nils-herrmann/clean_scirepeval huggingface dataset:
A Real Time Simulation and Modeling of Flood Hazard JASRUL
Year: 2008

The increasing flash flood hazards in major cities like Kuala Lumpur have caused tremendous damages to the society and this requires more essential countermeasures to be implemented. With the advancement in 3D Computer Graphics and fluid simulation technologies, movie experts can now produce realistic visual effects for fluid objects such as water. This paper describes a study made to model and simulate the flash flood incident that struck Kuala Lumpur on 10 June 2007 using 3D Computer Graphics and fluid simulation techniques. The main goal was to study and examine the stability and effectiveness of this approach as a solution tool for environmental studies. Particle based technique called Smoothed Particle Hydrodynamics (SPH) method were used to model the flash flood behavior. This was done using MAYA plug in software called GLU3D which was developed based on SPH architecture. Geographical Information System (GIS) data such as Light Detection and Ranging (LIDAR) Digital Elevation Model (DEM) and remote sensing imagery were used to model the study area. Results show an adequate realism of water movement for the area studied. A prototype of a flood system was developed using MAYA Application Programming Interface (API) to examine the real time effectiveness of flood movement. The study has verified the usability of 3D computer graphics and fluid simulation particle based technique for environmental study purposes. The main contribution the study was to show that this approach can produce a realistic visualization thus enable more precautions and countermeasures to prevent the disaster.  Three Dimensional (3D) Computer Graphics, Fluid Simulation, Computational Fluid Dynamics (CFD) Smoothed Particle Hydrodynamics (SPH) Application Programming Interface (API) Geographical Information System (GIS) Light Detection and Ranging (LIDAR) Digital Elevation Model (DEM)
Energy savings with outdoor temperature based smart ventilation control strategies in advanced California homes
Venue: Energy and Buildings
Year: 2019

We simulated the energy performance of smart ventilation controls based on outdoor temperature in homes located in California climate regions, designed to comply with the 2016 Title 24, Part 6 California Energy Code prescriptive requirements. The smart controls shift ventilation rates in time, but ensure an annual occupant pollutant exposure equal to, or less than, what would be experienced in a home with a constant ventilation rate equal to the whole house target airflow in the ASHRAE Standard 62.2 2016. Annual simulations of previously developed prototype homes with validated ventilation models were conducted via co simulation of EnergyPlus with CONTAM. Controller performance varied substantially by climate zone, airtightness and house prototype. Smart controls were generally ineffective in Climate Zone 1 (Arcata) due to its lack of a cooling season and low diurnal temperature swings. The most successful smart controls used parameters pre calculated using an optimization routine. In order to better inform state energy policy, we also determined savings weighted by the amount of new home construction in each climate. The best controls averaged about one third of ventilation related energy savings that increased to about 48 55% when weighted. Annual average savings were about 650 700 kWh/year for the best controllers. The vast majority of site energy savings were for heating end uses >90% of total savings) Whole house ventilation rates increased between 0% and 42% with typical increases in the 15 20% range. The best performing control strategies shifted ventilation rates seasonally and modulated flows within each season. Peak concentrations during low ventilation times were kept below an exposure level of 3, well below the maximum of 5 required to avoid acute exposure issues.
The origins of the Malagasy people, some certainties and a few mysteries
Year: 2018

The Malagasy language belongs to the Greater Barito East group of the Austronesian family, the language most closely connected to Malagasy dialects is Maanyan (Kalimantan) but Malay as well other Indonesian and Philippine languages are also related. The African contribution is very high in the Malagasy genetic make up (about 50% but negligible in the language. Because of the linguistic link, it is widely accepted that the island was settled by Indonesian sailors after a maritime trek but date and place of landing are still debated. The 50% Indonesian genetic contribution to present Malagasy points in a different direction then Maanyan for the Asian ancestry, therefore, the ethnic composition of the Austronesian settlers is also still debated. In this talk I mainly review the joint research of Filippo Petroni, Dima Volchenkov, S\"oren Wichmann and myself which tries to shed new light on these problems. The key point is the application of a new quantitative methodology which is able to find out the kinship relations among languages (or dialects) New techniques are also introduced in order to extract the maximum information from these relations concerning time and space patterns.
Dentin hypersensitivity: prevalence, etiology, pathogenesis, and management
Year: 2015

Dentin hypersensitivity is simply defined as a short sharply painful reaction of the exposed and innervated pulp dentin complex in response to stimuli being typically thermal, evaporative, tactile, osmotic, or chemical and which reaction cannot be attributed to any dental defect or pathology. To be hypersensitive, dentin must be exposed and the exposed tubules must be open and patent to both the oral cavity and the pulp. Exposure of dentin through the loss of gingival and periodontal tissue may be caused by either too meticulous or by neglected oral hygiene. Exposure of dentin by the loss of the protecting enamel is mainly caused by erosion, abrasion, and abfraction or a combination thereof. Clinical examination for dentin hypersensitivity would include a pain provocation test by a tactile stimulus, an evaporative air stimulus, or a cold stimulus. A number of other dental conditions can give rise to pain symptoms, which may mimic those of dentin hypersensitivity. Therefore, careful examination is necessary to exclude the conditions, which need different treatment options. When the patients do suffer from dentin hypersensitivity, there is broad range of treatment options comprising home use and professional approaches. It is advised to start with the less invasive home use therapies and only expand to professional in office treatments when the home use treatments are not effective. When decided to continue with in office treatments, again one should start with the least invasive ones. The working mechanisms fall under two basic categories being nerve desensitization (potassium salts and guanethidine) and occlusion of exposed dental tubules (chemically: strontium, fluoride, stannous, oxalate, calcium phospho silicate, arginine calcium carbonate, nano hydroxyapatite, and glutaraldehyde; mechanically: pumice paste, glassionomers, dentin bondings, and resins; laser therapy) Regenerative mucogingival therapy also remains an alternative, where hard and soft tissue conditions allow.
Measurement of Radiation Damage to 130nm Hybrid Pixel Detector Readout Chips
Year: 2009

We present the first measurements of the performance of the Medipix3 hybrid pixel readout chip after exposure to significant x ray flux. Specifically the changes in performance of the mixed mode pixel architecture, the digital periphery, digital to analogue converters and the e fuse technology were characterised. A high intensity, calibrated xray source was used to incrementally irradiate the separate regions of the detector whilst it was powered. This is the first total ionizing dose study of a large area pixel detector fabricated using the 130nm CMOS technology.
Guided Learning for the combination of weakly supervised and semi supervised learning
Year: 2019

We propose a simple but efficient method to combine semi supervised learning with weakly supervised learning for deep neural networks. Weakly supervised learning is to solve the task which requires fine level prediction with only coarse level annotations available. Designing deep neural networks for weakly supervised learning is always accompanied by a trade off between fine level information detection performance and coarse level classification accuracy. While combining weakly supervised learning with semi supervised learning using unlabeled data, in contrast to seeking for this trade off, we design two different models for different targets. One merely pursues finer information detection performance as the final target, while another one is more professional in achieving higher coarse level classification accuracy so that it is regarded as a more professional teacher to teach the former model using unlabeled data. We present an end to end semi supervised learning process termed Guided Learning for these two different models to improve the training efficiency. Our approach outperforms the first place result on DCASE2018 Task 4 which employs Mean Teacher with a well design CRNN network from 32.4% to 38.9% achieving state of the art performance.
To what extent have learners with severe, profound and multiple learning difficulties been excluded from the policy and practice of inclusive education?
Year: 2020

The article is a position paper on inclusive practice in education with respect to students with severe or profound and multiple learning difficulties (sld/pmld) It asks if children and young people with sld/pmld have been excluded from the policy and the practice of inclusive education. A review of the literature found that there is a research gap around inclusive education for learners with sld/pmld, and a review of historical and current practices indicated that this group of learners has indeed been excluded from both the policy and practice of inclusion in the United Kingdom with the use of curricula based on a mainstream linear and academic model reinforcing this exclusion. The study makes a theoretical and practical contribution to the continuing debate about inclusive education and will be of interest to teachers, parents, policy makers and the learners themselves.
A Pruning Based Algorithm for Computing Optimal Coalition Structures in Linear Production Domains
Venue: Canadian Conference on AI
Year: 2006

Computing optimal coalition structures is an important research problem in multi agent systems. It has rich application in real world problems, including logistics and supply chains. We study computing optimal coalition structures in linear production domains. The common goal of the agents is to maximize the system's profit. Agents perform two steps: i) deliberate profitable coalitions, and ii) exchange computed coalitions and generate coalition structures. In our previous studies, agents keep growing their coalitions from the singleton ones in the deliberation step. This work takes opposite approach that agents keep pruning unlikely profitable coalitions from the grand coalition. It also relaxes the strict condition of coalition center, which yields the minimal cost to the coalition. Here, agents merely keep generating profitable coalitions. Furthermore, we introduce new concepts, i.e. best coalitions and pattern, in our algorithm and provide an example of how it can work. Lastly, we show that our algorithm outperforms exhaustive search in generating optimal coalition structures in terms of elapsed time and number of coalition structures generated.
Attentive Fusion Enhanced Audio Visual Encoding for Transformer Based Robust Speech Recognition
Venue: 2020 Asia Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC)
Year: 2020

Audio visual information fusion enables a performance improvement in speech recognition performed in complex acoustic scenarios, e.g. noisy environments. It is required to explore an effective audio visual fusion strategy for audiovisual alignment and modality reliability. Different from the previous end to end approaches where the audio visual fusion is performed after encoding each modality, in this paper we propose to integrate an attentive fusion block into the encoding process. It is shown that the proposed audio visual fusion method in the encoder module can enrich audio visual representations, as the relevance between the two modalities is leveraged. In line with the transformer based architecture, we implement the embedded fusion block using a multi head attention based audiovisual fusion with one way or two way interactions. The proposed method can sufficiently combine the two streams and weaken the over reliance on the audio modality. Experiments on the LRS3 TED dataset demonstrate that the proposed method can increase the recognition rate by 0.55% 4.51% and 4.61% on average under the clean, seen and unseen noise conditions, respectively, compared to the state of the art approach.
Should, and how can, exercise be done during a coronavirus outbreak? An interview with Dr. Jeffrey A. Woods
Venue: Journal of Sport and Health Science
Year: 2020

Cite this artic J Sport Health Sci 2020;9:10 The Coronavirus disease (COVID 19) crisis is now present in China. It started in December, 2019 and has, so far, led 213 individuals died and at least 9066 infected in China by local time 17:26, January 30, 2020. It has also spread to a number of Asian countries, as well as to Canada, France, Germany, and the United States. As a result, the Chinese government has put several major cities in Hubei Province on lockdown and has thrown plans for the Lunar New Year holiday into chaos for millions of people. On January 30, 2020, the World Health Organization also declared the COVID 19 outbreak a global health emergency because it could spread to countries that are not prepared. Furthermore, to prevent the spread of the new and deadly virus, all cities in China now have shut down most public places and facilities, including parks, leaving many people with no place to exercise. As a result, people may wonder if one should exercise at all during the outbreak and if so, how? These questions made Journal of Sport and Health Science remember some well known studies done by my colleague, Dr. Jeffrey A. Woods and his team at the University of Illinois at UrbanaChampaign (UIUC) in which they found a protective effect of exercise on mortality due to influenza in mice. Dr. Woods is a Mottier Family Professor at UIUC. His research focuses on the effects of exercise on the immune system, the gut microbiome, and aging. He was among the first scholars to demonstrate that regular exercise can have an anti inflammatory effect on the body and showed that exercise can improve the immune response to the flu vaccine in older adults. I interviewed Dr. Woods for the "Should and How" questions.  Would you please tell the readers of Journal of Sport and Health Science a little bit about yourself, for example,
Neural Architecture Search for Deep Image Prior
Venue: Comput. Graph.
Year: 2021

We present a neural architecture search (NAS) technique to enhance the performance of unsupervised image de noising, in painting and super resolution under the recently proposed Deep Image Prior (DIP) We show that evolutionary search can automatically optimize the encoder decoder (E D) structure and meta parameters of the DIP network, which serves as a content specific prior to regularize these single image restoration tasks. Our binary representation encodes the design space for an asymmetric E D network that typically converges to yield a content specific DIP within 10 20 generations using a population size of 500. The optimized architectures consistently improve upon the visual quality of classical DIP for a diverse range of photographic and artistic content.
Predicting human gaze using quaternion DCT image signature saliency and face detection
Venue: 2012 IEEE Workshop on the Applications of Computer Vision (WACV)
Year: 2012

We combine and extend the previous work on DCT based image signatures and face detection to determine the visual saliency. To this end, we transfer the scalar definition of image signatures to quaternion images and thus introduce a novel saliency method using quaternion type II DCT image signatures. Furthermore, we use MCT based face detection to model the important influence of faces on the visual saliency using rotated elliptical Gaussian weight functions and evaluate several integration schemes. In order to demonstrate the performance of the proposed methods, we evaluate our approach on the Bruce Tsotsos (Toronto)  and Cerf (FIFA)  benchmark eye tracking data sets. Additionally, we present evaluation results on the Bruce Tsotsos data set of the most important spectral saliency approaches. We achieve state of the art results in terms of the well established area under curve (AUC) measure on the Bruce Tsotsos data set and come close to the ideal AUC on the Cerf data set with less than one millisecond to calculate the bottom up QDCT saliency map.
Combining Semantic Guidance and Deep Reinforcement Learning For Generating Human Level Paintings
Venue: CVPR
Year: 2021

Generation of stroke based non photorealistic imagery, is an important problem in the computer vision community. As an endeavor in this direction, substantial recent research efforts have been focused on teaching machines "how to paint" in a manner similar to a human painter. However, the applicability of previous methods has been limited to datasets with little variation in position, scale and saliency of the foreground object. As a consequence, we find that these methods struggle to cover the granularity and diversity possessed by real world images. To this end, we propose a Semantic Guidance pipeline with 1) a bi level painting procedure for learning the distinction between foreground and background brush strokes at training time. 2) We also introduce invariance to the position and scale of the foreground object through a neural alignment model, which combines object localization and spatial transformer networks in an end to end manner, to zoom into a particular semantic instance. 3) The distinguishing features of the in focus object are then amplified by maximizing a novel guided backpropagation based focus reward. The proposed agent does not require any supervision on human stroke data and successfully handles variations in foreground object attributes, thus, producing much higher quality canvases for the CUB 200 Birds and Stanford Cars 196 datasets. Finally, we demonstrate the further efficacy of our method on complex datasets with multiple foreground object instances by evaluating an extension of our method on the challenging Virtual KITTI dataset.
Efficiency of Board Composition on Firm Performance: Empirical Evidence from listed Manufacturing Firms of Bangladesh
Year: 2018

Corporate governance has received massive attention in academic research nowadays due to several recent corporate failures. Inefficiency of corporate governance mechanisms have driven the minds of the researchers and the policy makers to look with more insights into this area. Board composition, as part of corporate governance mechanism, plays a significant role to achieve company's goals or objectives and ensure transparency and accountability. The objective of this study is to find out the efficiency of board composition through board size, independent directors and female directors on firm performance in the listed manufacturing firms of Bangladesh. In this study, a sample of 162 firm years are considered as the sample during the period of 2011 to 2016. This study finds that large board is the significant explanatory variable in improving firm performance. This study also shows that board independence and female directors have no significant association with firm performance which implies that instrument of corporate governance mechanism particularly board composition is very weak. This study recommends that code of corporate governance, specially the role of independent directors and female directors, should be reformed in the light of cultural and institutional context along with the effective enforcement.
Automatic water filling and capping machine trainer kit programmable logic controller controller prototype for vocational high school learning validity
Venue: IOP Conference Series: Materials Science and Engineering
Year: 2019

The purpose of this article is to find out validity level PLC controlled automatic water filling and capping machine as trainer kit for learning in industry automation competence expertise in vocational high school. Research type used in this research is research and development (RD Analyze, Design, Develop, Implement, and Evaluate. Instrument used is questionnaire and observation sheet. Collected data is analyzed descriptively. Research result shown that validation toward automatic water filling and capping machine prototype as trainer kit for PC practical learning in vocational high school which done by media expert, material expert, and student shown result in very satisfactory category, indicated by: (1) media experts give average score 87.5% (very satisfactory) (2) material experts give average score of 86,9% (very satisfactory) and (3) student give average score of 95,3% (Very Good)
Bench to bedside translation of magnetic nanoparticles.
Year: 2014

Magnetic nanoparticles (MNPs) are a new and promising addition to the spectrum of biomedicines. Their promise revolves around the broad versatility and biocompatibility of the MNPs and their unique physicochemical properties. Guided by applied external magnetic fields, MNPs represent a cutting edge tool designed to improve diagnosis and therapy of a broad range of inflammatory, infectious, genetic and degenerative diseases. Magnetic hyperthermia, targeted drug and gene delivery, cell tracking, protein bioseparation and tissue engineering are but a few applications being developed for MNPs. MNPs toxicities linked to shape, size and surface chemistry are real and must be addressed before clinical use is realized. This article presents both the promise and perils of this new nanotechnology, with an eye towards opportunity in translational medical science.
Feasibility of autologous bone marrow mesenchymal stem cell derived extracellular matrix scaffold for cartilage tissue engineering.
Venue: Artificial organs
Year: 2013

Extracellular matrix (ECM) materials are widely used in cartilage tissue engineering. However, the current ECM materials are unsatisfactory for clinical practice as most of them are derived from allogenous or xenogenous tissue. This study was designed to develop a novel autologous ECM scaffold for cartilage tissue engineering. The autologous bone marrow mesenchymal stem cell derived ECM (aBMSC dECM) membrane was collected and fabricated into a three dimensional porous scaffold via cross linking and freeze drying techniques. Articular chondrocytes were seeded into the aBMSC dECM scaffold and atelocollagen scaffold, respectively. An in vitro culture and an in vivo implantation in nude mice model were performed to evaluate the influence on engineered cartilage. The current results showed that the aBMSC dECM scaffold had a good microstructure and biocompatibility. After 4 weeks in vitro culture, the engineered cartilage in the aBMSC dECM scaffold group formed thicker cartilage tissue with more homogeneous structure and higher expressions of cartilaginous gene and protein compared with the atelocollagen scaffold group. Furthermore, the engineered cartilage based on the aBMSC dECM scaffold showed better cartilage formation in terms of volume and homogeneity, cartilage matrix content, and compressive modulus after 3 weeks in vivo implantation. These results indicated that the aBMSC dECM scaffold could be a successful novel candidate scaffold for cartilage tissue engineering.