nils-herrmann/clean_scirepeval huggingface dataset:
An Examination of Regret in Bullying Tweets

Social media users who post bullying related tweets may later experience regret, potentially causing them to delete their posts. In this paper, we construct a corpus of bullying tweets and periodically check the existence of each tweet in order to infer if and when it becomes deleted. We then conduct exploratory analysis in order to isolate factors associated with deleted posts. Finally, we propose the construction of a regrettable posts predictor to warn users if a tweet might cause regret.
Education , Poverty and Terrorism : Is There a Causal Connection ?

The paper investigates whether there is a causal link between poverty or low education and participation in terrorist activities. After presenting a discussion of theoretical issues, we review evidence on the determinants of hate crimes, which are closely related to terrorism. This literature finds that the occurrence of hate crimes is largely independent of economic conditions. Next we analyze data on support for terrorism from public opinion polls conducted in the West Bank and Gaza Strip. These polls indicate that support for terrorism does not decrease among those with higher education and higher living standards. The core contribution of the paper is a statistical analysis of the determinants of participation in Hezbollah terrorist activities in Lebanon in the late 1980s and early 1990s. The evidence that we have assembled suggests that having a living standard above the poverty line or a secondary school or higher education is positively associated with participation in terrorism. Although our results are tentative and exploratory, they suggest that neither poverty nor education have a direct, causal impact on terrorism. The conclusion speculates on why economic conditions and education are largely unrelated to participation in, and support for, terrorism. This paper was prepared for the World Bank’s Annual Bank Conference on Development Economics, April 2002. We thank Claude Berrebi for excellent research assistance, Eli Hurvitz, Ayoub Mustafa, Adib Nehmeh and Zeina el Khalil for providing data, and Joshua Angrist, Guido Imbens and Elie Tamer for helpful discussions.
A real time system for dynamic hand gesture recognition with a depth sensor

Recent advances in depth sensing provide exciting opportunities for the development of new methods for human activity understanding. Yet, little work has been done in the area of hand gesture recognition which has many practical applications. In this paper we propose a real-time system for dynamic hand gesture recognition. It is fully automatic and robust to variations in speed and style as well as in hand orientations. Our approach is based on action graph, which shares similar robust properties with standard HMM but requires less training data by allowing states shared among different gestures. To deal with hand orientations, we have developed a new technique for hand segmentation and orientation normalization. The proposed system is evaluated on a challenging dataset of twelve dynamic American Sign Language (ASL) gestures.
Comprehension of drug toxicity: Software and databases

Quantitative structure-property/activity relationships (QSPRs/QSARs) are a tool (in silico) to rapidly predict various endpoints in general, and drug toxicity in particular. However, this dynamic evolution of experimental data (expansion of existing experimental data on drugs toxicity) leads to the problem of critical estimation of the data. The carcinogenicity, mutagenicity, liver effects and cardiac toxicity should be evaluated as the most important aspects of the drug toxicity. The toxicity is a multidimensional phenomenon. It is apparent that the main reasons for the increase in applications of in silico prediction of toxicity include the following: (i) the need to reduce animal testing; (ii) computational models provide reliable toxicity prediction; (iii) development of legislation that is related to use of new substances; (iv) filling data gaps; (v) reduction of cost and time; (vi) designing of new compounds; (vii) advancement of understanding of biology and chemistry. This mini-review provides analysis of existing databases and software which are necessary for use of robust computational assessments and robust prediction of potential drug toxicities by means of in silico methods.
Lions and Tigers and Bears: Capturing Non-rigid, 3D, Articulated Shape from Images

Animals are widespread in nature and the analysis of their shape and motion is important in many fields and industries. Modeling 3D animal shape, however, is difficult because the 3D scanning methods used to capture human shape are not applicable to wild animals or natural settings. Consequently, we propose a method to capture the detailed 3D shape of animals from images alone. The articulated and deformable nature of animals makes this problem extremely challenging, particularly in unconstrained environments with moving and uncalibrated cameras. To make this possible, we use a strong prior model of articulated animal shape that we fit to the image data. We then deform the animal shape in a canonical reference pose such that it matches image evidence when articulated and projected into multiple images. Our method extracts significantly more 3D shape detail than previous methods and is able to model new species, including the shape of an extinct animal, using only a few video frames. Additionally, the projected 3D shapes are accurate enough to facilitate the extraction of a realistic texture map from multiple frames.
Generating Realistic Training Images Based on Tonality-Alignment Generative Adversarial Networks for Hand Pose Estimation

Hand pose estimation from a monocular RGB image is an important but challenging task. A main factor affecting its performance is the lack of a sufficiently large training dataset with accurate hand-keypoint annotations. In this work, we circumvent this problem by proposing an effective method for generating realistic hand poses, and show that state-of-the-art algorithms for hand pose estimation can be greatly improved by utilizing the generated hand poses as training data. Specifically, we first adopt an augmented reality (AR) simulator to synthesize hand poses with accurate hand-keypoint labels. Although the synthetic hand poses come with precise joint labels, eliminating the need of manual annotations, they look unnatural and are not the ideal training data. To produce more realistic hand poses, we propose to blend a synthetic hand pose with a real background, such as arms and sleeves. To this end, we develop tonality-alignment generative adversarial networks (TAGANs), which align the tonality and color distributions between synthetic hand poses and real backgrounds, and can generate high quality hand poses. We evaluate TAGAN on three benchmarks, including the RHP, STB, and CMUPS hand pose datasets. With the aid of the synthesized poses, our method performs favorably against the state-ofthe-arts in both 2D and 3D hand pose estimations.
Industrial-Size Scheduling with ASP+CP

Answer Set Programming (ASP) combines a powerful, theoretically principled knowledge representation formalism and powerful solvers. To improve efficiency of computation on certain classes of problems, researchers have recently developed hybrid languages and solvers, combining ASP with language constructs and solving techniques from Constraint Programming (CP). The resulting ASP+CP solvers exhibit remarkable performance on “toy” problems. To the best of our knowledge, however, no hybrid ASP+CP language and solver have been used in practical, industrial-size applications. In this paper, we report on the first such successful application, consisting of the use of the hybrid ASP+CP system ezcsp to solve sophisticated industrial-size scheduling problems.
IC Design and Measurement of an Inductorless 48 V DC/DC Converter in Low-Cost CMOS Technology Facing Harsh Environments

With reference to 48-V systems, used in electric and hybrid vehicles and for telecom and networking power supply, this paper presents the circuit/transistor-level design, implementation, and test of an inductorless dc/dc converter. Its architecture is a cascade of three switched-capacitor converters with step-up/down capabilities plus linear converters, working in parallel at the end of the cascade. It provides multiple regulated voltages, which are isolated from input failures. The design also includes on-chip control unit and a serial interface toward an external host. Experimental tests carried out on the chip, fabricated in 0.35- $\mu \text{m}$  CMOS technology, proves that the design is suitable to face harsh operating environments, including the 48-V automotive one. Compared with the state-of-art, the proposed design stands for its wide input voltage regulation range, load/line regulation, power supply rejection ratio, and low electromagnetic interference emissions.
Computational modeling of synthetic microbial biofilms.

Microbial biofilms are complex, self-organized communities of bacteria, which employ physiological cooperation and spatial organization to increase both their metabolic efficiency and their resistance to changes in their local environment. These properties make biofilms an attractive target for engineering, particularly for the production of chemicals such as pharmaceutical ingredients or biofuels, with the potential to significantly improve yields and lower maintenance costs. Biofilms are also a major cause of persistent infection, and a better understanding of their organization could lead to new strategies for their disruption. Despite this potential, the design of synthetic biofilms remains a major challenge, due to the complex interplay between transcriptional regulation, intercellular signaling, and cell biophysics. Computational modeling could help to address this challenge by predicting the behavior of synthetic biofilms prior to their construction; however, multiscale modeling has so far not been achieved for realistic cell numbers. This paper presents a computational method for modeling synthetic microbial biofilms, which combines three-dimensional biophysical models of individual cells with models of genetic regulation and intercellular signaling. The method is implemented as a software tool (CellModeller), which uses parallel Graphics Processing Unit architectures to scale to more than 30,000 cells, typical of a 100 μm diameter colony, in 30 min of computation time.
Area Preserving Brain Mapping

Brain mapping transforms the brain cortical surface to canonical planar domains, which plays a fundamental role in morphological study. Most existing brain mapping methods are based on angle preserving maps, which may introduce large area distortions. This work proposes an area preserving brain mapping method based on Monge-Brenier theory. The brain mapping is intrinsic to the Riemannian metric, unique, and diffeomorphic. The computation is equivalent to convex energy minimization and power Voronoi diagram construction. Comparing to the existing approaches based on Monge-Kantorovich theory, the proposed one greatly reduces the complexity (from n2 unknowns to n ), and improves the simplicity and efficiency. Experimental results on caudate nucleus surface mapping and cortical surface mapping demonstrate the efficacy and efficiency of the proposed method. Conventional methods for caudate nucleus surface mapping may suffer from numerical instability, in contrast, current method produces diffeomorpic mappings stably. In the study of cortical surface classification for recognition of Alzheimer's Disease, the proposed method outperforms some other morphometry features.
Extracting problem solved concepts from patent documents

In this paper, we report the results for the experiments we carried out to automatically extract "problem solved concepts" from a patent document. We introduce two approaches for finding important information in a patent document. The main focus of our work is to devise methods that can efficiently find the problems an invention solves, as this can help in searching for the prior art and can be used as a mechanism for relevance feedback. We have used software and business process patents to carry out our studies.
A 63W 14MHz Class-E amplifier for wireless power transmission

This paper presents a 14MHz Class-E power amplifier to be used for wireless power transmission. The Class-E power amplifier was built to consider the VSWR and the frequency bandwidth. Tw o kinds of circuits were designed: the high and low quality factor amplifiers. The low quality factor amplifier is confirmed to have larger bandwidth than the high quality factor amplifier. It has also possessed less sensitive characteristics. Therefore, the low quality factor amplifier circuit was adopted and tested. The effect of gate driving input source is studied. The efficiency of the Class-E amplifier reaches 85.5% at 63W.
A review of antenna design and development for Indian regional navigational satellite system

The Indian regional navigational satellite system (IRNSS) developed by the Indian Space Research Organization (ISRO) is an autonomous regional satellite navigation system which is under the complete control of Government of India. The requirement of indigenous regional navigational satellite system is driven by the fact that access to Global Navigation Satellite System, like GPS is not guaranteed in hostile situations. Design of IRNSS antenna at user segment is mandatory for Indian region. The IRNSS satellites will be placed at a higher geostationary orbit to have a larger signal footprint and minimum satellites for regional mapping. IRNSS signals will consist of a Special Positioning Service and a Precision Service. Both will be carried on L5 band (1176.45 MHz) and S band (2492.08 MHz). As it is be long range communication system needs high frequency signals and high gain receiving antennas. So, different antennas can be designed to enhance the gain and directivity. Based on this the rectangular Microstrip patch antenna, planar array of patch antennas and planar, wideband feed slot spiral antenna are designed by using various software simulations. Use of array of spiral antennas will increase the gain position. Spiral antennas are comparatively small size and these antennas with its windings making it an extremely small structure. The performance of the designed antennas was compared in terms of return loss, bandwidth, directivity, radiation pattern and gain. In this paper, Review results of all antennas designed for IRNSS have presented.
Optical Flow Estimation Using a Spatial Pyramid Network

We learn to compute optical flow by combining a classical spatial-pyramid formulation with deep learning. This estimates large motions in a coarse-to-fine approach by warping one image of a pair at each pyramid level by the current flow estimate and computing an update to the flow. Instead of the standard minimization of an objective function at each pyramid level, we train one deep network per level to compute the flow update. Unlike the recent FlowNet approach, the networks do not need to deal with large motions, these are dealt with by the pyramid. This has several advantages. First, our Spatial Pyramid Network (SPyNet) is much simpler and 96% smaller than FlowNet in terms of model parameters. This makes it more efficient and appropriate for embedded applications. Second, since the flow at each pyramid level is small (
Purple Feed: Identifying High Consensus News Posts on Social Media

Although diverse news stories are actively posted on social media, readers often focus on the news which reinforces their pre-existing views, leading to 'filter bubble' effects. To combat this, some recent systems expose and nudge readers toward stories with different points of view. One example is the Wall Street Journal's 'Blue Feed, Red Feed' system, which presents posts from biased publishers on each side of a topic. However, these systems have had limited success. We present a complementary approach which identifies high consensus 'purple' posts that generate similar reactions from both 'blue' and 'red' readers. We define and operationalize consensus for news posts on Twitter in the context of US politics. We show that high consensus posts can be identified and discuss their empirical properties. We present a method for automatically identifying high and low consensus news posts on Twitter, which can work at scale across many publishers. To do this, we propose a novel category of audience leaning based features, which we show are well suited to this task. Finally, we present our 'Purple Feed' system which highlights high consensus posts from publishers on both sides of the political spectrum.
Soft video parsing by label distribution learning

In this paper, we tackle the problem of segmenting out a sequence of actions from videos. The videos contain background and actions which are usually composed of ordered sub-actions. We refer the sub-actions and the background as semantic units. Considering the possible overlap between two adjacent semantic units, we propose a bidirectional sliding window method to generate the label distributions for various segments in the video. The label distribution covers a certain number of semantic unit labels, representing the degree to which each label describes the video segment. The mapping from a video segment to its label distribution is then learned by a Label Distribution Learning (LDL) algorithm. Based on the LDL model, a soft video parsing method with segmental regular grammars is proposed to construct a tree structure for the video. Each leaf of the tree stands for a video clip of background or sub-action. The proposed method shows promising results on the THUMOS’14, MSR-II and UCF101 datasets and its computational complexity is much less than the compared state-of-the-art video parsing method.
A social media platform in higher education

This paper reports on the successful use of Graasp, a social media platform, by university students for their collaborative work. Graasp features a number of innovations, such as administrator-free creation of collaborative spaces, a context-aware recommendation and privacy management. In the context of a EU-funded project involving large test beds, we have been able to extend this platform with lightweight tools (widgets) aimed for learning and competence development and to validate its usefulness in a collaborative learning context.
A Publish/Subscribe Protocol for Event-Driven Communications in the Internet of Things

Internet of Things is a futuristic vision of networked objects able to take smart decisions and to assist humans in their daily activities. The founding factor of such a vision is the capacity of objects to exchange data by means of wireless networks, and the applied communication pattern is event-driven and multicast. Publish/subscribe services are a promising communication technology for building solutions according to the Internet of Things' vision. The available solutions consist in the adaptation of widely-known products for publish/subscribe services taken from other contexts and application domains, without considering the key peculiarities of the Internet of Things. Our driving idea is to consider beaconing as the basic communication means, and to build a publish/subscribe service based on it. Our solution can be used stand-alone for event-driven communications and/or integrated within standardized protocols to provide basic communication capabilities. We present a preliminary set of experiments showing the efficiency of our solution.
Topical Application of Oleuropein Induces Anagen Hair Growth in Telogen Mouse Skin

Oleuropein promoted cultured human follicle dermal papilla cell proliferation and induced LEF1 and Cyc-D1 mRNA expression and β-catenin protein expression in dermal papilla cells. Nuclear accumulation of β-catenin in dermal papilla cells was observed after oleuropein treatment. Topical application of oleuropein (0.4 mg/mouse/day) to C57BL/6N mice accelerated the hair-growth induction and increased the size of hair follicles in telogenic mouse skin. The oleuropein-treated mouse skin showed substantial upregulation of Wnt10b, FZDR1, LRP5, LEF1, Cyc-D1, IGF-1, KGF, HGF, and VEGF mRNA expression and β-catenin protein expression.



These results demonstrate that topical oleuroepin administration induced anagenic hair growth in telogenic C57BL/6N mouse skin. The hair-growth promoting effect of oleuropein in mice appeared to be associated with the stimulation of the Wnt10b/β-catenin signaling pathway and the upregulation of IGF-1, KGF, HGF, and VEGF gene expression in mouse skin tissue.
SEF Working paper : 12 / 2011 September 2011 The stability of big-five personality traits

We use a large, nationally-representative sample of working-age adults to demonstrate that personality (as measured by the Big Five) is stable over a four-year period. Average personality changes are small and do not vary substantially across age groups. Intra-individual personality change is generally unrelated to experiencing adverse life events and is unlikely to be economically meaningful. Like other non-cognitive traits, personality can be modeled as a stable input into many economic decisions.
Do We Need More Training Data or Better Models for Object Detection?

Datasets for training object recognition systems are steadily growing in size. This paper investigates the question of whether existing detectors will continue to improve as data grows, or if models are close to saturating due to limited model complexity and the Bayes risk associated with the feature spaces in which they operate. We focus on the popular paradigm of scanning-window templates defined on oriented gradient features, trained with discriminative classifiers. We investigate the performance of mixtures of templates as a function of the number of templates (complexity) and the amount of training data. We find that additional data does help, but only with correct regularization and treatment of noisy examples or “outliers” in the training data. Surprisingly, the performance of problem domain-agnostic mixture models appears to saturate quickly (∼10 templates and ∼100 positive training examples per template). However, compositional mixtures (implemented via composed parts) give much better performance because they share parameters among templates, and can synthesize new templates not encountered during training. This suggests there is still room to improve performance with linear classifiers and the existing feature space by improved representations and learning algorithms.
Distribution of the traction return current in AT electric railway systems

The problem of determination of the accurate distribution of the return current in AT (autotransformer) electric traction systems (supplied at 2/spl times/25 kV) for High Speed Railways is considered. The path of the traction return current flowing from the rolling stock axles back to the supply (i.e., substation) is composed of the traction rails and additional earth potential conductors. The overhead supply conductors in contact with the train pantograph are connected to a symmetrical circuit (the feeder) with the purpose of current balancing. This arrangement and its influence on current to earth are considered: the return current divides among rails (as signalling disturbing current) and earth, depending on the value of the electric parameters of the system and the earth and on the circuit arrangement and on the relative position of system devices. The amplitude (as a percentage of the total return current) of the disturbing current may be high enough to cause interference to signalling. This work investigates the behavior of the return current in AT electric railway systems, on the basis of a reference system for the variation of the most important electrical parameters.