nils-herrmann/clean_scirepeval huggingface dataset:
Investigation of Microstructure and Dispersoids/Precipitates in Additively Manufactured Aluminum Alloys
Venue: Microscopy and Microanalysis
Year: 2019

Additive manufacturing (AM) of metallic alloys is becoming an increasingly important area of interest for academia and industry due to its method of fabricating components with highly complex or customized geometries. The complicated thermo kinetic environment associated with AM is challenging for many traditional materials, particularly aluminum based alloys. Currently, many Al alloys manufactured by AM are Al Si alloys near the eutectic composition, while high strength 7xxx series alloys tends to exhibit excessive solidification cracking  However, opportunities are emerging in the design of novel Al alloys that are suitable for AM and take advantage of the AM process.
Existing Bridge Formulas for Truck Weight Regulation from International Jurisdictions and Resulting Load Stresses on Single Span Bridges
Year: 2016

AbstractThis paper identifies and characterizes existing bridge formulas from international regions and presents the results of an analysis of the allowable gross vehicle weights and bridge load stress effects on single span bridges resulting from these formulas. This is done to provide insight into this method of regulating truck size and weight to identify influencing factors and considerations for future decisions regarding the creation of new bridge formulas, or modification of existing ones. It is found that bridge formulas vary significantly in terms of the level of restrictiveness of allowable loads and imposed load effects due to the design criteria used in their development including truck configurations, bridge design methods, design loads, and allowable load rating. Bridge formulas should be designed to limit the imposed stresses on bridges based on criteria suitable to a jurisdiction's truck fleet and infrastructure characteristics in order to adequately regulate truck sizes and weights.
Seq2seq Vis: A Visual Debugging Tool for Sequence to Sequence Models
Venue: IEEE Transactions on Visualization and Computer Graphics
Year: 2019

Neural sequence to sequence models have proven to be accurate and robust for many sequence prediction tasks, and have become the standard approach for automatic translation of text. The models work with a five stage blackbox pipeline that begins with encoding a source sequence to a vector space and then decoding out to a new target sequence. This process is now standard, but like many deep learning methods remains quite difficult to understand or debug. In this work, we present a visual analysis tool that allows interaction and "what if" style exploration of trained sequence to sequence models through each stage of the translation process. The aim is to identify which patterns have been learned, to detect model errors, and to probe the model with counterfactual scenario. We demonstrate the utility of our tool through several real world sequence to sequence use cases on large scale models.
Next environment friendly cars: Application of solar power as automobile energy source
Venue: 2016 IEEE 43rd Photovoltaic Specialists Conference (PVSC)
Year: 2016

We investigate the benefits of utilizing solar power as an energy source for future automobiles. Since there are strict emission standards developed by governments, we must find an alternative energy source for the future automobiles. We show that replacing all passenger cars with hybrid vehicles equipped with a 800 W rated power solar module would reduce greenhouse gas (GHG) emission by 63% in Japan. We obtained an average power generation of 2.1 kWh/day over 100 days by solar modules installed on a Prius under the real environment, which was larger than the required power to achieve the GHG emission reduction. We also investigate an innovative statistic low concentrator with III V cells in order to reduce installation area for the automobiles. We show that our proposed design (asymmetric aspheric design) can expand the acceptance incident angle and increase the annual energy yield, while keeping thin structure, which is essential to automobile application.
0.52 11.86 Gbit/s OFDM modulation for power sharing VLC transmission by using VCSEL laser.
Venue: Optics express
Year: 2016

In this paper, we propose employing a 682 nm vertical cavity surface emitting laser (VCSEL) with 1 GHz bandwidth for high speed and power sharing wireless visible light communication (VLC) in the different transmission distances of 2 to 5 m. In the measurement, the data rate of 0.52 to 11.86 Gbit/s (0.44 to 10.8 Gbit/s in a net data rate) can be achieved by using spectral efficient orthogonal frequency division multiplexing (OFDM) modulation with bit loading algorithm. Therefore, 4 to 256 quadrature amplitude modulations (QAMs) are employed simultaneously in the modulation bandwidth for VCSEL based VLC. The proposed power sharing VLC system can be divided to four end users, when three beam splitters (BSs) are used simultaneously. Moreover, all of the measured bit error rates (BERs) are below the forward error correction (FEC) threshold (BER 3.8 x 10 3)
A Picture is Worth More Words Over Time: Multimodality and Narrative Structure Across Eight Decades of American Superhero Comics
Year: 2017

The visual narratives of comics involve complex multimodal interactions between written language and the visual language of images, where one or the other may guide the meaning and/or narrative structure. We investigated this interaction in a corpus analysis across eight decades of American superhero comics (1940 2010s) No change across publication date was found for multimodal interactions that weighted meaning towards text or across both text and images, where narrative structures were present across images. However, we found an increase over time of narrative sequences with meaning weighted to the visuals, and an increase of sequences without text at all. These changes coincided with an overall reduction in the number of words per panel, a shift towards panel framing with single characters and close ups rather than whole scenes, and an increase in shifts between temporal states between panels. These findings suggest that storytelling has shifted towards investing more information in the images, along with an increasing complexity and maturity of the visual narrative structures. This has shifted American comics from being textual stories with illustrations to being visual narratives that use text.
The acute effects of cannabis on human executive function
Venue: Behavioural pharmacology
Year: 2018

Cannabis remains the most frequently used illicit drug worldwide. It produces a broad range of acute effects, such as euphoria, increased heart rate and perceptual alterations. Over the last few decades, a substantial number of experiments have been conducted to provide insight into the acute effects of cannabis on cognition. Here, we systematically review studies that investigated the impact of administration of cannabis or [INCREMENT]9 tetrahydrocannabinol, the main psychoactive constituent of cannabis, on human executive function, in particular, on the three principal domains of inhibition, working memory and reasoning/association. Our findings suggest that cannabis use results in acute impairment of inhibition, with the strongest effects after pulmonary administration of higher doses of [INCREMENT]9 tetrahydrocannabinol. Results from neuroimaging studies indicate that these effects are predominantly modulated through neural processes in the inferior frontal gyrus. Working memory and reasoning/association are less clearly affected by cannabis administration, possibly because of compensational neural mechanisms to overcome the effects of cannabis intoxication on performance accuracy. Factors that may account for the variation in results are the extent to which a paradigm involves attentional processes, differences between studies in administration methods and variation in the patients' history of cannabis use.
Wholesale Competition, Open Access Regulation and Tacit Collusion: Experimental Evidence
Year: 2016

We consider the market scenario where wholesale access for a non integrated reseller is provided competitively by two vertically integrated firms. In a continuous time economic laboratory experiment with both student and expert participants we compare market outcomes under different modes of wholesale competition as well as under an open access regulation preventing a margin squeeze. We find that wholesale competition can facilitate tacit collusion, which yields wholesale and retail prices even above the monopoly level. However, we show that a simple price commitment rule can substantially reduce tacit collusion. Moreover, we do not find evidence that margin squeeze regulation benefits consumers.
Research on Path Planning of Mobile Robot Based on Artificial Neural Network
Year: 2019

Mobile robot is an important development direction in the field of robotics, which is widely used in industry, agriculture, military, education and so on. Path planning is one of the most important problems in mobile robot research. In this paper, the path planning of mobile robot is based on artificial neural network technology. Firstly, the basic theory of artificial neural network is analyzed. Then this paper analyses the path planning algorithm of mobile robot. Finally, the application of artificial neural network in path planning of mobile robots is described.
The 4 th International Workshop on Agent based Mobility Tra ffi c and Transportation Models Methodologies and Applications ABMTRANS Parallel Reinforcement Learning for Tra ffi c Signal Control
Year: 2015

Developing Adaptive Traffic Signal Control strategies for efficient urban traffic management is a challenging problem, which is not easily solved. Reinforcement Learning (RL) has been shown to be a promising approach when applied to traffic signal control (TSC) problems. When using RL agents for TSC, difficulties may arise with respect to convergence times and performance. This is especially pronounced on complex intersections with many different phases, due to the increased size of the state action space. Parallel Learning is an emerging technique in RL literature, which allows several learning agents to pool their experiences while learning concurrently on the same problem. Here we present an extension to a leading published work on RL for TSC, which leverages the benefits of Parallel Learning to increase exploration and reduce delay times and queue lengths. c (c) 2015  Peer review under responsibility of the Conference Program Chairs.
NF kB links innate immunity to the hypoxic response through transcriptional regulation of HIF 1a
Venue: Nature
Year: 2008

The hypoxic response is an ancient stress response triggered by low ambient oxygen (O2) (ref. 1) and controlled by hypoxia inducible transcription factor 1 (HIF 1) whose a subunit is rapidly degraded under normoxia but stabilized when O2 dependent prolyl hydroxylases (PHDs) that target its O2 dependent degradation domain are inhibited. Thus, the amount of HIF 1a, which controls genes involved in energy metabolism and angiogenesis, is regulated post translationally. Another ancient stress response is the innate immune response, regulated by several transcription factors, among which NF kB plays a central role. NF kB activation is controlled by IkB kinases (IKK) mainly IKK b, needed for phosphorylation induced degradation of IkB inhibitors in response to infection and inflammation. IKK b is modestly activated in hypoxic cell cultures when PHDs that attenuate its activation are inhibited. However, defining the relationship between NF kB and HIF 1a has proven elusive. Using in vitro systems, it was reported that HIF 1a activates NF kB, that NF kB controls HIF 1a transcription and that HIF 1a activation may be concurrent with inhibition of NF kB. Here we show, with the use of mice lacking IKK b in different cell types, that NF kB is a critical transcriptional activator of HIF 1a and that basal NF kB activity is required for HIF 1a protein accumulation under hypoxia in cultured cells and in the liver and brain of hypoxic animals. IKK b deficiency results in defective induction of HIF 1a target genes including vascular endothelial growth factor. IKK b is also essential for HIF 1a accumulation in macrophages experiencing a bacterial infection. Hence, IKK b is an important physiological contributor to the hypoxic response, linking it to innate immunity and inflammation.
Analisis Kebangkrutan dengan Menggunakan Metode Springate Pada Perusahaan Asuransi Jiwa di Indonesia Tahun 2016 2018
Venue: ASSET: Jurnal Manajemen dan Bisnis
Year: 2021

Penelitian ini bertujuan untuk mengetahui mengetahui adanya potensi kebangkrutan dengan metode Springate pada perusahaan asuransi jiwa di Indonesia yang terdaftar di OJKtahun 2016 2018. Adapun yang menjadi rumusan masalah dalamm penelitian ini adalah bagaimana potensi kebangkrutan dengan metode Springate pada perusahaan asuransi jiwa di Indonesia yang terdaftar di BEI tahun 2016 2018? Populasi dalam penelitian ini adalah perusahaan asuransi jiwa yang terdaftar di OJK pada tahun 2016 2018. Data yang diambil untuk melakukan penelitian ini adalah data sekunder berupa laporan keuangan tahunan yang diperoleh dari situs resmi www.ojk.co.id dan beberapa perusahaan dari situs resmi perusahaan yang bersangkutan. Hasil penelitian menunjukkan bahwa berdasarkan perhitungan variabel variabel X dalam metode Springate dapat disimpulkan bahwa sebanyak 87,5% atau 28 perusahaan asuransi jiwa yang memiliki potensi untuk mengalami kebangkrutan, sedangkan sisanya yaitu 12,5% atau 4 perusahaan masuk kedalam katagori perusahaan sehat. Data perhitungan metode Springate dari tahun 2016 2018 bisa digunakan oleh investor untuk memilih metode mana yang akan digunakan dalam mengukur potensi kebangkrutan perusahaan dan bisa digunakan untuk menilai perusahaan mana yang tidak berpotensi bangkrut dimasa depan. Kata Kunci Bankruptcy, Springate Method, Tabel
Initial Left Image R ight Image Hand Designed Cost Aggregation Disparity Map Disparity Computation A ggregated C ost Volume Extracting Guidance Aggregation Guidance Generaing Aggregation Proposals Aggregation Proposals Fusion Aggregated Cost Volume Disparity Map Disparity Computation A The Typical
Year: 2018

Deep neural networks have shown excellent performance for stereo matching. Many efforts focus on the feature extraction and similarity measurement of the matching cost computation step while less attention is paid on cost aggregation which is crucial for stereo matching. In this paper, we present a learning based cost aggregation method for stereo matching by a novel sub architecture in the end to end trainable pipeline. We reformulate the cost aggregation as a learning process of the generation and selection of cost aggregation proposals which indicate the possible cost aggregation results. The cost aggregation sub architecture is realized by a two stream network: one for the generation of cost aggregation proposals, the other for the selection of the proposals. The criterion for the selection is determined by the low level structure information obtained from a light convolutional network. The two stream network offers a global view guidance for the cost aggregation to rectify the mismatching value stemming from the limited view of the matching cost computation. The comprehensive experiments on challenge datasets such as KITTI and Scene Flow show that our method outperforms the state of the art methods.
A Novel Energy Efficient Authentic Reliable Routing Protocol (EEARRP) for Scalable Mobile Ad Hoc Networks
Year: 2020

This paper proposes a novel energy efficient authentic reliable routing protocol (EEARRP) to attain better authenticity in scalable mobile ad hoc networks. To decrease the signature verification expenditure and message communication expenditure a mechanism with message recovery is employed. The proposed authentic scheme is to minimize the size of the message transmitted when the signature length is reduced any more. To implement this, the energy efficient authentic reliable routing scheme with message recovery is used. Therefore, the communication overhead is minimized guaranteed in the above said scheme. The total length of the broadcast message is reduced by 24% As a result, the total energy consumption of our scheme can be reduced by up to 27% With the extensive simulation results using NS2 the proposed EEARRP attains better throughput, packet delivery ratio with reasonable decrease in total energy consumption and total overhead even in real time scalable environment.
Multi Class Uncertainty Calibration via Mutual Information Maximization based Binning
Venue: ICLR
Year: 2021

Post hoc calibration is a common approach for providing high quality confidence estimates of deep neural network predictions. Recent work has shown that widely used scaling methods underestimate their calibration error, while alternative Histogram Binning (HB) methods with verifiable calibration performance often fail to preserve classification accuracy. In the case of multi class calibration with a large number of classes K, HB also faces the issue of severe sample inefficiency due to a large class imbalance resulting from the conversion into K one vs rest class wise calibration problems. The goal of this paper is to resolve the identified issues of HB in order to provide verified and calibrated confidence estimates using only a small holdout calibration dataset for bin optimization while preserving multi class ranking accuracy. From an information theoretic perspective, we derive the I Max concept for binning, which maximizes the mutual information between labels and binned (quantized) logits. This concept mitigates potential loss in ranking performance due to lossy quantization, and by disentangling the optimization of bin edges and representatives allows simultaneous improvement of ranking and calibration performance. In addition, we propose a shared class wise (sCW) binning strategy that fits a single calibrator on the merged training sets of all K class wise problems, yielding reliable estimates from a small calibration set. The combination of sCW and I Max binning outperforms the state of the art calibration methods on various evaluation metrics across different benchmark datasets and models, even when using only a small set of calibration data, e.g. 1k samples for ImageNet.
Recent advances in application of graphitic carbon nitride based catalysts for degrading organic contaminants in water through advanced oxidation processes beyond photocatalysis: A critical review.
Venue: Water research
Year: 2020

Advanced oxidation processes (AOPs) have attracted much interest in the field of water treatment owing to their high removal efficiency for refractory organic contaminants. Graphitic carbon nitride (g C3N4) based catalysts with high performance and cost effectiveness are promising heterogeneous catalysts for AOPs. Most research on g C3N4 based catalysts focuses on photocatalytic oxidation, but increasingly researchers are paying attention to the application of g C3N4 based catalysts in other AOPs beyond photocatalysis. This review aims to concisely highlight recent state of the art progress of g C3N4 based catalysts in AOPs beyond photocatalysis. Emphasis is made on the application of g C3N4 based catalysts in three classical AOPs including Fenton based processes, catalytic ozonation and persulfates activation. The catalytic performance and involved mechanism of g C3N4 based catalysts in these AOPs are discussed in detail. Meanwhile, the effect of water chemistry including pH, water temperature, natural organic matter, inorganic anions and dissolved oxygen on the catalytic performance of g C3N4 based catalysts are summarized. Moreover, the reusability, stability and toxicity of g C3N4 based catalysts in water treatment are also mentioned. Lastly, perspectives on the major challenges and opportunities of g C3N4 based catalysts in these AOPs are proposed for better developments in the future research.
The Cedar Project: mortality among young Indigenous people who use drugs in British Columbia
Venue: Canadian Medical Association Journal
Year: 2017

Young Indigenous people, particularly those involved in the child welfare system, those entrenched in substance use and those living with HIV or hepatitis C, are dying prematurely. We report mortality rates among young Indigenous people who use drugs in British Columbia and explore predictors of mortality over time.  We analyzed data collected every 6 months between 2003 and 2014 by the Cedar Project, a prospective cohort study involving young Indigenous people who use illicit drugs in Vancouver and Prince George, BC. We calculated age standardized mortality ratios using Indigenous and Canadian reference populations. We identified predictors of mortality using time dependent Cox proportional hazard regression.  Among 610 participants, 40 died between 2003 and 2014, yielding a mortality rate of 670 per 100 000 person years.  Indigenous people who used drugs were 12.9 (95% confidence interval [CI] 9.2 17.5) times more likely to die than all Canadians the same age and were 7.8 (95% CI 5.6 10.6) times more likely to die than Indigenous people with Status in BC. Young women and those using drugs by injection were most affected. The leading causes of death were overdose (n 15 [38% illness (n 11 [28% and suicide (n 5 [12% Predictors of mortality included having hepatitis C at baseline (adjusted hazard ratio [HR] 2.76, 95% CI 1.47 5.16) previous attempted suicide (adjusted HR 1.88, 95% CI 1.01 3.50) and recent overdose (adjusted HR 2.85, 95% CI 1.00 8.09)  Young Indigenous people using drugs in BC are dying at an alarming rate, particularly young women and those using injection drugs. These deaths likely reflect complex intersections of historical and present day injustices, substance use and barriers to care.
Using a Knee Arthrometer to Evaluate Tissue specific Contributions to Knee Flexion Contracture in the Rat.
Venue: Journal of visualized experiments JoVE
Year: 2018

Normal knee range of motion (ROM) is critical to well being and allows one to perform basic activities such as walking, climbing stairs and sitting. Lost ROM is called a joint contracture and results in increased morbidity. Due to the difficulty of reversing established knee contractures, early detection is important, and hence, knowing risk factors for their development is essential. The rat represents a good model with which the effect of an intervention can be studied due to the similarity of rat knee anatomy to that of humans, the rat's ability to tolerate long durations of knee immobilization in flexion, and because mechanical data can be correlated with histologic and biochemical analysis of knee tissue. Using an automated arthrometer, we demonstrate a validated, precise, reproducible, user independent method of measuring the extension ROM of the rat knee joint at specific torques. This arthrometer can be used to determine the effects of interventions on knee joint ROM in the rat.
Three dimensional acoustic tweezers with acoustic vortex streaming
Year: 2021

Acoustic tweezers use ultrasound for contact free manipulation of particles from millimeter to sub micrometer scale. Particle trapping originated in either radiation forces or acoustic streaming fields. Acoustic tweezers based on single beam focused acoustic vortices have attracted considerable attention due to their selective trapping capability, but have proven difficult to use for 3D trapping without a complex transducer array and significant constraints on the trapped particle properties. Here we demonstrate the first 3D acoustic tweezer that uses a single transducer and combines the radiation force for trapping in two dimensions with the streaming force to provide levitation in the third dimension. The idea is demonstrated in both simulation and experiments, and the achieved levitation force reaches three orders of magnitude larger than for previous 3D trapping. This hybrid acoustic tweezer that integrates acoustic streaming adds a new twist to the approach and expands the range of particles that can be manipulated.