nils-herrmann/clean_scirepeval huggingface dataset:
Disfluencies and human language comprehension

Spoken language contains disfluencies, which include editing terms such as uh and um as well as repeats and corrections. In less than ten years the question of how disfluencies are handled by the human sentence comprehension system has gone from virtually ignored to a topic of major interest in computational linguistics and psycholinguistics. We discuss relevant empirical findings and describe a computational model that captures how disfluencies influence parsing and comprehension. The research reviewed shows that the parser, which presumably evolved to handle conversations, deals with disfluencies in a way that is efficient and linguistically principled. The success of this research program reinforces the current trend in cognitive science to view cognitive mechanisms as adaptations to real-world constraints and challenges.
Location Privacy via Differential Private Perturbation of Cloaking Area

The increasing use of mobile devices has triggered the development of location based services (LBS). By providing location information to LBS, mobile users can enjoy variety of useful applications utilizing location information, but might suffer the troubles of private information leakage. Location information of mobile users needs to be kept secret while maintaining utility to achieve desirable service quality. Existing location privacy enhancing techniques based on K-anonymity and Hilbertcurve cloaking area generation showed advantages in privacy protection and service quality but disadvantages due to the generation of large cloaking areas that makes query processing and communication less effective. In this paper we propose a novel location privacy preserving scheme that leverages some differential privacy based notions and mechanisms to publish the optimal size cloaking areas from multiple rotated and shifted versions of Hilbert curve. With experimental results, we show that our scheme significantly reduces the average size of cloaking areas compared to previous Hilbert curve method. We also show how to quantify adversary's ability to perform an inference attack on user location data and how to limit adversary's success rate under a designed threshold.
Detecting Overlapping Community Structures in Networks

Community structure has been recognized as an important statistical feature of networked systems over the past decade. A lot of work has been done to discover isolated communities from a network, and the focus was on developing of algorithms with high quality and good performance. However, there is less work done on the discovery of overlapping community structure, even though it could better capture the nature of network in some real-world applications. For example, people are always provided with varying characteristics and interests, and are able to join very different communities in their social network. In this context, we present a novel overlapping community structures detecting algorithm which first finds the seed sets by the spectral partition and then extends them with a special random walks technique. At every expansion step, the modularity function Q is chosen to measure the expansion structures. The function has become one of the popular standards in community detecting and is defined in Newman and Girvan ( 2004). We also give a theoretic analysis to the whole expansion process and prove that our algorithm gets the best community structures greedily. Extensive experiments are conducted in real-world networks with various sizes. The results show that overlapping is important to find the complete community structures and our method outperforms the C-means in quality.
Similar researcher search in academic environments

Entity search is an emerging IR and NLP task that involves the retrieval of entities of a specific type in response to a query. We address the similar researcher search" or the "researcher recommendation" problem, an instance of similar entity search" for the academic domain. In response to a researcher name' query, the goal of a researcher recommender system is to output the list of researchers that have similar expertise as that of the queried researcher. We propose models for computing similarity between researchers based on expertise profiles extracted from their publications and academic homepages. We provide results of our models for the recommendation task on two publicly-available datasets. To the best of our knowledge, we are the first to address content-based researcher recommendation in an academic setting and demonstrate it for Computer Science via our system, ScholarSearch.
Buprenorphine – an attractive opioid with underutilized potential in treatment of chronic pain

Despite proven clinical utility, buprenorphine has not been used widely for the treatment of chronic pain. Questions about "ceiling effect" or bell-shaped curve observed for analgesia in preclinical studies and potential withdrawal issues on combining with marketed μ-agonists continue to hinder progress in expanding full potential of buprenorphine in the treatment of cancer and noncancer pain. Mounting evidence from clinical studies and conclusions drawn by a panel of experts strongly support superior safety and efficacy profile of buprenorphine vs marketed opioids. No ceiling on analgesic effect has been reported in clinical studies. The receptor pharmacology and pharmacokinetics profile of buprenorphine is complex but unique and contributes to its distinct safety and efficacy. The buprenorphine pharmacology also allows it to be combined with other μ-receptor opioids for additivity in efficacy. Transdermal delivery products of buprenorphine have been preferred choices for the management of pain but new delivery options are under investigation for the treatment of both opioid dependence and chronic pain.
Multiple kernel clustering based on centered kernel alignment

Multiple kernel clustering (MKC), which performs kernel-based data fusion for data clustering, is an emerging topic. It aims at solving clustering problems with multiple cues. Most MKC methods usually extend existing clustering methods with a multiple kernel learning (MKL) setting. In this paper, we propose a novel MKC method that is different from those popular approaches. Centered kernel alignment—an effective kernel evaluation measure—is employed in order to unify the two tasks of clustering and MKL into a single optimization framework. To solve the formulated optimization problem, an efficient two-step iterative algorithm is developed. Experiments on several UCI datasets and face image datasets validate the effectiveness and efficiency of our MKC algorithm.
Facial Action Unit Detection Using Attention and Relation Learning

Attention mechanism has recently attracted increasing attentions in the area of facial action unit (AU) detection. By finding the region of interest (ROI) of each AU with the attention mechanism, AU related local features can be captured. Most existing attention based AU detection works use prior knowledge to generate fixed attentions or refine the predefined attentions within a small range, which limits their capacity to model various AUs. In this paper, we propose a novel end-to-end weakly-supervised attention and relation learning framework for AU detection with only AU labels, which has not been explored before. In particular, multi-scale features shared by each AU are learned firstly, and then both channel-wise attentions and spatial attentions are learned to select and extract AU related local features. Moreover, pixellevel relations for AUs are further captured to refine spatial attentions so as to extract more relevant local features. Extensive experiments on BP4D and DISFA benchmarks demonstrate that our framework (i) outperforms the state-of-the-art methods for AU detection, and (ii) can find the ROI of each AU and capture the relations among AUs adaptively.
Mitochondrial defects associated with β-alanine toxicity: relevance to hyper-beta-alaninemia

Hyper-beta-alaninemia is a rare metabolic condition that results in elevated plasma and urinary β-alanine levels and is characterized by neurotoxicity, hypotonia, and respiratory distress. It has been proposed that at least some of the symptoms are caused by oxidative stress; however, only limited information is available on the mechanism of reactive oxygen species generation. The present study examines the hypothesis that β-alanine reduces cellular levels of taurine, which are required for normal respiratory chain function; cellular taurine depletion is known to reduce respiratory function and elevate mitochondrial superoxide generation. To test the taurine hypothesis, isolated neonatal rat cardiomyocytes and mouse embryonic fibroblasts were incubated with medium lacking or containing β-alanine. β-alanine treatment led to mitochondrial superoxide accumulation in conjunction with a decrease in oxygen consumption. The defect in β-alanine-mediated respiratory function was detected in permeabilized cells exposed to glutamate/malate but not in cells utilizing succinate, suggesting that β-alanine leads to impaired complex I activity. Taurine treatment limited mitochondrial superoxide generation, supporting a role for taurine in maintaining complex I activity. Also affected by taurine is mitochondrial morphology, as β-alanine-treated fibroblasts undergo fragmentation, a sign of unhealthy mitochondria that is reversed by taurine treatment. If left unaltered, β-alanine-treated fibroblasts also undergo mitochondrial apoptosis, as evidenced by activation of caspases 3 and 9 and the initiation of the mitochondrial permeability transition. Together, these data show that β-alanine mediates changes that reduce ATP generation and enhance oxidative stress, factors that contribute to heart failure.
ContainerCloudSim: An environment for modeling and simulation of containers in cloud data centers

Containers are increasingly gaining popularity and becoming one of the major deployment models in cloud environments. To evaluate the performance of scheduling and allocation policies in containerized cloud data centers, there is a need for evaluation environments that support scalable and repeatable experiments. Simulation techniques provide repeatable and controllable environments, and hence, they serve as a powerful tool for such purpose. This paper introduces ContainerCloudSim, which provides support for modeling and simulation of containerized cloud computing environments. We developed a simulation architecture for containerized clouds and implemented it as an extension of CloudSim. We described a number of use cases to demonstrate how one can plug in and compare their container scheduling and provisioning policies in terms of energy efficiency and SLA compliance. Our system is highly scalable as it supports simulation of large number of containers, given that there are more containers than virtual machines in a data center.
Integrative Computational Network Analysis Reveals Site-Specific Mediators of Inflammation in Alzheimer's Disease

Alzheimer's disease (AD) is a major neurodegenerative disease and is one of the most common cause of dementia in older adults. Among several factors, neuroinflammation is known to play a critical role in the pathogenesis of chronic neurodegenerative diseases. In particular, studies of brains affected by AD show a clear involvement of several inflammatory pathways. Furthermore, depending on the brain regions affected by the disease, the nature and the effect of inflammation can vary. Here, in order to shed more light on distinct and common features of inflammation in different brain regions affected by AD, we employed a computational approach to analyze gene expression data of six site-specific neuronal populations from AD patients. Our network based computational approach is driven by the concept that a sustained inflammatory environment could result in neurotoxicity leading to the disease. Thus, our method aims to infer intracellular signaling pathways/networks that are likely to be constantly activated or inhibited due to persistent inflammatory conditions. The computational analysis identified several inflammatory mediators, such as tumor necrosis factor alpha (TNF-a)-associated pathway, as key upstream receptors/ligands that are likely to transmit sustained inflammatory signals. Further, the analysis revealed that several inflammatory mediators were mainly region specific with few commonalities across different brain regions. Taken together, our results show that our integrative approach aids identification of inflammation-related signaling pathways that could be responsible for the onset or the progression of AD and can be applied to study other neurodegenerative diseases. Furthermore, such computational approaches can enable the translation of clinical omics data toward the development of novel therapeutic strategies for neurodegenerative diseases.
Compressed double-array tries for string dictionaries supporting fast lookup

A string dictionary is a basic tool for storing a set of strings in many kinds of applications. Recently, many applications need space-efficient dictionaries to handle very large datasets. In this paper, we propose new compressed string dictionaries using improved double-array tries. The double-array trie is a data structure that can implement a string dictionary supporting extremely fast lookup of strings, but its space efficiency is low. We introduce approaches for improving the disadvantage. From experimental evaluations, our dictionaries can provide the fastest lookup compared to state-of-the-art compressed string dictionaries. Moreover, the space efficiency is competitive in many cases.
A Six-Axis MEMS Force–Torque Sensor With Micro-Newton and Nano-Newtonmeter Resolution

This paper describes the design of a six-axis microelectromechanical systems (MEMS) force-torque sensor. A movable body is suspended by flexures that allow deflections and rotations along the x-, y-, and z-axes. The orientation of this movable body is sensed by seven capacitors. Transverse sensing is used for all capacitors, resulting in a high sensitivity. A batch fabrication process is described as capable of fabricating these multiaxis sensors with a high yield. The force sensor is experimentally investigated, and a multiaxis calibration method is described. Measurements show that the resolution is on the order of a micro-Newton and nano-Newtonmeter. This is the first six-axis MEMS force sensor that has been successfully developed.
A Robust Real-Time Automatic License Plate Recognition Based on the YOLO Detector

Automatic License Plate Recognition (ALPR) has been a frequent topic of research due to many practical applications. However, many of the current solutions are still not robust in real-world situations, commonly depending on many constraints. This paper presents a robust and efficient ALPR system based on the state-of-the-art YOLO object detector. The Convolutional Neural Networks (CNNs) are trained and finetuned for each ALPR stage so that they are robust under different conditions (e.g., variations in camera, lighting, and background). Specially for character segmentation and recognition, we design a two-stage approach employing simple data augmentation tricks such as inverted License Plates (LPs) and flipped characters. The resulting ALPR approach achieved impressive results in two datasets. First, in the SSIG dataset, composed of 2,000 frames from 101 vehicle videos, our system achieved a recognition rate of 93.53% and 47 Frames Per Second (FPS), performing better than both Sighthound and OpenALPR commercial systems (89.80% and 93.03%, respectively) and considerably outperforming previous results (81.80%). Second, targeting a more realistic scenario, we introduce a larger public dataset1 dataset, designed to ALPR. This dataset contains 150 videos and 4,500 frames captured when both camera and vehicles are moving and also contains different types of vehicles (cars, motorcycles, buses and trucks). In our proposed dataset, the trial versions of commercial systems achieved recognition rates below 70%. On the other hand, our system performed better, with recognition rate of 78.33% and 35 FPS.The UFPR-ALPR dataset is publicly available to the research community at https://web.inf.ufpr.br/vri/databases/ufpr-alpr/ subject to privacy restrictions.
Tri-Branch Vein Structure Assisted Finger Vein Recognition

In the template matching of finger vein recognition, the probe will be accepted if the number of its overlapped vein points with the enrolled user is larger than the predefined threshold. However, the acceptance may be false owing to ignoring the structure of the vein pattern. We find that local vein branches near the bifurcation point of vein pattern vary largely between the imposter images. So, this paper tries to explore this kind of local vein structure to improve the recognition performance of the template matching. The bifurcation point and its local vein branches, named tri-branch vein structure, are extracted from the vein pattern, and fused with the whole vein pattern by a user-specific threshold-based filter framework. The experimental results on two public databases prove the effectiveness of the proposed framework for improving the performance of vein pattern-based finger vein recognition.
Stimulus relevance modulates contrast adaptation in visual cortex

A general principle of sensory processing is that neurons adapt to sustained stimuli by reducing their response over time. Most of our knowledge on adaptation in single cells is based on experiments in anesthetized animals. How responses adapt in awake animals, when stimuli may be behaviorally relevant or not, remains unclear. Here we show that contrast adaptation in mouse primary visual cortex depends on the behavioral relevance of the stimulus. Cells that adapted to contrast under anesthesia maintained or even increased their activity in awake naïve mice. When engaged in a visually guided task, contrast adaptation re-occurred for stimuli that were irrelevant for solving the task. However, contrast adaptation was reversed when stimuli acquired behavioral relevance. Regulation of cortical adaptation by task demand may allow dynamic control of sensory-evoked signal flow in the neocortex.
The Obstacles of Sustainable Business Model Innovations

The continuous innovation process of the Information and Communication Technology (ICT) sector shape the way businesses redefine their business models. Though, current drivers of innovation processes focus solely on a technical dimension, while disregarding social and environmental drivers. However, examples like Nokia, Yahoo or Hewlett-Packard show that even though a profitable business model exists, a sound strategic innovation process is needed to remain profitable in the long term. A sustainable business model innovation demands the incorporation of all dimensions of the triple bottom line. Nevertheless, current management processes do not take the responsible steps to remain sustainable and keep being in denial of the evolutionary direction in which the markets develop, because the effects are not visible in short term. The implications are of substantial effect and can bring the foundation of the company’s business model in danger. This work evaluates the decision process that lets businesses decide in favor of un-sustainable changes and points out the barriers that prevent the development towards a sustainable business model that takes the new balance of forces into account.
ECMdd: Evidential c-medoids clustering with multiple prototypes

In this work, a new prototype-based clustering method named Evidential C-Medoids (ECMdd), which belongs to the family of medoid-based clustering for proximity data, is proposed as an extension of Fuzzy C-Medoids (FCMdd) on the theoretical framework of belief functions. In the application of FCMdd and original ECMdd, a single medoid (prototype), which is supposed to belong to the object set, is utilized to represent one class. For the sake of clarity, this kind of ECMdd using a single medoid is denoted by sECMdd. In real clustering applications, using only one pattern to capture or interpret a class may not adequately model different types of group structure and hence limits the clustering performance. In order to address this problem, a variation of ECMdd using multiple weighted medoids, denoted by wECMdd, is presented. Unlike sECMdd, in wECMdd objects in each cluster carry various weights describing their degree of representativeness for that class. This mechanism enables each class to be represented by more than one object. Experimental results in synthetic and real data sets clearly demonstrate the superiority of sECMdd and wECMdd. Moreover, the clustering results by wECMdd can provide richer information for the inner structure of the detected classes with the help of prototype weights.
FROM POINT SAMPLES TO SURFACES-ON MESHING AND ALTERNATIVES

Terrestrial laser scanners deliver a dense point-wise sampling of an object’s surface. For many applications a surface-like reconstruction is required. The most typical example is the visualization of the scanned data. Traditional approaches use meshing algorithms to reconstruct and triangulate the surface represented by the points. Especially in cultural heritage, where complex objects with delicate structures are recorded in highly detailed scans, this process is not without problems. Often long and tedious manual clean-up procedures are required to achieve satisfactory results. After summarizing our experience with current meshing technology we therefore explore alternative approaches for surface reconstruction. An alternative approach presented within this paper is point splatting. We have developed an algorithm to compute a suitable surfel representation directly from the raw laser scanner data. This results in a speedy and fully automated procedure for surface reconstruction. The properties of the different approaches for surface reconstruction are discussed considering a practical example from the field of cultural heritage. The Panagia Kera in Kritsa near Agios Nikolaos on the island of Crete was chosen as a suitable example.
Kniest Dysplasia in a Girl Aged 6 Years

We report clinical, radiological and anthropological findings from the first Czech patient with Kniest dysplasia whose radio-clinical diagnosis was confirmed by DNA studies. Kniest dysplasia is an inherited disorder associated with defects in type of collagen II with specific clinical and characteristic radiographic findings. Our affected girl had dysmorphic and radiographic features consistent with Kniest disease: cleft palate, hip dysplasia, dysmorphic flat face, short trunk and extremities, spine deformity, platyspondyly, short and broad femoral necks. Mental development was normal. Body height was below norm (-3.2 SD) and muscular hypotrophy of the extremities and trunk was noticeable. Molecular studies supported the diagnosis of Kniest disease by identification of the COL2A1 mutation (c.1023+1G>A) in intron 16.
Sexuality, substance use, and susceptibility to victimization: risk for rape and sexual coercion in a prospective study of college women.

An 8-month prospective study examined behavioral, personality, and psychological variables thought to increase vulnerability for college women's experience of rape and verbal sexual coercion. Participants were 276 college women who completed self-report surveys. During 1 academic year, 9.5% of women were raped and 11.7% reported verbal sexual coercion. Elevated levels of sexual concerns, dysfunctional sexual behavior, and impaired self-reference were associated with both verbal sexual coercion and rape. Alcohol and marijuana use increased risk only for rape, whereas self-criticism and depression increased risk only for verbal coercion. Findings suggest that multiple aspects of sexuality, such as shame regarding sexuality and using sex to meet nonsexual needs, may increase risk for both types of sexual victimization. Results support conclusions that rape and verbal sexual coercion have both shared and unique risk factors. Implications for future research and intervention programs are discussed.