nils-herrmann/clean_scirepeval huggingface dataset:
FA ] 2 N ov 2 00 3 An iterative thresholding algorithm for linear inverse problems with a sparsity constraint

We consider linear inverse problems where the solution is assumed to have a sparse expansion on an arbitrary preassigned orthonormal basis. We prove that replacing the usual quadratic regularizing penalties by weighted ppenalties on the coefficients of such expansions, with 1 ≤ p ≤ 2, still regularizes the problem. Use of such p-penalized problems with p < 2 is often advocated when one expects the underlying ideal noiseless solution to have a sparse expansion with respect to the basis under consideration. To compute the corresponding regularized solutions, we analyze an iterative algorithm that amounts to a Landweber iteration with thresholding (or nonlinear shrinkage) applied at each iteration step. We prove that this algorithm converges in norm.
DISCO: Distributed multi-domain SDN controllers

Software-Defined Networking (SDN) is now envisioned for Wide Area Networks (WAN) and constrained overlay networks. Such networks require a resilient, scalable and easily extensible SDN control plane. In this paper, we propose DISCO, an extensible DIstributed SDN COntrol plane able to cope with the distributed and heterogeneous nature of modern overlay networks. A DISCO controller manages its own network domain and communicates with other controllers to provide end-to-end network services. This east-west communication is based on a lightweight and highly manageable control channel. We implemented DISCO on top of the Floodlight OpenFlow controller and the AMQP protocol and we evaluated it through an inter-domain topology disruption use case.
The Normalization of Occurrence and Co-occurrence Matrices in Bibliometrics using Cosine Similarities and Ochiai Coefficients

We prove that Ochiai similarity of the co-occurrence matrix is equal to cosine similarity in the underlying occurrence matrix. Neither the cosine nor the Pearson correlation should be used for the normalization of co-occurrence matrices because the similarity is then normalized twice, and therefore over-estimated; the Ochiai coefficient can be used instead. Results are shown using a small matrix (5 cases, 4 variables) for didactic reasons, and also Ahlgren et al.’s (2003) co-occurrence matrix of 24 authors in library and information sciences. The over-estimation is shown numerically and will be illustrated using multidimensional scaling and cluster dendograms. If the occurrence matrix is not available (such as in internet research or author co-citation analysis) using Ochiai for the normalization is preferable to using the cosine.
A fast shadow algorithm for area light sources using backprojection

The fast identification of shadow regions due to area light sources is necessary for realistic rendering and for discontinuity meshing for global illumination. A new shadow-determination algorithm is presented that uses a data structure, called a backprojection, to represent the visible portion of a light source from any point in the scene. A complete discontinuity meshing algorithm is described for polyhedral scenes and area light sources, which includes an important class of light/geometry interactions that have not been implemented before. A fast incremental algorithm for computing backprojections is also described. The use of spatial subdivision, and heuristics based on computed statistics of typical scenes, results in efficient mesh and backprojection computation. Results of the implementation show that the use of the backprojection and discontinuity meshing permits accelerated high-quality rendering of shadows using both ray-casting and polygon-rendering with interpolants.
Custom Pictorial Structures for Re-identification

We propose a novel methodology for re-identification, based on Pictorial Structures (PS). Whenever face or other biometric information is missing, humans recognize an individual by selectively focusing on the body parts, looking for part-to-part correspondences. We want to take inspiration from this strategy in a re-identification context, using PS to achieve this objective. For single image re-identification, we adopt PS to localize the parts, extract and match their descriptors. When multiple images of a single individual are available, we propose a new algorithm to customize the fit of PS on that specific person, leading to what we call a Custom Pictorial Structure (CPS). CPS learns the appearance of an individual, improving the localization of its parts, thus obtaining more reliable visual characteristics for re-identification. It is based on the statistical learning of pixel attributes collected through spatio-temporal reasoning. The use of PS and CPS leads to state-of-the-art results on all the available public benchmarks, and opens a fresh new direction for research on re-identification.
Enhancing MongoDB with Purpose-Based Access Control

Privacy has become a key requirement for data management systems. Nevertheless, NoSQL datastores, namely highly scalable non relational database management systems, which often support data management of Internet scale applications,still do not provide support for privacy policies enforcement. With this work, we begin to address this issue, by proposing an approach for the integration of purpose based policy enforcement capabilities into MongoDB, a popular NoSQL datastore. Our contribution consists of the enhancement of the MongoDB role based access control model with privacy concepts and related enforcement monitor. The proposed monitor is easily integrable into any MongoDB deployment through simple configurations. Experimental results show that our monitor enforces purpose-based access control with low overhead.
Scalable diffusion-aware optimization of network topology

How can we optimize the topology of a networked system to bring a flu under control, propel a video to popularity, or stifle a network malware in its infancy? Previous work on information diffusion has focused on modeling the diffusion dynamics and selecting nodes to maximize/minimize influence. Only a paucity of recent studies have attempted to address the network modification problems, where the goal is to either facilitate desirable spreads or curtail undesirable ones by adding or deleting a small subset of network nodes or edges. In this paper, we focus on the widely studied linear threshold diffusion model, and prove, for the first time, that the network modification problems under this model have supermodular objective functions. This surprising property allows us to design efficient data structures and scalable algorithms with provable approximation guarantees, despite the hardness of the problems in question. Both the time and space complexities of our algorithms are linear in the size of the network, which allows us to experiment with millions of nodes and edges. We show that our algorithms outperform an array of heuristics in terms of their effectiveness in controlling diffusion processes, often beating the next best by a significant margin.
AccelPrint: Imperfections of Accelerometers Make Smartphones Trackable

As mobile begins to overtake the fixed Internet access, ad networks have aggressively sought methods to track users on their mobile devices. While existing countermeasures and regulation focus on thwarting cookies and various device IDs, this paper submits a hypothesis that smartphone/tablet accelerometers possess unique fingerprints, which can be exploited for tracking users. We believe that the fingerprints arise from hardware imperfections during the sensor manufacturing process, causing every sensor chip to respond differently to the same motion stimulus. The differences in responses are subtle enough that they do not affect most of the higher level functions computed on them. Nonetheless, upon close inspection, these fingerprints emerge with consistency, and can even be somewhat independent of the stimulus that generates them. Measurements and classification on 80 standalone accelerometer chips, 25 Android phones, and 2 tablets, show precision and recall upward of 96%, along with good robustness to realworld conditions. Utilizing accelerometer fingerprints, a crowdsourcing application running in the cloud could segregate sensor data for each device, making it easy to track a user over space and time. Such attacks are almost trivial to launch, while simple solutions may not be adequate to counteract them.
A general framework for kernel similarity-based image denoising

Any image can be represented as a function defined on a discrete weighted graph whose vertices are image pixels. Each pixel can be linked to other pixels via graph edges with corresponding weights derived from similarities between image pixels (graph vertices) measured in some appropriate fashion. Image structure is encoded in the Laplacian matrix derived from these similarity weights. Taking advantage of this graph-based point of view, we present a general regularization framework for image denoising. A number of well-known existing denoising methods like bilateral, NLM, and LARK, can be described within this formulation. Moreover, we present an analysis for the filtering behavior of the proposed method based on the spectral properties of Laplacian matrices. Some of the well established iterative approaches for improving kernel-based denoising like diffusion and boosting iterations are special cases of our general framework. The proposed approach provides a better understanding of enhancement mechanisms in self similarity-based methods, which can be used for their further improvement. Experimental results verify the effectiveness of this approach for the task of image denoising.
Effects of teachers ’ mathematical knowledge on student achievement 1 Effects of Teachers ' Mathematical Knowledge for Teaching on Student Achievement

This study explored whether and how teachers’ mathematical knowledge for teaching contributes to gains in students’ mathematics achievement. The authors used a linear mixed-model methodology in which first and third graders’ mathematical achievement gains over a year were nested within teachers, who in turn were nested within schools. They found that teachers’ mathematical knowledge was significantly related to student achievement gains in both first and third grades after controlling for key studentand teacher-level covariates. This result, while consonant with findings from the educational production function literature, was obtained via a measure focusing on the specialized mathematical knowledge and skills used in teaching mathematics. This finding provides support for policy initiatives designed to improve students’ mathematics achievement by improving teachers’ mathematical knowledge.
Invalid retro-cues can eliminate the retro-cue benefit: Evidence for a hybridized account.

The contents of visual working memory (VWM) are capacity limited and require frequent updating. The retrospective cueing (retro-cueing) paradigm clarifies how directing internal attention among VWM items boosts VWM performance. In this paradigm a cue appears prior to retrieval, but after encoding and maintenance. The retro-cue effect (RCE) refers to superior VWM after valid versus neutral retro-cues. Here we investigated the effect of the invalid retro-cues' inclusion on VWM performance. We conducted 2 pairs of experiments, changing both probe type (recognition and recall) as well as presence and absence of invalid retro-cue trials. Furthermore, to fully characterize these effects over time, we used extended post-retro-cue delay durations. In the first set of experiments, probing VWM using recognition indicated that the RCE remained consistent in magnitude with or without invalid retro-cue trials. In the second set of experiments, VWM was probed with recall. Here, the RCE was eliminated when invalid retro-cues were included. This finer-grained measure of VWM fidelity showed that all items were subject to decay over time. We conclude that the invalid retro-cues impaired the protection of validly cues items, but they remain accessible, suggesting greater concordance with a prioritization account.
Youtube traffic characterization: a view from the edge

This paper presents a traffic characterization study of the popular video sharing service, YouTube. Over a three month period we observed almost 25 million transactions between users on an edge network and YouTube, including more than 600,000 video downloads. We also monitored the globally popular videos over this period of time.
 In the paper we examine usage patterns, file properties, popularity and referencing characteristics, and transfer behaviors of YouTube, and compare them to traditional Web and media streaming workload characteristics. We conclude the paper with a discussion of the implications of the observed characteristics. For example, we find that as with the traditional Web, caching could improve the end user experience, reduce network bandwidth consumption, and reduce the load on YouTube's core server infrastructure. Unlike traditional Web caching, Web 2.0 provides additional meta-data that should be exploited to improve the effectiveness of strategies like caching.
A Theory of Shape by Space Carving

In this paper we consider the problem of computing the 3D shape of an unknown, arbitrarily-shaped scene from multiple photographs taken at known but arbitrarily-distributed viewpoints. By studying the equivalence class of all 3D shapes that reproduce the input photographs, we prove the existence of a special member of this class, the photo hull, that (1) can be computed directly from photographs of the scene, and (2) subsumes all other members of this class. We then give a provably-correct algorithm, called Space Carving, for computing this shape and present experimental results on complex real-world scenes. The approach is designed to (1) capture photorealistic shapes that accurately model scene appearance from a wide range of viewpoints, and (2) account for the complex interactions between occlusion, parallax, shading, and their view-dependent effects on scene-appearance.
Analysis of topological characteristics of huge online social networking services

Social networking services are a fast-growing business in the Internet. However, it is unknown if online relationships and their growth patterns are the same as in real-life social networks. In this paper, we compare the structures of three online social networking services: Cyworld, MySpace, and orkut, each with more than 10 million users, respectively. We have access to complete data of Cyworld's ilchon (friend) relationships and analyze its degree distribution, clustering property, degree correlation, and evolution over time. We also use Cyworld data to evaluate the validity of snowball sampling method, which we use to crawl and obtain partial network topologies of MySpace and orkut. Cyworld, the oldest of the three, demonstrates a changing scaling behavior over time in degree distribution. The latest Cyworld data's degree distribution exhibits a multi-scaling behavior, while those of MySpace and orkut have simple scaling behaviors with different exponents. Very interestingly, each of the two e ponents corresponds to the different segments in Cyworld's degree distribution. Certain online social networking services encourage online activities that cannot be easily copied in real life; we show that they deviate from close-knit online social networks which show a similar degree correlation pattern to real-life social networks.
Approximation Algorithms for Min-Sum k-Clustering and Balanced k-Median

We consider two closely related fundamental clustering problems in this paper. In Min-Sum k -Clustering, one is given n points in a metric space and has to partition them into k clusters while minimizing the sum of pairwise distances between points in the same cluster. In the Balanced k -Median problem, the instance is the same and the objective is to obtain a partitioning into k clusters $$C_1,\ldots ,C_k$$ C 1 , … , C k , where each cluster $$C_i$$ C i is centered at a point $$c_i$$ c i , while minimizing the total assignment cost of the points in the metric; the cost of assigning a point j to a cluster $$C_i$$ C i is equal to $$|C_i|$$ | C i | times the distance between j and $$c_i$$ c i in the metric. In this article, we present an $$O(\log n)$$ O ( log n ) -approximation for both these problems. This is an improvement over the $$O(\epsilon ^{-1}\log ^{1 + \epsilon } n)$$ O ( ϵ - 1 log 1 + ϵ n ) -approximation (for any constant $$\epsilon > 0$$ ϵ > 0 ) obtained by Bartal, Charikar, and Raz . We also obtain a quasi-PTAS for Balanced k-Median in metrics with constant doubling dimension. As in the work of Bartal et al., our approximation for general metrics uses embeddings into tree metrics. The main technical contribution in this paper is an O(1)-approximation for Balanced k-Median in hierarchically separated trees (HSTs). Our improvement comes from a more direct dynamic programming approach that heavily exploits the properties of standard HSTs. In this way, we avoid the reduction to special types of HSTs that were considered by Bartal et al., thereby avoiding an additional $$O(\epsilon ^{-1} \log ^\epsilon n)$$ O ( ϵ - 1 log ϵ n ) loss.
Common genetic variants on 5p14.1 associate with autism spectrum disorders

Autism spectrum disorders (ASDs) represent a group of childhood neurodevelopmental and neuropsychiatric disorders characterized by deficits in verbal communication, impairment of social interaction, and restricted and repetitive patterns of interests and behaviour. To identify common genetic risk factors underlying ASDs, here we present the results of genome-wide association studies on a cohort of 780 families (3,101 subjects) with affected children, and a second cohort of 1,204 affected subjects and 6,491 control subjects, all of whom were of European ancestry. Six single nucleotide polymorphisms between cadherin 10 (CDH10) and cadherin 9 (CDH9)—two genes encoding neuronal cell-adhesion molecules—revealed strong association signals, with the most significant SNP being rs4307059 (P = 3.4 × 10-8, odds ratio = 1.19). These signals were replicated in two independent cohorts, with combined P values ranging from 7.4 × 10-8 to 2.1 × 10-10. Our results implicate neuronal cell-adhesion molecules in the pathogenesis of ASDs, and represent, to our knowledge, the first demonstration of genome-wide significant association of common variants with susceptibility to ASDs.
Hand Pose Estimation from Local Surface Normals

We present a hierarchical regression framework for estimating hand joint positions from single depth images based on local surface normals. The hierarchical regression follows the tree structured topology of hand from wrist to finger tips. We propose a conditional regression forest, i.e. the Frame Conditioned Regression Forest (FCRF) which uses a new normal difference feature. At each stage of the regression, the frame of reference is established from either the local surface normal or previously estimated hand joints. By making the regression with respect to the local frame, the pose estimation is more robust to rigid transformations. We also introduce a new efficient approximation to estimate surface normals. We verify the effectiveness of our method by conducting experiments on two challenging real-world datasets and show consistent improvements over previous discriminative pose estimation methods.
Chernoff-Hoeffding Bounds for Applications with Limited Independence

Chernoff-Hoeffding bounds are fundamental tools used in bounding the tail probabilities of the sums of bounded and independent random variables. We present a simple technique which gives slightly better bounds than these, and which more importantly requires only limited independence among the random variables, thereby importing a variety of standard results to the case of limited independence for free. Additional methods are also presented, and the aggregate results are sharp and provide a better understanding of the proof techniques behind these bounds. They also yield improved bounds for various tail probability distributions and enable improved approximation algorithms for jobshop scheduling. The “limited independence” result implies that a reduced amount of randomness and weaker sources of randomness are sufficient for randomized algorithms whose analyses use the Chernoff-Hoeffding bounds, e.g., the analysis of randomized algorithms for random sampling and oblivious packet routing.
A Compact Portable Microwave Life-Detection Device for Finding Survivors

In this letter, an ultra-sensitive compact portable microwave life-detection device is introduced and implemented with promising results. By utilizing Doppler effect-based systems, vital signs such as heartbeats and breathing can be detected and can be used for finding survivors under earthquake rubble, injured soldiers on battlefields and as lie detection device. This device is tested in both simulated and realistic situations, and it can accurately detect crucial signs of life through highly dense construction materials of about 1.5 m thick and standard density materials of about 10 m while operating at 1.15 GHz center frequency.
Synonym set extraction from the biomedical literature by lexical pattern discovery

Although there are a large number of thesauri for the biomedical domain many of them lack coverage in terms and their variant forms. Automatic thesaurus construction based on patterns was first suggested by Hearst , but it is still not clear how to automatically construct such patterns for different semantic relations and domains. In particular it is not certain which patterns are useful for capturing synonymy. The assumption of extant resources such as parsers is also a limiting factor for many languages, so it is desirable to find patterns that do not use syntactical analysis. Finally to give a more consistent and applicable result it is desirable to use these patterns to form synonym sets in a sound way. We present a method that automatically generates regular expression patterns by expanding seed patterns in a heuristic search and then develops a feature vector based on the occurrence of term pairs in each developed pattern. This allows for a binary classifications of term pairs as synonymous or non-synonymous. We then model this result as a probability graph to find synonym sets, which is equivalent to the well-studied problem of finding an optimal set cover. We achieved 73.2% precision and 29.7% recall by our method, out-performing hand-made resources such as MeSH and Wikipedia. We conclude that automatic methods can play a practical role in developing new thesauri or expanding on existing ones, and this can be done with only a small amount of training data and no need for resources such as parsers. We also concluded that the accuracy can be improved by grouping into synonym sets.
A Small and Slim Coaxial Probe for Single Rice Grain Moisture Sensing

A moisture detection of single rice grains using a slim and small open-ended coaxial probe is presented. The coaxial probe is suitable for the nondestructive measurement of moisture values in the rice grains ranging from from 9.5% to 26%. Empirical polynomial models are developed to predict the gravimetric moisture content of rice based on measured reflection coefficients using a vector network analyzer. The relationship between the reflection coefficient and relative permittivity were also created using a regression method and expressed in a polynomial model, whose model coefficients were obtained by fitting the data from Finite Element-based simulation. Besides, the designed single rice grain sample holder and experimental set-up were shown. The measurement of single rice grains in this study is more precise compared to the measurement in conventional bulk rice grains, as the random air gap present in the bulk rice grains is excluded.