leverage estimates of bounds on the largest and smallest eigenvalues of the Hessian approximation to balance its quality and conditioning. Our algorithm, VARCHEN, draws from previous work that proposed a novel stochastic damped L-BFGS algorithm called SdLBFGS. We establish almost sure convergence to a stationary point and a complexity bound. We empirically demonstrate that VARCHEN is more robust than SdLBFGS-VR and SVRG on a modified DavidNet problem -- a highly nonconvex and ill-conditioned problem that arises in the context of deep learning, and their performance is comparable on a logistic regression problem and a nonconvex support-vector machine problem.
Debiased-CAM for bias-agnostic faithful visual explanations of deep convolutional networks

Class activation maps (CAMs) explain convolutional neural network predictions by identifying salient pixels, but they become misaligned and misleading when explaining predictions on images under bias, such as images blurred accidentally or deliberately for privacy protection, or images with improper white balance. Despite model fine-tuning to improve prediction performance on these biased images, we demonstrate that CAM explanations become more deviated and unfaithful with increased image bias. We present Debiased-CAM to recover explanation faithfulness across various bias types and levels by training a multi-input, multi-task model with auxiliary tasks for CAM and bias level predictions. With CAM as a prediction task, explanations are made tunable by retraining the main model layers and made faithful by self-supervised learning from CAMs of unbiased images. The model provides representative, bias-agnostic CAM explanations about the predictions on biased images as if generated from their unbiased form. In four simulation studies with different biases and prediction tasks, Debiased-CAM improved both CAM faithfulness and task performance. We further conducted two controlled user studies to validate its truthfulness and helpfulness, respectively. Quantitative and qualitative analyses of participant responses confirmed Debiased-CAM as more truthful and helpful. Debiased-CAM thus provides a basis to generate more faithful and relevant explanations for a wide range of real-world applications with various sources of bias.
A 12-mode Universal Photonic Processor for Quantum Information Processing.

Photonic processors are pivotal for both quantum and classical information processing tasks using light. In particular, linear optical quantum information processing requires both largescale and low-loss programmable photonic processors. In this paper, we report the demonstration of the largest universal quantum photonic processor to date: a low-loss, 12-mode fully tunable linear interferometer with all-to-all coupling based on stoichiometric silicon nitride waveguides.
Flexible Few-Shot Learning of Contextual Similarity

Existing approaches to few-shot learning deal with tasks that have persistent, rigid notions of classes. Typically, the learner observes data only from a ﬁxed number of classes at training time and is asked to generalize to a new set of classes at test time. Two examples from the same class would always be assigned the same labels in any episode. In this work, we consider a realistic setting where the similarities between examples can change from episode to episode depending on the task context, which is not given to the learner. We deﬁne two new benchmark datasets for this ﬂexible few-shot scenario, where the tasks are based on images of faces (Celeb-A) and shoes (Zappos50K). While classiﬁcation baselines and episodic approaches learn representations that work well for standard few-shot learning, they suffer in our ﬂexible tasks as novel similarity deﬁnitions arise during testing. We propose to build upon recent contrastive unsupervised learning techniques and use a combination of instance and class invariance learning, aiming to obtain general and ﬂexible features. We ﬁnd that our approach performs strongly on our new ﬂexible few-shot learning benchmarks.
Identification and prognostic value of DLGAP5 in endometrial cancer

Background Endometrial cancer poses a serious threat to women’s health worldwide, and its pathogenesis, although actively explored, is not fully understood. DLGAP5 is a recently identified cell cycle-regulation gene not reported in endometrial cancer. This study was aiming to analyze the role of DLGAP5 in tumorigenesis and development and to investigate its prognostic significance of patients with endometrial cancer. Methodology Microarray datasets (GSE17025, GSE39099 and GSE63678) from the GEO database were used for comparative analysis, and their intersection was obtained by applying the Venn diagram, and DLGAP5 was selected as the target gene. Next, transcriptome data (n = 578) was downloaded from TCGA-UCEC to analyze the mRNA expression profile of DLGAP5. Then, immunohistochemical data provided by HPA were used to identify the different protein expression levels of DLGAP5 in tumor tissues and normal tissues. Subsequently, the prognostic meaning of DLGAP5 in patients with endometrial cancer was explored based on survival data from TCGA-UCEC (n = 541). Finally, the reliability of DLGAP5 expression was verified by RT-qPCR. Results Transcriptome data from TCGA-UCEC, immunohistochemical data from HPA, and RT-qPCR results from clinical samples were used for triple validation to confirm that the expression of DLGAP5 in endometrial cancer tissues was significantly higher than that in normal endometrial tissues. Kaplan–Meier survival analysis announced that the expression level of DLGAP5 was negatively correlated with the overall survival of patients with endometrial cancer. Conclusions DLGAP5 is a potential oncogene with cell cycle regulation, and its overexpression can predict the poor prognosis of patients with endometrial cancer. As a candidate target for the diagnosis and treatment of endometrial cancer, it is worthwhile to make further study to reveal the carcinogenicity of DLGAP5 and the mechanism of its resistance of organisms.
Cross-lingual Word Sense Disambiguation using mBERT Embeddings with Syntactic Dependencies

Cross-lingual word sense disambiguation (WSD) tackles the challenge of disambiguating ambiguous words across languages given context. The pre-trained BERT embedding model has been proven to be effective in extracting contextual information of words, and have been incorporated as features into many state-of-the-art WSD systems. In order to investigate how syntactic information can be added into the BERT embeddings to result in both semantics- and syntax-incorporated word embeddings, this project proposes the concatenated embeddings by producing dependency parse tress and encoding the relative relationships of words into the input embeddings. Two methods are also proposed to reduce the size of the concatenated embeddings. The experimental results show that the high dimensionality of the syntax-incorporated embeddings constitute an obstacle for the classification task, which needs to be further addressed in future studies.
A Custom 7nm CMOS Standard Cell Library for Implementing TNN-based Neuromorphic Processors

A set of highly-optimized custom macro extensions is developed for a 7nm CMOS cell library for implementing Temporal Neural Networks (TNNs) that can mimic brain-like sensory processing with extreme energy efficiency. A TNN prototype (13,750 neurons and 315,000 synapses) for MNIST requires only 1.56mm2 die area and consumes only 1.69mW.
Utilising Graph Machine Learning within Drug Discovery and Development

Graph Machine Learning (GML) is receiving growing interest within the pharmaceutical and biotechnology industries for its ability to model biomolecular structures, the functional relationships between them, and integrate multi-omic datasets - amongst other data types. Herein, we present a multidisciplinary academic-industrial review of the topic within the context of drug discovery and development. After introducing key terms and modelling approaches, we move chronologically through the drug development pipeline to identify and summarise work incorporating: target identification, design of small molecules and biologics, and drug repurposing. Whilst the field is still emerging, key milestones including repurposed drugs entering in vivo studies, suggest graph machine learning will become a modelling framework of choice within biomedical machine learning.
A Natural Introduction to Fine-Tuning

A well-known topic within the philosophy of physics is the problem of fine-tuning: the fact that the universal constants seem to take non-arbitrary values in order for live to thrive in our Universe. In this paper we will talk about this problem in general, giving some examples from physics. We will review some solutions like the design argument, logical probability, cosmological natural selection, etc. Moreover, we will also discuss why it's dangerous to uphold the Principle of Naturalness as a scientific principle. After going through this paper, the reader should have a general idea what this problem exactly entails whenever it is mentioned in other sources and we recommend the reader to think critically about these concepts.
Uniqueness for Riccati equations with unbounded operator coefficients

In this article we address the issue of uniqueness for differential and algebraic operator Riccati equations, under a distinctive set of assumptions on their unbounded coefficients. The class of boundary control systems characterized by these assumptions encompasses diverse significant physical interactions, all modeled by systems of coupled hyperbolic/parabolic partial differential equations. The proofs of uniqueness provided tackle and overcome the obstacles raised by the peculiar regularity properties of the composite dynamics. These results supplement the theories of the finite and infinite time horizon linear-quadratic problem devised by the authors jointly with Lasiecka, as the unique solution to the Riccati equation enters the closed loop form of the optimal control.
DAX: Deep Argumentative eXplanation for Neural Networks

Despite the rapid growth in attention on eXplainable AI (XAI) of late, explanations in the literature provide little insight into the actual functioning of Neural Networks (NNs), significantly limiting their transparency. We propose a methodology for explaining NNs, providing transparency about their inner workings, by utilising computational argumentation (a form of symbolic AI offering reasoning abstractions for a variety of settings where opinions matter) as the scaffolding underpinning Deep Argumentative eXplanations (DAXs). We define three DAX instantiations (for various neural architectures and tasks) and evaluate them empirically in terms of stability, computational cost, and importance of depth. We also conduct human experiments with DAXs for text classification models, indicating that they are comprehensible to humans and align with their judgement, while also being competitive, in terms of user acceptance, with existing approaches to XAI that also have an argumentative spirit.
Direct Depth Learning Network for Stereo Matching

Being a crucial task of autonomous driving, Stereo matching has made great progress in recent years. Existing stereo matching methods estimate disparity instead of depth. They treat the disparity errors as the evaluation metric of the depth estimation errors, since the depth can be calculated from the disparity according to the triangulation principle. However, we find that the error of the depth depends not only on the error of the disparity but also on the depth range of the points. Therefore, even if the disparity error is low, the depth error is still large, especially for the distant points. In this paper, a novel Direct Depth Learning Network (DDL-Net) is designed for stereo matching. DDL-Net consists of two stages: the Coarse Depth Estimation stage and the Adaptive-Grained Depth Refinement stage, which are all supervised by depth instead of disparity. Specifically, Coarse Depth Estimation stage uniformly samples the matching candidates according to depth range to construct cost volume and output coarse depth. Adaptive-Grained Depth Refinement stage performs further matching near the coarse depth to correct the imprecise matching and wrong matching. To make the Adaptive-Grained Depth Refinement stage robust to the coarse depth and adaptive to the depth range of the points, the Granularity Uncertainty is introduced to Adaptive-Grained Depth Refinement stage. Granularity Uncertainty adjusts the matching range and selects the candidates' features according to coarse prediction confidence and depth range. We verify the performance of DDL-Net on SceneFlow dataset and DrivingStereo dataset by different depth metrics. Results show that DDL-Net achieves an average improvement of 25% on the SceneFlow dataset and $12\%$ on the DrivingStereo dataset comparing the classical methods. More importantly, we achieve state-of-the-art accuracy at a large distance.
Maternal Microdeletion at the H19/Igf2 ICR in Mice Increases Offspring Susceptibility to In Utero Environmental Perturbation

Deficiency of methyl donor nutrients folate, choline, and methionine (methyl deficiency) during gestation can impair fetal development and perturb DNA methylation. Here, we assessed genetic susceptibility to methyl deficiency by comparing effects in wildtype C57BL/6J (B6) mice to mutant mice carrying a 1.3 kb deletion at the H19/Igf2 Imprinting Control Region (ICR) (H19ICRΔ2,3). The H19ICRΔ2,3 mutation mimics microdeletions observed in Beckwith-Wiedemann syndrome (BWS) patients, who exhibit epimutations in cis that cause loss of imprinting and fetal overgrowth. Dams were treated during pregnancy with 1 of 4 methyl sufficient (MS) or methyl deficient (MD) diets, with or without the antibiotic commonly used to deplete folate producing gut microbes. As expected, after ~9 weeks of treatment, dams in MD and MD + antibiotic groups exhibited substantially reduced plasma folate concentrations. H19ICRΔ2,3 mutant lines were more susceptible to adverse pregnancy outcomes caused by methyl deficiency (reduced birth rate and increased pup lethality) and antibiotic (decreased litter size and litter survival). Surprisingly, pup growth/development was only minimally affected by methyl deficiency, while antibiotic treatment caused inverse effects on B6 and H19ICRΔ2,3 lines. B6 pups treated with antibiotic exhibited increased neonatal and weanling bodyweight, while both wildtype and mutant pups of heterozygous H19ICRΔ2,3/+ dams exhibited decreased neonatal bodyweight that persisted into adulthood. Interestingly, only antibiotic-treated pups carrying the H19ICRΔ2,3 mutation exhibited altered DNA methylation at the H19/Igf2 ICR, suggesting ICR epimutation was not sufficient to explain the altered phenotypes. These findings demonstrate that genetic mutation of the H19/Igf2 ICR increases offspring susceptibility to developmental perturbation in the methyl deficiency model, maternal and pup genotype play an essential role, and antibiotic treatment in the model also plays a key independent role.
Delft University of Technology Analysis of multipath channel delay estimation using subspace fitting

The presence of rich scattering in indoor and urban radio propagation scenarios may cause a high arrival density of multipath components (MPCs). Often the MPCs arrive in clusters at the receiver, where MPCs within one cluster have similar angles and delays. The MPCs arriving within a single cluster are typically unresolvable in the delay domain. In this paper, we analyze the effects of unresolved MPCs on the bias of the delay estimation with a multiband subspace fitting algorithm. We treat the unresolved MPCs as a model error that results in perturbed subspace estimation. Starting from the first-order approximation of the perturbations, we derive the bias of the delay estimate of the line-of-sight (LOS) component. We show that it depends on the power and relative delay of the unresolved MPCs in the first cluster compared to the LOS component. Numerical experiments are included to show that the derived expression for the bias well describes the effects of unresolved MPCs on the delay estimation.
Categorical Perception: A Groundwork for Deep Learning

Abstract Classification is one of the major tasks that deep learning is successfully tackling. Categorization is also a fundamental cognitive ability. A well-known perceptual consequence of categorization in humans and other animals, categorical perception, is notably characterized by a within-category compression and a between-category separation: two items, close in input space, are perceived closer if they belong to the same category than if they belong to different categories. Elaborating on experimental and theoretical results in cognitive science, here we study categorical effects in artificial neural networks. We combine a theoretical analysis that makes use of mutual and Fisher information quantities and a series of numerical simulations on networks of increasing complexity. These formal and numerical analyses provide insights into the geometry of the neural representation in deep layers, with expansion of space near category boundaries and contraction far from category boundaries. We investigate categorical representation by using two complementary approaches: one mimics experiments in psychophysics and cognitive neuroscience by means of morphed continua between stimuli of different categories, while the other introduces a categoricality index that, for each layer in the network, quantifies the separability of the categories at the neural population level. We show on both shallow and deep neural networks that category learning automatically induces categorical perception. We further show that the deeper a layer, the stronger the categorical effects. As an outcome of our study, we propose a coherent view of the efficacy of different heuristic practices of the dropout regularization technique. More generally, our view, which finds echoes in the neuroscience literature, insists on the differential impact of noise in any given layer depending on the geometry of the neural representation that is being learned, that is, on how this geometry reflects the structure of the categories.
Sylvester Matrix Based Similarity Estimation Method for Automation of Defect Detection in Textile Fabrics

Fabric defect detection is a crucial quality control step in the textile manufacturing industry. In this article, machine vision system based on the Sylvester Matrix Based Similarity Method (SMBSM) is proposed to automate the defect detection process. The algorithm involves six phases, namely resolution matching, image enhancement using Histogram Specification and Median-Mean Based Sub-Image-Clipped Histogram Equalization, image registration through alignment and hysteresis process, image subtraction, edge detection, and fault detection by means of the rank of the Sylvester matrix. The experimental results demonstrate that the proposed method is robust and yields an accuracy of 93.4%, precision of 95.8%, with 2275 ms computational speed.
Adversarial Linear Contextual Bandits with Graph-Structured Side Observations

This paper studies the adversarial graphical contextual bandits, a variant of adversarial multi-armed bandits that leverage two categories of the most common side information: contexts and side observations. In this setting, a learning agent repeatedly chooses from a set of K actions after being presented with a d-dimensional context vector. The agent not only incurs and observes the loss of the chosen action, but also observes the losses of its neighboring actions in the observation structures, which are encoded as a series of feedback graphs. This setting models a variety of applications in social networks, where both contexts and graph-structured side observations are available. Two efficient algorithms are developed based on EXP3. Under mild conditions, our analysis shows that for undirected feedback graphs the first algorithm, EXP3-LGC-U, achieves a sub-linear regret with respect to the time horizon and the average independence number of the feedback graphs. A slightly weaker result is presented for the directed graph setting as well. The second algorithm, EXP3-LGC-IX, is developed for a special class of problems, for which the regret is the same for both directed as well as undirected feedback graphs. Numerical tests corroborate the efficiency of proposed algorithms.
Low-rank matrix estimation in multi-response regression with measurement errors: Statistical and computational guarantees.

In this paper, we investigate the matrix estimation problem in multi-response regression with measurement errors. A nonconvex error-corrected estimator is proposed to estimate the matrix parameter via a combination of the loss function and the nuclear norm regularization. Then under the low-rank constraint, we analyse the statistical and computational theoretical properties of global solution of the nonconvex regularized estimator from a general point. In the statistical aspect, we establish the recovery bound for the global solution of the nonconvex estimator, under restricted strong convexity on the loss function. In the computational aspect, we solve the nonconvex optimization problem via the proximal gradient method. The algorithm is proved to converge to a near-global solution and achieve a linear convergence rate. In addition, we also establish sufficient conditions for the general results to be held for specific types of corruptions, including the additive noise and missing data. Probabilistic consequences are obtained by applying the general results. Finally, we demonstrate our theoretical consequences by several numerical experiments on the corrupted errors-in-variables multi-response regression models. Simulation results show remarkable consistency with our theory under high-dimensional scaling.