nils-herrmann/clean_scirepeval huggingface dataset:
License Plate Detection and Recognition in Unconstrained Scenarios

Despite the large number of both commercial and academic methods for Automatic License Plate Recognition (ALPR), most existing approaches are focused on a specific license plate (LP) region (e.g. European, US, Brazilian, Taiwanese, etc.), and frequently explore datasets containing approximately frontal images. This work proposes a complete ALPR system focusing on unconstrained capture scenarios, where the LP might be considerably distorted due to oblique views. Our main contribution is the introduction of a novel Convolutional Neural Network (CNN) capable of detecting and rectifying multiple distorted license plates in a single image, which are fed to an Optical Character Recognition (OCR) method to obtain the final result. As an additional contribution, we also present manual annotations for a challenging set of LP images from different regions and acquisition conditions. Our experimental results indicate that the proposed method, without any parameter adaptation or fine tuning for a specific scenario, performs similarly to state-of-the-art commercial systems in traditional scenarios, and outperforms both academic and commercial approaches in challenging ones.
Taking the sting out of carrier sense: interference cancellation for wireless LANs

A fundamental problem with unmanaged wireless networks is high packet loss rates and poor spatial reuse, especially with bursty traffic typical of normal use. To address these limitations, we explore the notion of interference cancellation for unmanaged networks - the ability for a single receiver to disambiguate and successfully receive simultaneous overlapping transmissions from multiple unsynchronized sources. We describe a practical algorithm for interference cancellation, and implement it for ZigBee using software radios. In this setting, we find that our techniques can reduce packet loss rate and substantially increase spatial reuse. With carrier sense set to prevent concurrent sends, our approach reduces the packet loss rate during collisions from 14% to 8% due to improved handling of hidden terminals. Conversely, disabling carrier sense reduces performance for only 7% of all pairs of links and increases the delivery rate for the median pair of links in our testbed by a factor of 1.8 due to improved spatial reuse.
Evidence for nonlinear , diffusive sediment transport on hillslopes and implications for landscape morphology

Steep, soil-mantled hillslopes evolve through the downslope movement of soil, driven largely by slope-dependent ransport processes. Most landscape evolution models represent hillslope transport by linear diffusion, in which rates of sediment transport are proportional to slope, such that equilibrium hillslopes should have constant curvature between divides and channels. On many soil-mantled hillslopes, however, curvature appears to vary systematically, such that slopes are typically convex near the divide and become increasingly planar downslope. This suggests that linear diffusion is not an adequate model to describe the entire morphology of soil-mantled hillslopes. Here we show that the interaction between local disturbances (such as rainsplash and biogenic activity) and frictional and gravitational forces results in a diffusive transport law that depends nonlinearly on hillslope gradient. Our proposed transport law (1) approximates linear diffusion at low gradients and (2) indicates that sediment flux increases rapidly as gradient approaches a critical value. We calibrated and tested this transport law using high-resolution topographic data from the Oregon Coast Range. These data, obtained by airborne laser altimetry, allow us to characterize hillslope morphology at •2 m scale. At five small basins in our study area, hillslope curvature approaches zero with increasing gradient, consistent with our proposed nonlinear diffusive transport law. Hillslope gradients tend to cluster near values for which sediment flux increases rapidly with slope, such that large changes in erosion rate will correspond to small changes in gradient. Therefore average hillslope gradient is unlikely to be a reliable indicator of rates of tectonic forcing or baselevel owering. Where hillslope erosion is dominated by nonlinear diffusion, rates of tectonic forcing will be more reliably reflected in hillslope curvature near the divide rather than average hillslope gradient.
A study of evaluation metrics and datasets for video captioning

With the fast growing interest in deep learning, various applications and machine learning tasks are emerged in recent years. Video captioning is especially gaining a lot of attention from both computer vision and natural language processing fields. Generating captions is usually performed by jointly learning of different types of data modalities that share common themes in the video. Learning with the joining representations of different modalities is very challenging due to the inherent heterogeneity resided in the mixed information of visual scenes, speech dialogs, music and sounds, and etc. Consequently, it is hard to evaluate the quality of video captioning results. In this paper, we introduce well-known metrics and datasets for evaluation of video captioning. We compare the the existing metrics and datasets to derive a new research proposal for the evaluation of video descriptions.
Searching and Analysis of Interface and Visualization Metaphors

Vladimir L. Averbukh, Mihkail O. Bakhterev, Aleksandr Yu. Baydalin Institute of Mathematics and Mechanics, Inst. of Math. and Mech.,  Ural State Univercity, Inst. of Math. and Mech. Polina V. Nebogatikova, Anna V. Popova, Pavel A. Vasev Ural State Univercity, Ural State Univercity, Inst. of Math. and Mech. Russia (all)
A parallel implementation of the modified augmented Lagrangian preconditioner for the incompressible Navier–Stokes equations

We describe a parallel implementation of a block triangular preconditioner based on the modified augmented Lagrangian approach to the steady incompressible Navier–Stokes equations. The equations are linearized by Picard iteration and discretized with various finite element and finite difference schemes on two- and three-dimensional domains. We report strong scalability results for up to 64 cores.
Gaussian Prototypical Networks for Few-Shot Learning on Omniglot

We propose a novel architecture for k-shot classification on the Omniglot dataset. Building on prototypical networks, we extend their architecture to what we call Gaussian prototypical networks. Prototypical networks learn a map between images and embedding vectors, and use their clustering for classification. In our model, a part of the encoder output is interpreted as a confidence region estimate about the embedding point, and expressed as a Gaussian covariance matrix. Our network then constructs a direction and class dependent distance metric on the embedding space, using uncertainties of individual data points as weights. We show that Gaussian prototypical networks are a preferred architecture over vanilla prototypical networks with an equivalent number of parameters. We report state-ofthe-art performance in 1-shot and 5-shot classification both in 5-way and 20-way regime (for 5-shot 5-way, we are comparable to previous state-of-the-art) on the Omniglot dataset. We explore artificially down-sampling a fraction of images in the training set, which improves our performance even further. We therefore hypothesize that Gaussian prototypical networks might perform better in less homogeneous, noisier datasets, which are commonplace in real world applications.
Improved edge-coloring with three colors

We show an O(1.344) = O(2) algorithm for edge-coloring an n-vertex graph using three colors. Our algorithm uses polynomial space. This improves over the previous, O(2) algorithm of Beigel and Eppstein . We apply a very natural approach of generating inclusion-maximal matchings of the graph. The time complexity of our algorithm is estimated using the “measure and con-
Poly(vinyl chloride) processes and products.

Poly(vinyl chloride) resins are produced by four basic processes: suspension, emulsion, bulk and solution polymerization. PVC suspensions resins are usually relatively dust-free and granular with varying degrees of particle porosity. PVC emulsion resins are small particle powders containing very little free monomer. Bulk PVC resins are similar to suspension PVC resins, though the particles tend to be more porous. Solution PVC resins are smaller in particle size than suspension PVC with high porosity particles containing essentially no free monomer. The variety of PVC resin products does not lend itself to broad generalizations concerning health hazards. In studying occupational hazards the particular PVC process and the product must be considered and identified in the study.
ChemNet: A Transferable and Generalizable Deep Neural Network for Small-Molecule Property Prediction

With access to large datasets, deep neural networks (DNN) have achieved humanlevel accuracy in image and speech recognition tasks. However, in chemistry, availability of large standardized and labelled datasets is scarce, and many chemical properties of research interest, chemical data is inherently small and fragmented. In this work, we explore transfer learning techniques in conjunction with the existing Chemception CNN model, to create a transferable and generalizable deep neural network for small-molecule property prediction. Our latest model, ChemNet learns in a semi-supervised manner from inexpensive labels computed from the ChEMBL database. When fine-tuned to the Tox21, HIV and FreeSolv dataset, which are 3 separate chemical properties that ChemNet was not originally trained on, we demonstrate that ChemNet exceeds the performance of existing Chemception models and other contemporary DNN models. Furthermore, as ChemNet has been pre-trained on a large diverse chemical database, it can be used as a general-purpose plug-and-play deep neural network for the prediction of novel small-molecule chemical properties.
Spatial Workspace Collaboration: A SharedView Video Support System for Remote Collaboration Capability

Collaboration in three-dimensional space: “spatial workspace collaboration” is introduced and an approach supporting its use via a video mediated communication system is described. Verbal expression analysis is primarily focused on. Based on experiment results, movability of a focal point, sharing focal points, movability of a shared workspace, and the ability to confirm viewing intentions and movements were determined to be system requirements necessary to support spatial workspace collaboration. A newly developed SharedView system having the capability to support spatial workspace collaboration is also introduced, tested, and some experimental results described.
Diagnosis and management of patients with clefts: a comprehensive and interdisciplinary approach.

This article reviews the most current practice guidelines in the diagnosis and management of patients born with cleft lip and/or palate. Such patients frequently have multiple medical and social issues that benefit greatly from a team approach. Common challenges include feeding difficulty, nutritional deficiency, speech disorders, hearing problems, ear disease, dental anomalies, and both social and developmental delays, among others. Interdisciplinary evaluation and collaboration throughout a patient's development are essential.
6.5 A 1.8pJ/b 56Gb/s PAM-4 transmitter with fractionally spaced FFE in 14nm CMOS

As data rates in electrical links rise to 56Gb/s, standards are gravitating towards PAM-4 modulation to achieve higher spectral efficiency. Such approaches are not without drawbacks, as PAM-4 signaling results in reduced vertical margins as compared to NRZ. This makes data recovery more susceptible to residual, or uncompensated, intersymbol interference (ISI) when the PAM-4 waveform is sampled by the receiver. To overcome this, existing standards such as OIF CEI 56Gb/s very short reach (VSR) require forward error correction to meet the target link BER of 1E-15. This comes at the expense of higher latency, which is undesirable for chip-to-chip VSR links in compute applications. Therefore, different channel equalization strategies should be considered for PAM-4 electrical links. Employing ½-UI (T/2) tap delays in an FFE extends the filter bandwidth as compared to baud- or T-spaced taps , resulting in improved timing margins and lower residual ISI for 56Gb/s PAM-4 data sent across VSR channels. While T/2-spaced FFEs have been reported in optical receivers for dispersion compensation , the analog delay techniques used are not conducive to designing dense I/O and cannot support a wide range of data rates. This work demonstrates a 56Gb/s PAM-4 transmitter with a T/2-spaced FFE using high-speed clocking techniques to produce well-controlled tap delays that are data-rate agile. The transmitter also supports T-spaced tap delays, ensuring compatibility with existing standards.
Spatial Aliasing in Spherical Microphone Arrays

Performance of microphone arrays at the high-frequency range is typically limited by aliasing, which is a result of the spatial sampling process. This paper presents analysis of aliasing for spherical microphone arrays, which have been recently studied for a range of applications. The paper presents theoretical analysis of spatial aliasing for various sphere sampling configurations, showing how high-order spherical harmonic coefficients are aliased into the lower orders. Spatial antialiasing filters on the sphere are then introduced, and the performance of spatially constrained filters is compared to that of the ideal antialiasing filter. A simulation example shows how the effect of aliasing on the beam pattern can be reduced by the use of the antialiasing filters
Adaptive dynamic surface control of nonlinear systems with unknown dead zone in pure feedback form

In this paper, adaptive dynamic surface control (DSC) is developed for a class of pure-feedback nonlinear systems with unknown dead zone and perturbed uncertainties using neural networks. The explosion of complexity in traditional backstepping design is avoided by utilizing dynamic surface control and introducing integral-type Lyapunov function. It is proved that the proposed design method is able to guarantee semi-global uniform ultimate boundedness of all signals in the closed-loop system, with arbitrary small tracking error by appropriately choosing design constants. Simulation results demonstrate the effectiveness of the proposed approach.
Supply Chain Coordination with Contracts

1 This paper is an invited chapter to be published in the Handbooks in Operations Research and Management  Supply Chain Management, edited by Steve Graves and Ton de Kok and published by North-Holland. I would like to thank the many people that carefully read and commented on the ...rst draft of this manuscript: Ravi Anupindi, Fangruo Chen, Charles Corbett, James Dana, Ananth Iyer, Ton de Kok, Yigal Gerchak, Mark Ferguson, Marty Lariviere, Serguei Netessine, Ediel Pinker, Nils Rudi, Sridhar Seshadri, Terry Taylor and Kevin Weng. I am, of course, responsible for all remaining errors. Comments, of course, are still quite welcomed.
Card-660: Cambridge Rare Word Dataset - a Reliable Benchmark for Infrequent Word Representation Models

Rare word representation has recently enjoyed a surge of interest, owing to the crucial role that effective handling of infrequent words can play in accurate semantic understanding. However, there is a paucity of reliable benchmarks for evaluation and comparison of these techniques. We show in this paper that the only existing benchmark (the Stanford Rare Word dataset) suffers from low-confidence annotations and limited vocabulary; hence, it does not constitute a solid comparison framework. In order to fill this evaluation gap, we propose CAmbridge Rare word Dataset (CARD-660), an expert-annotated word similarity dataset which provides a highly reliable, yet challenging, benchmark for rare word representation techniques. Through a set of experiments we show that even the best mainstream word embeddings, with millions of words in their vocabularies, are unable to achieve performances higher than 0.43 (Pearson correlation) on the dataset, compared to a human-level upperbound of 0.90. We release the dataset and the annotation materials at https:// pilehvar.github.io/card-660/.
Quilting Stochastic Kronecker Product Graphs to Generate Multiplicative Attribute Graphs

We describe the first sub-quadratic sampling algorithm for the Multiplicative Attribute Graph Model (MAGM) of Kim and Leskovec (2010). We exploit the close connection between MAGM and the Kronecker Product Graph Model (KPGM) of Leskovec et al. (2010), and show that to sample a graph from a MAGM it suffices to sample small number of KPGM graphs and quilt them together. Under a restricted set of technical conditions our algorithm runs in O ( (log2(n)) 3 |E| ) time, where n is the number of nodes and |E| is the number of edges in the sampled graph. We demonstrate the scalability of our algorithm via extensive empirical evaluation; we can sample a MAGM graph with 8 million nodes and 20 billion edges in under 6 hours.
Stochastic Gradient Methods for Distributionally Robust Optimization with f-divergences

We develop efficient solution methods for a robust empirical risk minimization problem designed to give calibrated confidence intervals on performance and provide optimal tradeoffs between bias and variance. Our methods apply to distributionally robust optimization problems proposed by Ben-Tal et al., which put more weight on observations inducing high loss via a worst-case approach over a non-parametric uncertainty set on the underlying data distribution. Our algorithm solves the resulting minimax problems with nearly the same computational cost of stochastic gradient descent through the use of several carefully designed data structures. For a sample of size n, the per-iteration cost of our method scales as O(log n), which allows us to give optimality certificates that distributionally robust optimization provides at little extra cost compared to empirical risk minimization and stochastic gradient methods.
A large scale study of SVM based methods for abstract screening in systematic reviews

A major task in systematic reviews is abstract screening, i.e., excluding, often hundreds or thousand of, irrelevant citations returned from a database search based on titl es and abstracts. Thus, a systematic review platform that can auto mate the abstract screening process is of huge importance. Sever al methods have been proposed for this task. However, it is very hard to clearly understand the applicability of these methods in a systematic review platform because of the following chall enges: (1) the use of non-overlapping metrics for the evaluation of the proposed methods, (2) usage of features that are very har d to collect, (3) using a small set of reviews for the evaluatio n, and (4) no solid statistical testing or equivalence groupin g of the methods. In this paper, we use feature representation th at can be extracted per citation. We evaluate SVM based methods (commonly used) on a large set of reviews ( 61) and metrics (11) to provide equivalence grouping of methods based on a solid statistical test. Our analysis also includes a strong varia bility of the metrics using 500x2 cross validation. While some methods shine for different metrics and for different datasets, there is no single method that dominates the pack. Furthermore, we obse rve that in some cases relevant (included) citations can be foun d after screening only 15-20% of them via a certainty based sampling . A few included citations present outlying characteristicsand can only be found after a very large number of screening steps. Finally, we present an ensemble algorithm for producing a5star rating of citations based on their relevance. Such algo rithm combines the best methods from our evaluation and through it s 5-star rating outputs a more easy-to-consume prediction.
Bundle-branch block as a risk factor in noncardiac surgery.

Despite extensive data examining perioperative risk in patients with coronary artery disease, little attention has been devoted to the implications of conduction system abnormalities.



To define the clinical significance of bundle-branch block (BBB) as a perioperative risk factor.



Retrospective, cohort-controlled study of all noncardiac, nonophthalmologic, adult patients with BBB seen in our preoperative evaluation center. Medical charts were reviewed for data regarding cardiovascular disease, surgical procedure, type of anesthesia, intravascular monitoring, and perioperative complications.



Bundle-branch block was present in 455 patients. Right BBB (RBBB) was more common than left BBB (LBBB) (73.8% vs 26.2%). Three patients with LBBB and 1 patient with RBBB died; 1 patient had a supraventricular tachyarrhythmia. Three of the 4 deaths were sepsis related. There were 2 (0.4%) deaths in the control group. There was no difference in mortality between BBB and control groups (P = .32). Subgroup analysis suggested an increased risk for death in patients with LBBB vs controls (P = .06; odds ratio, 6.0; 95% confidence interval, 1.2-100.0) and vs RBBB (P = .06; odds ratio, 8.7; 95% confidence interval, 1.2-100.0).



The presence of BBB is not associated with a high incidence of postoperative cardiac complications. Perioperative mortality is not increased in patients with RBBB and not directly attributable to cardiac complications in patients with LBBB. These data suggest that the presence of BBB does not significantly increase the likelihood of cardiac complications following surgery, but that patients with LBBB may not tolerate the stress of perioperative noncardiac complications.