Miracle-dz/UniFAR-FTUs huggingface dataset:
Zeros of modular forms and Faber polynomials

We study the zeros of cusp forms of large weight for the modular group, which have a very large order of vanishing at infinity, so that they have a fixed number of finite zeros in the fundamental domain. We show that for large weight the zeros of these forms cluster near vertical lines, with the zeros of a weight form lying at height approximately . This is in contrast to previously known cases, such as Eisenstein series, where the zeros lie on the circular part of the boundary of the fundamental domain, or the case of cuspidal Hecke eigenforms where the zeros are uniformly distributed in the fundamental domain. Our method uses the Faber polynomials. We show that for our class of cusp forms, the associated Faber polynomials, suitably renormalized, converge to the truncated exponential polynomial of degree .
Response of creepmeters on the San Andreas fault near Parkfield to the earthquake

A total of 14 US Geological Survey creepmeters on the San Andreas fault in central California recorded coseismic steps coincident with the M = 6.7 May 2 earthquake. Creepmeters near Parkfield recorded the largest effects. Postearthquake creep rates slowed significantly, and creep at one station reversed to left lateral. About 4 months after the earthquake, decreased rates caused cumulative creep at four Parkfield stations to fall below long-term linear trends observed before May 2. At several stations, creep continued to be either left lateral or slower than normal for the rest of 1983. By January 31, 1984, all but two stations recorded resumption of right-lateral creep at reduced rates. As late as April 1, 1984, however, one station north of Parkfield continued to record left-lateral drift, and another station at the south end of the creeping section south of Parkfield recorded little or no movement. One interpretation of the creep slowdown after May 2 is that the Coalinga main shock released accumulated stress in the upper kilometer or so of the San Andreas fault near Parkfield, and several months elapsed before stress built up sufficiently to allow creep to resume. A multiple-linear-regression analysis of coseismic-step size as a function ofmore » distance from the creepmeter to an earthquake focus and (or) earthquake magnitude showed a linear correlation between step size and magnitude for the data from two stations. No correlation was found between step size and distance to focus for the data from any of the stations. Reduction in step sizes after May 2, despite numerous large aftershocks, suggests that stored local stress is the dominant factor in coseismic-step size. « less
"Beware of deception": Detecting Half-Truth and Debunking it through Controlled Claim Editing

The prevalence of half-truths, which are statements containing some truth but that are ultimately deceptive, has risen with the increasing use of the internet. To help combat this problem, we have created a comprehensive pipeline consisting of a half-truth detection model and a claim editing model. Our approach utilizes the T5 model for controlled claim editing;"controlled"here means precise adjustments to select parts of a claim. Our methodology achieves an average BLEU score of 0.88 (on a scale of 0-1) and a disinfo-debunk score of 85% on edited claims. Significantly, our T5-based approach outperforms other Language Models such as GPT2, RoBERTa, PEGASUS, and Tailor, with average improvements of 82%, 57%, 42%, and 23% in disinfo-debunk scores, respectively. By extending the LIAR PLUS dataset, we achieve an F1 score of 82% for the half-truth detection model, setting a new benchmark in the field. While previous attempts have been made at half-truth detection, our approach is, to the best of our knowledge, the first to attempt to debunk half-truths.
Classification Committee for Active Deep Object Detection

In object detection, the cost of labeling is much high because it needs not only to confirm the categories of multiple objects in an image but also to accurately determine the bounding boxes of each object. Thus, integrating active learning into object detection will raise pretty positive significance. In this paper, we propose a classification committee for active deep object detection method by introducing a discrepancy mechanism of multiple classifiers for samples' selection when training object detectors. The model contains a main detector and a classification committee. The main detector denotes the target object detector trained from a labeled pool composed of the selected informative images. The role of the classification committee is to select the most informative images according to their uncertainty values from the view of classification, which is expected to focus more on the discrepancy and representative of instances. Specifically, they compute the uncertainty for a specified instance within the image by measuring its discrepancy output by the committee pre-trained via the proposed Maximum Classifiers Discrepancy Group Loss (MCDGL). The most informative images are finally determined by selecting the ones with many high-uncertainty instances. Besides, to mitigate the impact of interference instances, we design a Focus on Positive Instances Loss (FPIL) to make the committee the ability to automatically focus on the representative instances as well as precisely encode their discrepancies for the same instance. Experiments are conducted on Pascal VOC and COCO datasets versus some popular object detectors. And results show that our method outperforms the state-of-the-art active learning methods, which verifies the effectiveness of the proposed method.
Accurate synthesis of Dysarthric Speech for ASR data augmentation

Dysarthria is a motor speech disorder often characterized by reduced speech intelligibility through slow, uncoordinated control of speech production muscles. Automatic Speech recognition (ASR) systems can help dysarthric talkers communicate more effectively. However, robust dysarthria-specific ASR requires a significant amount of training speech, which is not readily available for dysarthric talkers. This paper presents a new dysarthric speech synthesis method for the purpose of ASR training data augmentation. Differences in prosodic and acoustic characteristics of dysarthric spontaneous speech at varying severity levels are important components for dysarthric speech modeling, synthesis, and augmentation. For dysarthric speech synthesis, a modified neural multi-talker TTS is implemented by adding a dysarthria severity level coefficient and a pause insertion model to synthesize dysarthric speech for varying severity levels. To evaluate the effectiveness for synthesis of training data for ASR, dysarthria-specific speech recognition was used. Results show that a DNN-HMM model trained on additional synthetic dysarthric speech achieves WER improvement of 12.2% compared to the baseline, and that the addition of the severity level and pause insertion controls decrease WER by 6.5%, showing the effectiveness of adding these parameters. Overall results on the TORGO database demonstrate that using dysarthric synthetic speech to increase the amount of dysarthric-patterned speech for training has significant impact on the dysarthric ASR systems. In addition, we have conducted a subjective evaluation to evaluate the dysarthric-ness and similarity of synthesized speech. Our subjective evaluation shows that the perceived dysartrhic-ness of synthesized speech is similar to that of true dysarthric speech, especially for higher levels of dysarthria
Building RadiologyNET: Unsupervised annotation of a large-scale multimodal medical database

Background and objective: The usage of machine learning in medical diagnosis and treatment has witnessed significant growth in recent years through the development of computer-aided diagnosis systems that are often relying on annotated medical radiology images. However, the availability of large annotated image datasets remains a major obstacle since the process of annotation is time-consuming and costly. This paper explores how to automatically annotate a database of medical radiology images with regard to their semantic similarity. Material and methods: An automated, unsupervised approach is used to construct a large annotated dataset of medical radiology images originating from Clinical Hospital Centre Rijeka, Croatia, utilising multimodal sources, including images, DICOM metadata, and narrative diagnoses. Several appropriate feature extractors are tested for each of the data sources, and their utility is evaluated using k-means and k-medoids clustering on a representative data subset. Results: The optimal feature extractors are then integrated into a multimodal representation, which is then clustered to create an automated pipeline for labelling a precursor dataset of 1,337,926 medical images into 50 clusters of visually similar images. The quality of the clusters is assessed by examining their homogeneity and mutual information, taking into account the anatomical region and modality representation. Conclusion: The results suggest that fusing the embeddings of all three data sources together works best for the task of unsupervised clustering of large-scale medical data, resulting in the most concise clusters. Hence, this work is the first step towards building a much larger and more fine-grained annotated dataset of medical radiology images.
Robust Bayesian Tensor Factorization with Zero-Inflated Poisson Model and Consensus Aggregation

Tensor factorizations (TF) are powerful tools for the efficient representation and analysis of multidimensional data. However, classic TF methods based on maximum likelihood estimation underperform when applied to zero-inflated count data, such as single-cell RNA sequencing (scRNA-seq) data. Additionally, the stochasticity inherent in TFs results in factors that vary across repeated runs, making interpretation and reproducibility of the results challenging. In this paper, we introduce Zero Inflated Poisson Tensor Factorization (ZIPTF), a novel approach for the factorization of high-dimensional count data with excess zeros. To address the challenge of stochasticity, we introduce Consensus Zero Inflated Poisson Tensor Factorization (C-ZIPTF), which combines ZIPTF with a consensus-based meta-analysis. We evaluate our proposed ZIPTF and C-ZIPTF on synthetic zero-inflated count data and synthetic and real scRNA-seq data. ZIPTF consistently outperforms baseline matrix and tensor factorization methods in terms of reconstruction accuracy for zero-inflated data. When the probability of excess zeros is high, ZIPTF achieves up to $2.4\times$ better accuracy. Additionally, C-ZIPTF significantly improves the consistency and accuracy of the factorization. When tested on both synthetic and real scRNA-seq data, ZIPTF and C-ZIPTF consistently recover known and biologically meaningful gene expression programs.
Dynamical large deviations for long-range interacting inhomogeneous systems without collective effects

We consider the long-term evolution of an inhomogeneous long-range interacting $N$-body system. Placing ourselves in the dynamically hot limit, i.e. neglecting collective effects, we derive a large deviation principle for the system's empirical angle-averaged distribution function. This result extends the classical ensemble-averaged kinetic theory given by the so-called inhomogeneous Landau equation, as it specifies the probability of typical and large dynamical fluctuations. We detail the main properties of the associated large deviation Hamiltonian, in particular how it complies with the system's conservation laws and possesses a gradient structure.
STS-GAN: Can We Synthesize Solid Texture with High Fidelity from Arbitrary Exemplars?

Solid texture synthesis (STS), an effective way to extend a 2D exemplar to a 3D solid volume, exhibits advantages in numerous application domains. However, existing methods generally fail to accurately learn arbitrary textures, which may result in the failure to synthesize solid textures with high ﬁ-delity. In this paper, we propose a novel generative adversarial nets-based framework (STS-GAN) to hierarchically learn arbitrary solid textures. In STS-GAN, multi-scale discrimi-nators evaluate the similarity between patch from exemplar and slice from the generated volume, promoting the generator synthesizing realistic solid textures. Finally, experimental results demonstrate that the proposed method can generate high-ﬁdelity solid textures with similar visual characteristics to the exemplar.
SYENet: A Simple Yet Effective Network for Multiple Low-Level Vision Tasks with Real-time Performance on Mobile Device

With the rapid development of AI hardware accelerators, applying deep learning-based algorithms to solve various low-level vision tasks on mobile devices has gradually become possible. However, two main problems still need to be solved: task-specific algorithms make it difficult to integrate them into a single neural network architecture, and large amounts of parameters make it difficult to achieve real-time inference. To tackle these problems, we propose a novel network, SYENet, with only 6K parameters, to handle multiple low-level vision tasks on mobile devices in a real-time manner. The SYENet consists of two asymmetrical branches with simple building blocks. To effectively connect the results by asymmetrical branches, a Quadratic Connection Unit(QCU) is proposed. Furthermore, to improve performance, a new Outlier-Aware Loss is proposed to process the image. The proposed method proves its superior performance with the best PSNR as compared with other networks in real-time applications such as Image Signal Processing(ISP), Low-Light Enhancement(LLE), and Super-Resolution(SR) with 2K60FPS throughput on Qualcomm 8 Gen 1 mobile SoC(System-on-Chip). Particularly, for ISP task, SYENet got the highest score in MAI 2022 Learned Smartphone ISP challenge.
Visually-Aware Context Modeling for News Image Captioning

News Image Captioning aims to create captions from news articles and images, emphasizing the connection between textual context and visual elements. Recognizing the significance of human faces in news images and the face-name co-occurrence pattern in existing datasets, we propose a face-naming module for learning better name embeddings. Apart from names, which can be directly linked to an image area (faces), news image captions mostly contain context information that can only be found in the article. We design a retrieval strategy using CLIP to retrieve sentences that are semantically close to the image, mimicking human thought process of linking articles to images. Furthermore, to tackle the problem of the imbalanced proportion of article context and image context in captions, we introduce a simple yet effective method Contrasting with Language Model backbone (CoLaM) to the training pipeline. We conduct extensive experiments to demonstrate the efficacy of our framework. We out-perform the previous state-of-the-art (without external data) by 7.97/5.80 CIDEr scores on GoodNews/NYTimes800k. Our code is available at https://github.com/tingyu215/VACNIC.
GAEI-UNet: Global Attention and Elastic Interaction U-Net for Vessel Image Segmentation

Vessel image segmentation plays a pivotal role in medical diagnostics, aiding in the early detection and treatment of vascular diseases. While segmentation based on deep learning has shown promising results, effectively segmenting small structures and maintaining connectivity between them remains challenging. To address these limitations, we propose GAEI-UNet, a novel model that combines global attention and elastic interaction-based techniques. GAEI-UNet leverages global spatial and channel context information to enhance high-level semantic understanding within the U-Net architecture, enabling precise segmentation of small vessels. Additionally, we adopt an elastic interaction-based loss function to improve connectivity among these fine structures. By capturing the forces generated by misalignment between target and predicted shapes, our model effectively learns to preserve the correct topology of vessel networks. Evaluation on retinal vessel dataset -- DRIVE demonstrates the superior performance of GAEI-UNet in terms of SE and connectivity of small structures, without significantly increasing computational complexity. This research aims to advance the field of vessel image segmentation, providing more accurate and reliable diagnostic tools for the medical community. The implementation code is available on Code.
Benchmarking Neural Network Generalization for Grammar Induction

How well do neural networks generalize? Even for grammar induction tasks, where the target generalization is fully known, previous works have left the question open, testing very limited ranges beyond the training set and using different success criteria. We provide a measure of neural network generalization based on fully specified formal languages. Given a model and a formal grammar, the method assigns a generalization score representing how well a model generalizes to unseen samples in inverse relation to the amount of data it was trained on. The benchmark includes languages such as a^nb^n, a^nb^nc^n, a^nb^mc^{n+m}, and Dyck-1 and 2. We evaluate selected architectures using the benchmark and find that networks trained with a Minimum Description Length objective (MDL) generalize better and using less data than networks trained using standard loss functions. The benchmark is available at https://github.com/taucompling/bliss.
Detection of Binary Black Hole Mergers from the Signal-to-Noise Ratio Time Series Using Deep Learning

Gravitational wave detection has opened up new avenues for exploring and understanding some of the fundamental principles of the universe. The optimal method for detecting modelled gravitational-wave events involves template-based matched filtering and doing a multi-detector search in the resulting signal-to-noise ratio time series. In recent years, advancements in machine learning and deep learning have led to a flurry of research into using these techniques to replace matched filtering searches and for efficient and robust parameter estimation. This paper presents a novel approach that utilizes deep learning techniques to detect gravitational waves from the signal-to-noise ratio time series produced from matched filtering. We do this to investigate if an efficient deep-learning model could replace the computationally expensive post-processing in current search pipelines. We present a feasibility study where we look to detect gravitational waves from binary black hole mergers in simulated stationary Gaussian noise from the LIGO detector in Hanford, Washington. We show that our model can match the performance of a single-detector matched filtering search and that the ranking statistic from the output of our model was robust over unseen noise, exhibiting promising results for practical online implementation in the future. We discuss the possible implications of this work and its future applications to gravitational-wave detection.
Hierarchical Uncertainty Estimation for Medical Image Segmentation Networks

Learning a medical image segmentation model is an inherently ambiguous task, as uncertainties exist in both images (noise) and manual annotations (human errors and bias) used for model training. To build a trustworthy image segmentation model, it is important to not just evaluate its performance but also estimate the uncertainty of the model prediction. Most state-of-the-art image segmentation networks adopt a hierarchical encoder architecture, extracting image features at multiple resolution levels from fine to coarse. In this work, we leverage this hierarchical image representation and propose a simple yet effective method for estimating uncertainties at multiple levels. The multi-level uncertainties are modelled via the skip-connection module and then sampled to generate an uncertainty map for the predicted image segmentation. We demonstrate that a deep learning segmentation network such as U-net, when implemented with such hierarchical uncertainty estimation module, can achieve a high segmentation performance, while at the same time provide meaningful uncertainty maps that can be used for out-of-distribution detection.
CFT reconstruction of local bulk operators in half-Minkowski space

We construct a holographic map that reconstructs massless fields (scalars, Maxwell field \&Fierz-Pauli field) in half-Minkowski spacetime in $d+1$ dimensions terms of smeared primary operators in a large $N$ factorizable CFT in $\mathbb{R}^{d-1,1}$ spacetime dimensions. This map is based on a Weyl (rescaling) transformation from the Poincar\'e wedge of AdS to the Minkowski half-space; and on the HKLL smearing function, which reconstructs local bulk operators in the Poincar\'e AdS in terms of smeared operators on the conformal boundary of the Poincar\'e wedge. The massless scalar field is reconstructed up to the level of two-point functions, while the Maxwell field and massless spin-2 fields are reconstructed at the level of the one-point function. We also discuss potential ways the map can be generalized to higher dimensions, and to the full Minkowski space.
Infrared spectra of TiO_2 clusters for hot Jupiter atmospheres

Context. Clouds seem unavoidable in cool and dense environments, and hence, are necessary to explain observations of exoplanet atmospheres, most recently of WASP 96b with JWST. Understanding the formation of cloud condensation nuclei in non-terrestrial environments is therefore crucial to develop accurate models to interpret present and future observations. Aims. The goal of the paper is to support observations with infrared spectra for (TiO2)N clusters in order to study cloud formation in exoplanet atmospheres. Methods. Vibrational frequencies are derived from quantum-chemical calculations for 123 (TiO2)-clusters and their isomers, and line-broadening mechanisms are evaluated. Cluster spectra are calculated for several atmospheric levels for two example exoplanet atmospheres (WASP 121b-like and WASP 96b-like) to identify possible spectral fingerprints for cloud formation. Results. Rotational motion of and transitions in the clusters cause significant line broadening, so that individual vibrational lines are broadened beyond the spectral resolution of the medium resolution mode of the JWST mid-infrared instrument MIRI at R = 3000. However, each individual cluster isomer exhibits a"fingerprint"IR spectrum. In particular, larger (TiO2)-clusters have distinctly different spectra from smaller clusters. Morning and evening terminator for the same planet can exhibit different total absorbances due to different cluster sizes being more abundant. Conclusions. The largest (TiO2)-clusters are not necessarily the most abundant (TiO2)-clusters in the high-altitude regions of ultra-hot Jupiters, and the different cluster isomers will contribute to the local absorbance. Planets with a considerable day-night asymmetry will be most suitable to search for (TiO2)-cluster isomers in order to improve cloud formation modelling.