nils-herrmann/clean_scirepeval huggingface dataset:
Multicore processor based instrument control and data processing units design for the SPICA instruments
Venue: Astronomical Telescopes Instrumentation
Year: 2020

In this paper we describe the activities towards the design of a common framework for the Instrument Control and Data Processing Units for the three scientific payload instruments on board the joint ESA JAXA SPICA mission, currently at the end of its phase A study. In this context, we started a program to assess modular architectures based on the use of a quad core fault tolerant LEON4 SPARC V8 processor on a SpaceWire network. We will describe the results of our initial tests using both Asymmetric Multi processing (AMP) and Symmetric Multi Processing (SMP) configurations. In addition, the possibility to adopt the RTEMS real time operating system, already space qualified on single core processors, will be evaluated both in terms of latency performances and of dynamical allocation of the resources. Finally, we will present the outline of the way forward for the next phases of the SPICA project.
"Conservatives Overfit, Liberals Underfit" The Social Psychological Control of Affect and Uncertainty
Venue: ArXiv
Year: 2019

The presence of artificial agents in human social networks is growing. From chatbots to robots, human experience in the developed world is moving towards a socio technical system in which agents can be technological or biological, with increasingly blurred distinctions between. Given that emotion is a key element of human interaction, enabling artificial agents with the ability to reason about affect is a key stepping stone towards a future in which technological agents and humans can work together. This paper presents work on building intelligent computational agents that integrate both emotion and cognition. These agents are grounded in the well established social psychological Bayesian Affect Control Theory (BayesAct) The core idea of BayesAct is that humans are motivated in their social interactions by affective alignment: they strive for their social experiences to be coherent at a deep, emotional level with their sense of identity and general world views as constructed through culturally shared symbols. This affective alignment creates cohesive bonds between group members, and is instrumental for collaborations to solidify as relational group commitments. BayesAct agents are motivated in their social interactions by a combination of affective alignment and decision theoretic reasoning, trading the two off as a function of the uncertainty or unpredictability of the situation. This paper provides a high level view of dual process theories and advances BayesAct as a plausible, computationally tractable model based in social psychological theory. We introduce a revised BayesAct model that more deeply integrates social psychological theorising, and we demonstrate a component of the model as being sufficient to account for cognitive biases about fairness, dissonance and conformity. We show how the model can unify different exploration strategies in reinforcement learning.
Exploiting Aesthetic Features in Visual Contents for Movie Recommendation
Venue: IEEE Access
Year: 2019

As one of the most widely used recommender systems, movie recommendation plays an important role in our life. However, the data sparsity problem severely hinders the effectiveness of personalized movie recommendation, which requires more rich content information to be utilized. Posters and still frames, which directly display the visual contents of movies, have significant influences on movie recommendation. They not only reveal rich knowledge for understanding movies but also useful for understanding user preferences. However, existing recommendation methods rarely consider aesthetic features, which tell how the movie looks and feels, extracted from these pictures for the movie recommendation. To this end, in this paper, we propose an aesthetic aware unified visual content matrix factorization (called UVMF AES) to integrate visual feature learning and recommendation into a unified framework. Specifically, we first integrate the convolutional neural network (CNN) features and aesthetic features into probabilistic matrix factorization. Then we establish a unified optimization framework with these features for the movie recommendation. The experimental results on two real world datasets show that our proposed method UVMF AES is significantly superior to the state of the art methods on movie recommendation.
Scheduling on (Un )Related Machines with Setup Times
Venue: 2019 IEEE International Parallel and Distributed Processing Symposium (IPDPS)
Year: 2019

We consider a natural generalization of scheduling n jobs on m parallel machines so as to minimize the makespan. In our extension the set of jobs is partitioned into several classes and a machine requires a setup whenever it switches from processing jobs of one class to jobs of a different class. During such a setup, a machine cannot process jobs and the duration of a setup may depend on the machine as well as the class of the job to be processed next. For this problem, we study approximation algorithms for non identical machines. We develop a polynomial time approximation scheme for uniformly related machines. For unrelated machines we obtain an O(log n log m) approximation, which we show to be optimal (up to constant factors) unless NP RP. We also identify two special cases that admit constant factor approximations.
Assessment and Remodelling of a Conventional Building into a Green Building Using BIM
Year: 2017

The demand for sustainable buildings has been highly increasing over the past few years. To meet the sustainability requirements of a building, changes or decisions must be made to a building during the design and preconstruction stages. This can be possible using Building Information Modelling (BIM) To demonstrate the use of BIM in sustainable design, a villa is taken for model study. The conventional building performance is analysed using Autodesk Revit which included various parameters like energy use intensity, lifecycle energy use/cost, annual carbon emissions, monthly heating and cooling loads, monthly peak demand and monthly electricity and fuel consumption. By employing the use of green materials in the conventional building model, the conventional building model is modified into a green building model. Using BIM, the use of artificial resources can be greatly reduced by replacing them with the use of renewable energy resources thereby saving energy. To show how efficient BIM is, the results of the performance of the conventional and green building models are compared. BIM has proved to be efficient in achieving sustainability with earlier decision making and rectification of errors.
Hard carbon microspheres derived from resorcinol formaldehyde resin as high performance anode materials for sodium ion battery
Venue: Ionics
Year: 2020

Hard carbon is a good anode material for sodium batteries due to its disordered structures and large interlayer spacing. However, the high cost and low initial Coulomb efficiencies limit its large scale application. In this study, we introduce a new way to produce the resorcinol formaldehyde resin derived hard carbon materials by spray drying and carbonization methods. The SEM and XRD indicate that the hard carbon materials have fine spherule shape and larger layer spacing (d002 0.4 nm) With the electrochemical performance test, we confirmed that RFHC 1100 sample exhibited an outstanding reversible specific capacity of 321 mAh/g and high initial Coulomb efficiency of 82% at the current rate of 0.1 C. In addition, the sodium ion storage mechanism is analyzed through GITT. This work provides a simple and efficient way to convert resorcinol formaldehyde resins into high capacity hard carbon materials, which opens up new avenues for the preparation of anode materials for sodium ion batteries.
Acquired Cystic Disease Associated Renal Cell Carcinoma Extending to the Renal Pelvis Mimicking Urothelial Carcinoma on Computed Tomography (CT) Two Case Reports
Venue: The American journal of case reports
Year: 2020

Male, 66 year old Male, 67 year old Final Diagnosis: Acquired cystic disease associated renal cell carcinoma Symptoms: Hematuria Medication:  Mistake in diagnosis  Acquired cystic disease associated renal cell carcinoma (ACD associated RCC) which was added to the 2016 World Health Organization classification, is the most common subtype of RCC in patients undergoing long term dialysis. ACD associated RCC is underrecognized and reports of computed tomography (CT) and magnetic resonance imaging findings for the lesion are sparse. Similar to urothelial carcinoma, ACD associated RCC is poorly to slightly enhanced on dynamic CT. Here, we report 2 cases of ACD associated RCC filling the renal pelvis and mimicking urothelial carcinoma.  We describe 2 cases of ACD associated RCC filling the left renal pelvis in patients undergoing dialysis for more than 10 years. In both cases, the patient's chief complaint was hematuria, and a left renal pelvic mass with poor enhancement was seen on dynamic CT. In both cases, the preoperative diagnosis was urothelial carcinoma of the left renal pelvis. Total nephroureterectomy was performed, and the final diagnosis was ACD associated RCC.  ACD associated RCC is a common tumor in patients undergoing long term dialysis. When ACD associated RCC is located in the renal pelvis, the imaging findings are similar to those of urothelial carcinoma. Therefore, it is important for radiologists to include ACD associated RCC in the differential diagnosis.
Student staff co creation in higher education: an evidence informed model to support future design and implementation
Venue: Journal of Higher Education Policy and Management
Year: 2019

Increased marketisation and competition has renewed interest in how universities can partner, or co create, with students. To address this, and further conceptualise a model of co creation across inputs, processes, and outcomes, this article summarises the findings from 10 different case studies of student staff co creation (e.g. co producers of learning resources, peer mentors, co creators of the curriculum) in the Australasian higher education context. Our data include qualitative survey responses (n= 97) and interviews (n= 35) with students and staff. Based on these data, we present an evidence informed model of co creation that elucidates the key considerations in the co creation process. The model highlights and distinguishes two dual value creation dimensions that underlie co creation, co production, and value in use. The result is a model of co creation in higher education that can help guide administrators, researchers, and higher education stakeholders to better conceptualise, design, implement, and assess co creation activities.
Tri band Semicircular Slit Based Wearable Antenna for Defense Applications
Year: 2019

This article presents a novel tri band semicircular slit based wearable antenna covering defense and LTE applications. Initially, the proposed antenna is constructed with three adjusted minor semicircular slit cuts on the right top layer of the patch to obtain multiple responses at 2.48, 3.5, and 5 GHz. Simulations with iterative designs are carried using CST Microwave Studio EM simulator and experimentally measured 10 dB return loss impedance bandwidths are 34, 99, and 115 MHz, respectively, on the FR4 laminate. In addition, a parametric study is carried on to use the same prototype model as a wearable/off body antenna by choosing Jeans substrate with a dielectric constant of 1.7. At last, it is found that the optimized antenna with better radiation patterns preferably at low resonant band and also a stable high gain is obtained over all the operating bands.
Integrated reporting and the performativity of intellectual capital
Year: 2019

The research investigates how intellectual capital (IC) is problematised in the context of integrated reporting. Drawing on a performative approach to IC, the paper explores the role of organisational actors in defining, classifying and valuing IC within the process of preparing an integrated report (IR) The analysis relies on in depth interviews with IR preparers of a European oil and gas company that has been issuing IRs since 2012 and considers IC to be a core element of its business model and value creation story. The case study reveals that IC definition, classification and valuation stimulate ongoing interaction among various actors. An active role is played by the staff of the department responsible for the IR preparation process, but also by organisational actors who are not directly involved in this process and by external actors, such as the company's peers, IIRC representatives and the company's accounting advisors. Some sketches, matrixes and maps inspired by the IIRC Framework (i.e. the business model sketch, the KPIs matrix and the connectivity map) were pivotal in defining concepts and categories of IC and its connection to value creation, although the quantification of IC's effect on value creation remains disputed. In showing how organisational actors problematise and engage with IC inscriptions within the process of IR preparation, the paper enriches the scant research that examines the performativity of IC in the context of corporate external reporting and answers the call for more research on how IC affects the management of organisations.
SELF CALIBRATED NON CONTACT CAPACITIVE TYPE LEVEL SENSOR FOR LIQUID AT DIFFERENT TEMPERATURES
Year: 2019

A novel non contact, fringe capacitance based sensor is proposed to measure the liquid level in process industries, to avoid the effects of hazardous liquids chemicals. In practical situations, the sensors are required to be calibrated repetitively when the dielectric medium/dielectric constant changes owing to the fluctuations in the process/environmental parameters. This work deals with the design of low cost, noncontact, fringe capacitance based sensor for monitoring multi threshold levels employing self calibration feature. A sensor with Al electrodes is fabricated and interfaced with PLC S200 through switching unit. The performance is evaluated with same liquid at four different temperatures which leads to significant changes in dielectric constants. The results indicate that the concept of fringe capacitance facilitates the feature of self calibration. It is also observed that the PLC S200 monitors the existing threshold level with lowest error varying from 0.408% FSO to 0.816% FSO.
Accurate Proteome wide Label free Quantification by Delayed Normalization and Maximal Peptide Ratio Extraction, Termed MaxLFQ
Venue: Molecular Cellular Proteomics
Year: 2014

Protein quantification without isotopic labels has been a long standing interest in the proteomics field. However, accurate and robust proteome wide quantification with label free approaches remains a challenge. We developed a new intensity determination and normalization procedure called MaxLFQ that is fully compatible with any peptide or protein separation prior to LC MS analysis. Protein abundance profiles are assembled using the maximum possible information from MS signals, given that the presence of quantifiable peptides varies from sample to sample. For a benchmark dataset with two proteomes mixed at known ratios, we accurately detected the mixing ratio over the entire protein expression range, with greater precision for abundant proteins. The significance of individual label free quantifications was obtained via a t test approach. For a second benchmark dataset, we accurately quantify fold changes over several orders of magnitude, a task that is challenging with label based methods. MaxLFQ is a generic label free quantification technology that is readily applicable to many biological questions; it is compatible with standard statistical analysis workflows, and it has been validated in many and diverse biological projects. Our algorithms can handle very large experiments of 500+ samples in a manageable computing time. It is implemented in the freely available MaxQuant computational proteomics platform and works completely seamlessly at the click of a button.
Adopting a flipped classroom approach for teaching molar calculations to biochemistry and genetics students
Venue: Biochemistry and molecular biology education a bimonthly publication of the International Union of Biochemistry and Molecular Biology
Year: 2019

The flipped classroom is a relatively new active learning pedagogical intervention, gaining popularity as a blended learning methodology. The flipped classroom comprises two distinct parts, directed learning carried out at the student's own pace away from the classroom and an interactive, class based activity encouraging problem solving and experiential learning. This research presents a 1 year study to measure student performance and perception toward a flipped classroom approach to teaching core biochemical calculations to first year undergraduate biochemistry and genetics students. A post task questionnaire showed an overall positive student perception with an associated significant improvement in the end of module summative assessment. These results suggest that this teaching approach offers some advantages over more traditional teaching pedagogies.
Assaying neural activity of children during video game play in public spaces: a deep learning approach.
Venue: Journal of neural engineering
Year: 2019

Understanding neural activity patterns in the developing brain remains one of the grand challenges in neuroscience. Developing neural networks are likely to be endowed with functionally important variability associated with the environmental context, age, gender, and other variables. Therefore, we conducted experiments with typically developing children in a stimulating museum setting and tested the feasibility of using deep learning techniques to help identify patterns of brain activity associated with different conditions.  A four channel dry EEG based Mobile brain body imaging data of children at rest and during videogame play (VGP) was acquired at the Children's Museum of Houston. A data driven approach based on convolutional neural networks (CNN) was used to describe underlying feature representations in the EEG and their ability to discern task and gender. The variability of the spectral features of EEG during the rest condition as a function of age was also analyzed.  Alpha power (7 13 Hz) was higher during rest whereas theta power (4 7 Hz) was higher during VGP. Beta (13 18 Hz) power was the most significant feature, higher in females, when differentiating between males and females. Using data from both temporoparietal channels to classify between VGP and rest condition, leave one subject out cross validation accuracy of 67% was obtained. Age related changes in EEG spectral content during rest were consistent with previous developmental studies conducted in laboratory settings showing an inverse relationship between age and EEG power.  These findings are the first to acquire, quantify and explain brain patterns observed during VGP and rest in freely behaving children in a museum setting using a deep learning framework. The study shows how deep learning can be used as a data driven approach to identify patterns in the data and explores the issues and the potential of conducting experiments involving children in a natural and engaging environment.
Wi Go: accurate and scalable vehicle positioning using WiFi fine timing measurement
Venue: MobiSys
Year: 2020

Driver assistance and vehicular automation would greatly benefit from uninterrupted lane level vehicle positioning, especially in challenging environments like metropolitan cities. In this paper, we explore whether the WiFi Fine Time Measurement (FTM) protocol, with its robust, accurate ranging capability, can complement current GPS and odometry systems to achieve lane level positioning in urban canyons. We introduce Wi Go, a system that simultaneously tracks vehicles and maps WiFi access point positions by coherently fusing WiFi FTMs, GPS, and vehicle odometry information together. Wi Go also adaptively controls the FTM messaging rate from clients to prevent high bandwidth usage and congestion, while maximizing the tracking accuracy. Wi Go achieves lane level vehicle positioning (1.3 m median and 2.9 m 90 percentile error) an order of magnitude improvement over vehicle built in GPS, through vehicle experiments in the urban canyons of Manhattan, New York City, as well as in suburban areas (0.8 m median and 3.2 m 90 percentile error)
Obstetrics in colonial Philippines
Year: 2009

A historical background on the practice of obstetrics in the Philippines dating back to as early as the 18th century when the Philippines was a colony of Spain, towards the American regime in the early 20th century was provided. Online search of the Analytics in the Department of Science and Technology (DOST) Online Public Access Catalog, particularly the earlier issues of the Philippine Journal of Science, first published in 1906, was done, retrieving the works of the father of Philippine obstetrics, Fernando Calderon. A manual searching of the library collection of the Science and Technology Information Institute was also done, retrieving the rare two volumes of Scientists in the Philippines that featured a full length biography of the first lady physician in the country, Honoria Acosta Sison. The results of the study showed the dominant influence of the Spanish friars on all aspects of life including socio medical. Midwives received direct instructions from priests. The friars wrote manuals on how to assist women at birthing, which although ludicrous, were obeyed to the letter. This study of early medical literature in the Philippines educates the world in the feminist tradition of pain.
Encouraging the Recycling Process of Urban Waste by Means of Game Theory Techniques Using a Multi agent Architecture
Venue: IBERAMIA
Year: 2018

The increase in population has caused the amount of waste generated in cities to increase, generally in large cities. It is necessary to develop solutions to reduce the amount of waste, and to be able to recycle most of it, avoiding wasting natural resources. Thanks to the treatment of these wastes, it is possible to obtain information to reduce costs and reduce the amount of waste generated, allowing a large quantity of them to be recycled. It is necessary to develop solutions to reduce the amount of waste, and to be able to recycle most of it, avoiding wasting natural resources. One of the main advantages of the Internet is its ability to connect people in remote locations and in real time. This has allowed the emergence of numerous collaborative applications, previously unthinkable, that are capable of solving traditional problems. This paper aims to address the problem of waste treatment by encouraging recycling among the different users involved in the system. This objective is approached from a perspective in which participants are rewarded for the amount they recycle, in order to encourage their participation and involvement in the recycling process. A framework that integrates gamification methodologies and multi agent technology is presented with a case study demonstrating the benefits of the developed system. More specifically, a case study has been carried out simulating the functionality of this system by recreating the conditions of a real urban environment. The case study has made it possible to evaluate the effectiveness of the gamification system for citizen incentive in the recycling process.