nils-herrmann/clean_scirepeval huggingface dataset:
Alleviating Memory Refresh Overhead via Data Compression for High Performance and Energy Efficiency
Venue: IEEE Transactions on Parallel and Distributed Systems
Year: 2018

DRAM memory suffers from increasingly aggravating refresh penalty, which causes significant performance degradation and power consumption. As memory capacity increases, refresh penalty has become increasingly worse as more rows have to be refreshed. In this work, we propose an effective refresh approach called Compression Aware Refresh (CAR) to efficiently mitigate refresh overheads. We apply a data compression technique to store data in a compressed format so that the resultant sparse banks need only be partially refreshed. Because of compression, data blocks which are originally distributed across all the constituent chips of a rank only need to be stored in a subset of those chips, leaving banks in the remaining chips not fully occupied. As a result, the memory controller can safely skip refreshing memory rows which contain no useful data without compromising data integrity. Such a compression aware refresh scheme significantly reduces the refresh and thus improves overall memory performance and energy efficiency. To further take advantage of data compression, we adopt a rank subsetting technique to enable accesses to only those occupied chips for memory requests accessing compressed data blocks. Evaluations using benchmarks from SPEC CPU 2006 and the PARSEC 3.0 on recent DDR4 memory systems have shown that CAR achieves up to 1.66x performance improvement (11.7 percent on average) and up to 45.9 percent energy reduction (27.3 percent on average) and reduce memory traffic by up to 99.9 percent for zero cache lines intensive workloads with an average of 66.3 percent across all benchmarks.
AETv2: AutoEncoding Transformations for Self Supervised Representation Learning by Minimizing Geodesic Distances in Lie Groups
Venue: ArXiv
Year: 2019

Self supervised learning by predicting transformations has demonstrated outstanding performances in both unsupervised and (semi )supervised tasks. Among the state of the art methods is the AutoEncoding Transformations (AET) by decoding transformations from the learned representations of original and transformed images. Both deterministic and probabilistic AETs rely on the Euclidean distance to measure the deviation of estimated transformations from their groundtruth counterparts. However, this assumption is questionable as a group of transformations often reside on a curved manifold rather staying in a flat Euclidean space. For this reason, we should use the geodesic to characterize how an image transform along the manifold of a transformation group, and adopt its length to measure the deviation between transformations. Particularly, we present to autoencode a Lie group of homography transformations PG(2) to learn image representations. For this, we make an estimate of the intractable Riemannian logarithm by projecting PG(2) to a subgroup of rotation transformations SO(3) that allows the closed form expression of geodesic distances. Experiments demonstrate the proposed AETv2 model outperforms the previous version as well as the other state of the art self supervised models in multiple tasks.
Optimum first failure load design of one/two core sandwich plates under blast loads, and their ultimate loads
Venue: Composite Structures
Year: 2019

We consider a sandwich plate with face sheets made of unidirectional fiber reinforced composite with fibers being either glass, or carbon or aramid and the core made of balsa wood loaded by a blast pressure, and find optimal geometries and materials for maximizing the first failure load. While analyzing the problem, we assume that the areal density is fixed and use the Nest Site Selection optimization algorithm, a third order shear and normal deformable plate theory, a one step stress recovery scheme, and the Tsai Wu failure criterion. We also delineate the effect on the first failure load of inertia forces and uncertainties in values of various parameters. For a sandwich plate optimally designed for the first failure load, we find the ultimate load by progressively degrading elasticities of failed elements. We find that the optimal single core sandwich designs are symmetric about the mid surface with thick face sheets and the optimal two core sandwich designs have a thin middle face sheet, and thick top and bottom face sheets. The first failure load of the optimal clamped single core (two core) design is approximately 20% (30% more than that of the corresponding simply supported plate. For simply supported (clamped) sandwich structures, the failure initiates at the centroid (center of the clamped edge) of either the top or the bottom surfaces. It is also found that the first failure occurs in a face sheet (core) due to the in plane transverse axial stress (transverse shear stress) exceeding its critical value. The collapse load of a clamped (simply supported) sandwich structure is approximately 15% 30% (0% 17% higher than the first failure load. The maximum deflection of the collapsed structure may be in a direction opposite to that of the applied load. A novelty of the work is in considering inertia effects in ascertaining the first and the ultimate failure loads and quantifying the benefits, if any, of two core over one core sandwich structures, and determining effects of uncertainties in values of parameters.
NPGRJ_NG_165 1..6
Year: 2008

Bmi1 plays an essential part in the self renewal of hematopoietic and neural stem cells. To investigate its role in other adult stem cell populations, we generated a mouse expressing a tamoxifen inducible Cre from the Bmi1 locus. We found that Bmi1 is expressed in discrete cells located near the bottom of crypts in the small intestine, predominantly four cells above the base of the crypt +4 position) Over time, these cells proliferate, expand, self renew and give rise to all the differentiated cell lineages of the small intestine epithelium. The induction of a stable form of b catenin in these cells was sufficient to rapidly generate adenomas. Moreover, ablation of Bmi1+ cells using a Rosa26 conditional allele, expressing diphtheria toxin, led to crypt loss. These experiments identify Bmi1 as an intestinal stem cell marker in vivo. Unexpectedly, the distribution of Bmi1 expressing stem cells along the length of the small intestine suggested that mammals use more than one molecularly distinguishable adult stem cell subpopulation to maintain organ homeostasis.
Baseline advanced RAIM user algorithm and possible improvements
Venue: IEEE Transactions on Aerospace and Electronic Systems
Year: 2015

We present a baseline multiple fault and multiconstellation advanced receiver autonomous integrity monitoring user algorithm for vertical guidance. After reviewing the navigation requirements for localizer performance with vertical guidance down to 200 feet, we describe in detail how to process the pseudorange measurements, the nominal error models, and the prior fault probabilities to obtain the protection levels and other figures of merit. In particular, we show how to determine which fault modes must be monitored and a method for performing fault exclusion. Finally, we present a list of possible algorithm improvements and simplifications.
The Effects of Self Regulation, Motivation, Anxiety, and Attributions on Mathematics Achievement for Fifth and Sixth Grade Students
Year: 2007

For this quantitative study, a total of n 761 students (58.1% female) from selected fifth and sixth grade mathematics classrooms in Alabama were surveyed in order to investigate the relationships between self regulated learning, motivation, anxiety, attributions and achievement in mathematics. Data analyses revealed that significant contributions are made by motivation and anxiety on both test score and mathematics grade for fifth grade students. Specific factors (e.g. self efficacy, worry, other, and failure) were related to academic performance while failure attribution was significantly related to mathematics grade. As for sixth grade students, data analyses showed relationships exist between motivation, anxiety and academic performance with specific factors (i.e. self efficacy, intrinsic value, and worry) significantly predicting both test score and mathematics grade for sixth graders. The findings underlie the importance of motivation and anxiety for students and how these constructs interact to facilitate self regulation over the course of developing expertise in a domain, such as mathematics.
Pengaruh Samsat Keliling, Samsat Drive Thru, E Samsat Terhadap Penerimaan Pajak Kendaraan Bermotor Pada Kantor Samsat Jakarta Timur
Year: 2020

Abstrak: Pajak kendaraan bermotor termasuk pajak daerah yang dikelola oleh pemerintah tingkat satu atau provinsi. Dalam mengelola pajak kendaraan bermotor, pemerintah daerah mendirikan Kantor Samsat dimana kantor tersebut memberikan bermacam macam layanan seperti Samsat Keliling, Samsat Drive Thru dan E Samsat. Penelitian ini bertujuan untuk mengetahui pengaruh secara parsial maupun simultan Penerimaan Pajak dari Samsat keliling, Samsat Drive Thru dan E Samsat terhadap penerimaan pajak kendaraan bermotor di Jakarta Timur. Metode analisis menggunakan metode Diskriptif Kuantitatif dan di analisis menggunakan regresi linier berganda. Hasil penelitian menunjukkan secara parsial Samsat Drive Thru berpengaruh signifikan terhadap penerimaan pajak kendaraan bermotor, sedangkan Samsat keliling dan E Samsat secara parsial tidak berpengaruh terhadap penerimaan Pajak kendaraan bermotor pada Kantor Samsat Jakarta Timur. Secara Simultan Samsat Keliling, Samsat Drive Thru, E Samsat berpengaruh terhadap penerimaan pajak kendaraan bermotor pada Kantor Samsat Jakarta Timur Kata kunci: Samsat keliling, Samsat Drive Thru, E Samsat, Pajak kendaraan Bermotor Abstract: The motor vehicle tax includes local taxes which are managed by the first or provincial level government. In managing the motor vehicle tax, the local government established the Samsat Office where the office provided various services such as the Mobile Samsat, the Samsat Drive Thru and the E Samsat. This study aims to determine the effect of partial or simultaneous tax receipts from mobile Samsat, Samsat Drive Thru and E Samsat on motor vehicle tax in East Jakarta. The analysis method uses quantitative descriptive method and analyzed using multiple linear regression. The results showed partially Samsat Drive Thru had a significant effect on motor vehicle tax revenue, while mobile Samsat and E Samsat partially had no effect on motor vehicle tax receipts in the East Jakarta area. Simultaneously Samsat Keliling, Samsat Drive Thru, E Samsat affect the tax revenue of motor vehicles in the area samsat of East Jakarta  Mobile Samsat, Samsat Drive Thru, E Samsat, Motorized Vehicle Tax
Mechanosensitive mechanisms in transcriptional regulation
Venue: Journal of Cell Science
Year: 2012

Transcriptional regulation contributes to the maintenance of pluripotency, self renewal and differentiation in embryonic cells and in stem cells. Therefore, control of gene expression at the level of transcription is crucial for embryonic development, as well as for organogenesis, functional adaptation, and regeneration in adult tissues and organs. In the past, most work has focused on how transcriptional regulation results from the complex interplay between chemical cues, adhesion signals, transcription factors and their co regulators during development. However, chemical signaling alone is not sufficient to explain how three dimensional (3D) tissues and organs are constructed and maintained through the spatiotemporal control of transcriptional activities. Accumulated evidence indicates that mechanical cues, which include physical forces (e.g. tension, compression or shear stress) alterations in extracellular matrix (ECM) mechanics and changes in cell shape, are transmitted to the nucleus directly or indirectly to orchestrate transcriptional activities that are crucial for embryogenesis and organogenesis. In this Commentary, we review how the mechanical control of gene transcription contributes to the maintenance of pluripotency, determination of cell fate, pattern formation and organogenesis, as well as how it is involved in the control of cell and tissue function throughout embryogenesis and adult life. A deeper understanding of these mechanosensitive transcriptional control mechanisms should lead to new approaches to tissue engineering and regenerative medicine.
Experimental study on the cascaded thermal energy storage system using MWCNT enhanced phase change materials
Year: 2019

The proposed work is about the effectiveness of latent heat dispersion on energy storage using phase change materials with modified thermal stratifiers on charging inlet of the tank. A novel flow governing nozzle with swirl flow injection is designed and fabricated to enhance the steady state of heat flow inside the cascaded thermal energy storage (TES) system. Spherical phase change materials (PCM) capsules with added multiwall carbon nanotubes (MWCNT) particles used as an energy storage material inside the TES tank. Experimentation conducted for the variable process parameters with mass flow rates, temperature, and injection pressure to differentiate the initial stratification of the thermocline system. Results obtained from the analysis clarify that with the increase in swirl number through injection pressure, temperature, and flow rate increases the steady state stratification behavior inside the cascaded TES tank. It is clear that the effective charging rate of 35 min observed from the system at 1 wt% of MWCNT with the increase in injection pressure and flow rate of 4 bar and 3 L/min. It also noted that the swirl effect increases the heat dissipation on the PCM capsules by maintaining a concentric heat transfer with the radial flow over the layers of the PCM capsules. Discharging trials are carried out by the batch wise process to recover the stored thermal energy. Moreover, the cascaded latent heat thermal energy storage system using MWCNT filled PCM is best suitable for water heating applications to overcome the intermittency.
R Drop: Regularized Dropout for Neural Networks
Venue: ArXiv
Year: 2021

Dropout is a powerful and widely used technique to regularize the training of deep neural networks. In this paper, we introduce a simple regularization strategy upon dropout in model training, namely R Drop, which forces the output distributions of different sub models generated by dropout to be consistent with each other. Specifically, for each training sample, R Drop minimizes the bidirectional KL divergence between the output distributions of two sub models sampled by dropout. Theoretical analysis reveals that R Drop reduces the freedom of the model parameters and complements dropout. Experiments on 5 widely used deep learning tasks (18 datasets in total) including neural machine translation, abstractive summarization, language understanding, language modeling, and image classification, show that R Drop is universally effective. In particular, it yields substantial improvements when applied to fine tune large scale pre trained models, e.g. ViT, RoBERTa large, and BART, and achieves state of the art (SOTA) performances with the vanilla Transformer model on WMT14 English German translation (30.91 BLEU) and WMT14 English French translation (43.95 BLEU) even surpassing models trained with extra large scale data and expert designed advanced variants of Transformer models. Our code is available at GitHub1.
MDE 4 IoT Supporting the Internet of Things with Model Driven Engineering
Year: 2016

The Internet of Things (IoT) unleashes great opportunities to improve our way of living and working through a seamless and highly dynamic cooperation among heterogeneous Things including both computer based systems and physical objects. However, properly dealing with the design, development, deployment and runtime management of this breathing network of Things means to provide solutions for a multitude of challenges. Among them, we focus on: supporting complexity and heterogeneity management, supporting collaborative development, maximising reusability of design artefacts, and providing self adaptation of IoT systems. In this paper we propose Model Driven Engineering (MDE) as a key enabler for solving these challenges and supporting the lifecycle of IoT systems, from their design to runtime management. More specifically, we: (i) introduce MDE4IoT, a Model Driven Engineering Framework supporting the modelling of Things and self adaptation of Emergent Configurations of connected systems in the IoT; and (ii) show how MDE in general, and MDE4IoT in particular, can help in tackling the above mentioned challenges by providing the Smart Street Lights case that we use throughout the paper as concrete case.
Female Oppression and Marginalization in Ice Candy Man by Sidhwa and the Holy Woman by Shahraz
Year: 2015

How women are marginalized during the partition and after the partition is the major theoretical concern of this research. The present research reveals how Sidhwa and Shahraz address the gender oppression in Ice Candy Man and The Holy Woman respectively. Different researchers also argues that both writers describe the subjection of women to rape, conversion, forced marriages and various sexual abuses during partition and in the days of peace. The social structure of our society regards women as the honor of family. In case of marginalization she suffers the sexual violence; she gets profaned, resulting in the honor killing of the family. Sidhwa and Shahraz represent this oppression, marginalization, and violence as the shameful event. Shahraz and Sidhwa's writings shows how the gender oppression work through their bodies. The selected novels of both writers show how the women are oppressed, marginalized, controlled, and exploited by the men under the shade of cultural, social, and religious traditions. This research will expose both the violent and subtle ways in which patriarchy repress the female to control in Pakistani society. The main focus of this research is to show how the works of these two female writers are critically examined to understand their role in exploring the issues of women that they face in this patriarchal society through the literary medium.  women, oppression, marginalization, patriarchy, religious, social, traditions
Student engagement with automated written corrective feedback (AWCF) provided by Grammarly: A multiple case study
Year: 2020

Despite the increased use of automated writing evaluation (AWE) systems and similar programs for assessment purposes in second language (L2) writing classrooms, research on student engagement with automated feedback is scarce. This naturalistic case study explored two ESL college students' engagement with automated written corrective feedback (AWCF) provided by Grammarly when revising a final draft. Following previous research, student engagement was operationalized using three interconnected dimensions: behavioral, cognitive, and affective. Behavioral engagement was explored through the analysis of QuickTime based screencasts of students' Grammarly usage. Cognitive and affective engagement were measured through the analysis of students' comments during stimulated recall of the aforementioned screencasts and semi structured interview. Findings suggest that students had different levels of engagement with AWCF. One showed greater cognitive engagement through his questioning of AWCF. However, he did little to verify the accuracy of feedback which resulted in moderate changes to his draft. The other's overreliance on AWCF indicated more limited cognitive engagement which led to feedback's blind acceptance. Nevertheless, this also resulted in moderate changes to her draft.
Deep convolutional neural network for segmentation of thoracic organs at risk using cropped 3D images.
Venue: Medical physics
Year: 2019

Automatic segmentation of organs at risk (OARs) is a key step in radiation treatment planning to reduce human efforts and bias. Deep convolutional neural networks (DCNN) have shown great success in many medical image segmentation applications but there are still challenges in dealing with large 3D images for optimal results. The purpose of this study is to develop a novel DCNN method for thoracic OARs segmentation using cropped 3D images.  To segment the five organs (left and right lungs, heart, esophagus and spinal cord) from the thoracic CT scans, preprocessing to unify the voxel spacing and intensity was first performed, a 3D U Net was then trained on resampled thoracic images to localize each organ, then the original images were cropped to only contain one organ and served as the input to each individual organ segmentation network. The segmentation maps were then merged to get the final results. The network structures were optimized for each step, as well as the training and testing strategies. A novel testing augmentation with multiple iterations of image cropping was used. The networks were trained on 36 thoracic CT scans with expert annotations provided by the organizers of the 2017 AAPM Thoracic Auto segmentation Challenge and tested on the challenge testing dataset as well as a private dataset.  The proposed method earned second place in the live phase of the challenge and first place in the subsequent ongoing phase using a newly developed testing augmentation approach. It showed superior than human performance on average in terms of Dice scores (spinal cord: 0.893 0.044, right lung: 0.972 0.021, left lung: 0.979 0.008, heart: 0.925 0.015, esophagus: 0.726 0.094) mean surface distance (spinal cord: 0.662 0.248 mm, right lung: 0.933 0.574 mm, left lung: 0.586 0.285 mm, heart: 2.297 0.492 mm, esophagus: 2.341 2.380 mm) and 95% Hausdorff distance (spinal cord: 1.893 0.627 mm, right lung: 3.958 2.845 mm, left lung: 2.103 0.938 mm, heart: 6.570 1.501 mm, esophagus: 8.714 10.588 mm) It also achieved good performance in the private dataset and reduced the editing time to 7.5 min per patient following automatic segmentation.  The proposed DCNN method demonstrated good performance in automatic OAR segmentation from thoracic CT scans. It has the potential for eventual clinical adoption of deep learning in radiation treatment planning due to improved accuracy and reduced cost for OAR segmentation.
Spatial negotiations in domestic space of the home based garment industry in Kampung Tambora, Jakarta
Year: 2020

With a population of around 260.100 and covering an area of 5.4 km2, Kampung Tambora is one of the densest urban areas in Jakarta. The area is well known for its home based garment industry, which supports the livelihood of the community. Typically, several houses in one alley run the garment industry business, where both men and women work and live under the same roof. The study aims to understand how all actors in the home based garment industry negotiate with the limited space to fulfill domestic and working needs. Three houses were selected for observation to understand how all involved actors maximize the use of the limited space through spatial interruption in domestic space. The study found that the actors define the activities through the utilization of space, the allocation of spatial functions, and symbolizing objects. Multi story houses were built to expand space and enable the owners to work without disrupting the privacy of domestic activities. Although the actors of the home based enterprises found ways to cope with the issues of crowding, alternative ideas are needed to increase their well being and improve the environmental quality of similar high density urban settlements in many parts of Jakarta.