nils-herrmann/clean_scirepeval huggingface dataset:
Mixed pooling neural networks for color constancy

Color constancy is the ability of the human visual system to perceive constant colors for a surface despite changes in the spectrum of the illumination. In computer vision, the main approach consists in estimating the illuminant color and then to remove its impact on the color of the objects. Many image processing algorithms have been proposed to tackle this problem automatically. However, most of these approaches are handcrafted and mostly rely on strong empirical assumptions, e.g., that the average reflectance in a scene is gray. State-of-the-art approaches can perform very well on some given datasets but poorly adapt on some others. In this paper, we have investigated how neural networks-based approaches can be used to deal with the color constancy problem. We have proposed a new network architecture based on existing successful hand-crafted approaches and a large number of improvements to tackle this problem by learning a suitable deep model. We show our results on most of the standard benchmarks used in the color constancy domain.
Worthiness of Sentences in Scientific Reports

Does this sentence need citation? In this paper, we introduce the task of citation worthiness for scientific texts at a sentence-level granularity. The task is to detect whether a sentence in a scientific article needs to be cited or not. It can be incorporated into citation recommendation systems to help automate the citation process by marking sentences where needed. It may also be useful for publishers to regularize the citation process. We construct a dataset using the ACLAnthology Reference Corpus; consisting of over 1.1M “not_cite” and 85K “cite” sentences. We study the performance of a set of state-of-the-art sentence classifiers for the citation worthiness task and show the practical challenges. We also explore sectionwise difficulty of the task and analyze the performance of our best model on a published article.  Hamed Bonab, Hamed Zamani, Erik Learned-Miller, and James Allan. 2018.  In SIGIR ’18: The 41st International ACM SIGIR Conference on Research & Development in Information Retrieval, July 8–12, 2018,  Arbor, MI, USA. ACM, New York, NY, USA, 4 pages.
Automatic corn plant location and spacing measurement using laser line-scan technique

Identifying corn plant location and/or spacing is important for predicting yield potential and making decisions for in-season nitrogen application rate. In this study, an automatic corn stalk identification system based on a laser line-scan technique was developed to measure stalk locations during corn mid-growth stages. A laser line-scan technique is advantageous in this application because the line-scan data sets taken from various points of view of a plant stalk results in less interference and higher probability of plant recognition. Data were collected for two 10-meter-long corn rows at the growth stages of V8 and V10 using a mobile test platform in 2011. Each potential stalk cluster was identified in a scan and registered with the same stalks in previous scans. The final location of a stalk was the average of the measured locations in all scans. The current system setup with data processing algorithms achieved 24.0 and 10.0 % of mean total errors in plant counting at the V8 and V10 growth stages, respectively. The root-mean-squared error (RMSE) between system measured plant locations and manually measured ones were 2.3 and 2.6 cm at the V8 and V10 growth stages, respectively. The interplant spacing measured by the developed system had a good correlation with the manual measurement with an R 2 of 0.962 and 0.951 for the V8 and V10 growth stages, respectively. This system can be ultimately integrated in a variable-rate-spraying system to improve real-time, high spatial resolution variable-rate nitrogen applications.
PERFORMANCE LIMITS OF SWITCHED-CAPACITOR DC-DC CONVERTERS

Theoretical performance limits of twophase switched-capacitor (SC) dc-dc converters are discussed in this paper. For a given number of capacitors k, the complete set of attainable dc conversion ratios is found. The maximum step-up or stepdown ratio is given by the k t h Fibonacci number, while the bound on the number of switches required in any SC circuit is 3k 2. Practical implications, illustrated by several SC converter examples, include savings in the number of components required for a given application, and the ability to construct SC converters that can maintain the output voltage regulation and high conversion efficiency over a wide range of input voltage variations. Limits found for the output resistance and efflciency can be used for selection and comparison of SC converters.
Multi-view 3D Models from Single Images with a Convolutional Network

Kulkarni, T. D., Whitney , W. F., Kohli, P., Tenenbaum,  Deep convolutional inverse graphics network. NIPS 2015.  Dosovitskiy, A., Springenberg, J. T., Brox, T.: Learning to generate chairs with convolutional neural networks. CVPR 2015.  Yang,  E., Yang, M. H., Lee, H.: Weakly-supervised disentangling with recurrent transformations for 3D view synthesis. NIPS 2015. The network was trained on renderings of synthetic 3D models.
Electromagnetic Two-Dimensional Scanner Using Radial Magnetic Field

In this paper, we present the design, fabrication, and measurement results of a two-dimensional electromagnetic scanning micromirror actuated by radial magnetic field. The scanner is realized by combining a gimbaled single-crystal-silicon micromirror with a single turn electroplated metal coil, with a concentric permanent magnet assembly composed of two concentric permanent magnets and an iron yoke. The proposed scanner utilizes the radial magnetic field rather than using a lateral magnetic field oriented 45deg to the horizontal and vertical scan axes to achieve a biaxial magnetic actuation. The single turn coil fabricated with electroplated copper achieves a nominal resistance of 1.2 Omega. A two-dimensional scanner with mirror size of 1.5 mm in diameter was fabricated. Maximum optical scan angle of 8.8deg in horizontal direction and 8.3deg in vertical direction were achieved. Forced actuation of the gimbal at 60 Hz and resonant actuation of the micromirror at 19.1-19.7 kHz provide slow vertical scan and fast horizontal scan, respectively. The proposed scanner can be used in raster scanning laser display systems and other scanner applications.
Audio-visual speech recognition using lip movement extracted from side-face images

This paper proposes an audio-visual speech recognition method using lip movement extracted from side-face images to attempt to increase noise-robustness in mobile environments. Although most previous bimodal speech recognition methods use frontal face (lip) images, these methods are not easy for users since they need to hold a device with a camera in front of their face when talking. Our proposed method capturing lip movement using a small camera installed in a handset is more natural, easy and convenient. This method also effectively avoids a decrease of signal-to-noise ratio (SNR) of input speech. Visual features are extracted by optical-flow analysis and combined with audio features in the framework of HMM-based recognition. Phone HMMs are built by the multi-stream HMM technique. Experiments conducted using Japanese connected digit speech contaminated with white noise in various SNR conditions show effectiveness of the proposed method. Recognition accuracy is improved by using the visual information in all SNR conditions, and the best improvement is approximately 6% at 5dB SNR.
QUO VADIS , DYNAMIC CAPABILITIES ? ACONTENT-ANALYTICREVIEWOFTHECURRENTSTATEOF KNOWLEDGE AND RECOMMENDATIONS FOR FUTURE RESEARCH

Although the dynamic capabilities perspective has become one of the most frequently used theoretical lenses in management research, critics have repeatedly voiced their frustration with this literature, particularly bemoaning the lack of empirical knowledge and the underspecification of the construct of dynamic capabilities. But research on dynamic capabilities has advanced considerably since its early years, in which most contributions to this literature were purely conceptual. A plethora of empirical studies as well as further theoretical elaborations have shed substantial light on a variety of specific, measurable factors connected to dynamic capabilities. Our article starts out by analyzing these studies to develop a meta-framework that specifies antecedents, dimensions, mechanisms, moderators, and outcomes of dynamic capabilities identified in the literature to date. This framework provides a comprehensive and systematic synthesis of the dynamic capabilities perspective that reflects the richness of the research while at the same time unifying it into a cohesive, overarching model. Such an analysis has not yet been undertaken; no comprehensive framework with this level of detail has previously been presented for dynamic capabilities. Our analysis shows where research has made the most progress and where gaps and unresolved tensions remain. Based on this analysis, we propose a forward-looking research agenda that outlines directions for future research.
Texture analysis of medical images.

The analysis of texture parameters is a useful way of increasing the information obtainable from medical images. It is an ongoing field of research, with applications ranging from the segmentation of specific anatomical structures and the detection of lesions, to differentiation between pathological and healthy tissue in different organs. Texture analysis uses radiological images obtained in routine diagnostic practice, but involves an ensemble of mathematical computations performed with the data contained within the images. In this article we clarify the principles of texture analysis and give examples of its applications, reviewing studies of the technique.
Data Center Energy Consumption Modeling: A Survey

Data centers are critical, energy-hungry infrastructures that run large-scale Internet-based services. Energy consumption models are pivotal in designing and optimizing energy-efficient operations to curb excessive energy consumption in data centers. In this paper, we survey the state-of-the-art techniques used for energy consumption modeling and prediction for data centers and their components. We conduct an in-depth study of the existing literature on data center power modeling, covering more than 200 models. We organize these models in a hierarchical structure with two main branches focusing on hardware-centric and software-centric power models. Under hardware-centric approaches we start from the digital circuit level and move on to describe higher-level energy consumption models at the hardware component level, server level, data center level, and finally systems of systems level. Under the software-centric approaches we investigate power models developed for operating systems, virtual machines and software applications. This systematic approach allows us to identify multiple issues prevalent in power modeling of different levels of data center systems, including: i) few modeling efforts targeted at power consumption of the entire data center ii) many state-of-the-art power models are based on a few CPU or server metrics, and iii) the effectiveness and accuracy of these power models remain open questions. Based on these observations, we conclude the survey by describing key challenges for future research on constructing effective and accurate data center power models.
Face Detection in Color Images

ا ل صخمم  ددددهي  دددد د (فدددديف فأ ددددحاف ودددديف دددد فددلا هفددددألا اددددو ( ددددي ح ف ودددديفلا دددد ه وددددلا ىددددحبلا هافخ ما خ سا م ىيح ،صاخ لأل د د ودع ي بد يدز نفل تادف  هد بلا  د د اد لا وديفلا لد ًد ندع ً دضو . مد لاف ني ي لاده فدق لا  ف ىديحب هادب خثا ادو د خ دسدلا د فددلا هفدألا رفد هي د دسا ه ىدحبلا نددض مد هفددأ ما خ ددسا ندددض ودديفلا ددي ح اددو ادده د دده هي دد ف دد د خد هفددأ ددي دخف دد د خد يهدد ب رادد يأ ندد ني عفدد يد نددع ً ددضو . هفددألا دديوا هقف فو ا هيداددهب تددوق لا دديوا هقف فو هفددأ ددعفديد ، دد فددلا هفددألا ندد ىهخلأا عفديدلاف  يدقه ا هيداهب توق لا يدقه هفأ د ه او ني عفديدلا جئا نيب هاقد ءا هيإ م اده . او ( ي ح ف ويفلا  هفألا (ذه  دلاقلا ما خ ساب ني عفديدلا ند هفأ هل ويفلا ا هح ا يفا ز ديق سحف  . هاسي ،نيدي اب ايلا لاقلاف ادادلأا
A Survey of Data Mining and Knowledge Discovery Software Tools

Knowledge discovery in databases is a rapidly growing field, whose development is driven by strong research interests as well as urgent practical, social, and economical needs. While the last few years knowledge discovery tools have been used mainly in research environments, sophisticated software products are now rapidly emerging. In this paper, we provide an overview of common knowledge discovery tasks and approaches to solve these tasks. We propose a feature classification scheme that can be used to study knowledge and data mining software. This scheme is based on the software's general characteristics, database connectivity, and data mining characteristics. We then apply our feature classification scheme to investigate 43 software products, which are either research prototypes or commercially available. Finally, we specify features that we consider important for knowledge discovery software to possess in order to accommodate its users effectively, as well as issues that are either not addressed or insufficiently solved yet.
City Forensics: Using Visual Elements to Predict Non-Visual City Attributes

We present a method for automatically identifying and validating predictive relationships between the visual appearance of a city and its non-visual attributes (e.g. crime statistics, housing prices, population density etc.). Given a set of street-level images and (location, city-attribute-value) pairs of measurements, we first identify visual elements in the images that are discriminative of the attribute. We then train a predictor by learning a set of weights over these elements using non-linear Support Vector Regression. To perform these operations efficiently, we implement a scalable distributed processing framework that speeds up the main computational bottleneck (extracting visual elements) by an order of magnitude. This speedup allows us to investigate a variety of city attributes across 6 different American cities. We find that indeed there is a predictive relationship between visual elements and a number of city attributes including violent crime rates, theft rates, housing prices, population density, tree presence, graffiti presence, and the perception of danger. We also test human performance for predicting theft based on street-level images and show that our predictor outperforms this baseline with 33% higher accuracy on average. Finally, we present three prototype applications that use our system to (1) define the visual boundary of city neighborhoods, (2) generate walking directions that avoid or seek out exposure to city attributes, and (3) validate user-specified visual elements for prediction.
Sparse Nonparametric Density Estimation in High Dimensions Using the Rodeo

We consider the problem of estimating the joint density of a d-dimensional random vector X = (X1, X2, ..., Xd) when d is large. We assume that the density is a product of a parametric baseline component and a nonparametric component. The nonparametric component depends on an unknown subset of the variables. If this subset is small, then nonparametric estimates with fast rates of convergence are possible. Using a modification of a previously developed nonparametric regression framework called rodeo (regularization of derivative expectation operator), we propose a method to exploit this fact. The method selects the bandwidths in an incremental way making it computationally attractive. We empirically show that the density rodeo works well even for very high-dimensional problems. When the unknown density function satisfies some suitably defined sparsity conditions, our approach avoids the curse of dimensionality and achieves an optimal converge rate of the risk. Because it is a greedy algorithm, bandwidth selection is fast. When the parametric baseline is a very smooth distribution, we also provide theoretical guarantees on the behavior of the method.
App Miscategorization Detection: A Case Study on Google Play

An ongoing challenge in the rapidly evolving app market ecosystem is to maintain the integrity of app categories. At the time of registration, app developers have to select, what they believe, is the most appropriate category for their apps. Besides the inherent ambiguity of selecting the right category, the approach leaves open the possibility of misuse and potential gaming by the registrant. Periodically, the app store will refine the list of categories available and potentially reassign the apps. However, it has been observed that the mismatch between the description of the app and the category it belongs to, continues to persist. Although some common mechanisms (e.g., a complaint-driven or manual checking) exist, they limit the response time to detect miscategorized apps and still open the challenge on categorization. We introduce FRAC+: (FR)amework for (A)pp (C)ategorization. FRAC+ has the following salient features: (i) it is based on a data-driven topic model and automatically suggests the categories appropriate for the app store, and (ii) it can detect miscategorizated apps. Extensive experiments attest to the performance of FRAC+. Experiments on Google Play shows that FRAC+’s topics are more aligned with Google’s new categories and 0.35-1.10 percent game apps are detected to be miscategorized.
PLUG: flexible lookup modules for rapid deployment of new protocols in high-speed routers

New protocols for the data link and network layer are being proposed to address limitations of current protocols in terms of scalability, security, and manageability. High-speed routers and switches that implement these protocols traditionally perform packet processing using ASICs which offer high speed, low chip area, and low power. But with inflexible custom hardware, the deployment of new protocols could happen only through equipment upgrades. While newer routers use more flexible network processors for data plane processing, due to power and area constraints lookups in forwarding tables are done with custom lookup modules. Thus most of the proposed protocols can only be deployed with equipment upgrades. To speed up the deployment of new protocols, we propose a flexible lookup module, PLUG (Pipelined Lookup Grid). We can achieve generality without loosing efficiency because various custom lookup modules have the same fundamental features we retain: area dominated by memories, simple processing, and strict access patterns defined by the data structure. We implemented IPv4, Ethernet, Ethane, and SEATTLE in our dataflow-based programming model for the PLUG and mapped them to the PLUG hardware which consists of a grid of tiles. Throughput, area, power, and latency of PLUGs are close to those of specialized lookup modules.
Through the Twitter Glass: Detecting Questions in Micro-Text

In a separate study, we were interested in understanding people’s Q&A habits on Twitter. Finding questions within Twitter turned out to be a difficult challenge, so we considered applying some traditional NLP approaches to the problem. On the one hand, Twitter is full of idiosyncrasies, which make processing it difficult. On the other it is very restricted in length and tends to employ simple syntactic constructions, which could help the performance of NLP processing. In order to find out the viability of NLP and Twitter, we built a pipeline of tools to work specifically with Twitter input for the task of finding questions in tweets. This work is still preliminary, but in this paper we discuss the techniques we used and the lessons we learned.
vTPM: Virtualizing the Trusted Platform Module

We present the design and implementation of a system that enables trusted computing for an unlimited number of virtual machines on a single hardware platform. To this end, we virtualized the Trusted Platform Module (TPM). As a result, the TPM’s secure storage and cryptographic functions are available to operating systems and applications running in virtual machines. Our new facility supports higher-level services for establishing trust in virtualized environments, for example remote attestation of software integrity. We implemented the full TPM specification in software and added functions to create and destroy virtual TPM instances. We integrated our software TPM into a hypervisor environment to make TPM functions available to virtual machines. Our virtual TPM supports suspend and resume operations, as well as migration of a virtual TPM instance with its respective virtual machine across platforms. We present four designs for certificate chains to link the virtual TPM to a hardware TPM, with security vs. efficiency trade-offs based on threat models. Finally, we demonstrate a working system by layering an existing integrity measurement application on top of our virtual TPM facility.
A Broad-Coverage Challenge Corpus for Sentence Understanding through Inference

This paper introduces the Multi-Genre Natural Language Inference (MultiNLI) corpus, a dataset designed for use in the development and evaluation of machine learning models for sentence understanding. At 433k examples, this resource is one of the largest corpora available for natural language inference (a.k.a. recognizing textual entailment), improving upon available resources in both its coverage and difficulty. MultiNLI accomplishes this by offering data from ten distinct genres of written and spoken English, making it possible to evaluate systems on nearly the full complexity of the language, while supplying an explicit setting for evaluating cross-genre domain adaptation. In addition, an evaluation using existing machine learning models designed for the Stanford NLI corpus shows that it represents a substantially more difficult task than does that corpus, despite the two showing similar levels of inter-annotator agreement.
One-Shot Face Recognition via Generative Learning

One-shot face recognition measures the ability to recognize persons with only seeing them once, which is a hallmark of human visual intelligence. It is challenging for existing machine learning approaches to mimic this way, since limited data cannot well represent the data variance. To this end, we propose to build a large-scale face recognizer, which is capable to fight off the data imbalance difficulty. To seek a more effective general classifier, we develop a novel generative model attempting to synthesize meaningful data for one-shot classes by adapting the data variances from other normal classes. Specifically, we formulate conditional generative adversarial networks and the general Softmax classifier into a unified framework. Such a two-player minimax optimization can guide the generation of more effective data, which benefit the classifier learning for one-shot classes. The experimental results on a large-scale face benchmark with 21K persons verify the effectiveness of our proposed algorithm in one-shot classification, as our generative model significantly improves the recognition coverage rate from 25:65% to 94:84% at the precision of 99% for the one-shot classes, while still keeps an overall Top-1 accuracy at 99:80% for the normal classes.
Asynchronous Distributed Semi-Stochastic Gradient Optimization

Lemma 2 At a specific stage s and for a worker p, let g∗ i = ∇fi(w)−∇fi(w̃) +∇F (w̃), i ∈ Dp, then the following inequality holds, Ep [ Ei‖g i ‖ ] ≤ 2L [F (w̃)− F (w∗)] , Proof 2 Ei‖g i ‖ =Ei‖∇fi(w̃)−∇fi(w)−∇F (w̃)‖ =Ei‖∇fi(w̃)−∇fi(w)‖ − 2Ei〈∇F (w̃),∇fi(w̃)−∇fi(w)〉+ ‖∇F (w̃)‖ ≤2L (Fp(w̃)− Fp(w)− 〈∇Fp(w), w̃ − w∗〉) − 2〈∇F (w̃),∇Fp(w̃)−∇Fp(w)〉+ ‖∇F (w̃)‖, where the inequality is the result of applying Lemma 1. Further, taking expectation on both sides w.r.t. worker p, we get