nils-herrmann/clean_scirepeval huggingface dataset:
Minimizers of Cost-Functions Involving Nonsmooth Data-Fidelity Terms. Application to the Processing of Outliers

We present a theoretical study of the recovery of an unknown vector x ∈ Rp (such as a signal or an image) from noisy data y ∈ Rq by minimizing with respect to x a regularized costfunction F(x, y) = Ψ(x, y) + αΦ(x), where Ψ is a data-fidelity term, Φ is a smooth regularization term, and α > 0 is a parameter. Typically, Ψ(x, y) = ‖Ax− y‖2, where A is a linear operator. The data-fidelity terms Ψ involved in regularized cost-functions are generally smooth functions; only a few papers make an exception to this and they consider restricted situations. Nonsmooth data-fidelity terms are avoided in image processing. In spite of this, we consider both smooth and nonsmooth data-fidelity terms. Our goal is to capture essential features exhibited by the local minimizers of regularized cost-functions in relation to the smoothness of the data-fidelity term. In order to fix the context of our study, we consider Ψ(x, y) = ∑ i ψ(ai x − yi), where ai are the rows of A and ψ is Cm on R \ {0}. We show that if ψ′(0−) < ψ′(0+), then typical data y give rise to local minimizers x̂ of F(., y) which fit exactly a certain number of the data entries: there is a possibly large set ĥ of indexes such that ai x̂ = yi for every i ∈ ĥ. In contrast, if ψ is smooth on R, for almost every y, the local minimizers of F(., y) do not fit any entry of y. Thus, the possibility that a local minimizer fits some data entries is due to the nonsmoothness of the data-fidelity term. This is a strong mathematical property which is useful in practice. By way of application, we construct a cost-function allowing aberrant data (outliers) to be detected and to be selectively smoothed. Our numerical experiments advocate the use of nonsmooth data-fidelity terms in regularized cost-functions for special purposes in image and signal processing.
Sampling Matters in Deep Embedding Learning

Deep embeddings answer one simple question: How similar are two images? Learning these embeddings is the bedrock of verification, zero-shot learning, and visual search. The most prominent approaches optimize a deep convolutional network with a suitable loss function, such as contrastive loss or triplet loss. While a rich line of work focuses solely on the loss functions, we show in this paper that selecting training examples plays an equally important role. We propose distance weighted sampling, which selects more informative and stable examples than traditional approaches. In addition, we show that a simple margin based loss is sufficient to outperform all other loss functions. We evaluate our approach on the Stanford Online Products, CAR196, and the CUB200-2011 datasets for image retrieval and clustering, and on the LFW dataset for face verification. Our method achieves state-of-the-art performance on all of them.
Reliability of GaN High-Electron-Mobility Transistors: State of the Art and Perspectives

Failure modes and mechanisms of AlGaN/GaN high-electron-mobility transistors are reviewed. Data from three de-accelerated tests are presented, which demonstrate a close correlation between failure modes and bias point. Maximum degradation was found in "semi-on" conditions, close to the maximum of hot-electron generation which was detected with the aid of electroluminescence (EL) measurements. This suggests a contribution of hot-electron effects to device degradation, at least at moderate drain bias (VDS<30 V). A procedure for the characterization of hot carrier phenomena based on EL microscopy and spectroscopy is described. At high drain bias (VDS>30-50 V), new failure mechanisms are triggered, which induce an increase of gate leakage current. The latter is possibly related with the inverse piezoelectric effect leading to defect generation due to strain relaxation, and/or to localized permanent breakdown of the AlGaN barrier layer. Results are compared with literature data throughout the text.
Style Transfer for Anime Sketches with Enhanced Residual U-net and Auxiliary Classifier GAN

Recently, with the revolutionary neural style transferring methods, creditable paintings can be synthesized automatically from content images and style images. However, when it comes to the task of applying a painting's style to an anime sketch, these methods will just randomly colorize sketch lines as outputs and fail in the main task: specific style transfer. In this paper, we integrated residual U-net to apply the style to the gray-scale sketch with auxiliary classifier generative adversarial network (AC-GAN). The whole process is automatic and fast. Generated results are creditable in the quality of art style as well as colorization.
DEEP RESIDUAL LEARNING FOR TOMATO PLANT LEAF DISEASE IDENTIFICATION

Deep Learning for plant leaf analysis has been recently studied in various works. In most cases, Transfer Learning has been utilized, where the weights of networks, which are stored in the pre-trained models, are fine-tuned to use in the considered task. In this paper, Convolutional Neural Networks (CNNs), are employed to classify tomato plant leaf images based on the visible effects of diseases. In addition to Transfer Learning as an effective approach, training a CNN from scratch using the Deep Residual Learning method, is experimented. To do that, an architecture of CNN is proposed and applied to a subset of the PlantVillage dataset, including tomato plant leaf images. The results indicate that the suggested architecture outperforms VGG models, pre-trained on the ImageNet dataset, in both accuracy and the time required for re-training, and it can be used with a regular PC without any extra hardware required. A common feature visualization and verification technique is also applied to the results and further discussions are made to imply the importance of background pixels surrounding the leaves.
Are deep neural networks the best choice for modeling source code?

Current statistical language modeling techniques, including deep-learning based models, have proven to be quite effective for source code. We argue here that the special properties of source code can be exploited for further improvements. In this work, we enhance established language modeling approaches to handle the special challenges of modeling source code, such as: frequent changes, larger, changing vocabularies, deeply nested scopes, etc. We present a fast, nested language modeling toolkit specifically designed for software, with the ability to add & remove text, and mix & swap out many models. Specifically, we improve upon prior cache-modeling work and present a model with a much more expansive, multi-level notion of locality that we show to be well-suited for modeling software. We present results on varying corpora in comparison with traditional N-gram, as well as RNN, and LSTM deep-learning language models, and release all our source code for public use. Our evaluations suggest that carefully adapting N-gram models for source code can yield performance that surpasses even RNN and LSTM based deep-learning models.
Bio-Inspired Stochastic Computing Using Binary CBRAM Synapses

In this paper, we present an alternative approach to neuromorphic systems based on multilevel resistive memory synapses and deterministic learning rules. We demonstrate an original methodology to use conductive-bridge RAM (CBRAM) devices as, easy to program and low-power, binary synapses with stochastic learning rules. New circuit architecture, programming strategy, and probabilistic spike-timing dependent plasticity (STDP) learning rule for two different CBRAM configurations with-selector (1T-1R) and without-selector (1R) are proposed. We show two methods (intrinsic and extrinsic) for implementing probabilistic STDP rules. Fully unsupervised learning with binary synapses is illustrated through two example applications: 1) real-time auditory pattern extraction (inspired from a 64-channel silicon cochlea emulator); and 2) visual pattern extraction (inspired from the processing inside visual cortex). High accuracy (audio pattern sensitivity > 2, video detection rate > 95%) and low synaptic-power dissipation (audio 0.55 μW, video 74.2 μW) are shown. The robustness and impact of synaptic parameter variability on system performance are also analyzed.
Compression By Induction of Hierarchical Grammars

Adaptive compression methods build models of symbol sequences. In many areas of computer science, models of sequences are constructed for their explanatory value. In contrast, data compression schemes use models that are opaque in that they do not provide descriptions of the sequence that can be readily understood or applied in other domains. Statistical methods that compress text well invariably generate large models that are not so much a structural description of the sequence as a record of frequencies of short substrings. Macro models replace repeated text by references to earlier occurrences and generally work within a small moving window of symbols so that any implicit model is transient. In both cases the models are flat and do not provide abstractions by combining references into higher level phrases.
The wisdom of minority: discovering and targeting the right group of workers for crowdsourcing

Worker reliability is a longstanding issue in crowdsourcing, and the automatic discovery of high quality workers is an important practical problem. Most previous work on this problem mainly focuses on estimating the quality of each individual worker jointly with the true answer of each task. However, in practice, for some tasks, worker quality could be associated with some explicit characteristics of the worker, such as education level, major and age. So the following question arises: how do we automatically discover related worker attributes for a given task, and further utilize the findings to improve data quality? In this paper, we propose a general crowd targeting framework that can automatically discover, for a given task, if any group of workers based on their attributes have higher quality on average; and target such groups, if they exist, for future work on the same task. Our crowd targeting framework is complementary to traditional worker quality estimation approaches. Furthermore, an advantage of our framework is that it is more budget efficient because we are able to target potentially good workers before they actually do the task. Experiments on real datasets show that the accuracy of final prediction can be improved significantly for the same budget (or even less budget in some cases). Our framework can be applied to many real word tasks and can be easily integrated in current crowdsourcing platforms.
Optimal differential energy watermarking of DCT encoded images and video

This paper proposes the differential energy watermarking (DEW) algorithm for JPEG/MPEG streams. The DEW algorithm embeds label bits by selectively discarding high frequency discrete cosine transform (DCT) coefficients in certain image regions. The performance of the proposed watermarking algorithm is evaluated by the robustness of the watermark, the size of the watermark, and the visual degradation the watermark introduces. These performance factors are controlled by three parameters, namely the maximal coarseness of the quantizer used in pre-encoding, the number of DCT blocks used to embed a single watermark bit, and the lowest DCT coefficient that we permit to be discarded. We follow a rigorous approach to optimizing the performance and choosing the correct parameter settings by developing a statistical model for the watermarking algorithm. Using this model, we can derive the probability that a label bit cannot be embedded. The resulting model can be used, for instance, for maximizing the robustness against re-encoding and for selecting adequate error correcting codes for the label bit string.
Shared representations between self and other: a social cognitive neuroscience view

The abilities to identify with others and to distinguish between self and other play a pivotal role in intersubjective transactions. Here, we marshall evidence from developmental science, social psychology and neuroscience (including clinical neuropsychology) that support the view of a common representation network (both at the computational and neural levels) between self and other. However, sharedness does not mean identicality, otherwise representations of self and others would completely overlap, and lead to confusion. We argue that self-awareness and agency are integral components for navigating within these shared representations. We suggest that within this shared neural network the inferior parietal cortex and the prefrontal cortex in the right hemisphere play a special role in interpersonal awareness.
iOverbook: Intelligent Resource-Overbooking to Support Soft Real-Time Applications in the Cloud

Cloud service providers (CSPs) often overbook their resources with user applications despite having to maintain service-level agreements with their customers. Overbooking is attractive to CSPs because it helps to reduce power consumption in the data center by packing more user jobs in less number of resources while improving their profits. Overbooking becomes feasible because user applications tend to overestimate their resource requirements utilizing only a fraction of the allocated resources. Arbitrary resource overbooking ratios, however, may be detrimental to soft real-time applications, such as airline reservations or Netflix video streaming, which are increasingly hosted in the cloud. The changing dynamics of the cloud preclude an offline determination of overbooking ratios. To address these concerns, this paper presents iOverbook, which uses a machine learning approach to make systematic and online determination of overbooking ratios such that the quality of service needs of soft real-time systems can be met while still benefiting from overbooking. Specifically, iOverbook utilizes historic data of tasks and host machines in the cloud to extract their resource usage patterns and predict future resource usage along with the expected mean performance of host machines. To evaluate our approach, we have used a large usage trace made available by Google of one of its production data centers. In the context of the traces, our experiments show that iOverbook can help CSPs improve their resource utilization by an average of 12.5% and save 32% power in the data center.
De-anonymizing Web Browsing Data with Social Networks

Can online trackers and network adversaries de-anonymize web browsing data readily available to them? We show— theoretically, via simulation, and through experiments on real user data—that de-identified web browsing histories can be linked to social media profiles using only publicly available data. Our approach is based on a simple observation: each person has a distinctive social network, and thus the set of links appearing in one’s feed is unique. Assuming users visit links in their feed with higher probability than a random user, browsing histories contain tell-tale marks of identity. We formalize this intuition by specifying a model of web browsing behavior and then deriving the maximum likelihood estimate of a user’s social profile. We evaluate this strategy on simulated browsing histories, and show that given a history with 30 links originating from Twitter, we can deduce the corresponding Twitter profile more than 50% of the time. To gauge the real-world e↵ectiveness of this approach, we recruited nearly 400 people to donate their web browsing histories, and we were able to correctly identify more than 70% of them. We further show that several online trackers are embedded on su ciently many websites to carry out this attack with high accuracy. Our theoretical contribution applies to any type of transactional data and is robust to noisy observations, generalizing a wide range of previous de-anonymization attacks. Finally, since our attack attempts to find the correct Twitter profile out of over 300 million candidates, it is—to our knowledge—the largestscale demonstrated de-anonymization to date. CCS Concepts •Security and privacy ! Pseudonymity, anonymity and untraceability; •Information systems ! Online advertising; Social networks; Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from permissions@acm.org.
CleanNet: Transfer Learning for Scalable Image Classifier Training with Label Noise

In this paper, we study the problem of learning image classification models with label noise. Existing approaches depending on human supervision are generally not scalable as manually identifying correct or incorrect labels is time-consuming, whereas approaches not relying on human supervision are scalable but less effective. To reduce the amount of human supervision for label noise cleaning, we introduce CleanNet, a joint neural embedding network, which only requires a fraction of the classes being manually verified to provide the knowledge of label noise that can be transferred to other classes. We further integrate CleanNet and conventional convolutional neural network classifier into one framework for image classification learning. We demonstrate the effectiveness of the proposed algorithm on both of the label noise detection task and the image classification on noisy data task on several large-scale datasets. Experimental results show that CleanNet can reduce label noise detection error rate on held-out classes where no human supervision available by 41.5% compared to current weakly supervised methods. It also achieves 47% of the performance gain of verifying all images with only 3.2% images verified on an image classification task. Source code and dataset will be available at kuanghuei.github.io/CleanNetProject.
Precoccygeal epidermal inclusion cyst presenting as coccygodynia.

Although there are numerous aetiologies for coccygodynia described in the medical literature, precoccygeal epidermal inclusion cyst presenting as a coccygodynia has not been reported. We report a 30-year-old woman with intractable coccygodynia. Magnetic resonance imaging showed a circumscribed precoccygeal cystic lesion. The removed cyst was pearly-white in appearance and contained cheesy material. Histological evaluation established the diagnosis of epidermal inclusion cyst with mild nonspecific inflammation. The patient became asymptomatic and remained so at two years follow-up. This report suggests that precoccygeal epidermal inclusion cyst should be considered as one of the differential diagnosis of coccygodynia. Our experience suggests that patients with intractable coccygodynia should have a magnetic resonance imaging to rule out treatable causes of coccygodynia.
Search log analysis: What it is, what's been done, how to do it

The use of data stored in transaction logs of Web search engines, Intranets, and Web sites can provide valuable insight into understanding the information-searching process of online searchers. This understanding can enlighten information system design, interface development, and devising the information architecture for content collections. This article presents a review and foundation for conducting Web search transaction log analysis. A methodology is outlined consisting of three stages, which are collection, preparation, and analysis. The three stages of the methodology are presented in detail with discussions of goals, metrics, and processes at each stage. Critical terms in transaction log analysis for Web searching are defined. The strengths and limitations of transaction log analysis as a research method are presented. An application to log client-side interactions that supplements transaction logs is reported on, and the application is made available for use by the research community. Suggestions are provided on ways to leverage the strengths of, while addressing the limitations of, transaction log analysis for Web-searching research. Finally, a complete flat text transaction log from a commercial search engine is available as supplementary material with this manuscript.
YouTube as a participatory culture.

There is an explosion of youth subscriptions to original content-media-sharing Web sites such as YouTube. These Web sites combine media production and distribution with social networking features, making them an ideal place to create, connect, collaborate, and circulate. By encouraging youth to become media creators and social networkers, new media platforms such as YouTube offer a participatory culture in which youth can develop, interact, and learn. As youth development researchers, we must be cognizant of this context and critically examine what this platform offers that might be unique to (or redundant of) typical adolescent experiences in other developmental contexts.
A Tradeoff Analysis of a Cloud-Based Robot Navigation Assistant Using Stereo Image Processing

The use of Cloud Computing for computation offloading in the robotics area has become a field of interest today. The aim of this work is to demonstrate the viability of cloud offloading in a low level and intensive computing task: a vision-based navigation assistance of a service mobile robot. In order to do so, a prototype, running over a ROS-based mobile robot (Erratic by Videre Design LLC) is presented. The information extracted from on-board stereo cameras will be used by a private cloud platform consisting of five bare-metal nodes with AMD Phenom 965 × 4 CPU, with the cloud middleware Openstack Havana. The actual task is the shared control of the robot teleoperation, that is, the smooth filtering of the teleoperated commands with the detected obstacles to prevent collisions. All the possible offloading models for this case are presented and analyzed. Several performance results using different communication technologies and offloading models are explained as well. In addition to this, a real navigation case in a domestic circuit was done. The tests demonstrate that offloading computation to the Cloud improves the performance and navigation results with respect to the case where all processing is done by the robot.
An overview of pipeline leak detection and location systems

Leak detection in transmission pipelines is crucially important for safe operation. Delay in detecting leaks leads to loss of property and human life in fire hazards and loss of valuable material. Leaking of methane and hydrocarbon gas causes negative impacts on the eco system such as global warming and air pollution. Pipeline leak detection systems play a key role in minimization of the probability of occurrence of leaks and hence their impacts. Today there are many available technologies in the domain of leak detection. This paper provides an overview on external and internal leak detection and location systems and a summary of comparison regarding performance of each system.