nils-herrmann/clean_scirepeval huggingface dataset:
Keyword Search over Distributed Graphs with Compressed Signature

Graph keyword search has drawn many research interests, since graph models can generally represent both structured and unstructured databases and keyword searches can extract valuable information for users without the knowledge of the underlying schema and query language. In practice, data graphs can be extremely large, e.g., a Web-scale graph containing billions of vertices. The state-of-the-art approaches employ centralized algorithms to process graph keyword searches, and thus they are infeasible for such large graphs, due to the limited computational power and storage space of a centralized server. To address this problem, we investigate keyword search for Web-scale graphs deployed in a distributed environment. We first give a naive search algorithm to answer the query efficiently. However, the naive search algorithm uses a flooding search strategy that incurs large time and network overhead. To remedy this shortcoming, we then propose a signature-based search algorithm. Specifically, we design a vertex signature that encodes the shortest-path distance from a vertex to any given keyword in the graph. As a result, we can find query answers by exploring fewer paths, so that the time and communication costs are low. Moreover, we reorganize the graph data in the cluster after its initial random partitioning so that the signature-based techniques are more effective. Finally, our experimental results demonstrate the feasibility of our proposed approach in performing keyword searches over Web-scale graph data.
Using a generalized additive model with autoregressive terms to study the effects of daily temperature on mortality

Generalized Additive Model (GAM) provides a flexible and effective technique for modelling nonlinear time-series in studies of the health effects of environmental factors. However, GAM assumes that errors are mutually independent, while time series can be correlated in adjacent time points. Here, a GAM with Autoregressive terms (GAMAR) is introduced to fill this gap.



Parameters in GAMAR are estimated by maximum partial likelihood using modified Newton's method, and the difference between GAM and GAMAR is demonstrated using two simulation studies and a real data example. GAMM is also compared to GAMAR in simulation study 1.



In the simulation studies, the bias of the mean estimates from GAM and GAMAR are similar but GAMAR has better coverage and smaller relative error. While the results from GAMM are similar to GAMAR, the estimation procedure of GAMM is much slower than GAMAR. In the case study, the Pearson residuals from the GAM are correlated, while those from GAMAR are quite close to white noise. In addition, the estimates of the temperature effects are different between GAM and GAMAR.



GAMAR incorporates both explanatory variables and AR terms so it can quantify the nonlinear impact of environmental factors on health outcome as well as the serial correlation between the observations. It can be a useful tool in environmental epidemiological studies.
The NMDA receptor antagonist memantine as a symptomatological and neuroprotective treatment for Alzheimer's disease: preclinical evidence.

There is increasing evidence for the involvement of glutamate-mediated neurotoxicity in the pathogenesis of Alzheimer's disease (AD). We suggest that glutamate receptors of the N-methyl-D-aspartate (NMDA) type are overactivated in a tonic rather than a phasic manner in this disorder. This continuous mild activation may lead to neuronal damage and impairment of synaptic plasticity (learning). It is likely that under such conditions Mg(2+) ions, which block NMDA receptors under normal resting conditions, can no longer do so. We found that overactivation of NMDA receptors using a direct agonist or a decrease in Mg(2+) concentration produced deficits in synaptic plasticity (in vivo: passive avoidance test and/or in vitro: LTP in the CA1 region). In both cases, memantine-an uncompetitive NMDA receptor antagonists with features of an 'improved' Mg(2+) (voltage-dependency, kinetics, affinity)-attenuated this deficit. Synaptic plasticity was restored by therapeutically-relevant concentrations of memantine (1 microM). Moreover, doses leading to similar brain/serum levels provided neuroprotection in animal models relevant for neurodegeneration in AD such as neurotoxicity produced by inflammation in the NBM or beta-amyloid injection to the hippocampus. As such, if overactivation of NMDA receptors is present in AD, memantine would be expected to improve both symptoms (cognition) and to slow down disease progression because it takes over the physiological function of magnesium.
Expression of Toll-Like Receptors in Verruca and Molluscum Contagiosum

Recent studies indicate that several Toll-like receptors (TLRs) are implicated in recognizing viral structures and instigating immune responses against viral infections. The aim of this study is to examine the expression of TLRs and proinflammatory cytokines in viral skin diseases such as verruca vulgaris (VV) and molluscum contagiosum (MC). Reverse transcription-polymerase chain reaction and immunostaining of skin samples were performed to determine the expression of specific antiviral and proinflammatory cytokines as well as 5 TLRs (TLR2, 3, 4, 7, and 9). In normal human skin, TLR2, 4, and 7 mRNA was constitutively expressed, whereas little TLR3 and 9 mRNA was detected. Compared to normal skin (NS), TLR3 and 9 mRNA was clearly expressed in VV and MC specimens. Likewise, immunohistochemistry indicated that keratinocytes in NS constitutively expressed TLR2, 4, and 7; however, TLR3 was rarely detected and TLR9 was only weakly expressed, whereas 5 TLRs were all strongly expressed on the epidermal keratinocytes of VV and MC lesions. In addition, the mRNA expression of IFN-beta and TNF-alpha was upregulated in the VV and MC samples. Immunohistochemistry indicated that IFN-beta and TNF-alpha were predominantly localized in the granular layer in the VV lesions and adjacent to the MC bodies. Our results indicated that VV and MC skin lesions expressed TLR3 and 9 in addition to IFN-beta and TNF-alpha. These viral-induced proinflammatory cytokines may play a pivotal role in cutaneous innate immune responses.
forensic dentistry: A look at forensic dentistry – Part 1: The role of teeth in the determination of human identity

Forensic dentistry can be defined in many ways. One of the more elegant definitions is simply that forensic dentistry represents the overlap between the dental and the legal professions. This two-part series presents the field of forensic dentistry by outlining two of the major aspects of the profession: human identification and bite marks. This first paper examines the use of the human dentition and surrounding structures to enable the identification of found human remains. Conventional and novel techniques are presented.
Human Drivers Based Active-Passive Model for Automated Lane Change

Lane change maneuver is a complicated maneuver, and incorrect maneuvering is an important reason for expressway accidents and fatalities. In this scenario, automated lane change has great potential to reduce the number of accidents. Previous research in this area, typically, focuses on the generation of an optimal lane change trajectory, while ignoring the human behavior model. To understand the human lane change behavior model, we carried out experiments on Japanese expressways. By analyzing the human-driver lane change data, we propose a two-segment lane change model that mimics the human-driver. We categorize the driving environment based on the observation grid and propose different lane change behaviors to handle the different scenarios. We develop an intuitive method to select the suitable lane change behavior, for a given scenario, using active (accelerate/decelerate) and passive (wait) information derived from the distance and related velocity (dx/dv) graph. Additionally, we also identify the most desirable and safe conditions for doing lane change based on the human driver preference data. We evaluated the proposed model by performing lane change simulations in the PreScan environment, while considering the vehicle motion/control model. The simulation results show the proposed model is able to handle complicated lane change scenarios with human driver-like performance.
Indoor localization via multi-modal sensing on smartphones

Indoor localization is of great importance to a wide range of applications in shopping malls, office buildings and public places. The maturity of computer vision (CV) techniques and the ubiquity of smartphone cameras hold promise for offering sub-meter accuracy localization services. However, pure CV-based solutions usually involve hundreds of photos and pre-calibration to construct image database, a labor-intensive overhead for practical deployment. We present ClickLoc, an accurate, easy-to-deploy, sensor-enriched, image-based indoor localization system. With core techniques rooted in semantic information extraction and optimization-based sensor data fusion, ClickLoc is able to bootstrap with few images. Leveraging sensor-enriched photos, ClickLoc also enables user localization with a single photo of the surrounding place of interest (POI) with high accuracy and short delay. Incorporating multi-modal localization with Manifold Alignment and Trapezoid Representation, ClickLoc not only localizes efficiently, but also provides image-assisted navigation. Extensive experiments in various environments show that the 80-percentile error is within 0.26m for POIs on the floor plan, which sheds light on sub-meter level indoor localization.
A comparative look at model transformation languages

Model-driven development is an emerging area in software development that provides a way to express system requirements and architecture at a high level of abstraction through models. It involves using these models as the primary artifacts during the development process. One aspect that is holding back MDD from more wide-spread adoption is the lack of a well established and easy way of performing model to model (M2M) transformations. We propose to explore and compare popular M2M model transformation languages in existence: EMT , Kermeta, and ATL. Each of these languages support transformation of Ecore models within the Eclipse Modeling Framework (EMF). We attempt to implement the same transformation rule on identical meta models in each of these languages to achieve the appropriate transformed model. We provide our observations in using each tool to perform the transformation and comment on each language/tool’s expressive power, ease of use, and modularity. We conclude by noting that ATL is our language / tool of choice because it strikes a balance between ease of use and expressive power and still allows for modularity. We believe this, in conjunction with ATL’s role in the official Eclipse M2M project will lead to widespread use of ATL and, hopefully, a step forward in M2M transformations.
Atypical on-, off- and neutral cells in the rostral ventromedial medulla oblongata in rat

It is generally assumed that the response pattern of on-, off- and neutral cells in the rostral ventromedial medulla (RVM) to noxious stimulation is independent of stimulation site. But recent studies have shown that a remarkable number of RVM neurons do not have whole-body receptive fields. These so-called atypical neurons were extracellularly recorded in lightly anaesthetized rats. The receptive fields to noxious thermal and mechanical stimulation applied to the tail, the extremities and the craniofacial region were determined in 57 RVM neurons. In 24 atypical off-cells, 12 on-cells and 21 neutral cells, the response pattern evoked by noxious pinch to the nose, forehead and ear most frequently differed from the responses to noxious tail heat. The modulatory effects of intravenously administered morphine were examined in 21 cells. In contrast to the general assumption that morphine activates off-cells, inhibits on-cells and has no effect on neutral cells, in atypical RVM neurons 5 of 6 off-cells, 2 of 6 on-cells and 5 out 9 neutral cells showed a different response pattern to systemical administration of morphine. The results show that a RVM cell classification that is exclusively based on the behaviour to noxious tail heat can neither sufficiently predict the response pattern to different noxious stimuli, especially in the craniofacial region, nor reliably predict the modulatory effect of morphine in RVM neurons. The fact that the neutral cells responded in an off or on manner to noxious stimulation different from noxious tail heat and that morphine modulated activity in many neutral cells suggests that these cells are probably subtypes of on- and off-cells.
PHOCNet: A Deep Convolutional Neural Network for Word Spotting in Handwritten Documents

In recent years, deep convolutional neural networks have achieved state of the art performance in various computer vision tasks such as classification, detection or segmentation. Due to their outstanding performance, CNNs are more and more used in the field of document image analysis as well. In this work, we present a CNN architecture that is trained with the recently proposed PHOC representation. We show empirically that our CNN architecture is able to outperform state-of-the-art results for various word spotting benchmarks while exhibiting short training and test times.
Multiobjective Robust Control via Youla Parametrization

Using Youla Parametrization and Linear Matrix Inequalities (LMI) a Multiobjective Robust Control (MRC) design for continuous linear time invariant (LTI) systems with bounded uncertainties is described. The design objectives can be a combination of H∞-, H2-performances, constraints on the control signal, etc.. Based on an initial stabilizing controller all stabilizing controllers for the uncertain system can be described by the Youla parametrization. Given this representation, all objectives can be formulated by independent Lyapunov functions, increasing the degree of freedom for the control design.
Natural Polyphenols for Prevention and Treatment of Cancer

There is much epidemiological evidence that a diet rich in fruits and vegetables could lower the risk of certain cancers. The effect has been attributed, in part, to natural polyphenols. Besides, numerous studies have demonstrated that natural polyphenols could be used for the prevention and treatment of cancer. Potential mechanisms included antioxidant, anti-inflammation as well as the modulation of multiple molecular events involved in carcinogenesis. The current review summarized the anticancer efficacy of major polyphenol classes (flavonoids, phenolic acids, lignans and stilbenes) and discussed the potential mechanisms of action, which were based on epidemiological, in vitro, in vivo and clinical studies within the past five years.
AWL: Turning Spatial Aliasing From Foe to Friend for Accurate WiFi Localization

Owing to great potential in smart home and human-computer interactive applications, WiFi indoor localization has attracted extensive attentions in the past several years. The state-of-the-art systems have successfully achieved decimeter-level accuracies. However, the high accuracy is acquired at the cost of dense access point (AP) deployment, employing large size of frequency bandwidths or special-purpose radar signals which are not compatible with existing WiFi protocol, limiting their practical deployments. This paper presents the design and implementation of AWL, an accurate indoor localization system that enables a single WiFi AP to achieve decimeter-level accuracy with only one channel hopping. The key enabler of the system is we novelly employ channel hopping to create virtual antennas, without the need of adding more antennas or physically move the antennas' positions for a larger antenna array. We successfully utilize the widely known "bad" spatial aliasing to improve the AoA estimation accuracy. A novel multipath suppression scheme is also proposed to combat the severe multipath issue indoors. We build a prototype of AWL on WARP software-defined radio platform. Comprehensive experiments manifest that AWL achieves a median localization accuracy of 38 cm in a rich multipath indoor environment with only a single AP equipped with 6 antennas. In a small scale area, AWL is able to accurately track a moving device's trajectory, enabling applications such as writing/drawing in the air.
A modified Chevrel technique for ventral hernia repair: long-term results of a single centre cohort

To evaluate the short- and long-term results after a modified Chevrel technique for midline incisional hernia repair, regarding surgical technique, hospital stay, wound complications, recurrence rate, and postoperative quality of life. These results will be compared to the literature derived reference values regarding the original and modified Chevrel techniques. In this large retrospective, single surgeon, single centre cohort all modified Chevrel hernia repairs between 2000 and 2012 were identified. Results were obtained by reviewing patients’ medical charts. Postoperative quality of life was measured using the Carolina Comfort Scale. A multi-database literature search was conducted to compare the results of our series to the literature based reference values. One hundred and fifty-five patients (84 male, 71 female) were included. Eighty patients (52%) had a large incisional hernia (width ≥ 10 cm) according the definition of the European Hernia Society. Fourteen patients (9%) underwent a concomitant procedure. Median length-of-stay was 5 days. Within 30 days postoperative 36 patients (23.2%) had 39 postoperative complications of which 30 were mild (CDC I–II), and nine severe (CDC III–IV). Thirty-one surgical site occurrences were observed in thirty patients (19.4%) of which the majority were seroma (16 patients 10.3%). There was no hernia-related mortality during follow-up. Recurrence rate was 1.8% after a median follow-up of 52 months (12–128 months). Postoperative quality of life was rated excellent. The modified Chevrel technique for midline ventral hernias results in a moderate complication rate, low recurrence rate and high rated postoperative quality of life.
Operational Exercise Integration Recommendations for DoD Cyber Ranges

Permission has been given to destroy this document when it is no longer needed. Cyber-enabled and cyber-physical systems connect and engage virtually every mission-critical military capability today. And as more warfighting technologies become integrated and connected, both the risks and opportunities from a cyberwarfare continue to grow—motivating sweeping requirements and investments in cybersecurity assessment capabilities to evaluate technology vulner-abilities, operational impacts, and operator effectiveness. Operational testing of cyber capabilities, often in conjunction with major military exercises, provides valuable connections to and feedback from the operational warfighter community. These connections can help validate capability impact on the mission and, when necessary, provide course-correcting feedback to the technology development process and its stakeholders. However, these tests are often constrained in scope, duration, and resources and require a thorough and wholistic approach, especially with respect to cyber technology assessments, where additional safety and security constraints are often levied. This report presents a summary of the state of the art in cyber assessment technologies and methodologies and prescribes an approach to the employment of cyber range operational exercises (OPEXs). Numerous recommendations on general cyber assessment methodologies and cyber range design are included, the most significant of which are summarized below. • Perform bottom-up and top-down assessment formulation methodologies to robustly link mission and assessment objectives to metrics, success criteria, and system observables. • Include threat-based assessment formulation methodologies that define risk and security met-rics within the context of mission-relevant adversarial threats and mission-critical system assets. • Follow a set of cyber range design mantras to guide and grade the design of cyber range components. • Call for future work in live-to-virtual exercise integration and cross-domain modeling and simulation technologies. • Call for continued integration of developmental and operational cyber assessment events, development of reusable cyber assessment test tools and processes, and integration of a threat-based assessment approach across the cyber technology acquisition cycle. Finally, this recommendations report was driven by obsevations made by the MIT Lincoln Laboratory (MIT LL) Cyber Measurement Campaign (CMC) team during an operational demonstration event for the DoD Enterprise Cyber Range Environment (DECRE) Command and Control Information Systems (C2IS). 1 This report also incorporates a prior CMC report based on Pacific Command (PACOM) exercise observations, as well as MIT LL's expertise in cyber range development and cyber systems assessment. 2 1 CMC is explained in further detail  See References section at the end of the report.
Attenuated Pik 3 r 1 Expression Prevents Insulin Resistance and Adipose Tissue Macrophage Accumulation in Diet-Induced Obese Mice

Obese white adipose tissue (AT) is characterized by large-scale infiltration of proinflammatory macrophages, in parallel with systemic insulin resistance; however, the cellular stimulus that initiates this signaling cascade and chemokine release is still unknown. The objective of this study was to determine the role of the phosphoinositide 3-kinase (PI3K) regulatory subunits on AT macrophage (ATM) infiltration in obesity. Here, we find that the Pik3r1 regulatory subunits (i.e., p85a/p55a/p50a) are highly induced in AT from high-fat diet–fed obese mice, concurrent with insulin resistance. Global heterozygous deletion of the Pik3r1 regulatory subunits (aHZ), but not knockout of Pik3r2 (p85b), preserves whole-body, AT, and skeletal muscle insulin sensitivity, despite severe obesity. Moreover, ATM accumulation, proinflammatory gene expression, and ex vivo chemokine secretion in obese aHZ mice are markedly reduced despite endoplasmic reticulum (ER) stress, hypoxia, adipocyte hypertrophy, and Jun NH2-terminal kinase activation. Furthermore, bone marrow transplant studies reveal that these improvements in obese aHZ mice are independent of reduced Pik3r1 expression in the hematopoietic compartment. Taken together, these studies demonstrate that Pik3r1 expression plays a critical role in mediating AT insulin sensitivity and, more so, suggest that reduced PI3K activity is a key step in the initiation and propagation of the inflammatory response in obese AT.
Texture brush: an interactive surface texturing interface

This paper presents Texture Brush, an interactive interface for texturing 3D surfaces. We extend the conventional exponential map to a more general setting, in which the generator can be an arbitrary curve. Based on our extended exponential map, we develop a local parameterization method which naturally supports anisotropic texture mapping. With Texture Brush, the user can easily specify such local parameterization with a free-form stroke on the surface. We also propose a set of intuitive operations which are mainly based on 3D painting metaphor, including texture painting, texture cloning, texture animation design, and texture editing. Compared to the existing surface texturing techniques, our method enables a smoother and more natural work flow so that the user can focus on the design task itself without switching back and forth among different tools or stages. The encouraging experimental results and positive evaluation by artists demonstrate the efficacy of our Texture Brush for interactive texture mapping.