Miracle-dz/UniFAR-FTUs huggingface dataset:
DeepFashion2: A Versatile Benchmark for Detection, Pose Estimation, Segmentation and Re-Identification of Clothing Images

Understanding fashion images has been advanced by benchmarks with rich annotations such as DeepFashion, whose labels include clothing categories, landmarks, and consumer-commercial image pairs. However, DeepFashion has nonnegligible issues such as single clothing-item per image, sparse landmarks (4∼8 only), and no per-pixel masks, making it had significant gap from real-world scenarios. We fill in the gap by presenting DeepFashion2 to address these issues. It is a versatile benchmark of four tasks including clothes detection, pose estimation, segmentation, and retrieval. It has 801K clothing items where each item has rich annotations such as style, scale, view- point, occlusion, bounding box, dense landmarks (e.g. 39 for ‘long sleeve outwear’ and 15 for ‘vest’), and masks. There are also 873K Commercial-Consumer clothes pairs. The annotations of DeepFashion2 are much larger than its counterparts such as 8× of FashionAI Global Challenge. A strong baseline is proposed, called Match R- CNN, which builds upon Mask R-CNN to solve the above four tasks in an end-to-end manner. Extensive evaluations are conducted with different criterions in Deep- Fashion2. DeepFashion2 Dataset will be released at : https://github.com/switchablenorms/DeepFashion2
A Machine Learning Benchmark for Facies Classification

The recent interest in using deep learning for seismic interpretation tasks, such as facies classification, has been facing a significant obstacle, namely, the absence of large publicly available annotated data sets for training and testing models. As a result, researchers have often resorted to annotating their own training and testing data. However, different researchers may annotate different classes or use different train and test splits. In addition, it is common for papers that apply machine learning for facies classification to not contain quantitative results, and rather rely solely on visual inspection of the results. All of these practices have led to subjective results and have greatly hindered our ability to compare different machine-learning models against each other and understand the advantages and disadvantages of each approach. To address these issues, we open source a fully annotated 3D geologic model of the Netherlands F3 block. This model is based on study of the 3D seismic data in addition to 26 well logs, and it is grounded on the careful study of the geology of the region. Furthermore, we have developed two baseline models for facies classification based on a deconvolution network architecture and make their codes publicly available. Finally, we have developed a scheme for evaluating different models on this data set, and we have evaluated the results of our baseline models. In addition to making the data set and the code publicly available, our work helps advance research in this area by creating an objective benchmark for comparing the results of different machine-learning approaches for facies classification.
A Boost in Revealing Subtle Facial Expressions: A Consolidated Eulerian Framework

Facial Micro-expression Recognition (MER) distinguishes the underlying emotional states of spontaneous subtle facial expressions. Automatic MER is challenging because that the intensity of subtle facial muscle movement is extremely low and the duration of ME is transient.Recent works adopt motion magnification or temporal interpolation to resolve these issues. Nevertheless, existing works divide them into two separate modules due to their non-linearity. Though such operation eases the difficulty in implementation, it ignores their underlying connections and thus results in inevitable losses in both accuracy and speed. Instead, in this paper, we propose a consolidated Eulerian framework to reveal the subtle facial movements. It expands the temporal duration and amplifies the muscle movements in micro-expressions simultaneously. Compared to existing approaches, the proposed method can not only process ME clips more efficiently but also make subtle ME movements more distinguishable. Experiments on two public MER databases indicate that our model outperforms the state-of-the-art in both speed and accuracy.
Approximate k-Cover in Hypergraphs: Efficient Algorithms, and Applications

Given a weighted hypergraph $\mathcal{H}(V, \mathcal{E} \subseteq 2^V, w)$, the approximate $k$-cover problem seeks for a size-$k$ subset of $V$ that has the maximum weighted coverage by \emph{sampling only a few hyperedges} in $\mathcal{E}$. The problem has emerged from several network analysis applications including viral marketing, centrality maximization, and landmark selection. Despite many efforts, even the best approaches require $O(k n \log n)$ space complexities, thus, cannot scale to, nowadays, humongous networks without sacrificing formal guarantees. In this paper, we propose BCA, a family of algorithms for approximate $k$-cover that can find $(1-\frac{1}{e} -\epsilon)$-approximation solutions within an \emph{$O(\epsilon^{-2}n \log n)$ space}. That is a factor $k$ reduction on space comparing to the state-of-the-art approaches with the same guarantee. We further make BCA more efficient and robust on real-world instances by introducing a novel adaptive sampling scheme, termed DTA.
Attenuating Bias in Word Vectors

Word vector representations are well developed tools for various NLP and Machine Learning tasks and are known to retain significant semantic and syntactic structure of languages. But they are prone to carrying and amplifying bias which can perpetrate discrimination in various applications. In this work, we explore new simple ways to detect the most stereotypically gendered words in an embedding and remove the bias from them. We verify how names are masked carriers of gender bias and then use that as a tool to attenuate bias in embeddings. Further, we extend this property of names to show how names can be used to detect other types of bias in the embeddings such as bias based on race, ethnicity, and age.
Learning to Collaborate in Markov Decision Processes

We consider a two-agent MDP framework where agents repeatedly solve a task in a collaborative setting. We study the problem of designing a learning algorithm for the first agent (A1) that facilitates a successful collaboration even in cases when the second agent (A2) is adapting its policy in an unknown way. The key challenge in our setting is that the first agent faces non-stationarity in rewards and transitions because of the adaptive behavior of the second agent. We design novel online learning algorithms for agent A1 whose regret decays as $O(T^{\max\{1-\frac{3}{7} \cdot \alpha, \frac{1}{4}\}})$ with $T$ learning episodes provided that the magnitude of agent A2's policy changes between any two consecutive episodes are upper bounded by $O(T^{-\alpha})$. Here, the parameter $\alpha$ is assumed to be strictly greater than $0$, and we show that this assumption is necessary provided that the learning parity with noise problem is computationally hard. We show that sub-linear regret of agent A1 further implies near-optimality of the agents' joint return for MDPs that manifest the properties of a smooth game.
Minimum-Entropy Couplings and Their Applications

Given two discrete random variables <inline-formula> <tex-math notation="LaTeX">$X$ </tex-math></inline-formula> and <inline-formula> <tex-math notation="LaTeX">$Y$ </tex-math></inline-formula>, with probability distributions <inline-formula> <tex-math notation="LaTeX">$p=(p_{1}, \ldots , p_{n})$ </tex-math></inline-formula> and <inline-formula> <tex-math notation="LaTeX">$q=(q_{1}, \ldots , q_{m})$ </tex-math></inline-formula>, respectively, let us denote by <inline-formula> <tex-math notation="LaTeX">${\mathcal{ C}}(p, q)$ </tex-math></inline-formula> the set of all couplings of <inline-formula> <tex-math notation="LaTeX">$p$ </tex-math></inline-formula> and <inline-formula> <tex-math notation="LaTeX">$q$ </tex-math></inline-formula>, that is, the set of all bivariate probability distributions that have <inline-formula> <tex-math notation="LaTeX">$p$ </tex-math></inline-formula> and <inline-formula> <tex-math notation="LaTeX">$q$ </tex-math></inline-formula> as marginals. In this paper, we study the problem of finding a joint probability distribution in <inline-formula> <tex-math notation="LaTeX">${\mathcal{ C}}(p, q)$ </tex-math></inline-formula> of <italic>minimum entropy</italic> (equivalently, a coupling that <italic>maximizes</italic> the mutual information between <inline-formula> <tex-math notation="LaTeX">$X$ </tex-math></inline-formula> and <inline-formula> <tex-math notation="LaTeX">$Y$ </tex-math></inline-formula>), and we discuss several situations where the need for this kind of optimization naturally arises. Since the optimization problem is known to be NP-hard, we give an efficient algorithm to find a joint probability distribution in <inline-formula> <tex-math notation="LaTeX">${\mathcal{ C}}(p, q)$ </tex-math></inline-formula> with entropy exceeding the minimum possible at most by 1 bit, thus providing an approximation algorithm with an additive gap of at most 1 bit. Leveraging on this algorithm, we extend our result to the problem of finding a minimum-entropy joint distribution of arbitrary <inline-formula> <tex-math notation="LaTeX">$k\geq {2}$ </tex-math></inline-formula> discrete random variables <inline-formula> <tex-math notation="LaTeX">$X_{1}, \ldots , X_{k}$ </tex-math></inline-formula>, consistent with the known <inline-formula> <tex-math notation="LaTeX">$k$ </tex-math></inline-formula> marginal distributions of the individual random variables <inline-formula> <tex-math notation="LaTeX">$X_{1}, \ldots , X_{k}$ </tex-math></inline-formula>. In this case, our algorithm has an additive gap of at most <inline-formula> <tex-math notation="LaTeX">$\log k$ </tex-math></inline-formula> from optimum. We also discuss several related applications of our findings and extensions of our results to entropies different from the Shannon entropy.
SirenAttack: Generating Adversarial Audio for End-to-End Acoustic Systems

Despite their immense popularity, deep learning-based acoustic systems are inherently vulnerable to adversarial attacks, wherein maliciously crafted audios trigger target systems to misbehave. In this paper, we present SirenAttack, a new class of attacks to generate adversarial audios. Compared with existing attacks, SirenAttack highlights with a set of significant features: (i) versatile -- it is able to deceive a range of end-to-end acoustic systems under both white-box and black-box settings; (ii) effective -- it is able to generate adversarial audios that can be recognized as specific phrases by target acoustic systems; and (iii) stealthy -- it is able to generate adversarial audios indistinguishable from their benign counterparts to human perception. We empirically evaluate SirenAttack on a set of state-of-the-art deep learning-based acoustic systems (including speech command recognition, speaker recognition and sound event classification), with results showing the versatility, effectiveness, and stealthiness of SirenAttack. For instance, it achieves 99.45% attack success rate on the IEMOCAP dataset against the ResNet18 model, while the generated adversarial audios are also misinterpreted by multiple popular ASR platforms, including Google Cloud Speech, Microsoft Bing Voice, and IBM Speech-to-Text. We further evaluate three potential defense methods to mitigate such attacks, including adversarial training, audio downsampling, and moving average filtering, which leads to promising directions for further research.
Exploring Uncertainty in Conditional Multi-Modal Retrieval Systems

We cast visual retrieval as a regression problem by posing triplet loss as a regression loss. This enables epistemic uncertainty estimation using dropout as a Bayesian approximation framework in retrieval. Accordingly, Monte Carlo (MC) sampling is leveraged to boost retrieval performance. Our approach is evaluated on two applications: person re-identification and autonomous car driving. Comparable state-of-the-art results are achieved on multiple datasets for the former application. We leverage the Honda driving dataset (HDD) for autonomous car driving application. It provides multiple modalities and similarity notions for ego-motion action understanding. Hence, we present a multi-modal conditional retrieval network. It disentangles embeddings into separate representations to encode different similarities. This form of joint learning eliminates the need to train multiple independent networks without any performance degradation. Quantitative evaluation highlights our approach competence, achieving 6% improvement in a highly uncertain environment.
Towards Compact ConvNets via Structure-Sparsity Regularized Filter Pruning

The success of convolutional neural networks (CNNs) in computer vision applications has been accompanied by a significant increase of computation and memory costs, which prohibits its usage on resource-limited environments such as mobile or embedded devices. To this end, the research of CNN compression has recently become emerging. In this paper, we propose a novel filter pruning scheme, termed structured sparsity regularization (SSR), to simultaneously speedup the computation and reduce the memory overhead of CNNs, which can be well supported by various off-the-shelf deep learning libraries. Concretely, the proposed scheme incorporates two different regularizers of structured sparsity into the original objective function of filter pruning, which fully coordinates the global outputs and local pruning operations to adaptively prune filters. We further propose an Alternative Updating with Lagrange Multipliers (AULM) scheme to efficiently solve its optimization. AULM follows the principle of ADMM and alternates between promoting the structured sparsity of CNNs and optimizing the recognition loss, which leads to a very efficient solver (2.5x to the most recent work that directly solves the group sparsity-based regularization). Moreover, by imposing the structured sparsity, the online inference is extremely memory-light, since the number of filters and the output feature maps are simultaneously reduced. The proposed scheme has been deployed to a variety of state-of-the-art CNN structures including LeNet, AlexNet, VGG, ResNet and GoogLeNet over different datasets. Quantitative results demonstrate that the proposed scheme achieves superior performance over the state-of-the-art methods. We further demonstrate the proposed compression scheme for the task of transfer learning, including domain adaptation and object detection, which also show exciting performance gains over the state-of-the-arts.
Confidence Region of Singular Subspaces for Low-Rank Matrix Regression

Low-rank matrix regression refers to the instances of recovering a low-rank matrix based on specially designed measurements and the corresponding noisy outcomes. Numerous statistical methods have been developed over the recent decade for efficiently reconstructing the unknown low-rank matrices. It is often interesting, in certain applications, to estimate the unknown singular subspaces. In this paper, we revisit the low-rank matrix regression model and introduce a two-step procedure to construct confidence regions of the singular subspaces. We investigate distributions of the joint projection distance between the empirical singular subspaces and the unknown true singular subspaces. We prove asymptotical normality of the joint projection distance with data-dependent centering and normalization when <inline-formula> <tex-math notation="LaTeX">$r^{3/2}(m_{1}+m_{2})^{3/2}=o(n/\log n)$ </tex-math></inline-formula> where <inline-formula> <tex-math notation="LaTeX">$m_{1}, m_{2}$ </tex-math></inline-formula> denote the matrix row and column sizes, <inline-formula> <tex-math notation="LaTeX">$r$ </tex-math></inline-formula> is the rank and <inline-formula> <tex-math notation="LaTeX">$n$ </tex-math></inline-formula> is the number of independent random measurements. Consequently, data-dependent confidence regions of the true singular subspaces are established which attain pre-determined confidence levels asymptotically. Additionally, non-asymptotic convergence rates are also established. Numerical results are presented to show the merits of our methods.
Physical reservoir computing built by spintronic devices for temporal information processing

Spintronic nanodevices have ultrafast nonlinear dynamic and recurrence behaviors on a nanosecond scale that promises to enable spintronic reservoir computing (RC) system. Here two physical RC systems based on a single magnetic skyrmion memristor (MSM) and 24 spin-torque nano-oscillators (STNOs) were proposed and modeled to process image classification task and nonlinear dynamic system prediction, respectively. Based on our micromagnetic simulation results on the nonlinear responses of MSM and STNO with current pulses stimulation, the handwritten digits recognition task domesticates that an RC system using one single MSM has the outstanding performance on image classification. In addition, the complex unknown nonlinear dynamic problems can also be well solved by a physical RC system consisted of 24 STNOs confirmed in a second-order nonlinear dynamic system and NARMA10 tasks. The capability of both high accuracy and fast information processing promises to enable one type of brain-like chip based on spintronics for various artificial intelligence tasks.
How Many Passengers Can We Serve with Ride-sharing?

Ride-sharing can reduce traffic congestion and thus reduce gas emissions and save travel time. However, transportation system with ride-sharing is currently inefficient due to low occupancy rate, high travel demand and some other factors. Existing literature did not consider ride-sharing with multi-request grouped in one trip. In our paper, we firstly proposed a graph-based algorithm that can obtain an approximation solution in polynomial time and then proposed an exact algorithm to solve this problem with maximizing the number of passenegers served in $O(1.2312^{|\mathcal{E}|})$ time.
Ciphers and Executioners: How 3′-Untranslated Regions Determine the Fate of Messenger RNAs

The sequences and structures of 3′-untranslated regions (3′UTRs) of messenger RNAs govern their stability, localization, and expression. 3′UTR regulatory elements are recognized by a wide variety of trans-acting factors that include microRNAs (miRNAs), their associated machinery, and RNA-binding proteins (RBPs). In turn, these factors instigate common mechanistic strategies to execute the regulatory programs encoded by 3′UTRs. Here, we review classes of factors that recognize 3′UTR regulatory elements and the effector machineries they guide toward mRNAs to dictate their expression and fate. We outline illustrative examples of competitive, cooperative, and coordinated interplay such as mRNA localization and localized translation. We further review the recent advances in the study of mRNP granules and phase transition, and their possible significance for the functions of 3′UTRs. Finally, we highlight some of the most recent strategies aimed at deciphering the complexity of the regulatory codes of 3′UTRs, and identify some of the important remaining challenges.
An Application of Semi-empirical Physical Model of Tsunami-Bore Pressure on Buildings

Characteristic patterns of tsunami wave pressure on buildings is divided into three types, depending on its vertical profiles and time, which is observed after the tsunami impacted the buildings. The first one is the impulsive pressure, which is observed just after the tsunami impacted the buildings. The second one is the bore pressure, which is observed after the impulsive pressures. The third one is the quasi-steady pressure, which is observed after the bores go away from the buildings. In this study, based on characteristics of bore pressure observed in a hydraulic experiment, a semi-empirical physical model of bore pressure is developed by applying a turbulent bore theory. Also, we present an application method of the semi-empirical physical model to evaluations of bore pressure with usage of numerical results of inundation simulations of two-dimensional nonlinear shallow water equation models. Furthermore, we apply the semi-empirical physical model to evaluations of pressure acting on buildings in an inundation area by carrying out numerical simulations of tsunami inundation.
Survival in Papillary Thyroid Microcarcinoma: A Comparative Analysis Between the 7th and 8th Versions of the AJCC/UICC Staging System Based on the SEER Database

Background: Considerable modifications have been introduced in the new edition of the American Joint Committee on Cancer/Union for International Cancer Control (AJCC/UICC) TNM staging system. Based on the National Cancer Institute's Surveillance, Epidemiology, and End Results (SEER) database, this study aimed to compare the 7th and 8th editions of the AJCC/UICC TNM staging system for patients with papillary thyroid microcarcinoma (PTMC) and follicular variant papillary thyroid microcarcinoma (FVPTMC). Methods: A Data from 2004 to 2014 of 39,032 patients registered in the SEER database were included. The 7th and 8th editions of the AJCC/UICC staging system were compared in terms of TNM staging, age cutoff, and clinical staging. Patient survival was evaluated using Kaplan-Meier and multivariable Cox proportional hazards models. The American Thyroid Association (ATA) risk stratification system was integrated with the AJCC/UICC staging system for further investigation. Receiver operating characteristic (ROC) curves, Harrell's C-index, Akaike information criterion (AIC), and the Bayesian information criterion (BIC) were used to assess the models' performances. Results: Revised TNM categories, age cutoff, and clinical staging in the 8th edition resulted in reclassification of the overall stage. Applying the 8th edition, 1,278 stage III and 425 stage IV patients were reclassified as stage I; 950 stage III and 459 stage IV patients were reclassified as stage II; 77 stage IV patients were reclassified as stage III; and only 88 patients remained in stage IV. All patients in stage I, according to the 7th edition, remained in this stage when using the 8th edition. Patients classified into higher stages (III and IV) in the 8th edition showed a worse prognosis than those classified into same stages in the 7th edition. The 8th edition proved to be a better model with higher prognostic efficacy survival (higher AUC and C-index, lower AIC and BIC) than the 7th edition. When integrated with the ATA risk stratification system, the 8th edition still showed better discriminative power for patients in the higher risk group. Conclusion: Based on the SEER database, the 8th edition of the AJCC/UICC staging system has better prognostic efficacy than the 7th edition for patients with PTMC and FVPTMC.