Miracle-dz/UniFAR-FTUs huggingface dataset:
Hierarchical Auto-Regressive Model for Image Compression Incorporating Object Saliency and a Deep Perceptual Loss

We propose a new end-to-end trainable model for lossy image compression which includes a number of novel components. This approach incorporates 1) a hierarchical auto-regressive model; 2)it also incorporates saliency in the images and focuses on reconstructing the salient regions better; 3) in addition, we empirically demonstrate that the popularly used evaluations metrics such as MS-SSIM and PSNR are inadequate for judging the performance of deep learned image compression techniques as they do not align well with human perceptual similarity. We, therefore propose an alternative metric, which is learned on perceptual similarity data specific to image compression. Our experiments show that this new metric aligns significantly better with human judgments when compared to other hand-crafted or learned metrics. The proposed compression model not only generates images that are visually better but also gives superior performance for subsequent computer vision tasks such as object detection and segmentation when compared to other engineered or learned codecs.
Automatic Segmentation and Visualization of Choroid in OCT with Knowledge Infused Deep Learning

The choroid provides oxygen and nourishment to the outer retina thus is related to the pathology of various ocular diseases. Optical coherence tomography (OCT) is advantageous in visualizing and quantifying the choroid in vivo. However, its application in the study of the choroid is still limited for two reasons. (1) The lower boundary of the choroid (choroid-sclera interface) in OCT is fuzzy, which makes the automatic segmentation difficult and inaccurate. (2) The visualization of the choroid is hindered by the vessel shadows from the superficial layers of the inner retina. In this paper, we propose to incorporate medical and imaging prior knowledge with deep learning to address these two problems. We propose a biomarker-infused global-to-local network (Bio-Net) for the choroid segmentation, which not only regularizes the segmentation via predicted choroid thickness, but also leverages a global-to-local segmentation strategy to provide global structure information and suppress overfitting. For eliminating the retinal vessel shadows, we propose a deep-learning pipeline, which firstly locate the shadows using their projection on the retinal pigment epithelium layer, then the contents of the choroidal vasculature at the shadow locations are predicted with an edge-to-texture generative adversarial inpainting network. The results show our method outperforms the existing methods on both tasks. We further apply the proposed method in a clinical prospective study for understanding the pathology of glaucoma, which demonstrates its capacity in detecting the structure and vascular changes of the choroid related to the elevation of intra-ocular pressure.
Predicting the Speed of Epidemics Spreading in Networks

Global transport and communication networks enable information, ideas, and infectious diseases to now spread at speeds far beyond what has historically been possible. To effectively monitor, design, or intervene in such epidemic-like processes, there is a need to predict the speed of a particular contagion in a particular network, and to distinguish between nodes that are more likely to become infected sooner or later during an outbreak. Here, we study these quantities using a message-passing approach to derive simple and effective predictions that are validated against epidemic simulations on a variety of real-world networks with good agreement. In addition to individualized predictions for different nodes, we find an overall sudden transition from low density to almost full network saturation as the contagion progresses in time. Our theory is developed and explained in the setting of simple contagions on treelike networks, but we are also able to show how the method extends remarkably well to complex contagions and highly clustered networks.
Content Based Singing Voice Extraction from a Musical Mixture

We present a deep learning based methodology for extracting the singing voice signal from a musical mixture based on the underlying linguistic content. Our model follows an encoder-decoder architecture and takes as input the magnitude component of the spectrogram of a musical mixture with vocals. The encoder part of the model is trained via knowledge distillation using a teacher network to learn a content embedding, which is decoded to generate the corresponding vocoder features. Using this methodology, we are able to extract the unprocessed raw vocal signal from the mixture even for a processed mixture dataset with singers not seen during training. While the nature of our system makes it incongruous with traditional objective evaluation metrics, we use subjective evaluation via listening tests to compare the methodology to state-of-the-art deep learning based source separation algorithms. We also provide sound examples and source code for reproducibility.
Distributed Computation for Marginal Likelihood based Model Choice

We propose a general method for distributed Bayesian model choice, using the marginal likelihood, where each worker has access only to non-overlapping subsets of the data. Our approach approximates the model evidence for the full data set through Monte Carlo sampling from the posterior on every subset generating a model evidence per subset. The model evidences per worker are then consistently combined using a novel approach which corrects for the splitting using summary statistics of the generated samples. This divide-and-conquer approach allows Bayesian model choice in the large data setting, exploiting all available information but limiting communication between workers. Our work thereby complements the work on consensus Monte Carlo (Scott et al., 2016) by explicitly enabling model choice. In addition, we show how the suggested approach can be extended to model choice within a reversible jump setting that explores multiple feature combinations within one run.
EXTREME VALUES OF GEODESIC PERIODS ON ARITHMETIC HYPERBOLIC SURFACES

Abstract Given a closed geodesic on a compact arithmetic hyperbolic surface, we show the existence of a sequence of Laplacian eigenfunctions whose integrals along the geodesic exhibit nontrivial growth. Via Waldspurger’s formula we deduce a lower bound for central values of Rankin-Selberg L-functions of Maass forms times theta series associated to real quadratic fields.
Dynamical friction-driven orbital circularization in rotating discs: a semi-analytical description

We present and validate a novel semi-analytical approach to study the effect of dynamical friction (DF) on the orbits of massive perturbers in rotating stellar discs. We find that DF efficiently circularizes the orbit of co-rotating perturbers, while it constantly increases the eccentricity of counter-rotating ones until their angular momenta reverse, then once again promoting circularization. Such ‘drag toward circular co-rotation’ could shape the distribution of orientations of kinematically decoupled cores in disc galaxies, naturally leading to the observed larger fraction of co-rotating cores.
Understanding Global Loss Landscape of One-hidden-layer ReLU Neural Networks

For one-hidden-layer ReLU networks, we show that all local minima are global in each differentiable region, and these local minima can be unique or continuous, depending on data, activation pattern of hidden neurons and network size. We give criteria to identify whether local minima lie inside their defining regions, and if so (we call them genuine differentiable local minima), their locations and loss values. Furthermore, we give necessary and sufficient conditions for the existence of saddle points as well as non-differentiable local minima. Finally, we compute the probability of getting stuck in genuine local minima for Gaussian input data and parallel weight vectors, and show that it is exponentially vanishing when the weights are located in regions where data are not too scarce. This may give a hint to the question why gradient-based local search methods usually do not get trapped in local minima when training deep ReLU neural networks.
Lucid: A Practical, Lightweight Deep Learning Solution for DDoS Attack Detection

Distributed Denial of Service (DDoS) attacks are one of the most harmful threats in today’s Internet, disrupting the availability of essential services. The challenge of DDoS detection is the combination of attack approaches coupled with the volume of live traffic to be analysed. In this paper, we present a practical, lightweight deep learning DDoS detection system called Lucid, which exploits the properties of Convolutional Neural Networks (CNNs) to classify traffic flows as either malicious or benign. We make four main contributions; (1) an innovative application of a CNN to detect DDoS traffic with low processing overhead, (2) a dataset-agnostic preprocessing mechanism to produce traffic observations for online attack detection, (3) an activation analysis to explain Lucid’s DDoS classification, and (4) an empirical validation of the solution on a resource-constrained hardware platform. Using the latest datasets, Lucid matches existing state-of-the-art detection accuracy whilst presenting a 40x reduction in processing time, as compared to the state-of-the-art. With our evaluation results, we prove that the proposed approach is suitable for effective DDoS detection in resource-constrained operational environments.
Joint Embedding in Named Entity Linking on Sentence Level

Named entity linking is to map an ambiguous mention in documents to an entity in a knowledge base. The named entity linking is challenging, given the fact that there are multiple candidate entities for a mention in a document. It is difficult to link a mention when it appears multiple times in a document, since there are conflicts by the contexts around the appearances of the mention. In addition, it is difficult since the given training dataset is small due to the reason that it is done manually to link a mention to its mapping entity. In the literature, there are many reported studies among which the recent embedding methods learn vectors of entities from the training dataset at document level. To address these issues, we focus on how to link entity for mentions at a sentence level, which reduces the noises introduced by different appearances of the same mention in a document at the expense of insufficient information to be used. We propose a new unified embedding method by maximizing the relationships learned from knowledge graphs. We confirm the effectiveness of our method in our experimental studies.
ConvLab-2: An Open-Source Toolkit for Building, Evaluating, and Diagnosing Dialogue Systems

We present ConvLab-2, an open-source toolkit that enables researchers to build task-oriented dialogue systems with state-of-the-art models, perform an end-to-end evaluation, and diagnose the weakness of systems. As the successor of ConvLab, ConvLab-2 inherits ConvLab’s framework but integrates more powerful dialogue models and supports more datasets. Besides, we have developed an analysis tool and an interactive tool to assist researchers in diagnosing dialogue systems. The analysis tool presents rich statistics and summarizes common mistakes from simulated dialogues, which facilitates error analysis and system improvement. The interactive tool provides an user interface that allows developers to diagnose an assembled dialogue system by interacting with the system and modifying the output of each system component.
Complexity of Combinatorial Matrix Completion With Diameter Constraints

We thoroughly study a novel and still basic combinatorial matrix completion problem: Given a binary incomplete matrix, fill in the missing entries so that the resulting matrix has a specified maximum diameter (that is, upper-bounding the maximum Hamming distance between any two rows of the completed matrix) as well as a specified minimum Hamming distance between any two of the matrix rows. This scenario is closely related to consensus string problems as well as to recently studied clustering problems on incomplete data. We obtain an almost complete complexity dichotomy between polynomial-time solvable and NP-hard cases in terms of the minimum distance lower bound and the number of missing entries per row of the incomplete matrix. Further, we develop polynomial-time algorithms for maximum diameter three, which are based on Deza's theorem from extremal set theory. On the negative side we prove NP-hardness for diameter at least four. For the parameter number of missing entries per row, we show polynomial-time solvability when there is only one missing entry and NP-hardness when there can be at least two missing entries. In general, our algorithms heavily rely on Deza's theorem and the correspondingly identified sunflower structures pave the way towards solutions based on computing graph factors and solving 2-SAT instances.
On the I/O Complexity of the k-Nearest Neighbors Problem

We consider static, external memory indexes for exact and approximate versions of the k-nearest neighbor (k-NN) problem, and show new lower bounds under a standard indivisibility assumption: Polynomial space indexing schemes for high-dimensional k-NN in Hamming space cannot take advantage of block transfers: í(k) block reads are needed to to answer a query. For the l∞ metric the lower bound holds even if we allow c-appoximate nearest neighbors to be returned, for c ∈ (1, 3). The restriction to c < 3 is necessary: For every metric there exists an indexing scheme in the indexability model of Hellerstein et al. using space O(kn), where n is the number of points, that can retrieve k 3-approximate nearest neighbors using optimal ⌈k/B⌉ I/Os, where B is the block size. For specific metrics, data structures with better approximation factors are possible. For k-NN in Hamming space and every approximation factor c>1 there exists a polynomial space data structure that returns k c-approximate nearest neighbors in ⌈k/B⌉ I/Os. To show these lower bounds we develop two new techniques: First, to handle that approximation algorithms have more freedom in deciding which result set to return we develop a relaxed version of the λ-set workload technique of Hellerstein et al. This technique allows us to show lower bounds that hold in d ≥ n dimensions. To extend the lower bounds down to d = O(k log(n/k)) dimensions, we develop a new deterministic dimension reduction technique that may be of independent interest.
ALMA Imaging of the CO (7–6) Line Emission in the Submillimeter Galaxy LESS 073 at z = 4.755

In this paper we present our imaging observations on the CO (7−6) line and its underlying continuum emission of the young submillimeter galaxy LESS 073 at redshift 4.755, using the Atacama Large Millimeter/submillimeter Array. At the achieved resolution of ∼ (8 × 6 kpc2), the CO (7−6) emission is largely unresolved (with a deconvolved size of ), and the continuum emission is totally unresolved. The CO (7−6) line emission has an integrated flux of 0.86 ± 0.08 Jy km s−1, and a line width of 343 ± 40 km s−1. The continuum emission has a flux density of 0.51 mJy. By fitting the observed far-infrared spectral energy distribution (SED) of LESS 073 with a single-temperature modified blackbody function, we obtained a dust temperature K, 60–100 μm flux density ratio , and total infrared luminosity . The SED-fit-based is consistent with those estimated from various line ratios as advocated by our earlier work, indicating that the proposed line-ratio-based method can be used to practically derive for high-z sources. The total molecular gas mass of LESS 073 is , and the inferred gas depletion time is about 43 Myr.
Green exciton series in cuprous oxide

We numerically investigate the odd parity states of the green exciton series in cuprous oxide. Taking into account the coupling to the yellow series and especially to the yellow continuum, the green excitons are quasi-bound resonances with a finite lifetime which cannot be described with Hermitian operators. To calculate their positions and linewidths, we use the method of complex-coordinate rotation, leading to a non-Hermitian complex eigenvalue problem. We find that the behavior of the dominant P states is very well approximated by a modified Rydberg formula using a negative quantum defect. The corresponding linewidths induced by the coupling to the yellow continuum decrease with the third power of the principal quantum number.
3d $Spin(N)$ Seiberg dualities

We study low-energy aspects of 3d $\mathcal{N}=2$ $Spin(N)$ gauge theories with matters in vector and (conjugate) spinor representations. Extending the construction of the 4d $\mathcal{N}=1$ $Spin(N)$ Seiberg duality, we find 3d magnetic dual descriptions with tree-level superpotentials slightly different from the 4d ones. We test various consistency checks including RG flows to known 3d dualities and supersymmetry enhancement deformation which leads to a 3d $\mathcal{N}=4$ duality between $SU(2)$ with three hypermultiplets and $U(1)$ with four hypermultiplets.
Weighted Empirical Risk Minimization: Sample Selection Bias Correction based on Importance Sampling

We consider statistical learning problems, when the distribution $P'$ of the training observations $Z'_1,\; \ldots,\; Z'_n$ differs from the distribution $P$ involved in the risk one seeks to minimize (referred to as the test distribution) but is still defined on the same measurable space as $P$ and dominates it. In the unrealistic case where the likelihood ratio $\Phi(z)=dP/dP'(z)$ is known, one may straightforwardly extends the Empirical Risk Minimization (ERM) approach to this specific transfer learning setup using the same idea as that behind Importance Sampling, by minimizing a weighted version of the empirical risk functional computed from the 'biased' training data $Z'_i$ with weights $\Phi(Z'_i)$. Although the importance function $\Phi(z)$ is generally unknown in practice, we show that, in various situations frequently encountered in practice, it takes a simple form and can be directly estimated from the $Z'_i$'s and some auxiliary information on the statistical population $P$. By means of linearization techniques, we then prove that the generalization capacity of the approach aforementioned is preserved when plugging the resulting estimates of the $\Phi(Z'_i)$'s into the weighted empirical risk. Beyond these theoretical guarantees, numerical results provide strong empirical evidence of the relevance of the approach promoted in this article.
Deep Multi-Task Learning via Generalized Tensor Trace Norm

The trace norm is widely used in multi-task learning as it can discover low-rank structures among tasks in terms of model parameters. Nowadays, with the emerging of big datasets and the popularity of deep learning techniques, tensor trace norms have been used for deep multi-task models. However, existing tensor trace norms cannot discover all the low-rank structures and they require users to manually determine the importance of their components. To solve those two issues together, in this paper, we propose a Generalized Tensor Trace Norm (GTTN). The GTTN is defined as a convex combination of matrix trace norms of all possible tensor flattenings and hence it can discover all the possible low-rank structures. In the induced objective function, we will learn combination coefficients in the GTTN to automatically determine the importance. Experiments on real-world datasets demonstrate the effectiveness of the proposed GTTN.
Fast Generation of High-Fidelity RGB-D Images by Deep Learning With Adaptive Convolution

Using the raw data from consumer-level RGB-D cameras as input, we propose a deep-learning-based approach to efficiently generate RGB-D images with completed information in high resolution. To process the input images in low resolution with missing regions, new operators for adaptive convolution are introduced in our deep-learning network that consists of three cascaded modules—the completion module, the refinement module, and the super-resolution module. The completion module is based on an architecture of encoder–decoder, where the features of input raw RGB-D will be automatically extracted by the encoding layers of a deep neural network. The decoding layers are applied to reconstruct the completed depth map, which is followed by a refinement module to sharpen the boundary of different regions. For the super-resolution module, we generate RGB-D images in high resolution by multiple layers for feature extraction and a layer for upsampling. Benefited from the adaptive convolution operators proposed in this article, our results outperform the existing deep-learning-based approaches for RGB-D image complete and super-resolution. As an end-to-end approach, high-fidelity RGB-D images can be generated efficiently at the rate of 22 frames/s. Note to Practitioners—With the development of consumer-level RGB-D cameras, industries have started to employ these low-cost sensors in many robotic and automation applications. However, images generated by consumer-level RGB-D cameras are generally in low resolution. Moreover, the depth images often have incomplete regions when the surface of an object is transparent, highly reflective, or beyond the distance of sensing. With the help of our method, engineers are able to “repair” the images captured by consumer-level RGB-D cameras in high efficiency. As the typical deep-learning networks are employed in this approach, the proposed approach fits well with the GPU-based hardware architecture of deep-learning computation—therefore, it potentially can be integrated into the hardware of RGB-D cameras.
Active Learning for Sound Event Detection

This article proposes an active learning system for sound event detection (SED). It aims at maximizing the accuracy of a learned SED model with limited annotation effort. The proposed system analyzes an initially unlabeled audio dataset, from which it selects sound segments for manual annotation. The candidate segments are generated based on a proposed change point detection approach, and the selection is based on the principle of mismatch-first farthest-traversal. During the training of SED models, recordings are used as training inputs, preserving the long-term context for annotated segments. The proposed system clearly outperforms reference methods in the two datasets used for evaluation (TUT Rare Sound 2017 and TAU Spatial Sound 2019). Training with recordings as context outperforms training with only annotated segments. Mismatch-first farthest-traversal outperforms reference sample selection methods based on random sampling and uncertainty sampling. Remarkably, the required annotation effort can be greatly reduced on the dataset where target sound events are rare: by annotating only 2% of the training data, the achieved SED performance is similar to annotating all the training data.