nils-herrmann/clean_scirepeval huggingface dataset:
Rethinking Graph Neural Network Search from Message passing
Venue: ArXiv
Year: 2021

Graph neural networks (GNNs) emerged recently as a standard toolkit for learning from data on graphs. Current GNN designing works depend on immense human expertise to explore different message passing mechanisms, and require manual enumeration to determine the proper message passing depth. Inspired by the strong searching capability of neural architecture search (NAS) in CNN, this paper proposes Graph Neural Architecture Search (GNAS) with novel designed search space. The GNAS can automatically learn better architecture with the optimal depth of message passing on the graph. Specifically, we design Graph Neural Architecture Paradigm (GAP) with treetopology computation procedure and two types of finegrained atomic operations (feature filtering neighbor aggregation) from message passing mechanism to construct powerful graph network search space. Feature filtering performs adaptive feature selection, and neighbor aggregation captures structural information and calculates neighbors' statistics. Experiments show that our GNAS can search for better GNNs with multiple message passing mechanisms and optimal message passing depth. The searched network achieves remarkable improvement over state ofthe art manual designed and search based GNNs on five large scale datasets at three classical graph tasks. Codes can be found at https:/github.com/phython96/ GNAS MP.
Tunable rotating mode density measurement using magnetic levitation
Year: 2018

In this letter, a density measurement method by magnetic levitation using the rotation mechanism is presented. By rotating the entire magnetic levitation device that consists of four identical magnets, the horizontal centrifugal force and gravity can be balanced by the magnetic forces in the x direction and the z direction, respectively. The controllable magnified centripetal acceleration is investigated as a means to improve the measurement sensitivity without destabilization. Theoretical and experimental results show that the density measurement method can be flexible in characterizing small differences in density by tuning the eccentric distance or rotating speed. The rotating mode density measurement method using magnetic levitation has prospects of providing an operationally simple way in separations and quality control of objects with arbitrary shapes in materials science and industrial fields.
Implementation and Operation of a Dividing Wall Distillation Column
Year: 2011

The design and construction of a prototype of a dividing wall distillation column was possible by integrating previous knowledge in process intensification, energy savings, theoretical control properties, and closed loop dynamics of thermally coupled distillation sequences. In order to achieve the predicted energy savings for this class of complex distillation column, a dividing wall and a side tank were implemented in order to manipulate the internal flows associated with energy consumption. The reaction between ethanol and acetic acid was conducted within the prototype, and the experimental results indicate that a heterogeneous mixture of ethyl acetate and water is obtained as the top product. The temperature profile measured during the experimental run can be used for controlling the batch distillation column in cyclic operation mode.
Efficient Computation of Exposure Profiles on Real World and Risk Neutral Scenarios for Bermudan Swaptions
Year: 2016

This paper presents a computationally efficient technique for the computation of exposure distributions at any future time under the risk neutral and some observed real world probability measures; these are needed for the computation of credit valuation adjustment (CVA) and potential future exposure (PFE) In particular, we present a valuation framework for Bermudan swaptions. The essential idea is to approximate the required value function via a set of risk neutral scenarios and use this approximated value function on the set of observed real world scenarios. This technique significantly improves the computational efficiency by avoiding nested Monte Carlo simulation and using only basic methods such as regression. We demonstrate the benefits of this technique by computing exposure distributions for Bermudan swaptions under the Hull White and G2 models.
Robotic Fertilisation Using Localisation Systems Based on Point Clouds in Strip Cropping Fields
Year: 2020

The use of robotic systems in organic farming has taken on a leading role in recent years; the Sureveg CORE Organic Cofund ERA Net project seeks to evaluate the benefits of strip cropping to produce organic vegetables. This includes, among other objectives, the development of a robotic tool that facilitates the automation of the fertilisation process, allowing the individual treatment (at the plant level) In organic production, the slower nutrient release of the used fertilisers poses additional difficulties, as a tardy detection of deficiencies can no longer be corrected. To improve the detection, as well as counter the additional labour stemming from the strip cropping configuration, an integrated robotic tool is proposed to detect individual crop deficiencies and react on a single crop basis. For the development of this proof of concept, one of the main objectives of this work is implementing a robust localisation method within the vegetative environment based on point clouds, through the generation of general point cloud maps (G PC) and local point cloud maps (L PC) of a crop row. The plants' geometric characteristics were extracted from the G PC as a framework in which the robot's positioning is defined. Through the processing of real time lidar data, the L PC is then defined and compared to the predefined reference system previously deduced. Both subsystems are integrated with ROS (Robot Operating System) alongside motion planning, and an inverse kinematics CCD (Cyclic Coordinate Descent) solver, among others. Tests were performed using a simulated environment of the crop row developed in Gazebo, followed by actual measurements in a strip cropping field. During real time data acquisition, the localisation error is reduced from 13 mm to 11 mm within the first 120 cm of measurement. The encountered real time geometric characteristics were found to coincide with those in the G PC to an extend of 98.6%
Tejas: A java based versatile micro architectural simulator
Venue: 2015 25th International Workshop on Power and Timing Modeling, Optimization and Simulation (PATMOS)
Year: 2015

In this paper, we present the design of a new Java based, cycle accurate, heterogeneous architectural simulator, Tejas. Tejas is a trace driven simulator, which is platform independent. It can simulate binaries in any ISA and corresponding to virtually any operating system. It can itself run on virtually any machine. It is one of the fastest cycle accurate simulators available in academia. This is achieved through employing optimized data structures, improving the simulator's cache locality, and reducing the amount of wasteful work done. Tejas offers a rich library of architectural features that are modular and highly configurable. Tejas has been validated against real hardware (Dell PowerEdge R620 server) and has been shown to be more accurate than some of the most popular architectural simulators.
Use of Valgrind's Tool Suite and Profile Guided Optimization in a CFD Code
Year: 2018

In this work Valgrind's Tool Suite is used to profile memory usage (Cachegrind) and to graph function calls (Callgrind) when running Code Saturne with OpenMP. The object of this analysis is to detect possible code's bottlenecks in order to improve performance in shared memory environments. Additionally, tests were run using flat Message Passing Interface (MPI) to compare total memory usage between these parallelization strategies. Finally, the code is compiled using Profile Guided Optimization (PGO) with a representative set of workloads to evaluate if this technique improves application's performance.
Muslim Distinction: Imitation and the Anxiety of Jewish, Christian, and Other Influences
Year: 2012

Imitation and the Anxiety of Jewish, Christians, and Other Influences by Youshaa Patel Graduate Program in Religion Duke University Date:_______________________ Approved: ___________________________ Ebrahim Moosa, Supervisor ___________________________ Bruce Lawrence ___________________________ Leela Prasad __________________________ Carl Ernst An abstract of a dissertation submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy in the Graduate Program in Religion in the Graduate School at Duke University 2012
Rational ignorance: simpler models learn more from finite data
Venue: ArXiv
Year: 2017

We use the language of uninformative Bayesian prior choice to study the selection of appropriately simple effective models. We consider the prior which maximizes the mutual information between parameters and predictions, learning as much as possible from finite data. When many parameters are poorly constrained by data, this puts weight only on boundaries of the parameter manifold. Thus it selects a lower dimensional effective theory in a principled way, ignoring irrelevant parameter directions. Only in the limit where there is sufficient data to tightly constrain all parameters do we recover Jeffreys prior.
Human Rights and Artificial Intelligence: An Urgently Needed Agenda
Year: 2019

The increasing presence of artificial intelligence creates enormous challenges for human rights. Among the short term challenges are ways in which technology engages just about all rights on the UDHR, as exemplified through use of effectively discriminatory algorithms. Medium term challenges include changes in the nature of work that could call into question many people's status as participants in society. In the long term humans may have to live with machines that are intellectually and possibly morally superior, even though this is highly speculative. Artificial intelligence also gives a new relevance to moral debates that used to strike many as arcane.
Pain Facial Expression Recognition from Video Sequences Using Spatio temporal Local Binary Patterns and Tracking Fiducial Points
Year: 2020

Monitoring the facial expressions of patients in clinical environments is a necessity in addition to vital sign monitoring. Pain monitoring of patients by facial expressions from video sequences eliminates the need for another person to accompany patients. In this paper, a novel approach is presented to monitor the expression of face and notify in case of pain using tracking fiducial points of face in video sequences and spatio temporal Local Binary Patterns (LBPs) for eyes and eyebrows. The motion of eight fiducial points on facial features such as mouth, eyes, eyebrows are tracked by Lucas Kanade algorithm and the movement angles are recorded in a feature vector which along with the spatio temporal histogram of LBPs creates a concatenated feature vector. Spatio temporal LBPs boost the proposed algorithm to capture minor deformations on eyes and eyebrows. The feature vectors are then compared and classified using the Chi square similarity measure. Experimental results show that leveraging spatio temporal LBPs improves the accuracy by 12% on STOIC database.
VTA dopaminergic neurons regulate ethologically relevant sleep wake behaviors
Venue: Nature Neuroscience
Year: 2016

Dopaminergic ventral tegmental area (VTA) neurons are critically involved in a variety of behaviors that rely on heightened arousal, but whether they directly and causally control the generation and maintenance of wakefulness is unknown. We recorded calcium activity using fiber photometry in freely behaving mice and found arousal state dependent alterations in VTA dopaminergic neurons. We used chemogenetic and optogenetic manipulations together with polysomnographic recordings to demonstrate that VTA dopaminergic neurons are necessary for arousal and that their inhibition suppresses wakefulness, even in the face of ethologically relevant salient stimuli. Nevertheless, before inducing sleep, inhibition of VTA dopaminergic neurons promoted goal directed and sleep related nesting behavior. Optogenetic stimulation, in contrast, initiated and maintained wakefulness and suppressed sleep and sleep related nesting behavior. We further found that different projections of VTA dopaminergic neurons differentially modulate arousal. Collectively, our findings uncover a fundamental role for VTA dopaminergic circuitry in the maintenance of the awake state and ethologically relevant sleep related behaviors.
Evaluation of OpenDelos online video editing and publishing platform for Greek Open Courses and MOOCs
Venue: 2015 International Conference on Interactive Mobile Communication Technologies and Learning (IMCL)
Year: 2015

This paper presents briefly the project "Central Repository of Greek Open Courses" as well as the innovative OpenDelos platform. OpenDelos is an online video editing and publishing platform for MOOCs implemented in the context of this project. The main goal of this paper is to investigate users' acceptance of OpenDelos platform as an online video editing and publishing platform for MOOCs. The evaluation methodology is based on Technology Acceptance Model (TAM) The five dimensions of the studied model are based on external variables (sex, age, web experience, educational level) perceived ease of use (PEOU) perceived usefulness (PU) attitude towards using (ATU) and behavior intension (BI) According to the confirmed derived model, presented in this paper, all the initially mentioned hypotheses were verified.
Expanding the capability of reaction diffusion codes using pseudo traps and temperature partitioning: Applied to hydrogen uptake and release from tungsten
Year: 2018

Simulating the implantation and thermal desorption evolution in a reaction diffusion model requires solving a set of coupled differential equations that describe the trapping and release of atomic species in Plasma Facing Materials (PFMs) These fundamental equations are well outlined by the Tritium Migration Analysis Program (TMAP) which can model systems with no more than three active traps per atomic species. To overcome this limitation, we have developed a Pseudo Trap and Temperature Partition (PTTP) scheme allowing us to lump multiple inactive traps into one pseudo trap, simplifying the system of equations to be solved. For all temperatures, we show the trapping of atoms from solute is exactly accounted for when using a pseudo trap. However, a single effective pseudo trap energy can not well replicate the release from multiple traps, each with its own detrapping energy. However, atoms held in a high energy trap will remain trapped at relatively low temperatures, and thus there is a temperature range in which release from high energy traps is effectively inactive. By partitioning the temperature range into segments, a pseudo trap can be defined for each segment to account for multiple high energy traps that are actively trapping but are effectively not releasing atoms. With increasing temperature, as in controlled thermal desorption, the lowest energy trap is nearly emptied and can be removed from the set of coupled equations, while the next higher energy trap becomes an actively releasing trap. Each segment is thus calculated sequentially, with the last time step of a given segment solution being used as an initial input for the next segment as only the pseudo and actively releasing traps are modeled. This PTTP scheme is then applied to experimental thermal desorption data for tungsten (W) samples damaged with heavy ions, which display six distinct release peaks during thermal desorption. Without modifying the TMAP7 source code the PTTP scheme is shown to successfully model the D retention in all six traps. We demonstrate the full reconstruction from the plasma implantation phase through the controlled thermal desorption phase with detrapping energies near 0.9, 1.1, 1.4, 1.7, 1.9 and 2.1 eV for a W sample damaged at room temperature.
Narrowband UVB Phototherapy for Clinically Isolated Syndrome: A Trial to Deliver the Benefits of Vitamin D and Other UVB Induced Molecules
Venue: Front. Immunol.
Year: 2017

Low vitamin D and insufficient sun exposure are additive independent risk factors for the development of multiple sclerosis (MS) The usual measure of vitamin D status, serum 25 hydroxy vitamin D [25(OH)D] is also a marker of recent exposure to the UVB rays of sunshine. The main evidence for a protective effect for MS development of higher 25(OH)D comes from observational studies, but this study design cannot separate out whether 25(OH)D is acting as a marker of vitamin D status, sun exposure, or both. In light of a lack of definitive outcomes in MS patients after trials of vitamin D supplementation and the ability of narrowband UVB to induce vitamin D, as well as other immune regulatory molecules in skin, the Phototherapy for Clinically Isolated Syndrome (PhoCIS) trial was established to investigate the benefits of narrowband UVB, in addition to supplemented vitamin D, on MS development in individuals with Clinically Isolated Syndrome. We propose that the PhoCIS trial provides a fresh approach to re defining the reported associations of 25(OH)D levels with MS development and progression.
Mechanics of Anisotropic Semiflexible Networks
Year: 2013

The study of semiflexible networks has direct applications to cellular mechanics. In particular, the cytoskeleton of a cell is a prime example of a nematically ordered semiflexible networks. Using computer simulations, we were able generate a directionally ordered network and model its responses to shear. In doing so, we observed interesting nonlinear behaviour and a breakdown of rotational symmetry. Previous work had attributed this behavior due to buckling within the system, which caused filaments compressed by shear to buckle. This paper attempts to explore an interesting feature noticed within the network, namely, that as shear increased, the buckling seemed to propagate outward from a point of origin. To measure this, Matlab code was used to generate a correlation function from the buckling data, and it was found that the observed buckling did propagate outward. Currently more work is being done to model the mechanism behind this buckling propagation.
Real time simulation of construction workers using combined human body and hand tracking for robotic construction worker system
Year: 2018

Construction is an inherently less safe sector than other sectors because it exposes workers to harsh and dangerous working environments. The nature of the construction industry results in a comparatively high incidence of serious injuries and death caused by falls from a height, musculoskeletal disorders and being struck by objects. This paper presents a new concept that can tackle this problem in the future. The central hypothesis of this study is that it is possible to eliminate injuries if we move the human construction worker off site and remotely link his/her motions to a Robotic Construction Worker (RCW) on site. As a first steppingstone towards this ultimate goal, two systems essential for the RCW were developed in this study. First, a novel system that combines 3D body and hand position tracking was developed to capture the movements of human construction worker. This combination of tracking enables the capture of changes in the orientations and articulations of the entire human body. Second, a real time simulation system that connects a human construction worker off site to a virtual RCW was developed to demonstrate the proposed concept in a variety of construction scenarios. The simulation results demonstrate the future viability of the RCW concept and indicate the promise of this system for eliminating the health and safety risks faced by human construction workers.
A Study on News Headline Translation from the Cognitive Perspective Based on Conceptual Blending Theory
Year: 2015

In the light of cognitive linguistics, particularly the conceptual blending theory the author holds that translation is a cognitive activity closely related to human cognition and bodily experience. What the translator needs to do is re experience the cognition and mental or physical experience of the source language writer and transfer it into the target language with reference to the target language readers' cognition and experience. Translation is the blend of concepts concerned in the two language spaces vie the translator's mental work. The same is true with the process of news translation. Combining the conceptual blending theory with the practice of news translation, the author aims at on line exploring and investigating the process of translation and explains it from cognitive perspective.
Integration of Partial Information Hiding Technology with Multi level Trust Privacy Preserving Data Mining
Year: 2015

Privacy Preserving Data Mining (PPDM) addresses the problem of developing accurate models about aggregated data without access to precise information in individual data record. A widely studied perturbation based PPDM approach introduces additive perturbation. Previous solutions of this approach are limited in their tacit assumption of single level trust on data miners. In this work, the scope of perturbation based PPDM is expanded to Multilevel Trust (MLT PPDM) In this setting, the more trusted a data miner is, the less perturbed copy of the data it can access. Under this setting, a malicious data miner may have access to differently perturbed copies of the same data through various means, and may combine these diverse copies to jointly infer additional information about the original data that the data owner does not intend to release. Preventing such diversity attacks is the key challenge of providing MLT PPDM services. This challenge is addressed by properly correlating perturbation across copies at different trust levels. This solution allows a data owner to generate perturbed copies of its data for arbitrary trust levels on demand. This feature offers data owner"s maximum flexibility.