Miracle-dz/UniFAR-FTUs huggingface dataset:
New Techniques and Fine-Grained Hardness for Dynamic Near-Additive Spanners

Maintaining and updating shortest paths information in a graph is a fundamental problem with many applications. As computations on dense graphs can be prohibitively expensive, and it is preferable to perform the computations on a sparse skeleton of the given graph that roughly preserves the shortest paths information. Spanners and emulators serve this purpose. This paper develops fast dynamic algorithms for sparse spanner and emulator maintenance and provides evidence from fine-grained complexity that these algorithms are tight. Under the popular OMv conjecture, we show that there can be no decremental or incremental algorithm that maintains an $n^{1+o(1)}$ edge (purely additive) $+n^{\delta}$-emulator for any $\delta<1/2$ with arbitrary polynomial preprocessing time and total update time $m^{1+o(1)}$. Also, under the Combinatorial $k$-Clique hypothesis, any fully dynamic combinatorial algorithm that maintains an $n^{1+o(1)}$ edge $(1+\epsilon,n^{o(1)})$-spanner or emulator must either have preprocessing time $mn^{1-o(1)}$ or amortized update time $m^{1-o(1)}$. Both of our conditional lower bounds are tight. As the above fully dynamic lower bound only applies to combinatorial algorithms, we also develop an algebraic spanner algorithm that improves over the $m^{1-o(1)}$ update time for dense graphs. For any constant $\epsilon\in (0,1]$, there is a fully dynamic algorithm with worst-case update time $O(n^{1.529})$ that whp maintains an $n^{1+o(1)}$ edge $(1+\epsilon,n^{o(1)})$-spanner. Our new algebraic techniques and spanner algorithms allow us to also obtain (1) a new fully dynamic algorithm for All-Pairs Shortest Paths (APSP) with update and path query time $O(n^{1.9})$; (2) a fully dynamic $(1+\epsilon)$-approximate APSP algorithm with update time $O(n^{1.529})$; (3) a fully dynamic algorithm for near-$2$-approximate Steiner tree maintenance.
Proliferation of non-linear excitations in the piecewise-linear perceptron

We investigate the properties of local minima of the energy landscape of a continuous non-convex optimization problem, the spherical perceptron with piecewise linear cost function and show that they are critical, marginally stable and displaying a set of pseudogaps, singularities and non-linear excitations whose properties appear to be in the same universality class of jammed packings of hard spheres. The piecewise linear perceptron problem appears as an evolution of the purely linear perceptron optimization problem that has been recently investigated in [1]. Its cost function contains two non-analytic points where the derivative has a jump. Correspondingly, in the non-convex/glassy phase, these two points give rise to four pseudogaps in the force distribution and this induces four power laws in the gap distribution as well. In addition one can define an extended notion of isostaticity and show that local minima appear again to be isostatic in this phase. We believe that our results generalize naturally to more complex cases with a proliferation of non-linear excitations as the number of non-analytic points in the cost function is increased.
Sparse Recovery Analysis of Generalized $J$-Minimization with Results for Sparsity Promoting Functions with Monotonic Elasticity

In this paper we theoretically study exact recovery of sparse vectors from compressed measurements by minimizing a general nonconvex function that can be decomposed into the sum of single variable functions belonging to a class of smooth nonconvex sparsity promoting functions. Null space property (NSP) and restricted isometry property (RIP) are used as key theoretical tools. The notion of \emph{scale function} associated to a sparsity promoting function is introduced to generalize the state-of-the-art analysis technique of the $l_p$ minimization problem. The analysis is used to derive an upper bound on the null space constant (NSC) associated to this general nonconvex minimization problem, which is further utilized to derive sufficient conditions for exact recovery as upper bounds on the restricted isometry constant (RIC), as well as bounds on optimal sparsity $K$ for which exact recovery occurs. The derived bounds are explicitly calculated when the sparsity promoting function $f$ under consideration possesses the property that the associated \emph{elasticity function}, defined as, $\psi(x)=\frac{xdf(x)/dx}{f(x)}$, is monotonic in nature. Numerical simulations are carried out to verify the efficacy of the bounds and interesting conclusions are drawn about the comparative performances of different sparsity promoting functions for the problem of $1$-sparse signal recovery.
Multi-Window Data Augmentation Approach for Speech Emotion Recognition

We present a Multi-Window Data Augmentation (MWA-SER) approach for speech emotion recognition. MWA-SER is a unimodal approach that focuses on two key concepts; designing the speech augmentation method and building the deep learning model to recognize the underlying emotion of an audio signal. Our proposed multi-window augmentation approach generates additional data samples from the speech signal by employing multiple window sizes in the audio feature extraction process. We show that our augmentation method, combined with a deep learning model, improves speech emotion recognition performance. We evaluate the performance of our approach on three benchmark datasets: IEMOCAP, SAVEE, and RAVDESS. We show that the multi-window model improves the SER performance and outperforms a single-window model. The notion of finding the best window size is an essential step in audio feature extraction. We perform extensive experimental evaluations to find the best window choice and explore the windowing effect for SER analysis.
Use of Neural Networks for Stable, Accurate and Physically Consistent Parameterization of Subgrid Atmospheric Processes With Good Performance at Reduced Precision

A promising approach to improve climate‐model simulations is to replace traditional subgrid parameterizations based on simplified physical models by machine learning algorithms that are data‐driven. However, neural networks (NNs) often lead to instabilities and climate drift when coupled to an atmospheric model. Here, we learn an NN parameterization from a high‐resolution atmospheric simulation in an idealized domain by accurately calculating subgrid terms through coarse graining. The NN parameterization has a structure that ensures physical constraints are respected, such as by predicting subgrid fluxes instead of tendencies. The NN parameterization leads to stable simulations that replicate the climate of the high‐resolution simulation with similar accuracy to a successful random‐forest parameterization while needing far less memory. We find that the simulations are stable for different horizontal resolutions and a variety of NN architectures, and that an NN with substantially reduced numerical precision could decrease computational costs without affecting the quality of simulations.
Grouped Transformations in High-Dimensional Explainable ANOVA Approximation

Many applications are based on the use of efficient Fourier algorithms such as the fast Fourier transform and the nonequispaced fast Fourier transform. In a high-dimensional setting it is also already possible to deal with special sampling sets such as sparse grids or rank-1 lattices. In this paper we propose fast algorithms for high-dimensional scattered data points with corresponding frequency sets that consist of groups along the dimensions in the frequency domain. From there we propose a fast matrix-vector multiplication, the grouped Fourier transform, that finds theoretical foundation in the context of the analysis of variance (ANOVA) decomposition where there is a one-to-one correspondence from the ANOVA terms to our proposed groups. An application can be found in function approximation for high-dimensional functions where the number of the variable interactions is limited. We tested two different approximation approaches: Classical Tikhonov-regularization, namely, regularized least squares, and the technique of group lasso, which promotes sparsity in the groups. As for the latter, there are no explicit solution formulas which is why we applied the fast iterative shrinking-thresholding algorithm to obtain the minimizer. Numerical experiments in under-, overdetermined, and noisy settings indicate the applicability of our algorithms.
Adaptive Traffic Fingerprinting: Large-scale Inference under Realistic Assumptions

The widespread adoption of encrypted communications (e.g., the TLS protocol, the Tor anonymity network) fixed several critical security flaws and shielded the end-users from adversaries intercepting their transmitted data. While these protocols are very effective in protecting the confidentiality of the users' data (e.g., credit card numbers), it has been shown that they are prone (to different degrees) to adversaries aiming to breach the users' privacy. Traffic fingerprinting attacks allow an adversary to infer the webpage or the website loaded by a user based only on patterns in the user's encrypted traffic. In fact, many recent works managed to achieve a very high classification accuracy under optimal conditions for the adversary. This paper revisits the optimality assumptions made by those works and discusses various additional parameters that should be considered when evaluating a fingerprinting model. We propose three realistic scenarios simulating non-optimal fingerprinting conditions where various factors could affect the adversary's performance or operation. We then introduce a novel adaptive fingerprinting adversary and experimentally evaluate its accuracy and operation. Our experiments show that adaptive adversaries can reliably uncover the webpage visited by a user among several thousand potential pages, even under considerable distributional shift (e.g., the webpage contents change significantly over time). Such adversaries could infer the products a user browses on shopping websites or log the browsing habits of state dissidents on online forums and encyclopedias. Our technique achieves ~90% accuracy in a top-15 setting where the model distinguishes the article visited out of 6,000 Wikipedia webpages, while the same model achieves ~80% accuracy in a dataset of 13,000 classes that were not included in the training set.
Generative Adversarial Networks as stochastic Nash games

Generative adversarial networks (GANs) are a class of generative models with two antagonistic neural networks: the generator and the discriminator. These two neural networks compete against each other through an adversarial process that can be modelled as a stochastic Nash equilibrium problem. Since the associated training process is challenging, it is fundamental to design reliable algorithms to compute an equilibrium. In this paper, we propose a stochastic relaxed forward-backward algorithm for GANs and we show convergence to an exact solution or to a neighbourhood of it, if the pseudogradient mapping of the game is monotone. We apply our algorithm to the image generation problem where we observe computational advantages with respect to the extragradient scheme.
Comparison of dialectical behavior therapy and anti-anxiety medication on anxiety and digestive symptoms in patients with functional dyspepsia

Background: Functional dyspepsia is a common chronic digestive disorder. The purpose of this study was to compare the effectiveness of dialectical behavior therapy and anti-anxiety medication in patients with functional dyspepsia. Materials and Methods: The present study was a randomized, controlled clinical trial with sixty patients who were suffering from functional dyspepsia that identified by the ROME III criteria. Patients were divided into three groups by using pre- and posttest design, including Group A (dialectal treatment and pantoprazole), Group B (anxiolytic drug treatment and pantoprazole), and Group C (no intervention, only pantoprazole were used). The Beck Anxiety Inventory and the patient assessment of Gastrointestinal Symptom Severity Index Questionnaire were completed by the patients after receiving the written consent. Finally, the data were analyzed using the Statistical Package for the Social Sciences software version 20. Results: There was a significant improvement in the severity of dyspepsia after intervention in all three groups. The greatest decrease in the severity of functional dyspepsia was observed in the dialectical behavioral therapy group as compared to the other groups (Group A: −15.4 ± 6.61, Group B: −3.85 ± 2.77, and Group C: −7.8 ± 4.02; P = 0.001). Furthermore, the Beck Anxiety Inventory scores were statistically significantly improved in all three groups (Group A: −5.75 ± 2.53, Group B: −7.3 ± 3.19, and Group C: −2.60 ± 1.5; P = 0.001). There was a positive correlation between the change in dyspepsia score and change in anxiety score across different intervention groups (r = 0.55; P < 0.001). Conclusion: Dialectical behavioral therapy can be effective in reducing anxiety and improving the dyspepsia symptoms in patients with functional dyspepsia compared to anti-anxiety medication or conventional therapy. Therefore, communication between the physicians and psychologists and psychiatrists can have positive effects on the treatment of these patients.
PREVENTING CHRONIC DISEASE

Introduction Clustering of fast-food restaurants around schools facilitates fastfood consumption among students, which may cause obesity. We examined the prevalence of fast-food restaurants and identified the clusters of fast-food restaurants near secondary schools in Hong Kong. Methods We collected data of Western fast-food chain restaurants and 490 secondary schools in Hong Kong. Descriptive statistics and buffer analysis identified the prevalence of fast-food restaurants around the secondary schools within 400-m and 800-m buffers. Additional analyses compared schools stratified by the 3 main regions in Hong Kong, district-level population density, and median monthly household income. We used Getis-Ord GI* hot spot analysis to measure spatial clusters of fast-food restaurants around schools and Global Moran’s I to measure the spatial autocorrelation based on each school and the number of fast-food restaurants within the 400-m buffer. Results The average number of fast-food restaurants within 400 m and 800 m of a school was 2.0 and 6.3, respectively. Seven in 10 secondary schools had at least 1 fast-food restaurant within 400 m. The number of schools with no fast-food restaurants was higher in Hong Kong Island, considered the “rich region” in Hong Kong. Hot spots of clusters were significantly located in the high-density downtown areas. We observed significant spatial autocorrelation between fast-food restaurants and secondary schools in the areas with high density, low income, and high income (P < .001, z > 2.58). Conclusion Fast-food restaurants were substantially clustered around secondary schools in Hong Kong. Territory-wide studies about the health effect of fast-food clusters around schools on children and adolescents are warranted in Hong Kong.
Hydrostatic reduction of intussusception in children: a single centre experience

Introduction intussusception is a common surgical emergency in children especially in infants. Treatment of intussusception could be non-operative or operative. Non-operative treatment (hydrostatic reduction) of intussusception is increasingly being practiced in developing countries. Methods this was a review of our experience in the hydrostatic reduction of intussusception in children at a teaching hospital in Enugu, Nigeria. This study covered an 18-months period, October 2017 to March 2019. Patients on presentation were resuscitated, appropriate investigations done and prepared for surgery before the hydrostatic reduction (using normal saline) was carried out. Patients with features of peritonitis and marked abdominal distension were excluded from hydrostatic reduction. Results twenty patients who had 21 episodes of intussusception were analyzed. One patient had a recurrence that necessitated repeat hydrostatic reduction. Eighty percent of the patients were male. The mean and peak age of the patients was 8 months and 6 months respectively. Significant number of the patients presented after 48 hours of onset of their symptoms. Abdominal pain was the predominant presenting symptom. Twenty percent and fifteen percent of the patients had a history of preceding gastrointestinal and respiratory infections preceding the intussusception respectively. Ileocolic intussusception was the most common type and the most distal end of the intussusception was at the transverse colon. Hydrostatic reduction was successful in 13 patients (65%). Conclusion hydrostatic reduction is a simple and effective method of treatment of intussusception. However, early presentation and proper patient selection is necessary for optimal outcome.
Leveraging the Structure of Musical Preference in Content-Aware Music Recommendation

State-of-the-art music recommendation systems are based on collaborative filtering, which predicts a user’s interest from his listening habits and similarities with other users’ profiles. These approaches are agnostic to the song content, and therefore face the cold-start problem: they cannot recommend novel songs without listening history. To tackle this issue, content-aware recommendation incorporates information about the songs that can be used for recommending new items. Most methods falling in this category exploit either user-annotated tags, acoustic features or deeply-learned features. Consequently, these content features do not have a clear musical meaning, thus they are not necessarily relevant from a musical preference perspective. In this work, we propose instead to leverage a model of musical preference which originates from the field of music psychology. From low-level acoustic features we extract three factors (arousal, valence and depth), which have been shown appropriate for describing musical taste. Then we integrate those into a collaborative filtering framework for content-aware music recommendation. Experiments conducted on large-scale data show that this approach is able to address the cold-start problem, while using a compact and meaningful set of musical features.
Preventing Personal Data Theft from Images with Adversarial Machine Learning

—Facial recognition tools are becoming exceptionally accurate in identifying people from images. However, this comes at the cost of privacy for users of online services with photo management (e.g. social media platforms). Particularly troubling is the ability to leverage unsupervised learning to recognize faces even when the user has not labeled their images. This is made simpler by modern facial recognition tools, such as FaceNet, that use encoders to generate low dimensional embeddings that can be clustered to learn previously unknown faces. In this paper, we propose a strategy to generate non-invasive noise masks to apply to facial images for a newly introduced user, yielding adversarial examples and preventing the formation of identiﬁable clusters in the embedding space. We demonstrate the effectiveness of our method by showing that various classiﬁcation and clustering methods cannot reliably cluster the adversarial examples we generate.
Two-Stage Generative Adversarial Networks for Document Image Binarization with Color Noise and Background Removal

Document image enhancement and binarization methods are often used to improve the accuracy and efficiency of document image analysis tasks such as text recognition. Traditional non-machine-learning methods are constructed on low-level features in an unsupervised manner but have difficulty with binarization on documents with severely degraded backgrounds. Convolutional neural network-based methods focus only on grayscale images and on local textual features. In this paper, we propose a two-stage color document image enhancement and binarization method using generative adversarial neural networks. In the first stage, four color-independent adversarial networks are trained to extract color foreground information from an input image for document image enhancement. In the second stage, two independent adversarial networks with global and local features are trained for image binarization of documents of variable size. For the adversarial neural networks, we formulate loss functions between a discriminator and generators having an encoder-decoder structure. Experimental results show that the proposed method achieves better performance than many classical and state-of-the-art algorithms over the Document Image Binarization Contest (DIBCO) datasets, the LRDE Document Binarization Dataset (LRDE DBD), and our shipping label image dataset.
Astaxanthin prevents the methotrexate-induced reproductive toxicity by targeting oxidative stress in male mice

Abstract The future fertility of cancer patients after treatment is a serious concern amongst young cancer patients. Such chemotherapeutic agents as Methotrexate (MTX), a folic acid antagonist can cause long-term or permanent gonadal toxicity in male patients. Oxidative stress is a contributing mechanism to MTX-induced testicular damage. In the past decade, several studies have investigated the use of natural antioxidant agents to prevent the side effects of MTX. Astaxanthin (AST), a red-orange xanthophyll carotenoid, owns various clinical benefits and pharmacological effects including antioxidant, anti-tumor, anti-cancer, anti-diabetic, and anti-inflammatory properties. Since there has been no study so far in the literature on the effects of AST on MTX-induced testicular dysfunction, the present study evaluated the ameliorating effect of AST against MTX-induced testicular dysfunction. Biometric and semen parameters as well as, biochemical markers of oxidative stress were measured. Compared to the control group, MTX-treated group showed a significant decrease in sperm count, motility, viability, morphology, superoxide dismutase (SOD) and catalase (CAT) activities, and a significant increase in MDA levels. Pretreatment with AST for six consecutive days before MTX administration prevented these oxidative parameters. Thus, the current results suggest that MTX seems to impair the male fertility through oxidative damage in one hand, and AST pretreatment might improve the male fertility by preventing oxidative stress-induced fertility disorders, on the other hand. However, AST pretreatment might also protect germ cells against MTX-induced oxidative stress.
Neural Language Modeling for Contextualized Temporal Graph Generation

This paper presents the first study on using large-scale pre-trained language models for automated generation of an event-level temporal graph for a document. Despite the huge success of neural pre-training methods in NLP tasks, its potential for temporal reasoning over event graphs has not been sufficiently explored. Part of the reason is the difficulty in obtaining large training corpora with human-annotated events and temporal links. We address this challenge by using existing IE/NLP tools to automatically generate a large quantity (89,000) of system-produced document-graph pairs, and propose a novel formulation of the contextualized graph generation problem as a sequence-to-sequence mapping task. These strategies enable us to leverage and fine-tune pre-trained language models on the system-induced training data for the graph generation task. Our experiments show that our approach is highly effective in generating structurally and semantically valid graphs. Further, evaluation on a challenging hand-labeled, out-of-domain corpus shows that our method outperforms the closest existing method by a large margin on several metrics. We also show a downstream application of our approach by adapting it to answer open-ended temporal questions in a reading comprehension setting.