nils-herrmann/clean_scirepeval huggingface dataset:
Uniform and non-uniform quantization of Gaussian processes

Quantization of a continuous-value signal into a discrete form (or discretization of amplitude) is a standard task in all analog/digital devices. We consider quantization of a signal (or random process) in a probabilistic framework. The quantization method presented in this paper can be applied to signal coding and storage capacity problems. In order to demonstrate a general approach, both uniform and non-uniform quantization of a Gaussian process are studied in more detail and compared with a conventional piecewise constant approximation. We investigate asymptotic properties of some accuracy characteristics, such as a random quantization rate, in terms of the correlation structure of the original random process when quantization cellwidth tends to zero. Some examples and numerical experiments are presented.
A recursive branch-and-bound algorithm for the rectangular guillotine strip packing problem

A heuristic recursive algorithm for the two-dimensional rectangular strip packing problem is presented. It is based on a recursive structure combined with branch-and-bound techniques. Several lengths are tried to determine the minimal plate length to hold all the items. Initially the plate is taken as a block. For the current block considered, the algorithm selects an item, puts it at the bottom-left corner of the block, and divides the unoccupied region into two smaller blocks with an orthogonal cut. The dividing cut is vertical if the block width is equal to the plate width; otherwise it is horizontal. Both lower and upper bounds are used to prune unpromising branches. The computational results on a class of benchmark problems indicate that the algorithm performs better than several recently published algorithms.
Vigi4Med Scraper: A Framework for Web Forum Structured Data Extraction and Semantic Representation

The extraction of information from social media is an essential yet complicated step for data analysis in multiple domains. In this paper, we present Vigi4Med Scraper, a generic open source framework for extracting structured data from web forums. Our framework is highly configurable; using a configuration file, the user can freely choose the data to extract from any web forum. The extracted data are anonymized and represented in a semantic structure using Resource Description Framework (RDF) graphs. This representation enables efficient manipulation by data analysis algorithms and allows the collected data to be directly linked to any existing semantic resource. To avoid server overload, an integrated proxy with caching functionality imposes a minimal delay between sequential requests. Vigi4Med Scraper represents the first step of Vigi4Med, a project to detect adverse drug reactions (ADRs) from social networks founded by the French drug safety agency Agence Nationale de Sécurité du Médicament (ANSM). Vigi4Med Scraper has successfully extracted greater than 200 gigabytes of data from the web forums of over 20 different websites.
Preserving I/O prioritization in virtualized OSes

While virtualization helps to enable multi-tenancy in data centers, it introduces new challenges to the resource management in traditional OSes. We find that one important design in an OS, prioritizing interactive and I/O-bound workloads, can become ineffective in a virtualized OS. Resource multiplexing between multiple tenants breaks the assumption of continuous CPU availability in physical systems and causes two types of priority inversions in virtualized OSes. In this paper, we present xBalloon, a lightweight approach to preserving I/O prioritization. It uses a balloon process in the virtualized OS to avoid priority inversion in both short-term and long-term scheduling. Experiments in a local Xen environment and Amazon EC2 show that xBalloon improves I/O performance in a recent Linux kernel by as much as 136% on network throughput, 95% on disk throughput, and 125x on network tail latency.
LIRE: open source image retrieval in Java

Content based image retrieval has been around for some time. There are lots of different test data sets, lots of published methods and techniques, and manifold retrieval challenges, where content based image retrieval is of interest. LIRE is a Java library, that provides a simple way to index and retrieve millions of images based on the images' contents. LIRE is robust and well tested and is not only recommended by the websites of ImageCLEF and MediaEval, but is also employed in industry. This paper gives an overview on LIRE, its use, capabilities and reports on retrieval and runtime performance.
Bisimulation Can't Be Traced

Bisimulation is the primitive notion of equivalence between concurrent processes in Milner's Calculus of Communicating Systems (CCS); there is a nontrivial game-like protocol for distinguishing nonbisimular processes. In contrast, process distinguishability in Hoare's theory of Communicating Sequential Processes (CSP) is determined solely on the basis of traces of visible actions. We examine what additional operations are needed to explain bisimulation similarly—specifically in the case of finitely branching processes without silent moves. We formulate a general notion of Structured Operational Semantics for processes with Guarded recursion (GSOS), and demonstrate that bisimulation does not agree with trace congruence with respect to any set of GSOS-definable contexts. In justifying the generality and significance of GSOS's, we work out some of the basic proof theoretic facts which justify the SOS discipline.
Teaching UAVs to Race: End-to-End Regression of Agile Controls in Simulation

Automating the navigation of unmanned aerial vehicles (UAVs) in diverse scenarios has gained much attention in recent years. However, teaching UAVs to fly in challenging environments remains an unsolved problem, mainly due to the lack of training data. In this paper, we train a deep neural network to predict UAV controls from raw image data for the task of autonomous UAV racing in a photo-realistic simulation. Training is done through imitation learning with data augmentation to allow for the correction of navigation mistakes. Extensive experiments demonstrate that our trained network (when sufficient data augmentation is used) outperforms state-of-the-art methods and flies more consistently than many human pilots. Additionally, we show that our optimized network architecture can run in real-time on embedded hardware, allowing for efficient onboard processing critical for real-world deployment. From a broader perspective, our results underline the importance of extensive data augmentation techniques to improve robustness in end-to-end learning setups.
Connecting people and place : a new framework for reducing urban vulnerability to extreme heat

Climate change is predicted to increase the intensity and negative impacts of urban heat events, prompting the need to develop preparedness and adaptation strategies that reduce societal vulnerability to extreme heat. Analysis of societal vulnerability to extreme heat events requires an interdisciplinary approach that includes information about weather and climate, the natural and built environment, social processes and characteristics, interactions with stakeholders, and an assessment of community vulnerability at a local level. In this letter, we explore the relationships between people and places, in the context of urban heat stress, and present a new research framework for a multi-faceted, top-down and bottom-up analysis of local-level vulnerability to extreme heat. This framework aims to better represent societal vulnerability through the integration of quantitative and qualitative data that go beyond aggregate demographic information. We discuss how different elements of the framework help to focus attention and resources on more targeted health interventions, heat hazard mitigation and climate adaptation strategies.
Text2Shape: Generating Shapes from Natural Language by Learning Joint Embeddings

We present a method for generating colored 3D shapes from natural language. To this end, we first learn joint embeddings of freeform text descriptions and colored 3D shapes. Our model combines and extends learning by association and metric learning approaches to learn implicit cross-modal connections, and produces a joint representation that captures the many-to-many relations between language and physical properties of 3D shapes such as color and shape. To evaluate our approach, we collect a large dataset of natural language descriptions for physical 3D objects in the ShapeNet dataset. With this learned joint embedding we demonstrate text-to-shape retrieval that outperforms baseline approaches. Using our embeddings with a novel conditional Wasserstein GAN framework, we generate colored 3D shapes from text. Our method is the first to connect natural language text with realistic 3D objects exhibiting rich variations in color, texture, and shape detail.
A controlled-temperature hot-wire anemometer with voltage feedback linearization

This paper proposes a new architecture of a controlled-temperature hot-wire anemometer using voltage feedback linearization. The voltage feedback linearizes the sensor input-output relationship and the controller is designed to achieve null steady-state error and reduce the system response time. Analysis of the behavior of the architecture modeled using Simulink is presented for a NTC sensor. Simulation results are presented and discussed, and the architecture is compared with the classical constant-temperature anemometer (CTA) one.
Veganism, bone mineral density, and body composition: a study in Buddhist nuns

This cross-sectional study showed that, although vegans had lower dietary calcium and protein intakes than omnivores, veganism did not have adverse effect on bone mineral density and did not alter body composition. Whether a lifelong vegetarian diet has any negative effect on bone health is a contentious issue. We undertook this study to examine the association between lifelong vegetarian diet and bone mineral density and body composition in a group of postmenopausal women. One hundred and five Mahayana Buddhist nuns and 105 omnivorous women (average age = 62, range = 50–85) were randomly sampled from monasteries in Ho Chi Minh City and invited to participate in the study. By religious rule, the nuns do not eat meat or seafood (i.e., vegans). Bone mineral density (BMD) at the lumbar spine (LS), femoral neck (FN), and whole body (WB) was measured by DXA (Hologic QDR 4500). Lean mass, fat mass, and percent fat mass were also obtained from the DXA whole body scan. Dietary calcium and protein intakes were estimated from a validated food frequency questionnaire. There was no significant difference between vegans and omnivores in LSBMD (0.74 ± 0.14 vs. 0.77 ± 0.14 g/cm2; mean ± SD; P = 0.18), FNBMD (0.62 ± 0.11 vs. 0.63 ± 0.11 g/cm2; P = 0.35), WBBMD (0.88 ± 0.11 vs. 0.90 ± 0.12 g/cm2; P = 0.31), lean mass (32 ± 5 vs. 33 ± 4 kg; P = 0.47), and fat mass (19 ± 5 vs. 19 ± 5 kg; P = 0.77) either before or after adjusting for age. The prevalence of osteoporosis (T scores ≤ −2.5) at the femoral neck in vegans and omnivores was 17.1% and 14.3% (P = 0.57), respectively. The median intake of dietary calcium was lower in vegans compared to omnivores (330 ± 205 vs. 682 ± 417 mg/day, P < 0.001); however, there was no significant correlation between dietary calcium and BMD. Further analysis suggested that whole body BMD, but not lumbar spine or femoral neck BMD, was positively correlated with the ratio of animal protein to vegetable protein. These results suggest that, although vegans have much lower intakes of dietary calcium and protein than omnivores, veganism does not have adverse effect on bone mineral density and does not alter body composition.
OpenStack extensions for QoS and energy efficiency in edge computing

Mobility has always been a big challenge in cellular networks, because it is responsible for traffic fluctuations that eventually result into inconstant resource usage and the need for proper Quality of Service management. When applications get deployed at the network edge, the challenges even grow because software is harder to hand-over than traffic streams. Cloud technologies have been designed with different specifications, and should be properly revised to balance efficiency and effectiveness in distributed and capillary infrastructures. In this paper, we propose some extensions to OpenStack for power management and Quality of Service. Our framework provides additional APIs for setting the service level and interacting with power-saving mechanisms. It is designed to be easily integrated with modern software orchestration tools and workload consolidation algorithms. We report real measurements from an experimental proof-of-concept.
Parental warmth during childhood predicts coping and well-being in adulthood.

Numerous studies have shown that early life experiences can affect well-being later in life. Additionally, previous literature has emphasized the importance of exploring the role of mediators in developmental research (e.g., coping strategies). The present study used 3 waves of longitudinal data across 20 years from the national survey Midlife Development in the United States (N = 2,088) to examine the link between retrospectively reported parental warmth and well-being in adulthood by exploring 2 categories of coping strategies (emotion- and problem-focused strategies) as possible mediators. Three cross-lagged panel models, exhibiting good fit, were conducted in Mplus. Significant indirect effects were found where both negative and positive affect (Time 2) partially mediated the association between perceived parental warmth (Time 1) and emotion-focused coping (Time 3). Further, evidence for bidirectional effects were shown by the observed significant indirect effects of problem-focused coping (Time 2) partially explaining the association between perceived parental warmth (Time 1) and eudaimonic well-being (Time 3) as well as eudaimonic well-being (Time 2) partially explaining the link between parental warmth (Time 1) and problem-focused coping (Time 3). These findings suggest that it is important to consider early life experiences when examining both well-being and coping during adulthood.
SEGCloud: Semantic Segmentation of 3D Point Clouds

3D semantic scene labeling is fundamental to agents operating in the real world. In particular, labeling raw 3D point sets from sensors provides fine-grained semantics. Recent works leverage the capabilities of Neural Networks(NNs), but are limited to coarse voxel predictions and do not explicitly enforce global consistency. We present SEGCloud, an end-to-end framework to obtain 3D point-level segmentation that combines the advantages of NNs, trilinear interpolation(TI) and fully connected Conditional Random Fields (FC-CRF). Coarse voxel predictions from a 3D Fully Convolutional NN are transferred back to the raw 3D points via trilinear interpolation. Then the FC-CRF enforces global consistency and provides fine-grained semantics on the points. We implement the latter as a differentiable Recurrent NN to allow joint optimization. We evaluate the framework on two indoor and two outdoor 3D datasets (NYU V2, S3DIS, KITTI, Semantic3D.net), and show performance comparable or superior to the state-of-the-art on all datasets.
Findings of the Second Workshop on Neural Machine Translation and Generation

This document describes the findings of the Second Workshop on Neural Machine Translation and Generation, held in concert with the annual conference of the Association for Computational Linguistics (ACL 2018). First, we summarize the research trends of papers presented in the proceedings, and note that there is particular interest in linguistic structure, domain adaptation, data augmentation, handling inadequate resources, and analysis of models. Second, we describe the results of the workshop’s shared task on efficient neural machine translation (NMT), where participants were tasked with creating NMT systems that are both accurate and efficient.
Input characteristics of variable modulation index controlled current source inverters

Standard PWM current source inverters (CSIs) usually operate at fixed modulation index. The proposed modified current source inverter (MCSI) can operate with most pulse width modulation (PWM) techniques and with a variable mod­ ulation index, since the dc link inductor current freewheels on itself and not through the CSI. The use of variable modulation index control results in faster response times with no penalty on input power factor as compared to other variable modulation index schemes. This paper confirms this by investigating the input characteristics of the MCSI as seen from the ac mains. The quality of the input ac line currents is examined, and a design procedure for the input filters is given. Power factor and efficiency are discussed. Results are compared to those of other current source iuvester topologies. Experimental results obtained from a 5 kVA converter confirm the theoretical considerations.
Evaluation of Swallowing Using 320-Detector-Row Multislice CT. Part I: Single- and Multiphase Volume Scanning for Three-dimensional Morphological and Kinematic Analysis

A 320-detector-row multislice computed tomography (320-MSCT) scanner can acquire a volume data set covering a maximum range of 16 cm and can generate axial images 0.5-mm thick at 0.5-mm intervals. Three-dimensional (3D) images reconstructed from the thin axial slices include multiplanar reconstruction and 3D-CT. Single-phase 3D images are reconstructed from 0.175-s data, and multiphase 3D images are created in 29 phases at intervals of 0.1 s. Continuous replay of these 3D images produces four-dimensional moving images. In order to determine the feasibility of the morphologic and kinematic analyses of swallowing using 320-MSCT, single-phase volume scanning was performed on three patients and multiphase volume scanning was performed on one healthy volunteer. The single-phase 3D images clearly and accurately showed the structures involved in swallowing, and the multiphase 3D images were able to show the oral stage to the early esophageal stage of swallowing, allowing a kinematic analysis of swallowing. We developed a reclining chair that allows scanning to be performed with the subject in a semisitting position, which makes swallowing evaluation by 320-MSCT applicable not only to research on healthy swallowing but also to the clinical examination of dysphagia patients.
Biosignal Data Augmentation Based on Generative Adversarial Networks

In this paper, we propose a synthetic generationmethod for time-series data based on generative adversarial networks (GANs) and apply it to data augmentation for biosinal classification. GANs are a recently proposed framework for learning a generative model, where two neural networks, one generating synthetic data and the other discriminating synthetic and real data, are trained while competing with each other. In the proposed method, each neural network in GANs is developed based on a recurrent neural network using long short-term memories, thereby allowing the adaptation of the GANs framework to time-series data generation. In the experiments, we confirmed the capability of the proposed method for generating synthetic biosignals using the electrocardiogram and electroencephalogram datasets. We also showed the effectiveness of the proposed method for data augmentation in the biosignal classification problem.
Case Studies for Analyzing DLMS/COSEM-Based Smart Meter Vulnerabilities in Korea

Smart meters are at high risk to be an attack target or to be used as an attacking means of malicious users because they are placed at the closest location to users in the smart gridbased infrastructure. At present, Korea is proceeding with 'Smart Grid Advanced Metering Infrastructure (AMI) Construction Project', and has selected Device Language Message Specification/ COmpanion Specification for Energy Metering (DLMS/COSEM) protocol for the smart meter communication. However, the current situation is that the vulnerability analysis technique is still insufficient to be applied to DLMS/COSEM-based smart meters. Therefore, we propose a new fuzzing architecture for analyzing vulnerabilities which is applicable to actual DLMS/COSEM-based smart meter devices. In addition, this paper presents significant case studies for verifying proposed fuzzing architecture through conducting the vulnerability analysis of the experimental results from real DLMS/COSEM-based smart meter devices used in Korea SmartGrid Testbed.
Automatic floor plan detection and recognition

We present a system that detects floor plan automatically and realistically populated by a variety of objects of walls and windows. Given examples of floor plan, our system extracts, in advance, bearing wall, setting others objects which are not bearing wall into a non-bearing walls set. And then, to find contours in the non-bearing walls set. It recognize windows from these contours. The left objects of the set will to be identified walls including with the original bearing walls. The last step is to disintegrate wall into independent rectangular one by one. We demonstrate that our system can handle multiple realistic floor plan and, through decomposing and rebuilding, recognize walls, windows of a floor plan image. Based on high resolution images downloaded from Baidu, the experimental result shows that the average recognition rate of the proposed method is 90.21%, which proves the effectiveness of the proposed method.
Valproic acid induces CYP3A4 and MDR1 gene expression by activation of constitutive androstane receptor and pregnane X receptor pathways.

In our study, we tested the hypothesis whether valproic acid (VPA) in therapeutic concentrations has potential to affect expression of CYP3A4 and MDR1 via constitutive androstane receptor (CAR) and pregnane X receptor (PXR) pathways. Interaction of VPA with CAR and PXR nuclear receptors was studied using luciferase reporter assays, real-time reverse transcriptase polymerase chain reaction (RT-PCR), electrophoretic mobility shift assay (EMSA), and analysis of CYP3A4 catalytic activity. Using transient transfection reporter assays in HepG2 cells, VPA was recognized to activate CYP3A4 promoter via CAR and PXR pathways. By contrast, a significant effect of VPA on MDR1 promoter activation was observed only in CAR-cotransfected HepG2 cells. These data well correlated with up-regulation of CYP3A4 and MDR1 mRNAs analyzed by real-time RT-PCR in cells transfected with expression vectors encoding CAR or PXR and treated with VPA. In addition, VPA significantly up-regulated CYP3A4 mRNA in primary hepatocytes and augmented the effect of rifampicin. EMSA experiments showed VPA-mediated augmentation of CAR/retinoid X receptor alpha heterodimer binding to direct repeat 3 (DR3) and DR4 responsive elements of CYP3A4 and MDR1 genes, respectively. Finally, analysis of specific CYP3A4 catalytic activity revealed its significant increase in VPA-treated LS174T cells transfected with PXR. In conclusion, we provide novel insight into the mechanism by which VPA affects gene expression of CYP3A4 and MDR1 genes. Our results demonstrate that VPA has potential to up-regulate CYP3A4 and MDR1 through direct activation of CAR and/or PXR pathways. Furthermore, we suggest that VPA synergistically augments the effect of rifampicin in transactivation of CYP3A4 in primary human hepatocytes.