nils-herrmann/clean_scirepeval huggingface dataset:
Deep Neural Architecture for Multi-Modal Retrieval based on Joint Embedding Space for Text and Images

Recent advances in deep learning and distributed representations of images and text have resulted in the emergence of several neural architectures for cross-modal retrieval tasks, such as searching collections of images in response to textual queries and assigning textual descriptions to images. However, the multi-modal retrieval scenario, when a query can be either a text or an image and the goal is to retrieve both a textual fragment and an image, which should be considered as an atomic unit, has been significantly less studied. In this paper, we propose a gated neural architecture to project image and keyword queries as well as multi-modal retrieval units into the same low-dimensional embedding space and perform semantic matching in this space. The proposed architecture is trained to minimize structured hinge loss and can be applied to both cross- and multi-modal retrieval. Experimental results for six different cross- and multi-modal retrieval tasks obtained on publicly available datasets indicate superior retrieval accuracy of the proposed architecture in comparison to the state-of-art baselines.
Dynamic and Sensitivity Analysis of KNTU CDRPM: A Cable Driven Redundant Parallel Manipulator

KNTU CDRPM is a cable driven redundant parallel manipulator, which is under investigation for possible implementation of large workspace applications. This newly developed manipulators have several advantages compared to the conventional parable mechanisms. In this paper, the governing dynamic equation of motion of such structure is derived using the Newton-Euler formulation. Next, the dynamic equations of the system are used in simulations. It is shown that on the contrary to serial manipulators, dynamic equations of motion of cable-driven parallel manipulators can be only represented implicitly, and only special integration routines can be used for their simulations. In order to verify the accuracy and integrity of the derived dynamic equations, open- and closed-loop simulations for the system is performed and analyzed. Also, the effects of mechanical assembly tolerances on the closed-loop control performance of a cable driven parallel robot are studied in detail, and the sensitivity analysis of the precision in the construction and assembly of the system on the closed-loop behavior of the KNTU CDRPM is performed.
Personality and leadership: a qualitative and quantitative review.

This article provides a qualitative review of the trait perspective in leadership research, followed by a meta-analysis. The authors used the five-factor model as an organizing framework and meta-analyzed 222 correlations from 73 samples. Overall, the correlations with leadership were Neuroticism = -.24, Extraversion = .31, Openness to Experience = .24, Agreeableness = .08, and Conscientiousness = .28. Results indicated that the relations of Neuroticism, Extraversion, Openness to Experience, and Conscientiousness with leadership generalized in that more than 90% of the individual correlations were greater than 0. Extraversion was the most consistent correlate of leadership across study settings and leadership criteria (leader emergence and leadership effectiveness). Overall, the five-factor model had a multiple correlation of .48 with leadership, indicating strong support for the leader trait perspective when traits are organized according to the five-factor model.
Hedonic tone and activation level in the mood-creativity link: toward a dual pathway to creativity model.

To understand when and why mood states influence creativity, the authors developed and tested a dual pathway to creativity model; creative fluency (number of ideas or insights) and originality (novelty) are functions of cognitive flexibility, persistence, or some combination thereof. Invoking work on arousal, psychophysiological processes, and working memory capacity, the authors argue that activating moods (e.g., angry, fearful, happy, elated) lead to more creative fluency and originality than do deactivating moods (e.g., sad, depressed, relaxed, serene). Furthermore, activating moods influence creative fluency and originality because of enhanced cognitive flexibility when tone is positive and because of enhanced persistence when tone is negative. Four studies with different mood manipulations and operationalizations of creativity (e.g., brainstorming, category inclusion tasks, gestalt completion tests) support the model.
Facial Attractiveness: Beauty and the Machine

This work presents a novel study of the notion of facial attractiveness in a machine learning context. To this end, we collected human beauty ratings for data sets of facial images and used various techniques for learning the attractiveness of a face. The trained predictor achieves a significant correlation of 0.65 with the average human ratings. The results clearly show that facial beauty is a universal concept that a machine can learn. Analysis of the accuracy of the beauty prediction machine as a function of the size of the training data indicates that a machine producing human-like attractiveness rating could be obtained given a moderately larger data set.
Mechanisms of emotional arousal and lasting declarative memory

Neuroscience is witnessing growing interest in understanding brain mechanisms of memory formation for emotionally arousing events, a development closely related to renewed interest in the concept of memory consolidation. Extensive research in animals implicates stress hormones and the amygdaloid complex as key, interacting modulators of memory consolidation for emotional events. Considerable evidence suggests that the amygdala is not a site of long-term explicit or declarative memory storage, but serves to influence memory-storage processes in other brain regions, such as the hippocampus, striatum and neocortex. Human-subject studies confirm the prediction of animal work that the amygdala is involved with the formation of enhanced declarative memory for emotionally arousing events.
"Play PRBLMS": Identifying and Correcting Less Accessible Content in Voice Interfaces

Voice interfaces often struggle with specific types of named content. Domain-specific terminology and naming may push the bounds of standard language, especially in domains like music where artistic creativity extends beyond the music itself. Artists may name themselves with symbols (e.g. M S C RA) that most standard automatic speech recognition (ASR) systems cannot transcribe. Voice interfaces also experience difficulty surfacing content whose titles include non-standard spellings, symbols or other ASCII characters in place of English letters, or are written using a non-standard dialect. We present a generalizable method to detect content that current voice interfaces underserve by leveraging differences in engagement across input modalities. Using this detection method, we develop a typology of content types and linguistic practices that can make content hard to surface. Finally, we present a process using crowdsourced annotations to make underserved content more accessible.
Understanding Loneliness in Social Awareness Streams: Expressions and Responses

We studied the experience of loneliness as communicated by thousands of people on Twitter. Using a data set of public Twitter posts containing explicit expressions of loneliness, we qualitatively developed a categorization scheme for these expressions, showing how the context of loneliness expressed on Twitter relates to existing theories about loneliness. A quantitative analysis of the data exposed categories and patterns in communication practices around loneliness. For example, users expressing more severe, enduring loneliness are more likely to be female, and less likely to include requests for social interaction in their tweets. Further, we studied the responses to expressions of loneliness in Twitter’s social settings. Deriving from the same dataset, we examined factors that correlate with the existence and type of response, showing, for example, that men were more likely to receive responses to lonely tweets, and expressions of enduring loneliness are critically less likely to receive responses.
Visual Exploration of Spatial and Temporal Variations of Tweet Topic Popularity

We present a visual analytical approach to exploring variation of topic popularity in social media (such as Twitter) over space and time. Our approach includes an analytical pipeline and a multi-view visualization tool. As attempts of topic extraction from very short texts like tweets may not produce meaningful results, we aggregate the texts prior to applying topic modelling techniques. Interactive visualisations support detection of burst events in social media posting activities at different locations, show the spatial, temporal, quantitative, and semantic aspects of these events, and enable the user to explore how popularity of topics varies over cities and time. A case study has been conducted using a real-world tweet dataset.
Iterative thresholding based image segmentation using 2D improved Otsu algorithm

Threshold based segmentation methods can effectively separate objects from the background. The conventional Otsu's method can achieve good result only when the histogram of an image has two distinct peaks. In this paper, an iterative thresholding based on 2D improved Otsu method using a novel threshold value recognition function is proposed which is used to find the optimum threshold value in different types of histograms and separate into two classes. For the two classes separated by the above threshold value, mean values are computed. Based on the threshold value and the two mean values, the histogram of an image is divided iteratively into three regions, namely, the Foreground (FG) region, BG) region and To-Be-Processed (TBP) region. The pixels greater than the larger mean belongs to FG region, the pixel which are lesser than the smaller mean belongs to BG region. The pixel that lies between the two mean values belongs to the TBP region and only this region is processed at next iteration, the FG and BG region are not processed further. Again for that TBP region, the novel threshold value recognition function is used to find a new threshold value and then the two mean values are computed to separate into three classes, namely, the FG, BG and TBP region. Then the new TBP region is processed in a similar manner iteratively until the two mean values computed at successive iterations are not changed. Finally all the previously determined foreground regions are combined separately and the background regions are combined separately to give the segmented image. The experimental results show that the proposed method performs well in detecting the objects and has better anti-noise performance compared with the existing methods.
Identifying the Higgs Boson

This paper discusses the identification of the Higgs boson subatomic particle from jet pull energy colorflow images of the particles’ decay, as modeled by the ATLAS Experiment at the Large Hadron Collider in CERN . The Higgs field is an hypothesized energy field thought to permeate the entire universe. Without it, the Standard Model of particle physics would break down, as atomic particles would not have the required mass to attract each other, leading them to simply float around in the universe at the speed of light [20]. Its proof would completely alter our understanding of mass as a physical property, making the discovery of the Higgs field the fundamental unanswered question in particle physics in the last halfcentury [13]. The Standard Model suggests that if the Higgs field were to exist, then its quantum excitation, a particle referred to as the Higgs boson, would also have to exist [20]. The Large Hadron Collider (LHC), tasked with finding this particle, consisting of multiple super-powered electromagnets, collides charged particles traveling at near lightspeed. These collisions deform space upon impact, breaking the charged particles into subatomic constituents. It has been hypothesized that with a proton collision at high enough energy, the Higgs boson would decay in observable ways. The ATLAS detector at the LHC records 40 million proton collisions a second, making human curation of these events unfeasible [14]. An accurate classification system that would label the most promising observations as Higgs boson particle decays is, therefore, required. In collaboration with SLAC and the ATLAS Experiment at CERN we were given access to energy images for both Higgs boson and gluon decay, referred to as signal and background respectively, seen in Figures 1 and 2. The purpose of this project was to build a binary classifier which, given the colorflow energy image of the decay of an unknown particle, would accurately distinguish whether or not that particle was a Higgs boson. While we tested a wide array of supervised learning models, we focused primarily on ensemble methods. In particular we developed and fine-tuned an Adaptive Boosting classifier (AdaBoost) with a Random Forest classifier as its base estimator [8, 10]. We also utilized a number of image feature extraction mechanisms includ-
Acceptance of e-commerce services: the case of electronic brokerages

This paper examines human motivations underlying individual acceptance of business-to-consumer (B2C) electronic commerce services. Such acceptance is the key to the survival of firms in this intensely competitive industry. A modified theory of planned behavior (TPB) is used to hypothesize a model of e-commerce service acceptance, which is then tested using a field survey of 172 e-brokerage users. We found TPB useful in explaining e-commerce service acceptance; however acceptance motivations are significantly different from that of typical IS products. Based on a broader conceptualization of TPB’s subjective norm to include both external (mass-media) and interpersonal influences, we report that subjective norm is an important predictor of e-commerce acceptance, behavioral control has minimal impact on e-commerce acceptance, and external influence is a significant determinant of subjective norm. Implications of these findings in light of e-commerce research and practice are discussed.
The effect of substrate noise on VCO performance

This study characterizes the effect of substrate noise on a standard component of the RF front end: the voltage controlled oscillator (VCO), as well as evaluating the effect of VCO bias current and guard rings on noise performance. Frequency effects of substrate noise are also examined through the study of VCOs at three different center frequencies: 900 MHz, 2.4 GHz, and 5.2 GHz. Substrate noise is a serious problem that continues to plague mixed-signal designs. Components of the RF frontend are particularly sensitive to substrate noise as the effectiveness of standard isolation techniques degrades at higher frequencies. This study has shown that the phase noise of a VCO is adversely affected by substrate noise. In the extreme, the VCO can lock to the substrate noise. Guard rings can effectively attenuate substrate noise at lower frequencies. For example, at 900 MHz, as much as 25 dB of isolation is observed. At 5.2 GHz, the isolation reduces to 10 dB. Furthermore, the use of guard rings can improve the response of the VCO to injection locking.
A Combined Corner and Edge Detector

The problem we are addressing in Alvey Project MMI149 is that of using computer vision to understand the unconstrained 3D world, in which the viewed scenes will in general contain too wide a diversity of objects for topdown recognition techniques to work. For example, we desire to obtain an understanding of natural scenes, containing roads, buildings, trees, bushes, etc., as typified by the two frames from a sequence illustrated in Figure 1. The solution to this problem that we are pursuing is to use a computer vision system based upon motion analysis of a monocular image sequence from a mobile camera. By extraction and tracking of image features, representations of the 3D analogues of these features can be constructed.
Empathy and social functioning in late adulthood.

Both cognitive and affective empathy are regarded as essential prerequisites for successful social functioning, and recent studies have suggested that cognitive, but not affective, empathy may be adversely affected as a consequence of normal adult aging. This decline in cognitive empathy is of concern, as older adults are particularly susceptible to the negative physical and mental health consequences of loneliness and social isolation.



The present study compared younger (N = 80) and older (N = 49) adults on measures of cognitive empathy, affective empathy, and social functioning.



Whilst older adults' self-reported and performance-based cognitive empathy was significantly reduced relative to younger adults, there were no age-related differences in affective empathy. Older adults also reported involvement in significantly fewer social activities than younger adults, and cognitive empathy functioned as a partial mediator of this relationship.



These findings are consistent with theoretical models that regard cognitive empathy as an essential prerequisite for good interpersonal functioning. However, the cross-sectional nature of the study leaves open the question of causality for future studies.
Generating synthetic CTs from magnetic resonance images using generative adversarial networks.

While MR-only treatment planning using synthetic CTs (synCTs) offers potential for streamlining clinical workflow, a need exists for an efficient and automated synCT generation in the brain to facilitate near real-time MR-only planning. This work describes a novel method for generating brain synCTs based on generative adversarial networks (GANs), a deep learning model that trains two competing networks simultaneously, and compares it to a deep convolutional neural network (CNN).



Post-Gadolinium T1-Weighted and CT-SIM images from fifteen brain cancer patients were retrospectively analyzed. The GAN model was developed to generate synCTs using T1-weighted MRI images as the input using a residual network (ResNet) as the generator. The discriminator is a CNN with five convolutional layers that classified the input image as real or synthetic. Fivefold cross-validation was performed to validate our model. GAN performance was compared to CNN based on mean absolute error (MAE), structural similarity index (SSIM), and peak signal-to-noise ratio (PSNR) metrics between the synCT and CT images.



GAN training took ~11 h with a new case testing time of 5.7 ± 0.6 s. For GAN, MAEs between synCT and CT-SIM were 89.3 ± 10.3 Hounsfield units (HU) and 41.9 ± 8.6 HU across the entire FOV and tissues, respectively. However, MAE in the bone and air was, on average, ~240-255 HU. By comparison, the CNN model had an average full FOV MAE of 102.4 ± 11.1 HU. For GAN, the mean PSNR was 26.6 ± 1.2 and SSIM was 0.83 ± 0.03. GAN synCTs preserved details better than CNN, and regions of abnormal anatomy were well represented on GAN synCTs.



We developed and validated a GAN model using a single T1-weighted MR image as the input that generates robust, high quality synCTs in seconds. Our method offers strong potential for supporting near real-time MR-only treatment planning in the brain.
Modeling and Predicting User Actions in Recommender Systems

Many collaborative filtering recommender systems collect and use users' explicitly entered preferences in the form of ratings for items. However, in many real world scenarios, this form of feedback can be difficult to obtain or unavailable (e.g., news portals). In this case recommendations must be built by leveraging more abundant implicit feedback data, which only indirectly signal users' preferences or opinions. A record in such datasets is a result of an action performed by a user on an item (e.g., the item was clicked or viewed). State-of-the-art implicit feedback recommender systems predict whether the user will act on a target item and interpret this prediction as a discovered preference for the item. These models are trained by observations of user actions of one single type. For instance, they predict that a user will watch a video using a dataset of observed video watch actions. In this paper we conjecture that multiple types of user actions may be jointly exploited to predict one target type of actions. We present a general prediction model (MMF - Multiple action types Matrix Factorization) that implements this conjecture and we illustrate some practical examples. The empirical evaluation of MMF, which was conducted on a large real world dataset, shows that using multiple actions is beneficial and it can outperform a state-of-the-art implicit feedback model that uses only the target action data.
Semisupervised and Weakly Supervised Road Detection Based on Generative Adversarial Networks

Road detection is a key component of autonomous driving; however, most fully supervised learning road detection methods suffer from either insufficient training data or high costs of manual annotation. To overcome these problems, we propose a semisupervised learning (SSL) road detection method based on generative adversarial networks (GANs) and a weakly supervised learning (WSL) method based on conditional GANs. Specifically, in our SSL method, the generator generates the road detection results of labeled and unlabeled images, and then they are fed into the discriminator, which assigns a label on each input to judge whether it is labeled. Additionally, in WSL method we add another network to predict road shapes of input images and use them in both generator and discriminator to constrain the learning progress. By training under these frameworks, the discriminators can guide a latent annotation process on the unlabeled data; therefore, the networks can learn better representations of road areas and leverage the feature distributions on both labeled and unlabeled data. The experiments are carried out on KITTI ROAD benchmark, and the results show our methods achieve the state-of-the-art performances.
Discriminative K-means for Clustering

We present a theoretical study on the discriminative clustering framework, recently proposed for simultaneous subspace selection via linear discriminant analysis (LDA) and clustering. Empirical results have shown its favorable performance in comparison with several other popular clustering algorithms. However, the inherent relationship between subspace selection and clustering in this framework is not well understood, due to the iterative nature of the algorithm. We show in this paper that this iterative subspace selection and clustering is equivalent to kernel K-means with a specific kernel Gram matrix. This provides significant and new insights into the nature of this subspace selection procedure. Based on this equivalence relationship, we propose the Discriminative K-means (DisKmeans) algorithm for simultaneous LDA subspace selection and clustering, as well as an automatic parameter estimation procedure. We also present the nonlinear extension of DisKmeans using kernels. We show that the learning of the kernel matrix over a convex set of pre-specified kernel matrices can be incorporated into the clustering formulation. The connection between DisKmeans and several other clustering algorithms is also analyzed. The presented theories and algorithms are evaluated through experiments on a collection of benchmark data sets.