nils-herrmann/clean_scirepeval huggingface dataset:
TM4 microarray software suite.

Powerful specialized software is essential for managing, quantifying, and ultimately deriving scientific insight from results of a microarray experiment. We have developed a suite of software applications, known as TM4, to support such gene expression studies. The suite consists of open-source tools for data management and reporting, image analysis, normalization and pipeline control, and data mining and visualization. An integrated MIAME-compliant MySQL database is included. This chapter describes each component of the suite and includes a sample analysis walk-through.
An approximation algorithm for the hamiltonian walk problem on maximal planar graphs

A hamiltonian walk of a graph is a shortest closed walk that passes through every vertex at least once, and the length is the total number of traversed edges. The hamiltonian walk problem in which one would like to find a hamiltonian walk of a given graph is NP-complete. The problem is a generalized hamiltonian cycle problem and is a special case of the traveling salesman problem. Employing the techniques of divide-and-conquer and augmentation, we present an approximation algorithm for the problem on maximal planar graphs. The algorithm finds, in Ow2) time, a closed spanning walk of a given arbitrary maximal planar graph, and the length of the obtained walk is at most i@ 3) if the graph has p (Z 9) vertices. Hence the worst-case bound is i.
Seq2Seq Mimic Games: A Signaling Perspective

We study the emergence of communication in multiagent adversarial settings inspired by the classic Imitation game. A class of three player games is used to explore how agents based on sequence to sequence (Seq2Seq) models can learn to communicate information in adversarial settings. We propose a modeling approach, an initial set of experiments and use signaling theory to support our analysis. In addition, we describe how we operationalize the learning process of actor-critic Seq2Seq based agents in these communicational games.
The effect of network topology on the spread of epidemics

Many network phenomena are well modeled as spreads of epidemics through a network. Prominent examples include the spread of worms and email viruses, and, more generally, faults. Many types of information dissemination can also be modeled as spreads of epidemics. In this paper we address the question of what makes an epidemic either weak or potent. More precisely, we identify topological properties of the graph that determine the persistence of epidemics. In particular, we show that if the ratio of cure to infection rates is larger than the spectral radius of the graph, then the mean epidemic lifetime is of order log n, where n is the number of nodes. Conversely, if this ratio is smaller than a generalization of the isoperimetric constant of the graph, then the mean epidemic lifetime is of order e/sup na/, for a positive constant a. We apply these results to several network topologies including the hypercube, which is a representative connectivity graph for a distributed hash table, the complete graph, which is an important connectivity graph for BGP, and the power law graph, of which the AS-level Internet graph is a prime example. We also study the star topology and the Erdos-Renyi graph as their epidemic spreading behaviors determine the spreading behavior of power law graphs.
The effects of mindfulness meditation training on multitasking in a high-stress information environment

We describe an experiment to determine the effects of meditation training on the multitasking behavior of knowledge workers. Three groups each of 12-15 human resources personnel were tested: (1) those who underwent an 8-week training course on mindfulness-based meditation, (2) those who endured a wait period, were tested, and then underwent the same 8-week training, and (3) those who had 8-weeks of training in body relaxation. We found that only those trained in meditation stayed on tasks longer and made fewer task switches, as well as reporting less negative emotion after task performance, as compared with the other two groups. In addition, both the meditation and the relaxation groups showed improved memory for the tasks they performed.
Velocity Estimation for Ultralightweight Tendon-Driven Series Elastic Robots

Accurate velocity estimation is an important basis for robot control, but especially challenging for highly elastically driven robots. These robots show large swing or oscillation effects if they are not damped appropriately during the performed motion. In this letter, we consider an ultralightweight tendon-driven series elastic robot arm equipped with low-resolution joint position encoders. We propose an adaptive Kalman filter for velocity estimation that is suitable for these kinds of robots with a large range of possible velocities and oscillation frequencies. Based on an analysis of the parameter characteristics of the measurement noise variance, an update rule based on the filter position error is developed that is easy to adjust for use with different sensors. Evaluation of the filter both in simulation and in robot experiments shows a smooth and accurate performance, well suited for control purposes.
Kinematics and workspace of 3-RUU parallel manipulator

Kinematics design of a lower-DOF parallel robot parallel, a 3-UUR pure rotational parallel manipulator, is presented. We first analyzed the instantaneous motions of 3-RUU parallel robot at initial configuration by reciprocal screw, after the inverse kinematics and forward kinematics are analyzed. At the basis on the kinematics, the workspace of parallel robot is presented in the paper. The main influences for the workspace of parallel robot consist of circumscribed circle radius of fixed platform Ra, circumscribed circle radius of moving fixed platform Rb, length of connecting rod l and the angles Θ among connecting rod. The performances of such 3-RUU mechanisms are very different for different configurations. The curve of simulation which prove the academic analysis is given by Adams.
Sensible Organizations: Technology and Methodology for Automatically Measuring Organizational Behavior

We present the design, implementation, and deployment of a wearable computing platform for measuring and analyzing human behavior in organizational settings. We propose the use of wearable electronic badges capable of automatically measuring the amount of face-to-face interaction, conversational time, physical proximity to other people, and physical activity levels in order to capture individual and collective patterns of behavior. Our goal is to be able to understand how patterns of behavior shape individuals and organizations. By using on-body sensors in large groups of people for extended periods of time in naturalistic settings, we have been able to identify, measure, and quantify social interactions, group behavior, and organizational dynamics. We deployed this wearable computing platform in a group of 22 employees working in a real organization over a period of one month. Using these automatic measurements, we were able to predict employees' self-assessments of job satisfaction and their own perceptions of group interaction quality by combining data collected with our platform and e-mail communication data. In particular, the total amount of communication was predictive of both of these assessments, and betweenness in the social network exhibited a high negative correlation with group interaction satisfaction. We also found that physical proximity and e-mail exchange had a negative correlation of r = -0.55&nbsp;(p 0.01), which has far-reaching implications for past and future research on social networks.
Less hashing, same performance: Building a better Bloom filter

A standard technique from the hashing literature is to use two hash functions h1(x) and h2(x) to simulate additional hash functions of the form gi(x) = h1(x) + ih2(x). We demonstrate that this technique can be usefully applied to Bloom filters and related data structures. Specifically, only two hash functions are necessary to effectively implement a Bloom filter without any loss in the asymptotic false positive probability. This leads to less computation and potentially less need for randomness in practice.
Why do people seek anonymity on the internet?: informing policy and design

In this research we set out to discover why and how people seek anonymity in their online interactions. Our goal is to inform policy and the design of future Internet architecture and applications. We interviewed 44 people from America, Asia, Europe, and Africa who had sought anonymity and asked them about their experiences. A key finding of our research is the very large variation in interviewees' past experiences and life situations leading them to seek anonymity, and how they tried to achieve it. Our results suggest implications for the design of online communities, challenges for policy, and ways to improve anonymity tools and educate users about the different routes and threats to anonymity on the Internet.
Transcriptional control of a plant stem cell niche.

Despite the independent evolution of multicellularity in plants and animals, the basic organization of their stem cell niches is remarkably similar. Here, we report the genome-wide regulatory potential of WUSCHEL, the key transcription factor for stem cell maintenance in the shoot apical meristem of the reference plant Arabidopsis thaliana. WUSCHEL acts by directly binding to at least two distinct DNA motifs in more than 100 target promoters and preferentially affects the expression of genes with roles in hormone signaling, metabolism, and development. Striking examples are the direct transcriptional repression of CLAVATA1, which is part of a negative feedback regulation of WUSCHEL, and the immediate regulation of transcriptional repressors of the TOPLESS family, which are involved in auxin signaling. Our results shed light on the complex transcriptional programs required for the maintenance of a dynamic and essential stem cell niche.
Object Detection with YOLO on Artwork Dataset

I design a small object detection network, which is simplified from YOLO(You Only Look Once[15]) network. YOLO is a fast and elegant network that can extract meta features, predict bounding boxes and assign scores to bounding boxes. Compared with RCNN, it doesn’t have complex pipline, which is easier for me to implement. Start from a ImageNet pretrained model, I train my YOLO on PASCAL VOC2007 training dataset. And validate my YOLO on PASCAL VOC2007 validation dataset. Finally, I evaluate my YOLO on an artwork dataset(Picasso dataset). With the best parameters, I got 40% precision and 35% recall.
Modelling Soil Water Dynamics in the Unsaturated Zone --state of the Art

soil water dynamics in the unsaturated zone-State of the art. This paper reviews the principles underlying water dynamics in the unsaturated zone and gives an overview of simulation modelling of soil water flow in the unsaturated zone. The basic relations describing kinetics of flow and governing equations of flow in the un-saturated zone are presented in a general form considering unsteady multidimensional anisotropic and nonhomogeneous flow in the presence of sinks/sources. The influence of overburden potential, characterizing swelling soils, on water transport through the unsaturated zone is discussed. Outlines of thermally-induced water flow are presented with an extension to the specific case of frozen soils. Complex relationships describing the hydrological system are reduced into the one-dimensional vertical flow cases, for which the mathematical models are defined. The entire model has the form of a set of partial differential equations together with auxiliary conditions, that describe the system's geometry, parameters, boundary conditions and in case of transient flow also the initial conditions. Simulation is defined as operation with such a mathematical model. Numerical approximations to solve governing equations of unsaturated water flow are emphasized. Recently-introduced numerical methods are discussed pointing out their advantages and limitations. Atmospheric boundary conditions of the modelled system are described with a maximizing procedure of the flux through the soil-air interface. Several options to define lower boundary conditions are discussed and a special type of lower boundary condition is given for the case that unsaturated flow is coupled with a regional groundwater model. Simulation models require data concerning input, calibration and verification. The current status of collecting model parameters is discussed as well as measurement of common verification data, i.e. phreatic surface, matric head, water content and actual evapotranspiration. Preferential flow of water through unsaturated soil has considerable consequences for simulating the field water balance. The attempts to simulate such hydrological systems are also evaluated. Some transient flow problems are affected by hysteresis. Successful attempts to build hysteresis into dynamic simulation models are still scarce, though recently a number of solutions have been presented. Finally, several practical examples of simulation of flow problems are presented. These examples are taken from everyday water management practice and document the wide range of applicability of simulation models.
Proactive Learning for Named Entity Recognition

The goal of active learning is to minimise the cost of producing an annotated dataset, in which annotators are assumed to be perfect, i.e., they always choose the correct labels. However, in practice, annotators are not infallible, and they are likely to assign incorrect labels to some instances. Proactive learning is a generalisation of active learning that can model different kinds of annotators. Although proactive learning has been applied to certain labelling tasks, such as text classification, there is little work on its application to named entity (NE) tagging. In this paper, we propose a proactive learning method for producing NE annotated corpora, using two annotators with different levels of expertise, and who charge different amounts based on their levels of experience. To optimise both cost and annotation quality, we also propose a mechanism to present multiple sentences to annotators at each iteration. Experimental results for several corpora show that our method facilitates the construction of high-quality NE labelled datasets at minimal cost.
Set Transformer

Many machine learning tasks such as multiple instance learning, 3D shape recognition and few-shot image classification are defined on sets of instances. Since solutions to such problems do not depend on the permutation of elements of the set, models used to address them should be permutation invariant. We present an attention-based neural network module, the Set Transformer, specifically designed to model interactions among elements in the input set. The model consists of an encoder and a decoder, both of which rely on attention mechanisms. In an effort to reduce computational complexity, we introduce an attention scheme inspired by inducing point methods from sparse Gaussian process literature. It reduces computation time of self-attention from quadratic to linear in the number of elements in the set. We show that our model is theoretically attractive and we evaluate it on a range of tasks, demonstrating increased performance compared to recent methods for set-structured data.
Biodegradation of superabsorbent polymers in soil.

Biodegradation of two superabsorbent polymers, a crosslinked, insoluble polyacrylate and an insoluble polyacrylate/ polyacrylamide copolymer, in soil by the white-rot fungus, Phanerochaete chrysosporium was investigated. The polymers were both solubilized and mineralized by the fungus but solubilization and mineralization of the copolymer was much more rapid than of the polyacrylate. Soil microbes poorly solublized the polymers and were unable to mineralize either intact polymer. However, soil microbes cooperated with the fungus during polymer degradation in soil, with the fungus solubilizing the polymers and the soil microbes stimulating mineralization. Further, soil microbes were able to significantly mineralize both polymers after solubilization by P. chrysosporium grown under conditions that produced fungal peroxidases or cellobiose dehydrogenase, or after solubilization by photochemically generated Fenton reagent. The results suggest that biodegradation of these polymers in soil is best under conditions that maximize solubilization.
CrowdInside: automatic construction of indoor floorplans

The existence of a worldwide indoor floorplans database can lead to significant growth in location-based applications, especially for indoor environments. In this paper, we present CrowdInside: a crowdsourcing-based system for the automatic construction of buildings floorplans. CrowdInside leverages the smart phones sensors that are ubiquitously available with humans who use a building to automatically and transparently construct accurate motion traces. These accurate traces are generated based on a novel technique for reducing the errors in the inertial motion traces by using the points of interest in the indoor environment, such as elevators and stairs, for error resetting. The collected traces are then processed to detect the overall floorplan shape as well as higher level semantics such as detecting rooms and corridors shapes along with a variety of points of interest in the environment.
 Implementation of the system in two testbeds, using different Android phones, shows that CrowdInside can detect the points of interest accurately with 0.2% false positive rate and 1.3% false negative rate. In addition, the proposed error resetting technique leads to more than 12 times enhancement in the median distance error compared to the state-of-the-art. Moreover, the detailed floorplan can be accurately estimated with a relatively small number of traces. This number is amortized over the number of users of the building. We also discuss possible extensions to CrowdInside for inferring even higher level semantics about the discovered floorplans.
Assessment of Right Ventricular Afterload in COPD.

We aimed to study whether pulmonary hypertension (PH) and elevated pulmonary vascular resistance (PVR) could be predicted by conventional echo Doppler and novel tissue Doppler imaging (TDI) in a population of chronic obstructive pulmonary disease (COPD) free of LV disease and co-morbidities.



Echocardiography and right heart catheterization was performed in 100 outpatients with COPD. By echocardiography the time-integral of the TDI index, right ventricular systolic velocity (RVSmVTI) and pulmonary acceleration-time (PAAcT) were measured and adjusted for heart rate. The COPD patients were randomly divided in a derivation (n = 50) and a validation cohort (n = 50).



PH (mean pulmonary artery pressure (mPAP) ≥ 25mmHg) and elevated PVR ≥ 2Wood unit (WU) were predicted by satisfactory area under the curve for RVSmVTI of 0.93 and 0.93 and for PAAcT of 0.96 and 0.96, respectively. Both echo indices were 100% feasible, contrasting 84% feasibility for parameters relying on contrast enhanced tricuspid-regurgitation. RVSmVTI and PAAcT showed best correlations to invasive measured mPAP, but less so to PVR. PAAcT was accurate in 90- and 78% and RVSmVTI in 90- and 84% in the calculation of mPAP and PVR, respectively.



Heart rate adjusted-PAAcT and RVSmVTI are simple and reproducible methods that correlate well with pulmonary artery pressure and PVR and showed high accuracy in detecting PH and increased PVR in patients with COPD. Taken into account the high feasibility of these two echo indices, they should be considered in the echocardiographic assessment of COPD patients.
Digital taste and smell communication

In this paper, we introduce a new technology, which allows people to share taste and smell sensations digitally with a remote person through existing networking technologies such as the Internet. By introducing this technology, we expect people to share their smell and taste experiences with their family and friends remotely. Sharing these senses are immensely beneficial since those are strongly associated with individual memories, emotions, and everyday experiences. As the initial step, we developed a control system, an actuator, which could digitally stimulate the sense of taste remotely. The system uses two approaches to stimulate taste sensations digitally: the electrical and thermal stimulations on tongue. Primary results suggested that sourness and saltiness are the main sensations that could be evoked through this device. Furthermore, this paper focuses on future aspects of such technology for remote smell actuation followed by applications and possibilities for further developments.
Relationships among burnout, job involvement, and organizational citizenship behavior.

R. Cropanzano, D. E. Rupp, and Z. S. Byrne (2003) found that emotional exhaustion (i.e., 1 dimension of burnout) negatively affects organizational citizenship behavior (OCB). The authors extended this research by investigating relationships among 3 dimensions of burnout (emotional exhaustion, depersonalization, and diminished personal accomplishment) and OCB. They also affirmed the mediating effect of job involvement on these relationships. Data were collected from 296 paired samples of service employees and their supervisors from 12 hotels and restaurants in Taiwan. Findings demonstrated that emotional exhaustion and diminished personal accomplishment were related negatively to OCB, whereas depersonalization had no independent effect on OCB. Job involvement mediated the relationships among emotional exhaustion, diminished personal accomplishment, and OCB.
Intelligent Computing Theory

Performance self tuning in database systems is a challenge work since it is hard to identify tuning parameters and make a balance to choose proper configuration values for them. In this paper, we propose a neural network based algorithm for performance self-tuning. We first extract Automatic Workload Repository report automatically, and then identify key system performance parameters and performance indicators. We then use the collected data to construct a Neural Network model. Finally, we develop a selftuning algorithm to tune these parameters. Experimental results for oracle database system in TPC-C workload environment show that the proposed method can dynamically improve the performance.
Real-time Monocular Dense Mapping for Augmented Reality

Monocular simultaneous localization and mapping (SLAM) is a key enabling technique for many augmented reality (AR) applications. However, conventional methods for monocular SLAM can obtain only sparse or semi-dense maps in highly-textured image areas. Poorly-textured regions which widely exist in indoor and man-made urban environments can be hardly reconstructed, impeding interactions between virtual objects and real scenes in AR apps. In this paper,we present a novel method for real-time monocular dense mapping based on the piecewise planarity assumption for poorly textured regions. Specifically, a semi-dense map for highly-textured regions is first calculated by pixel matching and triangulation [6, 7]. Large textureless regions extracted by Maximally Stable Color Regions (MSCR) , which is a homogeneous-color region detector, are approximated using piecewise planar models which are estimated by the corresponding semi-dense 3D points and the proposed multi-plane segmentation algorithm. Plane models associated with the same 3D area across multiple overlapping views are linked and fused to ensure a consistent and accurate 3D reconstruction. Experimental results on two public datasets [15, 23] demonstrate that our method is 2.3X~2.9X faster than the state-of-the-art method DPPTAM , and meanwhile achieves better reconstruction accuracy and completeness. We also apply our method to a real AR application and live experiments with a hand-held camera demonstrate the effectiveness and efficiency of our method in practical scenario.