nils-herrmann/clean_scirepeval huggingface dataset:
ShadowCuts: Photometric Stereo with Shadows

We present an algorithm for performing Lambertian photometric stereo in the presence of shadows. The algorithm has three novel features. First, a fast graph cuts based method is used to estimate per pixel light source visibility. Second, it allows images to be acquired with multiple illuminants, and there can be fewer images than light sources. This leads to better surface coverage and improves the reconstruction accuracy by enhancing the signal to noise ratio and the condition number of the light source matrix. The ability to use fewer images than light sources means that the imaging effort grows sublinearly with the number of light sources. Finally, the recovered shadow maps are combined with shading information to perform constrained surface normal integration. This reduces the low frequency bias inherent to the normal integration process and ensures that the recovered surface is consistent with the shadowing configuration The algorithm works with as few as four light sources and four images. We report results for light source visibility detection and high quality surface reconstructions for synthetic and real datasets.
VIP-Tree: An Effective Index for Indoor Spatial Queries

Due to the growing popularity of indoor location-based services, indoor data management has received significant research attention in the past few years. However, we observe that the existing indexing and query processing techniques for the indoor space do not fully exploit the properties of the indoor space. Consequently, they provide below par performance which makes them unsuitable for large indoor venues with high query workloads. In this paper, we propose two novel indexes called Indoor Partitioning Tree (IPTree) and Vivid IP-Tree (VIP-Tree) that are carefully designed by utilizing the properties of indoor venues. The proposed indexes are lightweight, have small pre-processing cost and provide nearoptimal performance for shortest distance and shortest path queries. We also present efficient algorithms for other spatial queries such as k nearest neighbors queries and range queries. Our extensive experimental study on real and synthetic data sets demonstrates that our proposed indexes outperform the existing algorithms by several orders of magnitude.
Depth camera tracking with contour cues

We present an approach for tracking camera pose in real time given a stream of depth images. Existing algorithms are prone to drift in the presence of smooth surfaces that destabilize geometric alignment. We show that useful contour cues can be extracted from noisy and incomplete depth input. These cues are used to establish correspondence constraints that carry information about scene geometry and constrain pose estimation. Despite ambiguities in the input, the presented contour constraints reliably improve tracking accuracy. Results on benchmark sequences and on additional challenging examples demonstrate the utility of contour cues for real-time camera pose estimation.
Multi-level Adaptive Active Learning for Scene Classification

Semantic scene classification is a challenging problem in computer vision. In this paper, we present a novel multi-level active learning approach to reduce the human annotation effort for training robust scene classification models. Different from most existing active learning methods that can only query labels for selected instances at the target categorization level, i.e., the scene class level, our approach establishes a semantic framework that predicts scene labels based on a latent objectbased semantic representation of images, and is capable to query labels at two different levels, the target scene class level (abstractive high level) and the latent object class level (semantic middle level). Specifically, we develop an adaptive active learning strategy to perform multi-level label query, which maintains the default label query at the target scene class level, but switches to the latent object class level whenever an “unexpected” target class label is returned by the labeler. We conduct experiments on two standard scene classification datasets to investigate the efficacy of the proposed approach. Our empirical results show the proposed adaptive multi-level active learning approach can outperform both baseline active learning methods and a state-of-the-art multi-level active learning method.
An Analogy Ontology for Integrating Analogical Processing and First-Principles Reasoning

This paper describes an <i>analogy ontology</i>, a formal representation of some key ideas in analogical processing, that supports the integration of analogical processing with first-principles reasoners. The ontology is based on Gentner's <i>structure-mapping</i> theory, a psychological account of analogy and similarity. The semantics of the ontology are enforced via procedural attachment, using cognitive simulations of structure-mapping to provide analogical processing services. Queries that include analogical operations can be formulated in the same way as standard logical inference, and analogical processing systems in turn can call on the services of first-principles reasoners for creating cases and validating their conjectures. We illustrate the utility of the analogy ontology by demonstrating how it has been used in three systems: A crisis management analogical reasoner that answers questions about international incidents, a course of action analogical critiquer that provides feedback about military plans, and a comparison question-answering system for knowledge capture. These systems rely on large, general-purpose knowledge bases created by other research groups, thus demonstrating the generality and utility of these ideas.
High-voltage pulser for ultrasound medical imaging applications

This paper presents the design and implementation of a fully integrated high-voltage (HV) front-end transducer for MEMS ultrasonic applications. Each of the front-end transducers in the array includes a HV transmitting driver and a 50MHz capacitive micro-machined ultrasound transducer (CMUT). The design adapts low voltage integrated circuit (IC) design techniques to create integrated high voltage interfaces. Effective methods to implement high voltage interfaces to integrated CMOS/DMOS circuits are presented. Using a 0.18μm CMOS/DMOS process, simulation results of this interface IC show that can generated a 30V high voltage pulse from a 1.8V input triggering signal to drive a 50MHz MEMS ultrasound transducer with the largest 44pF capacitive load.
Generalized spectral bounds for sparse LDA

We present a discrete spectral framework for the sparse or cardinality-constrained solution of a generalized Rayleigh quotient. This NP-hard combinatorial optimization problem is central to supervised learning tasks such as sparse LDA, feature selection and relevance ranking for classification. We derive a new generalized form of the Inclusion Principle for variational eigenvalue bounds, leading to exact and optimal sparse linear discriminants using branch-and-bound search. An efficient greedy (approximate) technique is also presented. The generalization performance of our sparse LDA algorithms is demonstrated with real-world UCI ML benchmarks and compared to a leading SVM-based gene selection algorithm for cancer classification.
Width-Modulated Microstrip-Line Based Mantle Cloaks for Thin Single- and Multiple Cylinders

A thin mantle cloak is described for electromagnetic footprint reduction for arrays of thin cylinders. The cloaking is obtained by covering the conformal geometry with a metasurface having as a unit cell that is a width modulated microstrip line. Enfoldings at different angles have been considered and numerically studied both for single and multiple cylinders. Frequency scalability and polarization sensitivity of the geometry has also been considered. Experimental results obtained at K-band are in good agreement with numerical data and demonstrate the applicability of the proposed approach for wide-band (≈16%) cloaking applications. The performance of the proposed solution has been compared with available data in the literature, and its superiority has been demonstrated.
Alternative Perspectives in the Organizational Sciences : " Inquiry from the Inside " f f 1 2 , 3 And " Inquiry from the Outside

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Image Denoising Using the Higher Order Singular Value Decomposition

In this paper, we propose a very simple and elegant patch-based, machine learning technique for image denoising using the higher order singular value decomposition (HOSVD). The technique simply groups together similar patches from a noisy image (with similarity defined by a statistically motivated criterion) into a 3D stack, computes the HOSVD coefficients of this stack, manipulates these coefficients by hard thresholding, and inverts the HOSVD transform to produce the final filtered image. Our technique chooses all required parameters in a principled way, relating them to the noise model. We also discuss our motivation for adopting the HOSVD as an appropriate transform for image denoising. We experimentally demonstrate the excellent performance of the technique on grayscale as well as color images. On color images, our method produces state-of-the-art results, outperforming other color image denoising algorithms at moderately high noise levels. A criterion for optimal patch-size selection and noise variance estimation from the residual images (after denoising) is also presented.
A bibliometric index based on the collaboration distance between cited and citing authors

The popular h-index used to measure scientific output can be described in terms of a pool of evaluated objects (the papers), a quality function on the evaluated objects (the number of citations received by each paper) and a sentencing line crossing the origin, whose intersection with the graph of the quality function yields the index value (in the h-index this is a line with slope 1). Based on this abstraction, we present a new index, the c-index, in which the evaluated objects are the citations received by an author, a group of authors, a journal, etc., the quality function of a citation is the collaboration distance between the authors of the cited and the citing papers, and the sentencing line can take slopes between 0 and ∞. As a result, the new index counts only those citations which are significant enough, where significance is proportional to collaboration distance. Several advantages of the new c-index with respect to previous proposals are discussed.
Query suggestions using query-flow graphs

The query-flow graph [Boldi et al., CIKM 2008] is an aggregated representation of the latent querying behavior contained in a query log. Intuitively, in the query-flow graph a directed edge from query <i>q</i><sub><i>i</i></sub> to query <i>q</i><sub><i>j</i></sub> means that the two queries are likely to be part of the same search mission. Any path over the query-flow graph may be seen as a possible search task, whose likelihood is given by the strength of the edges along the path. An edge (<i>q</i><sub><i>i</i></sub>, <i>q<sub>j</sub></i>) is also labelled with some information: e.g., the probability that user moves from <i>q</i><sub><i>i</i></sub> to <i>q</i><sub><i>j</i></sub>, or the type of the transition, for instance, the fact that <i>q</i><sub><i>j</i></sub> is a specialization of <i>q</i><sub><i>i</i></sub>.
 In this paper we propose, and experimentally study, query recommendations based on short random walks on the query-flow graph. Our experiments show that these methods can match in precision, and often improve, recommendations based on query-click graphs, without using users' clicks. Our experiments also show that it is important to consider transition-type labels on edges for having good quality recommendations.
 Finally, one feature that we had in mind while devising our methods was that of providing diverse sets of recommendations: the experimentation that we conducted provides encouraging results in this sense.
Isolated High Step-Up DC–DC Converter With Low Voltage Stress

Fuel cell stacks and photovoltaic panels generate rather low dc voltages and these voltages need to be boosted before converted to ac voltage. Therefore, high step-up ratio dc-dc converters are preferred in renewable energy systems. A new Z-source-based topology that can boost the input voltage to desired levels with low duty ratios is proposed in this paper. The topology utilizes coupled inductor. The leakage inductance energy can efficiently be discharged. Since the device stresses are low in this topology, low-voltage MOSFETs with small RDS(on) values can be selected to reduce the conduction loss. These features improve the converter efficiency. Also, the converter has a galvanic isolation between source and load. The operating principles and steady-state analysis of continuous and discontinuous conduction modes are discussed in detail. Finally, experimental results are given for a prototype converter that converts 25 V dc to 400 V dc at various power levels with over 90% efficiency to verify the effectiveness of the theoretical analysis.
A Provably Efficient Algorithm for Training Deep Networks

We consider deep neural networks (formally equivalent to sum-product networks [19]), in which the output of each node is a quadratic function of its inputs. Similar to other deep architectures, these networks can compactly represent any function on a finite training set. The main goal of this paper is the derivation of a provably efficient, layer-by-layer, algorithm for training such networks, which we denote as the Basis Learner. Unlike most, if not all, previous algorithms for training deep neural networks, our algorithm comes with formal polynomial time convergence guarantees. Moreover, the algorithm is a universal learner in the sense that the training error is guaranteed to decrease at every iteration, and can eventually reach zero under mild conditions. We present practical implementations of this algorithm, as well as preliminary but quite promising experimental results. We also compare our deep architecture to other shallow architectures for learning polynomials, in particular kernel learning.
PowerSpy: Location Tracking using Mobile Device Power Analysis

Modern mobile platforms like Android enable applications to read aggregate power usage on the phone. This information is considered harmless and reading it requires no user permission or notification. We show that by simply reading the phone’s aggregate power consumption over a period of a few minutes an application can learn information about the user’s location. Aggregate phone power consumption data is extremely noisy due to the multitude of components and applications that simultaneously consume power. Nevertheless, by using machine learning algorithms we are able to successfully infer the phone’s location. We discuss several ways in which this privacy leak can be remedied.
Frequency-Synchronized Resonant Converters for the Supply of Multiwinding Coils in Induction Cooking Appliances

The induction heating principle has been successfully introduced for about 20 years in domestic cooking appliances. The technical developments now concern the improvement of the current appliances and the introduction of new functions. So, one tendency is the design of multiwinding induction coils that are very adaptive concerning the shape and the power capability. This paper describes different original converter topologies designed to supply multiwinding coils, and, in addition, multicoil systems. The proposed topologies are based on particular use and associations of zero-voltage switching series-resonant converters. They are all characterized by a frequency-synchronized operating mode, in order to prevent any acoustic harm that could result from beating frequency-interferences
PUBCRAWL: Protecting Users and Businesses from CRAWLers

Web crawlers are automated tools that browse the web to retrieve and analyze information. Although crawlers are useful tools that help users to find content on the web, they may also be malicious. Unfortunately, unauthorized (malicious) crawlers are increasingly becoming a threat for service providers because they typically collect information that attackers can abuse for spamming, phishing, or targeted attacks. In particular, social networking sites are frequent targets of malicious crawling, and there were recent cases of scraped data sold on the black market and used for blackmailing. In this paper, we introduce PUBCRAWL, a novel approach for the detection and containment of crawlers. Our detection is based on the observation that crawler traffic significantly differs from user traffic, even when many users are hidden behind a single proxy. Moreover, we present the first technique for crawler campaign attribution that discovers synchronized traffic coming from multiple hosts. Finally, we introduce a containment strategy that leverages our detection results to efficiently block crawlers while minimizing the impact on legitimate users. Our experimental results in a large, wellknown social networking site (receiving tens of millions of requests per day) demonstrate that PUBCRAWL can distinguish between crawlers and users with high accuracy. We have completed our technology transfer, and the social networking site is currently running PUBCRAWL in production.
A Two-Stage Resonant Inverter With Control of the Phase Angle and Magnitude of the Output Voltage

A high-efficiency two-stage resonant inverter with effective control of both the magnitude and phase angle of the output voltage was proposed in this paper for high-frequency ac (HFAC) power-distribution applications, where a number of resonant inverters need to be paralleled. In order to parallel multiple resonant inverters of the same operation frequency, each inverter module needs independent control of the phase angle and magnitude of the output voltage. It is also desirable that the output voltage has very low total harmonics distortion, as well as high efficiency over wide input and load ranges. The proposed resonant inverter consists of two stages. The first stage is a two-switch dc/dc converter with zero-voltage transition, and the second stage is a half-bridge resonant dc/ac inverter with fixed duty ratio. A series-parallel resonant tank is used to achieve high waveform quality of the output voltage. The magnitude of the output voltage is regulated through the duty-ratio control of the first stage with pulsewidth modulation. The phase angle of the output voltage is regulated through a pulse-phase-modulation control of the second stage. The proposed resonant inverter has the advantages of better waveform quality, wide range of input and load variations for soft-switching, and independent control of the phase angle and magnitude of the output voltage, making it an attractive candidate for applications where a number of resonant inverters need to be placed in parallel to the HFAC bus and a number of distributed loads are connected to the HFAC bus. The performance is verified with both simulation and experiments.
Reinforcement Learning in Neural Networks: A Survey

In recent years, researches on reinforcement learning (RL) have focused on bridging the gap between adaptive optimal control and bio-inspired learning techniques. Neural network reinforcement learning (NNRL) is among the most popular algorithms in the RL framework. The advantage of using neural networks enables the RL to search for optimal policies more efficiently in several real-life applications. Although many surveys investigated general RL, no survey is specifically dedicated to the combination of artificial neural networks and RL. This paper therefore describes the state of the art of NNRL algorithms, with a focus on robotics applications. In this paper, a comprehensive survey is started with a discussion on the concepts of RL. Then, a review of several different NNRL algorithms is presented. Afterwards, the performances of different NNRL algorithms are evaluated and compared in learning prediction and learning control tasks from an empirical aspect and the paper concludes with a discussion on open issues.
Efficient interactive fuzzy keyword search

Traditional information systems return answers after a user submits a complete query. Users often feel "left in the dark" when they have limited knowledge about the underlying data, and have to use a try-and-see approach for finding information. A recent trend of supporting autocomplete in these systems is a first step towards solving this problem. In this paper, we study a new information-access paradigm, called "interactive, fuzzy search," in which the system searches the underlying data "on the fly" as the user types in query keywords. It extends autocomplete interfaces by (1) allowing keywords to appear in multiple attributes (in an arbitrary order) of the underlying data; and (2) finding relevant records that have keywords matching query keywords approximately. This framework allows users to explore data as they type, even in the presence of minor errors. We study research challenges in this framework for large amounts of data. Since each keystroke of the user could invoke a query on the backend, we need efficient algorithms to process each query within milliseconds. We develop various incremental-search algorithms using previously computed and cached results in order to achieve an interactive speed. We have deployed several real prototypes using these techniques. One of them has been deployed to support interactive search on the UC Irvine people directory, which has been used regularly and well received by users due to its friendly interface and high efficiency.
Learning with Drift Detection

Most of the work in machine learning assume that examples are generated at random according to some stationary probability distribution. In this work we study the problem of learning when the class-probability distribution that generate the examples changes over time. We present a method for detection of changes in the probability distribution of examples. A central idea is the concept of context: a set of contiguous examples where the distribution is stationary. The idea behind the drift detection method is to control the online error-rate of the algorithm. The training examples are presented in sequence. When a new training example is available, it is classified using the actual model. Statistical theory guarantees that while the distribution is stationary, the error wil decrease. When the distribution changes, the error will increase. The method controls the trace of the online error of the algorithm. For the actual context we define a warning level, and a drift level. A new context is declared, if in a sequence of examples, the error increases reaching the warning level at example kw, and the drift level at example kd. This is an indication of a change in the distribution of the examples. The algorithm learns a new model using only the examples since kw. The method was tested with a set of eight artificial datasets and a real world dataset. We used three learning algorithms: a perceptron, a neural network and a decision tree. The experimental results show a good performance detecting drift and also with learning the new concept. We also observe that the method is independent of the learning algorithm.