nils-herrmann/clean_scirepeval huggingface dataset:
Strokelets: A Learned Multi-scale Representation for Scene Text Recognition

Driven by the wide range of applications, scene text detection and recognition have become active research topics in computer vision. Though extensively studied, localizing and reading text in uncontrolled environments remain extremely challenging, due to various interference factors. In this paper, we propose a novel multi-scale representation for scene text recognition. This representation consists of a set of detectable primitives, termed as strokelets, which capture the essential substructures of characters at different granularities. Strokelets possess four distinctive advantages: (1) Usability: automatically learned from bounding box labels, (2) Robustness: insensitive to interference factors, (3) Generality: applicable to variant languages, and (4) Expressivity: effective at describing characters. Extensive experiments on standard benchmarks verify the advantages of strokelets and demonstrate the effectiveness of the proposed algorithm for text recognition.
After the promise: the STD consequences of adolescent virginity pledges.

To examine the effectiveness of virginity pledges in reducing STD infection rates among young adults (ages 18-24).



Data are drawn from the National Longitudinal Study of Adolescent Health, a nationally representative study of students enrolled in grades 7-12 in 1995. During a follow-up survey in 2001-2002, respondents provided urine samples, which were tested for Human Papilloma Virus, Chlamydia, Gonorrhea, and Trichomoniasis. We report descriptive results for the relationship of pledge status and sexually transmitted disease (STD) rates as well as health behaviors commonly associated with STD infection.



Pledgers are consistently less likely to be exposed to risk factors across a wide range of indicators, but their STD infection rate does not differ from nonpledgers. Possible explanations are that pledgers are less likely than others to use condoms at sexual debut and to be tested and diagnosed with STDs.



Adopting virginity pledges as intervention may not be the optimal approach to preventing STD acquisition among young adults.
Design Exploration of Hybrid CMOS and Memristor Circuit by New Modified Nodal Analysis

Design of hybrid circuits and systems based on CMOS and nano-device requires rethinking of fundamental circuit analysis to aid design exploration. Conventional circuit analysis with modified nodal analysis (MNA) cannot consider new nano-devices such as memristor together with the traditional CMOS devices. This paper has introduced a new MNA method with magnetic flux (Φ) as new state variable. New SPICE-like circuit simulator is thereby developed for the design of hybrid CMOS and memristor circuits. A number of CMOS and memristor-based designs are explored, such as oscillator, chaotic circuit, programmable logic, analog-learning circuit, and crossbar memory, where their functionality, performance, reliability and power can be efficiently verified by the newly developed simulator. Specifically, one new 3-D-crossbar architecture with diode-added memristor is also proposed to improve integration density and to avoid sneak path during read-write operation.
pi-ADL: an Architecture Description Language based on the higher-order typed pi-calculus for specifying dynamic and mobile software architectures

A key aspect of the design of any software system is its architecture. An architecture description, from a runtime perspective, should provide a formal specification of the architecture in terms of components and connectors and how they are composed together. Further, a dynamic or mobile architecture description must provide a specification of how the architecture of the software system can change at runtime. Enabling specification of dynamic and mobile architectures is a large challenge for an Architecture Description Language (ADL). This article describes π-ADL, a novel ADL that has been designed in the ArchWare European Project to address specification of dynamic and mobile architectures. It is a formal, well-founded theoretically language based on the higher-order typed π-calculus. While most ADLs focus on describing software architectures from a structural viewpoint, π-ADL focuses on formally describing architectures encompassing both the structural and behavioural viewpoints. The π-ADL design principles, concepts and notation are presented.
Simultaneous feature learning and hash coding with deep neural networks

Similarity-preserving hashing is a widely-used method for nearest neighbour search in large-scale image retrieval tasks. For most existing hashing methods, an image is first encoded as a vector of hand-engineering visual features, followed by another separate projection or quantization step that generates binary codes. However, such visual feature vectors may not be optimally compatible with the coding process, thus producing sub-optimal hashing codes. In this paper, we propose a deep architecture for supervised hashing, in which images are mapped into binary codes via carefully designed deep neural networks. The pipeline of the proposed deep architecture consists of three building blocks: 1) a sub-network with a stack of convolution layers to produce the effective intermediate image features; 2) a divide-and-encode module to divide the intermediate image features into multiple branches, each encoded into one hash bit; and 3) a triplet ranking loss designed to characterize that one image is more similar to the second image than to the third one. Extensive evaluations on several benchmark image datasets show that the proposed simultaneous feature learning and hash coding pipeline brings substantial improvements over other state-of-the-art supervised or unsupervised hashing methods.
A probabilistic model for component-based shape synthesis

We present an approach to synthesizing shapes from complex domains, by identifying new plausible combinations of components from existing shapes. Our primary contribution is a new generative model of component-based shape structure. The model represents probabilistic relationships between properties of shape components, and relates them to learned underlying causes of structural variability within the domain. These causes are treated as latent variables, leading to a compact representation that can be effectively learned without supervision from a set of compatibly segmented shapes. We evaluate the model on a number of shape datasets with complex structural variability and demonstrate its application to amplification of shape databases and to interactive shape synthesis.
Block-Sparse Signals: Uncertainty Relations and Efficient Recovery

We consider efficient methods for the recovery of block-sparse signals-i.e., sparse signals that have nonzero entries occurring in clusters-from an underdetermined system of linear equations. An uncertainty relation for block-sparse signals is derived, based on a block-coherence measure, which we introduce. We then show that a block-version of the orthogonal matching pursuit algorithm recovers block -sparse signals in no more than steps if the block-coherence is sufficiently small. The same condition on block-coherence is shown to guarantee successful recovery through a mixed -optimization approach. This complements previous recovery results for the block-sparse case which relied on small block-restricted isometry constants. The significance of the results presented in this paper lies in the fact that making explicit use of block-sparsity can provably yield better reconstruction properties than treating the signal as being sparse in the conventional sense, thereby ignoring the additional structure in the problem.
Shape matching and object recognition using low distortion correspondences

We approach recognition in the framework of deformable shape matching, relying on a new algorithm for finding correspondences between feature points. This algorithm sets up correspondence as an integer quadratic programming problem, where the cost function has terms based on similarity of corresponding geometric blur point descriptors as well as the geometric distortion between pairs of corresponding feature points. The algorithm handles outliers, and thus enables matching of exemplars to query images in the presence of occlusion and clutter. Given the correspondences, we estimate an aligning transform, typically a regularized thin plate spline, resulting in a dense correspondence between the two shapes. Object recognition is then handled in a nearest neighbor framework where the distance between exemplar and query is the matching cost between corresponding points. We show results on two datasets. One is the Caltech 101 dataset (Fei-Fei, Fergus and Perona), an extremely challenging dataset with large intraclass variation. Our approach yields a 48% correct classification rate, compared to Fei-Fei et al 's 16%. We also show results for localizing frontal and profile faces that are comparable to special purpose approaches tuned to faces.
Region-based CNN for Logo Detection

Logo detection has been extensively studied because of its applications in many fields. Most existing studies for logo detection are based on hand-designed local features which have certain limitations. Recently, Convolutional Neural Networks (CNN), especially the series of region-based CNN (R-CNN) have achieved great success in general object detection due to its power of feature learning and representation. There are also few work on logo detection based on R-CNN, showing its superiority. However, logo detection presents its own challenges compared to general object detection. In this paper, we aim to experimentally investigate the appropriate architecture and settings of R-CNN for logo detection, by comparing several popular frameworks of R-CNN and considering the characteristics of logo objects in images. We expect our results will be helpful for designing a state-of-the-art logo detection system using handy machine learning techniques.
Modulation of Visual Responses by Behavioral State in Mouse Visual Cortex

Studies of visual processing in rodents have conventionally been performed on anesthetized animals, precluding examination of the effects of behavior on visually evoked responses. We have now studied the response properties of neurons in primary visual cortex of awake mice that were allowed to run on a freely rotating spherical treadmill with their heads fixed. Most neurons showed more than a doubling of visually evoked firing rate as the animal transitioned from standing still to running, without changes in spontaneous firing or stimulus selectivity. Tuning properties in the awake animal were similar to those measured previously in anesthetized animals. Response magnitude in the lateral geniculate nucleus did not increase with locomotion, demonstrating that the striking change in responsiveness did not result from peripheral effects at the eye. Interestingly, some narrow-spiking cells were spontaneously active during running but suppressed by visual stimuli. These results demonstrate powerful cell-type-specific modulation of visual processing by behavioral state in awake mice.
Type classes as objects and implicits

Type classes were originally developed in Haskell as a disciplined alternative to ad-hoc polymorphism. Type classes have been shown to provide a type-safe solution to important challenges in software engineering and programming languages such as, for example, retroactive extension of programs. They are also recognized as a good mechanism for concept-based generic programming and, more recently, have evolved into a mechanism for type-level computation.
 This paper presents a lightweight approach to type classes in object-oriented (OO) languages with generics using the CONCEPT pattern and implicits (a type-directed implicit parameter passing mechanism). This paper also shows how Scala's type system conspires with implicits to enable, and even surpass, many common extensions of the Haskell type class system, making Scala ideally suited for generic programming in the large.
Paranoid Android: versatile protection for smartphones

Smartphone usage has been continuously increasing in recent years. Moreover, smartphones are often used for privacy-sensitive tasks, becoming highly valuable targets for attackers. They are also quite different from PCs, so that PC-oriented solutions are not always applicable, or do not offer comprehensive security. We propose an alternative solution, where security checks are applied on remote security servers that host exact replicas of the phones in virtual environments. The servers are not subject to the same constraints, allowing us to apply multiple detection techniques simultaneously. We implemented a prototype of this security model for Android phones, and show that it is both practical and scalable: we generate no more than 2KiB/s and 64B/s of trace data for high-loads and idle operation respectively, and are able to support more than a hundred replicas running on a single server.
Autonomous navigation and sign detector learning

This paper presents an autonomous robotic system that incorporates novel Computer Vision, Machine Learning and Data Mining algorithms in order to learn to navigate and discover important visual entities. This is achieved within a Learning from Demonstration (LfD) framework, where policies are derived from example state-to-action mappings. For autonomous navigation, a mapping is learnt from holistic image features (GIST) onto control parameters using Random Forest regression. Additionally, visual entities (road signs e.g. STOP sign) that are strongly associated to autonomously discovered modes of action (e.g. stopping behaviour) are discovered through a novel Percept-Action Mining methodology. The resulting sign detector is learnt without any supervision (no image labeling or bounding box annotations are used). The complete system is demonstrated on a fully autonomous robotic platform, featuring a single camera mounted on a standard remote control car. The robot carries a PC laptop, that performs all the processing on board and in real-time.
Instagram #Instasad?: Exploring Associations Among Instagram Use, Depressive Symptoms, Negative Social Comparison, and Strangers Followed

As the use and influence of social networking continues to grow, researchers have begun to explore its consequences for psychological well-being. Some research suggests that Facebook use can have negative consequences for well-being. Instagram, a photo-sharing social network created in 2010, has particular characteristics that may make users susceptible to negative consequences. This study tested a theoretically grounded moderated meditation model of the association between Instagram use and depressive symptoms through the mechanism of negative social comparison, and moderation by amount of strangers one follows. One hundred and seventeen 18-29 year olds completed online questionnaires containing demographics, frequency of Instagram use, amount of strangers followed on Instagram, the Center for Epidemiological Resources Scale for Depression, and the Social Comparison Rating Scale. Instagram use was marginally positively associated with depressive symptoms, and positive social comparison was significantly negatively associated with depressive symptoms. Amount of strangers followed moderated the associations of Instagram use with social comparison (significantly) and depressive symptoms (marginally), and further significantly moderated the indirect association of Instagram use with depressive symptoms through social comparison. Findings generally suggest that more frequent Instagram use has negative associations for people who follow more strangers, but positive associations for people who follow fewer strangers, with social comparison and depressive symptoms. Implications of negative associations of social networking for people who follow strangers and the need for more research on Instagram use given its increasing popularity are explored.
Single-cell transcriptomics reveals bimodality in expression and splicing in immune
cells

Recent molecular studies have shown that, even when derived from a seemingly homogenous population, individual cells can exhibit substantial differences in gene expression, protein levels and phenotypic output, with important functional consequences. Existing studies of cellular heterogeneity, however, have typically measured only a few pre-selected RNAs or proteins simultaneously, because genomic profiling methods could not be applied to single cells until very recently. Here we use single-cell RNA sequencing to investigate heterogeneity in the response of mouse bone-marrow-derived dendritic cells (BMDCs) to lipopolysaccharide. We find extensive, and previously unobserved, bimodal variation in messenger RNA abundance and splicing patterns, which we validate by RNA-fluorescence in situ hybridization for select transcripts. In particular, hundreds of key immune genes are bimodally expressed across cells, surprisingly even for genes that are very highly expressed at the population average. Moreover, splicing patterns demonstrate previously unobserved levels of heterogeneity between cells. Some of the observed bimodality can be attributed to closely related, yet distinct, known maturity states of BMDCs; other portions reflect differences in the usage of key regulatory circuits. For example, we identify a module of 137 highly variable, yet co-regulated, antiviral response genes. Using cells from knockout mice, we show that variability in this module may be propagated through an interferon feedback circuit, involving the transcriptional regulators Stat2 and Irf7. Our study demonstrates the power and promise of single-cell genomics in uncovering functional diversity between cells and in deciphering cell states and circuits.
Learning Word-Like Units from Joint Audio-Visual Analysis

Given a collection of images and spoken audio captions, we present a method for discovering word-like acoustic units in the continuous speech signal and grounding them to semantically relevant image regions. For example, our model is able to detect spoken instances of the words “lighthouse” within an utterance and associate them with image regions containing lighthouses. We do not use any form of conventional automatic speech recognition, nor do we use any text transcriptions or conventional linguistic annotations. Our model effectively implements a form of spoken language acquisition, in which the computer learns not only to recognize word categories by sound, but also to enrich the words it learns with semantics by grounding them in images.
Artificial Neural Networks Applied to Taxi Destination Prediction

We describe our first-place solution to the ECML/PKDD discovery challenge on taxi destination prediction. The task consisted in predicting the destination of a taxi based on the beginning of its trajectory, represented as a variable-length sequence of GPS points, and diverse associated meta-information, such as the departure time, the driver id and client information. Contrary to most published competitor approaches, we used an almost fully automated approach based on neural networks and we ranked first out of 381 teams. The architectures we tried use multi-layer perceptrons, bidirectional recurrent neural networks and models inspired from recently introduced memory networks. Our approach could easily be adapted to other applications in which the goal is to predict a fixed-length output from a variable-length sequence. Random order, this does not reflect the weights of contributions. ar X iv :1 50 8. 00 02 1v 2 [ cs .L G ] 2 1 Se p 20 15 2 Artificial Neural Networks Applied to Taxi Destination Prediction
Application of Katharine Kolkaba Comfort theory to nursing care of patient

aring is in practice since existence but caring as profession named Nursing was scientifically Coined by the founder of modern nursing who in her writings “what it is and what it is not”declared the boundaries along with its Constitutional elements. Those elements with appropriate association not only revealed the nature of caring dogma it also shows that it is doing something base on thinking something or in simple words this is practice rely on theory. According to McCrae, (2011) For the sake of assuming valuable status ofprofession in society nursing struggle to legitimate its position by generation and application of theory. Nursing literature is rich regarding theory practice association. although constant body of argument, counter argument and lack of agreed conclusion regarding nursing theory creates a bit confusion among nurses but ideally nursing theory should set the code of every day practice of nurses(Giltinane, 2013) as lack of a valued unique contribution nurses make to health care based on a specialized body of knowledge will portrayed nursing role as subordinate to doctor (Mccrae, 2011) Eminent nurses’ theorist, scientist, Researchers, Clinicians, Educators and intellectual intelligentsia aid evidence to different perspectives of theory practice link. Some unanimous convincing narratives argue that the relationship is reciprocal and according to (Mckenna, 2005) Nursing theory takes its origin from practice, than reverse to practice for testing and at the end become set to guide the practice.
Compatibility Family Learning for Item Recommendation and Generation

Compatibility between items, such as clothes and shoes, is a major factor among customer’s purchasing decisions. However, learning “compatibility” is challenging due to (1) broader notions of compatibility than those of similarity, (2) the asymmetric nature of compatibility, and (3) only a small set of compatible and incompatible items are observed. We propose an end-to-end trainable system to embed each item into a latent vector and project a query item into K compatible prototypes in the same space. These prototypes reflect the broad notions of compatibility. We refer to both the embedding and prototypes as “Compatibility Family”. In our learned space, we introduce a novel Projected Compatibility Distance (PCD) function which is differentiable and ensures diversity by aiming for at least one prototype to be close to a compatible item, whereas none of the prototypes are close to an incompatible item. We evaluate our system on a toy dataset, two Amazon product datasets, and Polyvore outfit dataset. Our method consistently achieves state-of-the-art performance. Finally, we show that we can visualize the candidate compatible prototypes using a Metric-regularized Conditional Generative Adversarial Network (MrCGAN), where the input is a projected prototype and the output is a generated image of a compatible item. We ask human evaluators to judge the relative compatibility between our generated images and images generated by CGANs conditioned directly on query items. Our generated images are significantly preferred, with roughly twice the number of votes as others.
Multimodal Convolutional Neural Networks for Matching Image and Sentence

In this paper, we propose multimodal convolutional neural networks (m-CNNs) for matching image and sentence. Our m-CNN provides an end-to-end framework with convolutional architectures to exploit image representation, word composition, and the matching relations between the two modalities. More specifically, it consists of one image CNN encoding the image content and one matching CNN modeling the joint representation of image and sentence. The matching CNN composes different semantic fragments from words and learns the inter-modal relations between image and the composed fragments at different levels, thus fully exploit the matching relations between image and sentence. Experimental results demonstrate that the proposed m-CNNs can effectively capture the information necessary for image and sentence matching. More specifically, our proposed m-CNNs significantly outperform the state-of-the-art approaches for bidirectional image and sentence retrieval on the Flickr8K and Flickr30K datasets.