Miracle-dz/UniFAR-FTUs huggingface dataset:
Exploiting the Partly Scratch-off Lottery Ticket for Quantization-Aware Training

Quantization-aware training (QAT) receives extensive popularity as it well retains the performance of quantized networks. In QAT, the contemporary experience is that all quantized weights are updated for an entire training process. In this paper, this experience is challenged based on an interesting phenomenon we observed. Specifically, a large portion of quantized weights reaches the optimal quantization level after a few training epochs, which we refer to as the partly scratch-off lottery ticket. This straightforward-yet-valuable observation naturally inspires us to zero out gradient calculations of these weights in the remaining training period to avoid meaningless updating. To effectively find the ticket, we develop a heuristic method, dubbed lottery ticket scratcher (LTS), which freezes a weight once the distance between the full-precision one and its quantization level is smaller than a controllable threshold. Surprisingly, the proposed LTS typically eliminates 50%-70% weight updating and 25%-35% FLOPs of the backward pass, while still resulting on par with or even better performance than the compared baseline. For example, compared with the baseline, LTS improves 2-bit MobileNetV2 by 5.05%, eliminating 46% weight updating and 23% FLOPs of the backward pass. Code is at url{https://github.com/zysxmu/LTS}.
ConvFormer: Combining CNN and Transformer for Medical Image Segmentation

Convolutional neural network (CNN) based methods have achieved great successes in medical image segmentation, but their capability to learn global representations is still limited due to using small effective receptive fields of convolution operations. Transformer based methods are capable of modelling long-range dependencies of information for capturing global representations, yet their ability to model local context is lacking. Integrating CNN and Transformer to learn both local and global representations while exploring multi-scale features is instrumental in further improving medical image segmentation. In this paper, we propose a hierarchical CNN and Transformer hybrid architecture, called ConvFormer, for medical image segmentation. ConvFormer is based on several simple yet effective designs. (1) A feed forward module of Deformable Transformer (DeTrans) is re-designed to introduce local information, called Enhanced DeTrans. (2) A residual-shaped hybrid stem based on a combination of convolutions and Enhanced DeTrans is developed to capture both local and global representations to enhance representation ability. (3) Our encoder utilizes the residual-shaped hybrid stem in a hierarchical manner to generate feature maps in different scales, and an additional Enhanced DeTrans encoder with residual connections is built to exploit multi-scale features with feature maps of different scales as input. Experiments on several datasets show that our ConvFormer, trained from scratch, outperforms various CNN- or Transformer-based architectures, achieving state-of-the-art performance.
On form invariance of the Kirchhoff-Love plate equation

We propose an alternative approach called backward transformation for the design of platonic cloaks, without resorting to in-plane body forces and pre-stresses, which can lead to unphysical features. It is shown that the Kirchhoff-Love equation for an anisotropic heterogeneous plate is actually form invariant for a class of transformations with a vanishing Hessian. This formalism is detailed and numerically validated with three-dimensional simulations in the time domain. Results published in the literature fall in the class of transformations proposed here.
GC-GRU-N for Traffic Prediction using Loop Detector Data

—Because trafﬁc characteristics display stochastic nonlinear spatiotemporal dependencies, trafﬁc prediction is a challenging task. In this paper develop a graph convolution – gated recurrent unit (GC-GRU-N) network to extract the essential spatio-temporal features. we use Seattle loop detector data aggregated over 15 minutes and reframe the problem through space and time. The model performance is compared o benchmark models; Historical Average, Long - Short Term Memory (LSTM) and Transformers. The proposed model ranked second with the fastest inference time and a very close performance to ﬁrst place (Transformers). Our model also achieves a running time that is six-times faster than transformers. Finally, we present a comparative study of our model and the available benchmarks using metrics such as training time, inference time, MAPE, MAE and RMSE. Spatial and temporal aspects are also analyzed for each of the trained models.
A Hierarchical Deep Neural Network for Detecting Lines of Codes with Vulnerabilities

Software vulnerabilities, caused by unintentional flaws in source codes, are the main root cause of cyberattacks. Source code static analysis has been used extensively to detect the unintentional defects, i.e. vulnerabilities, introduced into the source codes by software developers. In this paper, we propose a deep learning approach to detect vulnerabilities from their LLVM IR representations based on the techniques that have been used in natural language processing. The proposed approach uses a hierarchical process to first identify source codes with vulnerabilities, and then it identifies the lines of codes that contribute to the vulnerability within the detected source codes. This proposed two-step approach reduces the false alarm of detecting vulnerable lines. Our extensive experiment on real-world and synthetic codes collected in NVD and SARD shows high accuracy (about 98%) in detectina source code vulnerabilities111The code repository: https://github.com/arashmahyari/PLP.
Detection of faint broad emission lines in type 2 AGN – II. On the measurement of the black hole mass of type 2 AGN and the unified model

We report the virial measurements of the BH mass of a sample of 17 type 2 AGN, drawn from the Swift/BAT 70-month 14-195 keV hard X-ray catalogue, where a faint BLR component has been measured via deep NIR (0.8-2.5 $\mu$m) spectroscopy. We compared the type 2 AGN with a control sample of 33 type 1 AGN. We find that the type 2 AGN BH masses span the 5$<$ log(M$_{BH}$ /M$_{\odot}$) $< $7.5 range, with an average log(M$_{BH}$/M$_{\odot}$) = 6.7, which is $\sim$ 0.8 dex smaller than found for type 1 AGN. If type 1 and type 2 AGN of the same X-ray luminosity log($L_{14-195}$/erg s$^{-1}$) $\sim$ 43.5 are compared, type 2 AGN have 0.5 dex smaller BH masses than type 1 AGN. Although based on few tens of objects, this result disagrees with the standard AGN unification scenarios in which type 1 and type 2 AGN are the same objects observed along different viewing angles with respect to a toroidal absorbing material.
Deep learning for table detection and structure recognition: A survey

Tables are everywhere, from scientific journals, papers, websites, and newspapers all the way to items we buy at the supermarket. Detecting them is thus of utmost importance to automatically understanding the content of a document. The performance of table detection has substantially increased thanks to the rapid development of deep learning networks. The goals of this survey are to provide a profound comprehension of the major developments in the field of Table Detection, offer insight into the different methodologies, and provide a systematic taxonomy of the different approaches. Furthermore, we provide an analysis of both classic and new applications in the field. Lastly, the datasets and source code of the existing models are organized to provide the reader with a compass on this vast literature. Finally, we go over the architecture of utilizing various object detection and table structure recognition methods to create an effective and efficient system, as well as a set of development trends to keep up with state-of-the-art algorithms and future research. We have also set up a public GitHub repository where we will be updating the most recent publications, open data, and source code. The GitHub repository is available at https://github.com/abdoelsayed2016/table-detection-structure-recognition.
Spatio-spectral limiting on hypercubes: eigenspaces

The operator that first truncates to a neighborhood of the origin in the spectral domain then truncates to a neighborhood of the origin in the spatial domain is investigated in the case of Boolean cubes. This operator is self adjoint on a space of bandlimited signals. The eigenspaces of this iterated projection operator are studied and are shown to depend fundamentally on the neighborhood structure of the cube when regarded as a metric graph with path distance equal to Hamming distance.
LEAN-DMKDE: Quantum Latent Density Estimation for Anomaly Detection

This paper presents an anomaly detection model that combines the strong statistical foundation of density-estimation-based anomaly detection methods with the representation-learning ability of deep-learning models. The method combines an autoencoder, that learns a low-dimensional representation of the data, with a density-estimation model based on density matrices in an end-to-end architecture that can be trained using gradient-based optimization techniques. A systematic experimental evaluation was performed on different benchmark datasets. The experimental results show that the method is able to outperform other state-of-the-art methods.
Improving Feature-based Visual Localization by Geometry-Aided Matching

Feature matching is crucial in visual localization, where 2D-3D correspondence plays a major role in determining the accuracy of camera pose. A sufficient number of well-distributed 2D-3D correspondences is essential for accurate pose estimation due to noise. However, existing 2D-3D feature matching methods rely on finding nearest neighbors in the feature space and removing outliers using hand-crafted heuristics, which may lead to potential matches being missed or the correct matches being filtered out. In this work, we propose a novel method called Geometry-Aided Matching (GAM), which incorporates both appearance information and geometric context to address this issue and to improve 2D-3D feature matching. GAM can greatly boost the recall of 2D-3D matches while maintaining high precision. We apply GAM to a new hierarchical visual localization pipeline and show that GAM can effectively improve the robustness and accuracy of localization. Extensive experiments show that GAM can find more real matches than hand-crafted heuristics and learning baselines. Our proposed localization method achieves state-of-the-art results on multiple visual localization datasets. Experiments on Cambridge Landmarks dataset show that our method outperforms the existing state-of-the-art methods and is six times faster than the top-performed method. The source code is available at https://github.com/openxrlab/xrlocalization.
Development of a new quantum trajectory molecular dynamics framework

An extension to the wave packet description of quantum plasmas is presented, where the wave packet can be elongated in arbitrary directions. A generalized Ewald summation is constructed for the wave packet models accounting for long-range Coulomb interactions and fermionic effects are approximated by purpose-built Pauli potentials, self-consistent with the wave packets used. We demonstrate its numerical implementation with good parallel support and close to linear scaling in particle number, used for comparisons with the more common wave packet employing isotropic states. Ground state and thermal properties are compared between the models with differences occurring primarily in the electronic subsystem. Especially, the electrical conductivity of dense hydrogen is investigated where a 15% increase in DC conductivity can be seen in our wave packet model compared with other models. This article is part of the theme issue ‘Dynamic and transient processes in warm dense matter’.
Self-supervised Egomotion and Depth Learning via Bi-directional Coarse-to-Fine Scale Recovery

Accurately perceiving location and scene is crucial for autonomous driving and mobile robots. Recent advances in deep learning have made it possible to learn egomotion and depth from monocular images in a self-supervised manner, without requiring highly precise labels to train the networks. However, monocular vision methods suffer from a limitation known as scale-ambiguity, which restricts their application when absolute-scale is necessary. To address this, we propose SelfOdom, a self-supervised dual-network framework that can robustly and consistently learn and generate pose and depth estimates in global scale from monocular images. In particular, we introduce a novel coarse-to-fine training strategy that enables the metric scale to be recovered in a two-stage process. Furthermore, SelfOdom is flexible and can incorporate inertial data with images, which improves its robustness in challenging scenarios, using an attention-based fusion module. Our model excels in both normal and challenging lighting conditions, including difficult night scenes. Extensive experiments on public datasets have demonstrated that SelfOdom outperforms representative traditional and learning-based VO and VIO models.
MCNET: Fuse Multiple Cues for Multichannel Speech Enhancement

In multichannel speech enhancement, both spectral and spatial information are vital for discriminating between speech and noise. How to fully exploit these two types of information and their temporal dynamics remains an interesting research problem. As a solution to this problem, this paper proposes a multi-cue fusion network named McNet, which cascades four modules to respectively exploit the full-band spatial, narrowband spatial, sub-band spectral, and full-band spectral information. Experiments show that each module in the proposed network has its unique contribution and, as a whole, notably outperforms other state-of-the-art methods.
Mode pumping in photonic lattices using a single tailored auxiliary waveguide

In this work, we propose a completely general method to pump the gapped topological modes of a lattice of optical waveguides by controlling the propagation constant of an auxiliary waveguide and its coupling to the main lattice. In this way, we can transform a single-waveguide excitation on the auxiliary waveguide into a specific mode of the lattice. We also demonstrate the possibility of transferring supermodes between two waveguide lattices using the same method. We illustrate the results by pumping and transferring the topological modes of Su-Schrieffer-Heeger lattices. For both scenarios, we show that purities above 99\% can be achieved with parameter values within experimental reach. Additionally, we demonstrate how the technique can be used to pump the bulk modes of the lattice for low number of waveguides or enable mode conversion between waveguide lattices.
Psychophysiology-aided Perceptually Fluent Speech Analysis of Children Who Stutter

This first-of-its-kind paper presents a novel approach named PASAD that detects changes in perceptually fluent speech acoustics of young children. Particularly, analysis of perceptually fluent speech enables identifying the speech-motor-control factors that are considered as the underlying cause of stuttering disfluencies. Recent studies indicate that the speech production of young children, especially those who stutter, may get adversely affected by situational physiological arousal. A major contribution of this paper is leveraging the speaker's situational physiological responses in real-time to analyze the speech signal effectively. The presented PASAD approach adapts a Hyper-Network structure to extract temporal speech importance information leveraging physiological parameters. In addition, a novel non-local acoustic spectrogram feature extraction network identifies meaningful acoustic attributes. Finally, a sequential network utilizes the acoustic attributes and the extracted temporal speech importance for effective classification. We collected speech and physiological sensing data from 73 preschool-age children who stutter (CWS) and who don't stutter (CWNS) in different conditions. PASAD's unique architecture enables visualizing speech attributes distinct to a CWS's fluent speech and mapping them to the speaker's respective speech-motor-control factors (i.e., speech articulators). Extracted knowledge can enhance understanding of children's fluent speech, speech-motor-control (SMC), and stuttering development. Our comprehensive evaluation shows that PASAD outperforms state-of-the-art multi-modal baseline approaches in different conditions, is expressive and adaptive to the speaker's speech and physiology, generalizable, robust, and is real-time executable on mobile and scalable devices.
ED-FAITH: Evaluating Dialogue Summarization on Faithfulness

Abstractive summarization models typically generate content unfaithful to the input, thus highlighting the significance of evaluating the faithfulness of generated summaries. Most faithfulness metrics are only evaluated on news domain, can they be transferred to other summarization tasks? In this work, we first present a systematic study of faithfulness metrics for dialogue summarization. We evaluate common faithfulness metrics on dialogue datasets and observe that most metrics correlate poorly with human judgements despite performing well on news datasets. Given these findings, to improve existing metrics' performance on dialogue summarization, we first finetune on in-domain dataset, then apply unlikelihood training on negative samples, and show that they can successfully improve metric performance on dialogue data. Inspired by the strong zero-shot performance of the T0 language model, we further propose T0-Score -- a new metric for faithfulness evaluation, which shows consistent improvement against baseline metrics across multiple domains.
Conditional variational autoencoder to improve neural audio synthesis for polyphonic music sound

Deep generative models for audio synthesis have recently been signiﬁcantly improved. However, the task of modeling raw-waveforms remains a difﬁcult problem, especially for audio waveforms and music signals. Recently, the realtime audio variational autoencoder (RAVE) method was developed for high-quality audio waveform synthesis. The RAVE method is based on the variational autoencoder and utilizes the two-stage training strategy. Unfortunately, the RAVE model is limited in re-producing wide-pitch polyphonic music sound. Therefore, to enhance the reconstruction performance, we adopt the pitch ac-tivation data as an auxiliary information to the RAVE model. To handle the auxiliary information, we propose an enhanced RAVE model with a conditional variational autoencoder structure and an additional fully-connected layer. To evaluate the proposed structure, we conducted a listening experiment based on multiple stimulus tests with hidden references and an anchor (MUSHRA) with the MAESTRO. The obtained results indicate that the proposed model exhibits a more signiﬁcant performance and stability improvement than the conventional RAVE model.
Simulating noise on a quantum processor: interactions between a qubit and resonant two-level system bath

Material defects fundamentally limit the coherence times of superconducting qubits, and manufacturing completely defect-free devices is not yet possible. Therefore, understanding the interactions between defects and a qubit in a real quantum processor design is essential. We build a model that incorporates the standard tunneling model, the electric field distributions in the qubit, and open quantum system dynamics, and draws from the current understanding of two-level system (TLS) theory. Specifically, we start with one million TLSs distributed on the surface of a qubit and pick the 200 systems that are most strongly coupled to the qubit. We then perform a full Lindbladian simulation that explicitly includes the coherent coupling between the qubit and the TLS bath to model the time dependent density matrix of resonant TLS defects and the qubit. We find that the 200 most strongly coupled TLSs can accurately describe the qubit energy relaxation time. This work confirms that resonant TLSs located in areas where the electric field is strong can significantly affect the qubit relaxation time, even if they are located far from the Josephson junction (JJ). Similarly, a strongly-coupled resonant TLS located in the JJ does not guarantee a reduced qubit relaxation time if a more strongly coupled TLS is far from the JJ. In addition to the coupling strengths between TLSs and the qubit, the model predicts that the geometry of the device and the TLS relaxation time play a significant role in qubit dynamics. Our work can provide guidance for future quantum processor designs with improved qubit coherence times.
A Basic Algorithm for Generating Individualized Numerical Scale (BAGINS)

Linguistic labels are effective means of expressing qualitative assessments because they account for the uncertain nature of human preferences. However, to perform computations with linguistic labels, they must first be converted to numbers using a scale function. Within the context of the Analytic Hierarchy Process (AHP), the most popular scale used to represent linguistic labels numerically is the linear 1-9 scale, which was proposed by Saaty. However, this scale has been criticized by several researchers, and various alternatives are proposed in the literature. There is a growing interest in scale individualization rather than relying on a generic fixed scale since the perceptions of the decision maker regarding these linguistic labels are highly subjective. The methods proposed in the literature for scale individualization focus on minimizing the transitivity errors, i.e., consistency. In this research, we proposed a novel, easy-to-learn, easy-to-implement, and computationally less demanding scale individualization approach based on compatibility. We also developed an experimental setup and introduced two new metrics that can be used by researchers that contribute to the theory of AHP. To assess the value of scale individualization in general, and the performance of the proposed novel approach in particular, numerical and two empirical studies are conducted. The results of the analyses demonstrate that the scale individualization outperforms the conventional fixed scale approach and validates the benefit of the proposed novel heuristic.
Toward Expanding the Scope of Radiology Report Summarization to Multiple Anatomies and Modalities

Radiology report summarization (RRS) is a growing area of research. Given the Findings section of a radiology report, the goal is to generate a summary (called an Impression section) that highlights the key observations and conclusions of the radiology study. However, RRS currently faces essential limitations.First, many prior studies conduct experiments on private datasets, preventing reproduction of results and fair comparisons across different systems and solutions. Second, most prior approaches are evaluated solely on chest X-rays. To address these limitations, we propose a dataset (MIMIC-RRS) involving three new modalities and seven new anatomies based on the MIMIC-III and MIMIC-CXR datasets. We then conduct extensive experiments to evaluate the performance of models both within and across modality-anatomy pairs in MIMIC-RRS. In addition, we evaluate their clinical efficacy via RadGraph, a factual correctness metric.