Miracle-dz/UniFAR-FTUs huggingface dataset:
DDI-100: Dataset for Text Detection and Recognition

With the increasing popularity of document analysis and recognition systems, text detection (TD) and optical character recognition (OCR) in document images become challenging tasks. However, according to our best knowledge, no publicly available datasets for these particular problems exist. In this paper, we introduce a Distorted Document Images dataset (DDI-100) and provide a detailed analysis of the DDI-100 in its current state. To create the dataset we collected 7000 unique document pages, and extended it by applying different types of distortions and geometric transformations. In total, DDI-100 contains more than 100,000 document images together with binary text masks, text and character locations in terms of bounding boxes. We conduct the experiments with several TD and OCR approaches trained on the introduced dataset. Obtained results demonstrate the usefulness of DDI-100 dataset to achieve high-quality results using a small amount of real data.
Future prospects for the minimal supersymmetric standard model

Experimental collaborations for the large hadron collider conducted various searches for supersymmetry. In the absence of signals, lower limits were put on sparticle masses but usually within frameworks with (over-)simplifications relative to the entire indications by supersymmetry models. For complementing current interpretations of experimental bounds, we introduce a 30-parameter version of the R-parity conserving Minimal Supersymmetric Standard Model (MSSM-30). Using a sample of the MSSM-30 which are in harmony with cold dark matter, flavor and precision electroweak constraints, we explicitly show the prospects for assessing neutralino candidate dark matter in contrast to future searches for supersymmetry. The MSSM-30-parameter regions that are beyond reach to dark matter direct detection experiments could be probed by future hadron–hadron colliders.
Linear Convergence for Distributed Optimization Under the Polyak-{\L}ojasiewicz Condition

— This paper considers the distributed optimization problem of minimizing a global cost function formed by a sum of local smooth cost functions by using local information exchange. A standard condition for proving linear convergence of distributed ﬁrst-order algorithms is strong convexity of the cost functions. This may not hold for many practical applications, such as least squares and logistic regression. In this paper, we propose a distributed ﬁrst-order algorithm and show that it converges linearly under the condition that the global cost function satisﬁes the Polyak-Łojasiewicz condition. This condition is weaker than strong convexity and the global minimizer is not necessarily to be unique. The theoretical result is illustrated by numerical simulations.
nnAudio: An on-the-Fly GPU Audio to Spectrogram Conversion Toolbox Using 1D Convolutional Neural Networks

In this paper, we present <monospace>nnAudio</monospace>, a new neural network-based audio processing framework with graphics processing unit (GPU) support that leverages 1D convolutional neural networks to perform time domain to frequency domain conversion. It allows on-the-fly spectrogram extraction due to its fast speed, without the need to store any spectrograms on the disk. Moreover, this approach also allows back-propagation on the waveforms-to-spectrograms transformation layer, and hence, the transformation process can be made trainable, further optimizing the waveform-to-spectrogram transformation for the specific task that the neural network is trained on. All spectrogram implementations scale as Big-O of linear time with respect to the input length. <monospace>nnAudio</monospace>, however, leverages the compute unified device architecture (CUDA) of 1D convolutional neural network from <monospace>PyTorch</monospace>, its short-time Fourier transform (STFT), Mel spectrogram, and constant-Q transform (CQT) implementations are an order of magnitude faster than other implementations using only the central processing unit (CPU). We tested our framework on three different machines with NVIDIA GPUs, and our framework significantly reduces the spectrogram extraction time from the order of seconds (using a popular python library <monospace>librosa</monospace>) to the order of milliseconds, given that the audio recordings are of the same length. When applying nnAudio to variable input audio lengths, an average of 11.5 hours are required to extract 34 spectrogram types with different parameters from the MusicNet dataset using <monospace>librosa</monospace>. An average of 2.8 hours is required for <monospace>nnAudio</monospace>, which is still four times faster than <monospace>librosa</monospace>. Our proposed framework also outperforms existing GPU processing libraries such as <monospace>Kapre</monospace> and <monospace>torchaudio</monospace> in terms of processing speed.
A Study of Multilingual Neural Machine Translation

Multilingual neural machine translation (NMT) has recently been investigated from different aspects (e.g., pivot translation, zero-shot translation, fine-tuning, or training from scratch) and in different settings (e.g., rich resource and low resource, one-to-many, and many-to-one translation). This paper concentrates on a deep understanding of multilingual NMT and conducts a comprehensive study on a multilingual dataset with more than 20 languages. Our results show that (1) low-resource language pairs benefit much from multilingual training, while rich-resource language pairs may get hurt under limited model capacity and training with similar languages benefits more than dissimilar languages; (2) fine-tuning performs better than training from scratch in the one-to-many setting while training from scratch performs better in the many-to-one setting; (3) the bottom layers of the encoder and top layers of the decoder capture more language-specific information, and just fine-tuning these parts can achieve good accuracy for low-resource language pairs; (4) direct translation is better than pivot translation when the source language is similar to the target language (e.g., in the same language branch), even when the size of direct training data is much smaller; (5) given a fixed training data budget, it is better to introduce more languages into multilingual training for zero-shot translation.
EVA: an encrypted vector arithmetic language and compiler for efficient homomorphic computation

Fully-Homomorphic Encryption (FHE) offers powerful capabilities by enabling secure offloading of both storage and computation, and recent innovations in schemes and implementations have made it all the more attractive. At the same time, FHE is notoriously hard to use with a very constrained programming model, a very unusual performance profile, and many cryptographic constraints. Existing compilers for FHE either target simpler but less efficient FHE schemes or only support specific domains where they can rely on expert-provided high-level runtimes to hide complications. This paper presents a new FHE language called Encrypted Vector Arithmetic (EVA), which includes an optimizing compiler that generates correct and secure FHE programs, while hiding all the complexities of the target FHE scheme. Bolstered by our optimizing compiler, programmers can develop efficient general-purpose FHE applications directly in EVA. For example, we have developed image processing applications using EVA, with a very few lines of code. EVA is designed to also work as an intermediate representation that can be a target for compiling higher-level domain-specific languages. To demonstrate this, we have re-targeted CHET, an existing domain-specific compiler for neural network inference, onto EVA. Due to the novel optimizations in EVA, its programs are on average 5.3× faster than those generated by CHET. We believe that EVA would enable a wider adoption of FHE by making it easier to develop FHE applications and domain-specific FHE compilers.
Neural ODEs for Image Segmentation with Level Sets

We propose a novel approach for image segmentation that combines Neural Ordinary Differential Equations (NODEs) and the Level Set method. Our approach parametrizes the evolution of an initial contour with a NODE that implicitly learns from data a speed function describing the evolution. In addition, for cases where an initial contour is not available and to alleviate the need for careful choice or design of contour embedding functions, we propose a NODE-based method that evolves an image embedding into a dense per-pixel semantic label space. We evaluate our methods on kidney segmentation (KiTS19) and on salient object detection (PASCAL-S, ECSSD and HKU-IS). In addition to improving initial contours provided by deep learning models while using a fraction of their number of parameters, our approach achieves F scores that are higher than several state-of-the-art deep learning algorithms.
Language Independent Sentiment Analysis

Social media platforms and online forums generate a rapid and increasing amount of textual data. Businesses, government agencies, and media organizations seek to perform sentiment analysis on this rich text data. The results of these analytics are used for adapting marketing strategies, customizing products, security, and various other decision makings. Sentiment analysis has been extensively studied and various methods have been developed for it with great success. These methods, however, apply to texts written in a specific language. This limits the applicability to a particular demographic and geographic region. In this paper, we propose a general approach for sentiment analysis on data containing texts from multiple languages. This enables all the applications to utilize the results of sentiment analysis in a language oblivious or language-independent fashion.
Review on Diabetes Mellitus and Oral Diseases

Diabetes mellitus is used to identify a group of disorders characterized by elevated levels of glucose in the blood. The metabolic disorders can cause a series of systemic diseases in patients with severe diabetes mellitus. Oral common complications usually manifested as periodontal disease, oral candidiasis, dental caries, etc. The periodontal disease was classified as the sixth complications among diabetes patients, even affects the life quality of those patients. This review focus on the oral health status of patients with diabetes and emphasizes on the relationship of the diabetes and periodontal disease, and suggests that patients with diabetes the importance of oral health and oral health for patients with diabetes to provide a basis.
Deep Learning Based Android Malware Detection Framework

With the development in the field of smartphones and ever growing base of Internet, various softwares are left prone to many malicious activities like pharming, phishing, ransomware, spam, spoofing, spyware, eavesdropping, etc. These threats have not spared the smartphones which are equally prone to them. In this work, we aim to detect these malwares with accuracy and efficiency. This being essentially a classification problem, we use various machine learning methods for this task. We observe that across models, Attention based Artificial Neural Networks (ANN), or broadly speaking, Deep Learning, are most suitable for this problem. Attention based ANNs are an amalgamation of accuracy and efficiency, the crux of our work. The accuracy achieved by our model is around 96.75\%. Our model runs the test on Android Package Files (APKs) to determine whether a particular application is malicious or not by doing behavior analysis on android application under consideration.
Polynomial-Time Exact MAP Inference on Discrete Models with Global Dependencies

Considering the worst-case scenario, junction tree algorithm remains the most general solution for exact MAP inference with polynomial run-time guarantees. Unfortunately, its main tractability assumption requires the treewidth of a corresponding MRF to be bounded strongly limiting the range of admissible applications. In fact, many practical problems in the area of structured prediction require modelling of global dependencies by either directly introducing global factors or enforcing global constraints on the prediction variables. That, however, always results in a fully-connected graph making exact inference by means of this algorithm intractable. Previous work [1]-[4] focusing on the problem of loss-augmented inference has demonstrated how efficient inference can be performed on models with specific global factors representing non-decomposable loss functions within the training regime of SSVMs. In this paper, we extend the framework for an efficient exact inference proposed in in [3] by allowing much finer interactions between the energy of the core model and the sufficient statistics of the global terms with no additional computation costs. We demonstrate the usefulness of our method in several use cases, including one that cannot be handled by any of the previous approaches. Finally, we propose a new graph transformation technique via node cloning which ensures a polynomial run-time for solving our target problem independently of the form of a corresponding clique tree. This is important for the efficiency of the main algorithm and greatly improves upon the theoretical guarantees of the previous works.
Towards a quantum-inspired proof for IP = PSPACE

We explore quantum-inspired interactive proof systems where the prover is limited. Namely, we improve on a result by \cite{AG17} showing a quantum-inspired interactive protocol ($\IP$) for $PreciseBQP$ where the prover is only assumed to be a $\PreciseBQP$ machine, and show that the result can be strengthened to show an $\IP$ for $\NP^{\PP}$ with a prover which is only assumed to be an $\NP^{\PP}$ machine - which was not known before. We also show how the protocol can be used to directly verify $\QMA$ computations, thus connecting the sum-check protocol by \cite{AAV13} with the result of \cite{AG17,LFKN90}. Our results shed light on a quantum-inspired proof for $\IP=\PSPACE$, as $\PreciseQMA$ captures the full $\PSPACE$ power.
Circular and Linear ${\rm e^+ e^-}$ Colliders: Another Story of Complementarity

The remarkable synergy and complementarity between the circular ${\rm e^+ e^-}$ and pp colliders has been extensively discussed. In this short document, we investigate the complementarity between the proposed circular and linear ${\rm e^+ e^-}$ colliders at the electroweak and TeV scale. This complementarity could be exploited on a world-wide scale, if both a large circular and a linear infrastructures were available. A possible implementation of such a complementary program is shown.
Holographic technicolor model and dark matter

We investigate the strongly coupled minimal walking technicolor model (MWT) in the framework of a bottom-up holographic model, where the global $SU(4)$ symmetry breaks to $SO(4)$ subgroup. In the holographic model, we found that 125GeV composite Higgs particles and small Peskin-Takeuchi $S$ parameter can be achieved simultaneously. In addition, the model predicts a large number of particles at the TeV scale, including dark matter candidate Technicolor Interacting Massive Particles (TIMPs). If we consider the dark matter nuclear spin-independent cross-section in the range of $10^{-45}\sim 10 ^ {-48} $cm$^2$, which can be detected by future experiments, the mass range of TIMPs predicted by the holographic technicolor model is 2 $\sim$ 4 TeV.
On the Coverage and Capacity of Ultra-Dense Networks With Directional Transmissions

We investigate the performance of a downlink ultra-dense network (UDN) with directional transmissions via stochastic geometry. Considering the dual-slope path loss model and sectored beamforming pattern, we derive the expressions and asymptotic characteristics of the coverage probability and constrained area spectrum efficiency (ASE). Several special scenarios, namely the physically feasible path loss model and adjustable beam pattern, are also analyzed. Although signal-to-interference-plus-noise ratio collapsing still exists when the path loss exponent in the near-field is no larger than 2, using strategies like beam pattern adaption, can avoid the decrease of the coverage probability and constrained ASE even when the base station density approaches infinity.
The CT Findings of Bronchial Anthracofibrosis: Comparison of Cases with or without Active Tuberculosis

Purpose: To assess the CT findings of bronchial anthracofibrosis with or without active tuberculosis. Materials and Methods: Analysis of the CT scans of 35 patients with bronchoscopically proven anthracofibrosis revealed that 25 were negative for active tuberculosis and ten were positive. Bronchial and pleural lesions, pulmonary parenchymal lesions, and mediastinal/hilar lymph nodes were retrospectively evaluated. Results: Common radiologic findings of anthracofibrosis included diffuse bronchial narrowing (50%), wall thickening (37%), and mass-like soft tissue density in central bronchi (8%). The right middle lobe was most commonly involved (31%). Common findings of pulmonary parenchymal lesions included consolidation (37%), atelectasis (31%), and ground-glass opacity (10%). These bronchial or pulmonary parenchymal findings were common in patients with or without active tuberculosis. Findings of multiple poorly defined small nodules, including branching opacities and consolidation with internal low density were seen only in patients with active tuberculosis (p
Symmetric block-low-rank layers for fully reversible multilevel neural networks

Factors that limit the size of the input and output of a neural network include memory requirements for the network states/activations to compute gradients, as well as memory for the convolutional kernels or other weights. The memory restriction is especially limiting for applications where we want to learn how to map volumetric data to the desired output, such as video-to-video. Recently developed fully reversible neural networks enable gradient computations using storage of the network states for a couple of layers only. While this saves a tremendous amount of memory, it is the convolutional kernels that take up most memory if fully reversible networks contain multiple invertible pooling/coarsening layers. Invertible coarsening operators such as the orthogonal wavelet transform cause the number of channels to grow explosively. We address this issue by combining fully reversible networks with layers that contain the convolutional kernels in a compressed form directly. Specifically, we introduce a layer that has a symmetric block-low-rank structure. In spirit, this layer is similar to bottleneck and squeeze-and-expand structures. We contribute symmetry by construction, and a combination of notation and flattening of tensors allows us to interpret these network structures in linear algebraic fashion as a block-low-rank matrix in factorized form and observe various properties. A video segmentation example shows that we can train a network to segment the entire video in one go, which would not be possible, in terms of memory requirements, using non-reversible networks and previously proposed reversible networks.
White Noise Analysis of Neural Networks

A white noise analysis of modern deep neural networks is presented to unveil their biases at the whole network level or the single neuron level. Our analysis is based on two popular and related methods in psychophysics and neurophysiology namely classification images and spike triggered analysis. These methods have been widely used to understand the underlying mechanisms of sensory systems in humans and monkeys. We leverage them to investigate the inherent biases of deep neural networks and to obtain a first-order approximation of their functionality. We emphasize on CNNs since they are currently the state of the art methods in computer vision and are a decent model of human visual processing. In addition, we study multi-layer perceptrons, logistic regression, and recurrent neural networks. Experiments over four classic datasets, MNIST, Fashion-MNIST, CIFAR-10, and ImageNet, show that the computed bias maps resemble the target classes and when used for classification lead to an over twofold performance than the chance level. Further, we show that classification images can be used to attack a black-box classifier and to detect adversarial patch attacks. Finally, we utilize spike triggered averaging to derive the filters of CNNs and explore how the behavior of a network changes when neurons in different layers are modulated. Our effort illustrates a successful example of borrowing from neurosciences to study ANNs and highlights the importance of cross-fertilization and synergy across machine learning, deep learning, and computational neuroscience.
Large-scale Multi-modal Person Identification in Real Unconstrained Environments

Person identification (P-ID) under real unconstrained noisy environments is a huge challenge. In multiple-feature learning with Deep Convolutional Neural Networks (DCNNs) or Machine Learning method for large-scale person identification in the wild, the key is to design an appropriate strategy for decision layer fusion or feature layer fusion which can enhance discriminative power. It is necessary to extract different types of valid features and establish a reasonable framework to fuse different types of information. In traditional methods, different persons are identified based on single modal features to identify, such as face feature, audio feature, and head feature. These traditional methods cannot realize a highly accurate level of person identification in real unconstrained environments. The study aims to propose a fusion module to fuse multi-modal features for person identification in real unconstrained environments.
Cooperative Training of Fast Thinking Initializer and Slow Thinking Solver for Multi-Modal Conditional Learning.

This paper studies the supervised learning of the conditional distribution of a high-dimensional output given an input, where the output and input may belong to two different modalities, e.g., the output is an photo image and the input is a sketch image. We solve this problem by cooperative training of a fast thinking initializer and slow thinking solver. The initializer generates the output directly by a non-linear transformation of the input as well as a noise vector that accounts for latent variability in the output. The slow thinking solver learns an objective function in the form of a conditional energy function, so that the output can be generated by optimizing the objective function, or more rigorously by sampling from the conditional energy-based model. We propose to learn the two models jointly, where the fast thinking initializer serves to initialize the sampling of the slow thinking solver, and the solver refines the initial output by an iterative algorithm. The solver learns from the difference between the refined output and the observed output, while the initializer learns from how the solver refines its initial output. We demonstrate the effectiveness of the proposed method on various multi-modal conditional learning tasks, e.g., class-to-image generation, image-to-image translation, and image recovery.