Miracle-dz/UniFAR-FTUs huggingface dataset:
Study on implementation of one-piece lean line design using simulation techniques: A practical approach

This paper discusses the simulation study carried out for proposing one-piece lean line layout with features of Lean Manufacturing. The lean initiatives that can be addressed are, introducing Kanban replenishment system, better work-in-process, changing the layout, visual management techniques, standardized work for the reduction of cycle time, number of workers, number of setups. To improve the layout in a pump manufacturing industry, VISIO 2007 and Pro Model-Process Simulator Software are used. Pro Model simulation modeling software is a powerful yet easy-to-use simulation tools for modeling all types of systems and processes. Pro Model software is designed to model manufacturing systems ranging from small job shops and machining cells to large mass production, flexible manufacturing systems, and supply chain systems. The study has discussed the lacuna in the existing flow line and has suggested a new one-piece lean line design using the simulation software. The validation is carried out by creating the wooden prototypes representing the cells and involving the workers to do the assembly of the pump. Standard time study charts are used to calculate the cycle time and change over time.
Using Black-Box Compression Algorithms for Phase Retrieval

Compressive phase retrieval refers to the problem of recovering a structured <inline-formula> <tex-math notation="LaTeX">$n$ </tex-math></inline-formula>-dimensional complex-valued vector from its phase-less under-determined linear measurements. The non-linearity of the measurement process makes designing theoretically-analyzable efficient phase retrieval algorithms challenging. As a result, to a great extent, existing recovery algorithms only take advantage of simple structures such as sparsity and its convex generalizations. The goal of this article is to move beyond simple models through employing compression codes. Such codes are typically developed to take advantage of complex signal models to represent the signals as efficiently as possible. In this work, it is shown how an existing compression code can be treated as a black box and integrated into an efficient solution for phase retrieval. First, COmpressive PhasE Retrieval (COPER) optimization, a computationally-intensive compression-based phase retrieval method, is proposed. COPER provides a theoretical framework for studying compression-based phase retrieval. The number of measurements required by COPER is connected to <inline-formula> <tex-math notation="LaTeX">$\kappa $ </tex-math></inline-formula>, the <inline-formula> <tex-math notation="LaTeX">$\alpha $ </tex-math></inline-formula>-dimension (closely related to the rate-distortion dimension) of a given family of compression codes. To finds the solution of COPER, an efficient iterative algorithm called gradient descent for COPER (GD-COPER) is proposed. It is proven that under some mild conditions on the initialization and the compression code, if the number of measurements is larger than <inline-formula> <tex-math notation="LaTeX">$ C \kappa ^{2} \log ^{2}~n$ </tex-math></inline-formula>, where <inline-formula> <tex-math notation="LaTeX">$C$ </tex-math></inline-formula> is a constant, GD-COPER obtains an accurate estimate of the input vector in polynomial time. In the simulation results, JPEG2000 is integrated in GD-COPER to confirm the state-of-the-art performance of the resulting algorithm on real-world images.
MobileGAN: Skin Lesion Segmentation Using a Lightweight Generative Adversarial Network

Determining the precise boundaries of skin lesions in dermoscopic images needs to cope with many challenges, such as the presence of hair, inconspicuous lesion edges, low contrast, and the variability in colour, texture, and shapes of these lesions. Recent developed skin lesion segmentation algorithms based on deep learning are computationally expensive in terms of time and memory. Consequently, running these algorithms requires a powerful GPU and high memory bandwidth, which is not available in dermoscopy devices. Thus, this paper aims at achieving high precise segmentation with minimum resources by presenting a lightweight and efficient generative adversarial network (GAN) model, called MobileGAN. Specifically, MobileGAN combines 1-D kernel factorized networks, position and channel attention, and multiscale aggregation mechanisms with a GAN model. The 1-D kernel factorized network reduces the computational cost of 2D filtering. The position and channel attention modules enhance the discriminant ability in between the lesion and non-lesion feature representations in spatial and channel dimensions. Besides, a multiscale block is introduced to aggregate the coarse-to-fine features of input images and reduce the effect of artifacts. MobileGAN is evaluated on two public datasets: ISBI 2017 and the ISIC 2018. Although MobileGAN has only 2.35 million parameters, the experimental results demonstrate that the proposed model provides comparable results to state-of-the-art skin lesion segmentation in terms of an accuracy, Dice and Jaccard similarity coefficient of 97.61%, 90.63% and 81.98% respectively. Moreover, MobileGAN can run at over 110 frames per second (FPS) in a single GTX1080Ti GPU, which is faster than the traditional deep learning models, and therefore MobileGAN is suitable for practical applications.
Time-to-Event Prediction with Neural Networks and Cox Regression

New methods for time-to-event prediction are proposed by extending the Cox proportional hazards model with neural networks. Building on methodology from nested case-control studies, we propose a loss function that scales well to large data sets, and enables fitting of both proportional and non-proportional extensions of the Cox model. Through simulation studies, the proposed loss function is verified to be a good approximation for the Cox partial log-likelihood. The proposed methodology is compared to existing methodologies on real-world data sets, and is found to be highly competitive, typically yielding the best performance in terms of Brier score and binomial log-likelihood. A python package for the proposed methods is available at this https URL.
MULEX: Disentangling Exploitation from Exploration in Deep RL

An agent learning through interactions should balance its action selection process between probing the environment to discover new rewards and using the information acquired in the past to adopt useful behaviour. This trade-off is usually obtained by perturbing either the agent's actions (e.g., e-greedy or Gibbs sampling) or the agent's parameters (e.g., NoisyNet), or by modifying the reward it receives (e.g., exploration bonus, intrinsic motivation, or hand-shaped rewards). Here, we adopt a disruptive but simple and generic perspective, where we explicitly disentangle exploration and exploitation. Different losses are optimized in parallel, one of them coming from the true objective (maximizing cumulative rewards from the environment) and others being related to exploration. Every loss is used in turn to learn a policy that generates transitions, all shared in a single replay buffer. Off-policy methods are then applied to these transitions to optimize each loss. We showcase our approach on a hard-exploration environment, show its sample-efficiency and robustness, and discuss further implications.
Understanding GCC builtins to develop better tools

C programs can use compiler builtins to provide functionality that the C language lacks. On Linux, GCC provides several thousands of builtins that are also supported by other mature compilers, such as Clang and ICC. Maintainers of other tools lack guidance on whether and which builtins should be implemented to support popular projects. To assist tool developers who want to support GCC builtins, we analyzed builtin use in 4,913 C projects from GitHub. We found that 37% of these projects relied on at least one builtin. Supporting an increasing proportion of projects requires support of an exponentially increasing number of builtins; however, implementing only 10 builtins already covers over 30% of the projects. Since we found that many builtins in our corpus remained unused, the effort needed to support 90% of the projects is moderate, requiring about 110 builtins to be implemented. For each project, we analyzed the evolution of builtin use over time and found that the majority of projects mostly added builtins. This suggests that builtins are not a legacy feature and must be supported in future tools. Systematic testing of builtin support in existing tools revealed that many lacked support for builtins either partially or completely; we also discovered incorrect implementations in various tools, including the formally verified CompCert compiler.
DuDoNet: Dual Domain Network for CT Metal Artifact Reduction

Computed tomography (CT) is an imaging modality widely used for medical diagnosis and treatment. CT images are often corrupted by undesirable artifacts when metallic implants are carried by patients, which creates the problem of metal artifact reduction (MAR). Existing methods for reducing the artifacts due to metallic implants are inadequate for two main reasons. First, metal artifacts are structured and non-local so that simple image domain enhancement approaches would not suffice. Second, the MAR approaches which attempt to reduce metal artifacts in the X-ray projection (sinogram) domain inevitably lead to severe secondary artifact due to sinogram inconsistency. To overcome these difficulties, we propose an end-to-end trainable Dual Domain Network (DuDoNet) to simultaneously restore sinogram consistency and enhance CT images. The linkage between the sigogram and image domains is a novel Radon inversion layer that allows the gradients to back-propagate from the image domain to the sinogram domain during training. Extensive experiments show that our method achieves significant improvements over other single domain MAR approaches. To the best of our knowledge, it is the first end-to-end dual-domain network for MAR.
Nonlinear one-way edge-mode interactions for frequency mixing in topological photonic crystals

Topological photonics aims to utilize topological photonic bands and corresponding edge modes to implement robust light manipulation, which can be readily achieved in the linear regime of light-matter interaction. Importantly, unlike solid-state physics, the common test bed for new ideas in topological physics, topological photonics provides an ideal platform to study wave mixing and other nonlinear interactions. These are well-known topics in classical nonlinear optics but largely unexplored in the context of topological photonics. Here, we investigate nonlinear interactions of one-way edge modes in frequency mixing processes in topological photonic crystals. We present a detailed analysis of the band topology of two-dimensional photonic crystals with hexagonal symmetry and demonstrate that nonlinear optical processes, such as second- and third-harmonic generation, can be conveniently implemented via one-way edge modes in this setup. Moreover, we demonstrate that more exotic phenomena, such as slow-light enhancement of nonlinear interactions and harmonic generation upon interaction of backward-propagating (left-handed) edge modes, can also be realized. Our work opens up new avenues towards topology-protected frequency mixing processes in photonics.
Effects of Foraging in Personalized Content-based Image Recommendation

A major challenge of recommender systems is to help users locating interesting items. Personalized recommender systems have become very popular as they attempt to predetermine the needs of users and provide them with recommendations to personalize their navigation. However, few studies have addressed the question of what drives the users' attention to specific content within the collection and what influences the selection of interesting items. To this end, we employ the lens of Information Foraging Theory (IFT) to image recommendation to demonstrate how the user could utilize visual bookmarks to locate interesting images. We investigate a personalized content-based image recommendation system to understand what affects user attention by reinforcing visual attention cues based on IFT. We further find that visual bookmarks (cues) lead to a stronger scent of the recommended image collection. Our evaluation is based on the Pinterest image collection.
Exact Augmented Lagrangian Duality for Mixed Integer Quadratic Programming

Mixed integer quadratic programming (MIQP) is the problem of minimizing a convex quadratic function over mixed integer points in a rational polyhedron. This paper focuses on the augmented Lagrangian dual (ALD) for MIQP. ALD augments the usual Lagrangian dual with a weighted nonlinear penalty on the dualized constraints. We first prove that ALD will reach a zero duality gap asymptotically as the weight on the penalty goes to infinity under some mild conditions on the penalty function. We next show that a finite penalty weight is enough for a zero gap when we use any norm as the penalty function. Finally, we prove a polynomially bound on the weight on the penalty term to obtain a zero gap.
Feasibility of cmOCT angiographic technique using 200 kHz VCSEL source for in vivo microcirculation imaging applications

Optical coherence tomography (OCT) angiography is a well-established in vivo imaging technique to assess the overall vascular morphology of tissues and is an emerging field of research for the assessment of blood flow dynamics and functional parameters such as oxygen saturation. In this study, we present a modified scanning based correlation mapping OCT (cmOCT) using a 200 kHz high speed swept source OCT system operating at 1300 nm and demonstrate its wide field imaging capability in ocular angiographic studies.
Beam Allocation for Millimeter-Wave MIMO Tracking Systems

In this paper, we propose a new beam allocation strategy aiming to maximize the average successful tracking probability (ASTP) of a single user time-varying millimeter-wave MIMO system. In contrast to most existing works that employ one transmitting-receiving (Tx-Rx) beam pair once only, we investigate a more general framework, where the Tx-Rx beam pairs are allowed to be used repeatedly in each beam training period to improve the received signal powers in specific directions. In the case of orthogonal Tx-Rx beam pairs, a power-based estimator is employed to track the time-varying AoA and AoD of the channel, and the resulting training beam pair sequence design problem is formulated as an integer nonlinear programming (I-NLP) problem. By dividing the feasible region into a set of subregions, the formulated I-NLP is decomposed into a series of concave sub I-NLPs, which can be solved by recursively invoking a nonlinear branch-and-bound algorithm. To reduce the computational cost, we relax the integer constraints of each sub I-NLP and obtain a low-complexity solution via solving the Karush-Kuhn-Tucker conditions of their relaxed problems. For the case when the Tx-Rx beam pairs are overlapped in the angular space, we estimate the updated AoA and AoD via an orthogonal matching pursuit (OMP) algorithm. Moreover, since no explicit expression for the ASTP exists for the OMP-based estimator, we derive a closed-form lower bound of the ASTP, based on which a favorable beam pair allocation strategy can be obtained. Numerical results demonstrate the superiority of the proposed beam allocation strategy over existing benchmarks.
Unsupervised Adversarial Graph Alignment with Graph Embedding

Graph alignment, also known as network alignment, is a fundamental task in social network analysis. Many recent works have relied on partially labeled cross-graph node correspondences, i.e., anchor links. However, due to the privacy and security issue, the manual labeling of anchor links for diverse scenarios may be prohibitive. Aligning two graphs without any anchor links is a crucial and challenging task. In this paper, we propose an Unsupervised Adversarial Graph Alignment (UAGA) framework to learn a cross-graph alignment between two embedding spaces of different graphs in a fully unsupervised fashion (\emph{i.e.,} no existing anchor links and no users' personal profile or attribute information is available). The proposed framework learns the embedding spaces of each graph, and then attempts to align the two spaces via adversarial training, followed by a refinement procedure. We further extend our UAGA method to incremental UAGA (iUAGA) that iteratively reveals the unobserved user links based on the pseudo anchor links. This can be used to further improve both the embedding quality and the alignment accuracy. Moreover, the proposed methods will benefit some real-world applications, \emph{e.g.,} link prediction in social networks. Comprehensive experiments on real-world data demonstrate the effectiveness of our proposed approaches UAGA and iUAGA for unsupervised graph alignment.
Isomorphism problems for tensors, groups, and cubic forms: completeness and reductions

In this paper we consider the problems of testing isomorphism of tensors, $p$-groups, cubic forms, algebras, and more, which arise from a variety of areas, including machine learning, group theory, and cryptography. These problems can all be cast as orbit problems on multi-way arrays under different group actions. Our first two main results are: 1. All the aforementioned isomorphism problems are equivalent under polynomial-time reductions, in conjunction with the recent results of Futorny-Grochow-Sergeichuk (Lin. Alg. Appl., 2019). 2. Isomorphism of $d$-tensors reduces to isomorphism of 3-tensors, for any $d \geq 3$. Our results suggest that these isomorphism problems form a rich and robust equivalence class, which we call Tensor Isomorphism-complete, or TI-complete. We then leverage the techniques used in the above results to prove two first-of-their-kind results for Group Isomorphism (GpI): 3. We give a reduction from GpI for $p$-groups of exponent $p$ and small class ($c < p$) to GpI for $p$-groups of exponent $p$ and class 2. The latter are widely believed to be the hardest cases of GpI, but as far as we know, this is the first reduction from any more general class of groups to this class. 4. We give a search-to-decision reduction for isomorphism of $p$-groups of exponent $p$ and class 2 in time $|G|^{O(\log \log |G|)}$. While search-to-decision reductions for Graph Isomorphism (GI) have been known for more than 40 years, as far as we know this is the first non-trivial search-to-decision reduction in the context of GpI. Our main technique for (1), (3), and (4) is a linear-algebraic analogue of the classical graph coloring gadget, which was used to obtain the search-to-decision reduction for GI. This gadget construction may be of independent interest and utility. The technique for (2) gives a method for encoding an arbitrary tensor into an algebra.
Predicting video saliency using crowdsourced mouse-tracking data

This paper presents a new way of getting high-quality saliency maps for video, using a cheaper alternative to eye-tracking data. We designed a mouse-contingent video viewing system which simulates the viewers’ peripheral vision based on the position of the mouse cursor. The system enables the use of mouse-tracking data recorded from an ordinary computer mouse as an alternative to real gaze fixations recorded by a more expensive eye-tracker. We developed a crowdsourcing system that enables the collection of such mouse-tracking data at large scale. Using the collected mouse-tracking data we showed that it can serve as an approximation of eye-tracking data. Moreover, trying to increase the efficiency of collected mouse-tracking data we proposed a novel deep neural network algorithm that improves the quality of mouse-tracking saliency maps.
Many-body calculations of two-photon, two color matrix elements for attosecond delays

Interferometric measurements based on the combination of extreme ultraviolet (XUV) attosecond pulse trains with weak infrared laser fields, have been successfully used to study time-resolved and recently angle-resolved photoemission in various atomic systems. These measurements rely on quantum interference between electron wavepackets created by absorption of XUV radiation, with further absorption or emission of laser photons. Here we present many-body calculations of the full two-photon process.
RFBNet: Deep Multimodal Networks with Residual Fusion Blocks for RGB-D Semantic Segmentation

RGB-D semantic segmentation methods conventionally use two independent encoders to extract features from the RGB and depth data. However, there lacks an effective fusion mechanism to bridge the encoders, for the purpose of fully exploiting the complementary information from multiple modalities. This paper proposes a novel bottom-up interactive fusion structure to model the interdependencies between the encoders. The structure introduces an interaction stream to interconnect the encoders. The interaction stream not only progressively aggregates modality-specific features from the encoders but also computes complementary features for them. To instantiate this structure, the paper proposes a residual fusion block (RFB) to formulate the interdependences of the encoders. The RFB consists of two residual units and one fusion unit with gate mechanism. It learns complementary features for the modality-specific encoders and extracts modality-specific features as well as cross-modal features. Based on the RFB, the paper presents the deep multimodal networks for RGB-D semantic segmentation called RFBNet. The experiments on two datasets demonstrate the effectiveness of modeling the interdependencies and that the RFBNet achieved state-of-the-art performance.
Consistent Estimation of the Max-Flow Problem: Towards Unsupervised Image Segmentation

Advances in the image-based diagnostics of complex biological and manufacturing processes have brought unsupervised image segmentation to the forefront of enabling automated, on the fly decision making. However, most existing unsupervised segmentation approaches are either computationally complex or require manual parameter selection (e.g., flow capacities in max-flow/min-cut segmentation). In this work, we present a fully unsupervised segmentation approach using a continuous max-flow formulation over the image domain while optimally estimating the flow parameters from the image characteristics. More specifically, we show that the maximum a posteriori estimate of the image labels can be formulated as a continuous max-flow problem given the flow capacities are known. The flow capacities are then iteratively obtained by employing a novel Markov random field prior over the image domain. We present theoretical results to establish the posterior consistency of the flow capacities. We compare the performance of our approach using brain tumor image segmentation, defect identification in additively manufactured components using electron microscopic images, and segmentation of multiple real-world images. Comparative results with several state-of-the-art supervised as well as unsupervised methods suggest that the present method performs statistically similar to the supervised methods, but results in more than 90 percent improvement in the Dice score when compared to the state-of-the-art unsupervised methods.