nils-herrmann/clean_scirepeval huggingface dataset:
Generating Natural Questions About an Image

There has been an explosion of work in the vision & language community during the past few years from image captioning to video transcription, and answering questions about images. These tasks focus on literal descriptions of the image. To move beyond the literal, we choose to explore how questions about an image often address abstract events that the objects evoke. In this paper, we introduce the novel task of ‘Visual Question Generation’ (VQG), where the system is tasked with asking a natural and engaging question when shown an image. We provide three datasets which cover a variety of images from object-centric to event-centric, providing different and more abstract training data than the stateof-the-art captioning systems have used thus far. We train and test several generative and retrieval models to tackle the task of VQG. Evaluation results show that while such models ask reasonable questions given various images, there is still a wide gap with human performance. Our proposed task offers a new challenge to the community which we hope can spur further interest in exploring deeper connections between vision & language.
How to compare movement? A review of physical movement similarity measures in geographic information science and beyond

In geographic information science, a plethora of different approaches and methods is used to assess the similarity of movement. Some of these approaches term two moving objects similar if they share akin paths. Others require objects to move at similar speed and yet others consider movement similar if it occurs at the same time. We believe that a structured and comprehensive classification of movement comparison measures is missing. We argue that such a classification not only depicts the status quo of qualitative and quantitative movement analysis, but also allows for identifying those aspects of movement for which similarity measures are scarce or entirely missing. In this review paper we, first, decompose movement into its spatial, temporal, and spatiotemporal movement parameters. A movement parameter is a physical quantity of movement, such as speed, spatial path, or temporal duration. For each of these parameters we then review qualitative and quantitative methods of how to compare movement. Thus, we provide a systematic and comprehensive classification of different movement similarity measures used in geographic information science. This classification is a valuable first step toward a GIS toolbox comprising all relevant movement comparison methods.
Substrate integrated waveguide power divider, circulator and coupler in [10-15] GHz band

The Substrate Integrated Waveguide (SIW) technology is an attractive approach for the design of high performance microwave and millimeter wave components, as it combines the advantages of planar technology, such as low fabrication costs, with the low loss inherent to the waveguide solution. In this study, a substrate integrated waveguide power divider, circulator and coupler are conceived and optimized in [10-15] GHz band by Ansoft HFSS code. Thus, results of this modeling are presented, discussed and allow to integrate these devices in planar circuits.
A Fast Approach for Semantic Similar Short Texts Retrieval

Retrieving semantic similar short texts is a crucial issue to many applications, e.g., web search, ads matching, questionanswer system, and so forth. Most of the traditional methods concentrate on how to improve the precision of the similarity measurement, while current real applications need to efficiently explore the top similar short texts semantically related to the query one. We address the efficiency issue in this paper by investigating the similarity strategies and incorporating them into the FAST framework (efficient FrAmework for semantic similar Short Texts retrieval). We conduct comprehensive performance evaluation on real-life data which shows that our proposed method outperforms the state-ofthe-art techniques.
Self-taught hashing for fast similarity search

The ability of fast similarity search at large scale is of great importance to many Information Retrieval (IR) applications. A promising way to accelerate similarity search is semantic hashing which designs compact binary codes for a large number of documents so that semantically similar documents are mapped to similar codes (within a short Hamming distance). Although some recently proposed techniques are able to generate high-quality codes for documents known in advance, obtaining the codes for previously unseen documents remains to be a very challenging problem. In this paper, we emphasise this issue and propose a novel Self-Taught Hashing (STH) approach to semantic hashing: we first find the optimal l-bit binary codes for all documents in the given corpus via unsupervised learning, and then train l classifiers via supervised learning to predict the l-bit code for any query document unseen before. Our experiments on three real-world text datasets show that the proposed approach using binarised Laplacian Eigenmap (LapEig) and linear Support Vector Machine (SVM) outperforms state-of-the-art techniques significantly.
Perspective Making and Perspective Taking in Communities of Knowing Ceo Publication T 93-18 (238) Perspective Making and Perspective Taking in Communities of Knowing Perspective Making and Perspective Taking in Communities of Knowing

Communication in knowledge intensive firms involves perspective making and perspective taking within and between their communities of knowing. In this paper we look to science as an example of knowledge work in a community of knowing, and draw implications for the design of electronic communication systems and policies to support perspective making and perspective taking. The role of narrative in perspective making and of reflexivity in perspective taking are highlighted. Two models of communication, the conduit model and the language games model, are proposed for guiding the design of electronic communication media, and their relative strengths and weaknesses are considered.
Autophagy in Cancer Stem Cells: A Potential Link Between Chemoresistance, Recurrence, and Metastasis

Cancer cells require an uninterrupted nutritional supply for maintaining their proliferative needs and this high demand in concurrence with inadequate supply of blood and nutrition induces stress in these cells. These cells utilize various strategies like high glycolytic flux, redox signaling, and modulation of autophagy to avoid cell death and overcome nutritional deficiency. Autophagy allows the cell to generate ATP and other essential biochemical building blocks necessary under such adverse conditions. It is emerging as a decisive process in the development and progression of pathophysiological conditions that are associated with increased cancer risk. However, the precise role of autophagy in tumorigenesis is still debatable. Autophagy is a novel cytoprotective process to augment tumor cell survival under nutrient or growth factor starvation, metabolic stress, and hypoxia. The tumor hypoxic environment may provide site for the enrichment/expansion of the cancer stem cells (CSCs) and successive rapid tumor progression. CSCs are characteristically resistant to conventional anticancer therapy, which may contribute to treatment failure and tumor relapse. CSCs have the potential to regenerate for an indefinite period, which can impel tumor metastatic invasion. From last decade, preclinical research has focused on the diversity in CSC content within tumors that could affect their chemo- or radio-sensitivity by impeding with mechanisms of DNA repair and cell cycle progression. The aim of this review is predominantly directed on the recent developments in the CSCs during cancer treatment, role of autophagy in maintenance of CSC populations and their implications in the development of promising new cancer treatment options in future.
Trajectory Bundling for Animated Transitions

Animated transition has been a popular design choice for smoothly switching between different visualization views or layouts, in which movement trajectories are created as cues for tracking objects during location shifting. Tracking moving objects, however, becomes difficult when their movement paths overlap or the number of tracking targets increases. We propose a novel design to facilitate tracking moving objects in animated transitions. Instead of simply animating an object along a straight line, we create "bundled" movement trajectories for a group of objects that have spatial proximity and share similar moving directions. To study the effect of bundled trajectories, we untangle variations due to different aspects of tracking complexity in a comprehensive controlled user study. The results indicate that using bundled trajectories is particularly effective when tracking more targets (six vs. three targets) or when the object movement involves a high degree of occlusion or deformation. Based on the study, we discuss the advantages and limitations of the new technique, as well as provide design implications.
Nonlinear residual acoustic echo suppression for high levels of harmonic distortion

Linear adaptive filters are often used for acoustic echo cancellation (AEC) but sometimes fail to perform well in notebook computers and inexpensive telephony devices. Low-quality speakers and poorly-designed enclosures that produce vibrations often generate harmonic distortion, and this nonlinear effect degrades the performance of linear AEC algorithms considerably. In this work, we present a new AEC architecture that consists of a linear, subband adaptive AEC filter followed a nonlinear residual echo suppression (RES) stage specifically designed to address harmonic distortion. In addition to suppressing the residual echo in the primary subband, the proposed model also suppresses the residual echo in a window of bands surrounding the higher order harmonics. Results show considerable improvement over other proposed algorithms, and the new algorithm has much lower implementation costs compared to nonlinear AEC models based on Volterra filters and a previously proposed, nonlinear residual echo suppression algorithm.
LSD-induced entropic brain activity predicts subsequent personality change.

Personality is known to be relatively stable throughout adulthood. Nevertheless, it has been shown that major life events with high personal significance, including experiences engendered by psychedelic drugs, can have an enduring impact on some core facets of personality. In the present, balanced-order, placebo-controlled study, we investigated biological predictors of post-lysergic acid diethylamide (LSD) changes in personality. Nineteen healthy adults underwent resting state functional MRI scans under LSD (75µg, I.V.) and placebo (saline I.V.). The Revised NEO Personality Inventory (NEO-PI-R) was completed at screening and 2 weeks after LSD/placebo. Scanning sessions consisted of three 7.5-min eyes-closed resting-state scans, one of which involved music listening. A standardized preprocessing pipeline was used to extract measures of sample entropy, which characterizes the predictability of an fMRI time-series. Mixed-effects models were used to evaluate drug-induced shifts in brain entropy and their relationship with the observed increases in the personality trait openness at the 2-week follow-up. Overall, LSD had a pronounced global effect on brain entropy, increasing it in both sensory and hierarchically higher networks across multiple time scales. These shifts predicted enduring increases in trait openness. Moreover, the predictive power of the entropy increases was greatest for the music-listening scans and when "ego-dissolution" was reported during the acute experience. These results shed new light on how LSD-induced shifts in brain dynamics and concomitant subjective experience can be predictive of lasting changes in personality.
Evaluation of 3D Feature Descriptors for Multi-modal Data Registration

We propose a framework for 2D/3D multi-modal data registration and evaluate 3D feature descriptors for registration of 3D datasets from different sources. 3D datasets of outdoor environments can be acquired using a variety of active and passive sensor technologies. Registration of these datasets into a common coordinate frame is required for subsequent modelling and visualisation. 2D images are converted into 3D structure by stereo or multiview reconstruction techniques and registered to a unified 3D domain with other datasets in a 3D world. Multi-modal datasets have different density, noise, and types of errors in geometry. This paper provides a performance benchmark for existing 3D feature descriptors across multi-modal datasets. This analysis highlights the limitations of existing 3D feature detectors and descriptors which need to be addressed for robust multi-modal data registration. We analyse and discuss the performance of existing methods in registering various types of datasets then identify future directions required to achieve robust multi-modal data registration.
An empirical evaluation of entropy-based traffic anomaly detection

Entropy-based approaches for anomaly detection are appealing since they provide more fine-grained insights than traditional traffic volume analysis. While previous work has demonstrated the benefits of entropy-based anomaly detection, there has been little effort to comprehensively understand the detection power of using entropy-based analysis of multiple traffic distributions in conjunction with each other. We consider two classes of distributions: flow-header features (IP addresses, ports, and flow-sizes), and behavioral features (degree distributions measuring the number of distinct destination/source IPs that each host communicates with). We observe that the timeseries of entropy values of the address and port distributions are strongly correlated with each other and provide very similar anomaly detection capabilities. The behavioral and flow size distributions are less correlated and detect incidents that do not show up as anomalies in the port and address distributions. Further analysis using synthetically generated anomalies also suggests that the port and address distributions have limited utility in detecting scan and bandwidth flood anomalies. Based on our analysis, we discuss important implications for entropy-based anomaly detection.
Out-of-bag Estimation

In bagging, predictors are constructed using bootstrap samples from the training set and then aggregated to form a bagged predictor. Each bootstrap sample leaves out about 37% of the examples. These left-out examples can be used to form accurate estimates of important quantities. For instance, they can be used to give much improved estimates of node probabilities and node error rates in decision trees. Using estimated outputs instead of the observed outputs improves accuracy in regression trees. They can also be used to give nearly optimal estimates of generalization errors for bagged predictors.  We assume that there is a training set T= {(y n ,x n), n=1, ... ,N} and a method for constructing a predictor Q(x,T) using the given training set. The output variable y can either be a class label (classification) or numerical (regression). In bagging (Breiman[1996a]) a sequence of training sets T B,1 , ... , T B,K are generated of the same size as T by bootstrap selection from T. Then K predictors are constructed such that the kth predictor Q(x,T k,B) is based on the kth bootstrap training set. It was shown that if these predictors are aggregated-averaging in regression or voting in classification, then the resultant predictor can be considerably more accurate than the original predictor.
Single-View Metrology: Algorithms and Applications

This paper addresses the problem of extracting three-dimensional geometric information from a single, uncalibrated image of a scene. This work, building upon [7], is divided into two parts. The first part describes, in simple steps, the basic algorithms to obtain partial or complete geometric reconstruction from single perspective images of a scene. The second part presents a panorama of applications of single-view metrology and discusses its relationship with different disciplines such as architecture, history of art and forensic science. Furthermore, techniques for increasing the level of automation of the reconstruction process are herein described. Several examples on photographs and historical paintings demonstrate the power and flexibility of the proposed techniques.
Multiple Sensor Fusion and Classification for Moving Object Detection and Tracking

The accurate detection and classification of moving objects is a critical aspect of advanced driver assistance systems. We believe that by including the object classification from multiple sensor detections as a key component of the object's representation and the perception process, we can improve the perceived model of the environment. First, we define a composite object representation to include class information in the core object's description. Second, we propose a complete perception fusion architecture based on the evidential framework to solve the detection and tracking of moving objects problem by integrating the composite representation and uncertainty management. Finally, we integrate our fusion approach in a real-time application inside a vehicle demonstrator from the interactIVe IP European project, which includes three main sensors: radar, lidar, and camera. We test our fusion approach using real data from different driving scenarios and focusing on four objects of interest: pedestrian, bike, car, and truck.
30 + years of research and practice of outsourcing – Exploring the past and anticipating the future

Received in revised form 24 June 2008 Accepted 31 July 2008 Available online 5 April 2009 Outsourcing is a phenomenon that as a practice originated in the 1950s, but it was not until the 1980s when the strategy became widely adopted in organizations. Since then, the strategy has evolved from a strictly cost focused approach towards more cooperative nature, in which cost is only one, often secondary, decision-making criterion. In the development of the strategy, three broad and somewhat overlapping, yet distinct phases can be identified: the era of the Big Bang, the era of the Bandwagon, and the era of Barrierless Organizations. This paper illustrates that the evolution of the practice has caused several contradictions among researchers, as well as led to the situation where the theoretical background of the phenomenon has recently become much richer. Through examining existing research, this paper intends to identify the development of outsourcing strategy from a practical as well as a theoretical perspective from its birth up to today. In addition, through providing insights from managers in the information technology industry, this paper aims at providing a glimpse from the future – that is – what may be the future directions and research issues in this complex phenomenon?
Radar Signal Processing for Jointly Estimating Tracks and Micro-Doppler Signatures

The aim of the radar systems is to collect information about their surroundings. In many scenarios besides static targets there are numerous moving objects with very different characteristics, such as extent, movement behavior or micro-Doppler spread. It would be most desirable to have algorithms that extract all information on static and moving object automatically, without a system operator. In this paper, we present measurements conducted with a commercially available high-resolution multi-channel linear frequency-modulated continuous-wave radar and algorithms that do not only produce radar images but a description of the scenario on a higher level. After conventional spectrum estimation and thresholding, we present a clustering stage that combines individual detections and generates representations of each target individually. This stage is followed by a Kalman filter based multi-target tracking block. The tracker allows us to follow each target and collect its properties over time. With this method of jointly estimating tracks and characteristics of each individual target in a scenario, inputs for classifiers can be generated. Which, in turn, will be able to generate information that could be used for driver assistance or alarm trigger systems.
Emotion in the criminal psychopath: startle reflex modulation.

Startle-elicited blinks were measured during presentation of affective slides to test hypotheses concerning emotional responding in psychopaths. Subjects were 54 incarcerated sexual offenders divided into nonpsychopathic, psychopathic, and mixed groups based on file and interview data. Consistent with findings for normal college students, nonpsychopaths and mixed subjects showed a significant linear relationship between slide valence and startle magnitude, with startle responses largest during unpleasant slides and smallest during pleasant slides. This effect was absent in psychopaths. Group differences in startle modulation were related to affective features of psychopathy, but not to antisocial behavior per se. Psychopathy had no effect on autonomic or self-report responses to slides. These results suggest an abnormality in the processing of emotional stimuli by psychopaths that manifests itself independently of affective report.
Active Inference and Learning in the Cerebellum

This letter offers a computational account of Pavlovian conditioning in the cerebellum based on active inference and predictive coding. Using eyeblink conditioning as a canonical paradigm, we formulate a minimal generative model that can account for spontaneous blinking, startle responses, and (delay or trace) conditioning. We then establish the face validity of the model using simulated responses to unconditioned and conditioned stimuli to reproduce the sorts of behavior that are observed empirically. The scheme’s anatomical validity is then addressed by associating variables in the predictive coding scheme with nuclei and neuronal populations to match the (extrinsic and intrinsic) connectivity of the cerebellar (eyeblink conditioning) system. Finally, we try to establish predictive validity by reproducing selective failures of delay conditioning, trace conditioning, and extinction using (simulated and reversible) focal lesions. Although rather metaphorical, the ensuing scheme can account for a remarkable range of anatomical and neurophysiological aspects of cerebellar circuitry—and the specificity of lesion-deficit mappings that have been established experimentally. From a computational perspective, this work shows how conditioning or learning can be formulated in terms of minimizing variational free energy (or maximizing Bayesian model evidence) using exactly the same principles that underlie predictive coding in perception.
Task versus relationship conflict, team performance, and team member satisfaction: a meta-analysis.

This study provides a meta-analysis of research on the associations between relationship conflict, task conflict, team performance, and team member satisfaction. Consistent with past theorizing, results revealed strong and negative correlations between relationship conflict, team performance, and team member satisfaction. In contrast to what has been suggested in both academic research and introductory textbooks, however, results also revealed strong and negative (instead of the predicted positive) correlations between task conflict team performance, and team member satisfaction. As predicted, conflict had stronger negative relations with team performance in highly complex (decision making, project, mixed) than in less complex (production) tasks. Finally, task conflict was less negatively related to team performance when task conflict and relationship conflict were weakly, rather than strongly, correlated.