nils-herrmann/clean_scirepeval huggingface dataset:
Spectrum sensing and resource allocation for multicarrier cognitive radio systems under interference and power constraints
Venue: EURASIP J. Adv. Signal Process.
Year: 2014

Multicarrier waveforms have been commonly recognized as strong candidates for cognitive radio. In this paper, we study the dynamics of spectrum sensing and spectrum allocation functions in cognitive radio context using very practical signal models for the primary users (PUs) including the effects of power amplifier nonlinearities. We start by sensing the spectrum with energy detection based wideband multichannel spectrum sensing algorithm and continue by investigating optimal resource allocation methods. Along the way, we examine the effects of spectral regrowth due to the inevitable power amplifier nonlinearities of the PU transmitters. The signal model includes frequency selective block fading channel models for both secondary and primary transmissions. Filter bank based wideband spectrum sensing techniques are applied for detecting spectral holes and filter bank based multicarrier (FBMC) modulation is selected for transmission as an alternative multicarrier waveform to avoid the disadvantage of limited spectral containment of orthogonal frequency division multiplexing (OFDM) based multicarrier systems. The optimization technique used for the resource allocation approach considered in this study utilizes the information obtained through spectrum sensing and knowledge of spectrum leakage effects of the underlying waveforms, including a practical power amplifier model for the PU transmitter. This study utilizes a computationally efficient algorithm to maximize the SU link capacity with power and interference constraints. It is seen that the SU transmission capacity depends critically on the spectral containment of the PU waveform, and these effects are quantified in a case study using an 802.11 g WLAN scenario.
Ultrasonication pre treatment of combined effluents from palm oil, pulp and paper mills for improving photofermentative biohydrogen production
Year: 2016

The improvement of batch photofermentative biohydrogen production was investigated using ultrasonication pre treatment on a combined effluent of palm oil and pulp and paper mills. The effects of the amplitude (30 90% and ultrasonication duration (5 60 min) were investigated in terms of their influences on the biohydrogen yield and chemical oxygen demand (COD) removal. The recommended ultrasonication parameters were found at the higher ranges of amplitude and duration (A70T45) Using A70T45 ultrasonication, the production of biohydrogen at 30 degC could be enhanced up to 8.72 mL H 2 /mL medium with a total COD removal of 36.9% During pre treatment at A70T45, an energy input of 775 J/mL was supplied to disintegrate complex compounds into simpler structures. As a result, an increase in the soluble organic matter concentration was achieved, which led to enhanced biohydrogen production. On the other hand, the lowest biohydrogen yield (4.67 mL H 2 /mL medium and total COD removal (28.8% were obtained in the control without pre treatment. The enthalpy of the photofermentation process was estimated to be 141.1 kJ/mol with a threshold temperature of 30.9 degC based on a modified Arrhenius approach.
SPI GCN: A Simple Permutation Invariant Graph Convolutional Network
Year: 2019

A wide range of machine learning problems involve handling graph structured data. Existing machine learning approaches for graphs, however, often imply computing expensive graph similarity measures, preprocessing input graphs, or explicitly ordering graph nodes. In this work, we present a novel and simple convolutional neural network architecture for supervised learning on graphs that is provably invariant to node permutation. The proposed architecture operates directly on arbitrary graphs and performs no node sorting. It also uses a simple multi layer perceptron for prediction as opposed to conventional convolution layers commonly used in other deep learning approaches for graphs. Despite its simplicity, our architecture is competitive with state of the art graph kernels and existing graph neural networks on benchmark graph classification data sets. Our approach clearly outperforms other deep learning algorithms for graphs on multiple multiclass classification tasks. We also evaluate our approach on a real world original application in materials science, on which we achieve extremely reasonable results.
Afrofuturism: Reimagining Art Curricula for Black Existence
Year: 2020

13 Every day, there are new tragic events that lead me to believe that we are in a 21st century dystopia of racial hate. My reprieve from these futureless visions has only come during engagements with the arts, specifi cally art created by Black artists who center Blackness and Afrodiasporic histories. In September 2018, I served as a community docent for Mickalene Th omas's exhibition, I Can't See You Without Me, at the Wexner Center for the Arts in Columbus, Ohio. For an hour, a couple of times a week, I was in the presence of Mickalene's muses, all Black women, diff erent shades of brown, who made me feel strong, visible, relevant, and central to all existence, past, present, and future. Th omas's aesthetics, patterns, and materials felt like home, and she off ered a future in which Black women could assertively and inconsequently stare back into the eyes of White audiences who gazed on them and judged their brown skin. A similar liberating aesthetic experience happened in January 2019 while I attended a live jazz performance by musical artist Mark Lomax. Lomax's work, entitled "400: An Afrikan Epic," had three movements that told "the story of black America stretching from Pre colonial Afrika, through the 400 years aft er the start of the Transatlantic slave trade in 1619, and into the Afrofuture while celebrating the creativity of a people who continue to endure" (MarkLomax.com, 2019, para. 2) Lomax's musical arrangements guided me on a contemplative, imaginary journey about the past, before slavery; the present; and, most importantly, the future, which situated Africa at the center of all human advancements. Th e rhythms and syncopations of Lomax's compositions lift ed my spirit to another land, to soil that felt comfortable and safe and mine to sow for future generations.
Stress Test Evaluation of Transformer based Models in Natural Language Understanding Tasks
Venue: LREC
Year: 2020

There has been significant progress in recent years in the field of Natural Language Processing thanks to the introduction of the Transformer architecture. Current state of the art models, via a large number of parameters and pre training on massive text corpus, have shown impressive results on several downstream tasks. Many researchers have studied previous (non Transformer) models to understand their actual behavior under different scenarios, showing that these models are taking advantage of clues or failures of datasets and that slight perturbations on the input data can severely reduce their performance. In contrast, recent models have not been systematically tested with adversarial examples in order to show their robustness under severe stress conditions. For that reason, this work evaluates three Transformer based models (RoBERTa, XLNet, and BERT) in Natural Language Inference (NLI) and Question Answering (QA) tasks to know if they are more robust or if they have the same flaws as their predecessors. As a result, our experiments reveal that RoBERTa, XLNet and BERT are more robust than recurrent neural network models to stress tests for both NLI and QA tasks. Nevertheless, they are still very fragile and demonstrate various unexpected behaviors, thus revealing that there is still room for future improvement in this field.
PASE: PostgreSQL Ultra High Dimensional Approximate Nearest Neighbor Search Extension
Venue: SIGMOD Conference
Year: 2020

Similarity search has been widely used in various fields, particularly in the Alibaba ecosystem. The open source solutions to a similarity search of vectors can only support a query with a single vector, whereas real life scenarios generally require a processing of compound queries. Moreover, existing open source implementations only provide runtime libraries, which have difficulty meeting the requirements of industrial applications. To address these issues, we designed a novel scheme for extending the index type of PostgreSQL (PG) which enables a similar vector search and achieves a high performance level and strong reliability of PG. Two representative types of nearest neighbor search (NNS) algorithms are presented herein. These algorithms achieve a high performance, and afford advantages such as the support of composite queries and seamless integration of existing business data. The other NNS algorithms can be easily implemented under the proposed framework. Experiments were conducted on large datasets to illustrate the efficiency of the proposed retrieval mechanism.
Joint Radar Target Detection and Parameter Estimation with MIMO OTFS
Venue: 2020 IEEE Radar Conference (RadarConf20)
Year: 2020

Motivated by future automotive applications, we study the joint target detection and parameter estimation problem using orthogonal time frequency space (OTFS) a digital modulation format robust to time frequency selective channels. Assuming the transmitter is equipped with a mono static MIMO radar, we propose an efficient maximum likelihood based approach to detect targets and estimate the corresponding delay, Doppler, and angle of arrival parameters. In order to reduce the computational complexity associated to the high dimensional search, our scheme proceeds in two steps, i.e. target detection and coarse parameter estimation followed by refined parameter estimation. Interestingly, our numerical results demonstrate that the proposed scheme is able to identify multiple targets if they are separated in at least one domain out of three (delay, Doppler, and angle) while achieving the Cramer Rao lower bound for the parameter estimation.
A PATIENT DERIVED IPSC MODEL OF A RARE TTC3 MUTATION
Venue: Alzheimer's Dementia
Year: 2017

brains. However, other genes on human chr21 likely modulate the age of onset, severity and modality of the clinical picture, as DS individuals have later or absent onset of clinical AD, and less intracerebral haemorrhage pathology, than euploid individuals with the familial early onset AD caused by a duplication of APP gene (dupAPP) Our aim is to identify such modulator genes on chr21 using cellular models. Neurons derived from isogenic hiPSCs we generated from a mosaic DS individual, unpublished iPSCs from segmental trisomy 21 DS and non DS individuals, as well as from DS individuals with extremely early or late dementia onset are used in 2D, 3D and cerebral organoid paradigms. Optogenetic stimulation of Channelrhodopsin 2 engineered hiPSCs is used to determine the neuronal activity dependent cellular phenotype modulation. CRISPR/Cas9 mediated reduction from trisomy to disomy for individual selected candidate genes on chr21 are performed on isogenic T21 iPSCs. We detect T21 caused neurodevelopmental delay, increased b amyloid and phospho Tau presence, complex mitochondrial dysfunction, accelerated DNA damage and abnormal endosomes, and some of these phenotypes exacerbate in 3D and cerebral organoid paradigms, and/or can be reproduced in primary human neuroectodermal cells expanded and differentiated in vitro.  Cellular phenotypes relevant for AD pathology caused by trisomy21 can be reproduced and quantitatively assessed in iPSC derived and primary NSC derived neurons and differences sharpened by the use of cerebral organoid technology. Isogenic iPSCs allow detection of subtle changes in phenotypes and evaluation of single gene's trisomy contribution using CRISPR/Cas9 editing. Segmental trisomy 21 iPSCs allow for assignment of phenotype effects to a region of chromosome 21, and can faster eliminate some candidate genes.
Noninvasive Brain Computer Interfaces Based on Sensorimotor Rhythms
Venue: Proceedings of the IEEE
Year: 2015

Brain computer interfaces (BCIs) have been explored in the field of neuroengineering to investigate how the brain can use these systems to control external devices. We review the principles and approaches we have taken to develop a sensorimotor rhythm electroencephalography (EEG) based brain computer interface (BCI) The methods include developing BCI systems incorporating the control of physical devices to increase user engagement, improving BCI systems by inversely mapping scalp recorded EEG signals to the cortical source domain, integrating BCI with noninvasive neuromodulation strategies to improve learning, and incorporating mind body awareness training to enhance BCI learning and performance. The challenges and merits of these strategies are discussed, together with recent findings. Our work indicates that the sensorimotor rhythm based noninvasive BCI has the potential to provide communication and control capabilities as an alternative to physiological motor pathways.
An efficient combined approach for medical brain tumour segmentation
Venue: 2017 International Conference on Communication and Signal Processing (ICCSP)
Year: 2017

Brain tumour is known to be the major setback which counts as the major fatality cause for human beings. Proper and timely diagnosis can prevent the life of a person to an extent. Segmentation is one of the important techniques to find the location and size of tumour in the brain. Automatic tumour detection in MRI is very important in today's world health scenario which boosts up the required medical application. The major road block in MR Images is lucidity and sensitivity for a better vivid image. In recent years, technology has opened a wide range of options to understand and improve segmentation of brain tumour. The diagnosis method consists of three stages: preprocessing of Magnetic Resonance images, feature extraction and segmentation. The methods like thresholding, k means and others whose limitations are being curbed into a more efficient and accurate measure of location and size of the tumour. Brain tumour is detected by the new proposed algorithm known as Spatial Fuzzy C Mean Clustering (SFCM) The proposed hybrid technique highlights the advancement and reliability of front end image segmentation and detection.
CLVSA: A Convolutional LSTM Based Variational Sequence to Sequence Model with Attention for Predicting Trends of Financial Markets
Venue: IJCAI
Year: 2019

Financial markets are a complex dynamical system. The complexity comes from the interaction between a market and its participants, in other words, the integrated outcome of activities of the entire participants determines the markets trend, while the markets trend affects activities of participants. These interwoven interactions make financial markets keep evolving. Inspired by stochastic recurrent models that successfully capture variability observed in natural sequential data such as speech and video, we propose CLVSA, a hybrid model that consists of stochastic recurrent networks, the sequence to sequence architecture, the self and inter attention mechanism, and convolutional LSTM units to capture variationally underlying features in raw financial trading data. Our model outperforms basic models, such as convolutional neural network, vanilla LSTM network, and sequence to sequence model with attention, based on backtesting results of six futures from January 2010 to December 2017. Our experimental results show that, by introducing an approximate posterior, CLVSA takes advantage of an extra regularizer based on the Kullback Leibler divergence to prevent itself from overfitting traps.
Ficus carica leaves extract inhibited pancreatic b cell apoptosis by inhibiting AMPK/JNK/caspase 3 signaling pathway and antioxidation.
Venue: Biomedicine pharmacotherapy Biomedecine pharmacotherapie
Year: 2019

The aim of this study was to explore the inhibitory effects of Ficus carica leaves (FCL) extract on AMPK/JNK/caspase3 signaling pathway and antioxidation in pancreatic b cells. H&E staining, insulin immunohistochemistry, and TUNEL methods were used to investigate the effects of FCL on pancreatic histopathology in type 1 diabetic mice. The expression levels of caspase 3, AMPK, and JNK protein in the pancreatic tissue and MIN6 cells [induced by palmitic acid (PA) and hydrogen peroxide] were determined. Flow cytometry was used to detect the effects of FCL on apoptosis and ROS production of MIN6 cells. FCL (2 g/kg, continuous gavage for 6 weeks) significantly improved the pancreatic tissue injury in type 1 diabetic mice and reduced the expression levels of apoptosis related proteins such as FasL, caspase8, Bax/Bcl 2, Cyt C, caspase 3, p AMPK, and p JNK. FCL inhibited cell apoptosis induced by PA and the protein expression levels of caspase 3, p AMPK, and p JNK. The AMPK agonist AICAR could reverse the protective effects of FCL on MIN6 cells. The AMPK inhibitor compound C had a similar effect on MIN6 cells as that of FCL. FCL could inhibit cell apoptosis induced by hydrogen peroxide and reduced the production of ROS. In conclusion, FCL could inhibit pancreatic b cell apoptosis by inhibiting the AMPK/JNK/caspase 3 signaling pathway and by antioxidation properties.
The immediate impact of the 2019 novel coronavirus (COVID 19) outbreak on subjective sleep status
Venue: Sleep Medicine
Year: 2020

An outbreak of the 2019 novel coronavirus (COVID 19) has been ongoing in China since January 2020. The threat of infection affects the work and life of most of the population and may also damage sleep. This study aims to examine the subjective sleep status and mental health of the population during the peak of the COVID 19 epidemic.  The data were collected through an online questionnaire with a sample of 5461 individuals in China from February 5, 2020, to February 23, 2020. Participants were divided into four groups based on their degree of threat from COVID 19: Group 1 was most closely associated with COVID 19, including inpatients diagnosed with COVID 19, first line hospital workers and first line management staff; Group 2 included outpatients diagnosed with COVID 19 and patients who developed a fever and visited the hospital; Group 3 included people related to Group 1 or 2, such as their colleagues, relatives, friends and rescuers; and Group 4 was the farthest removed from contact with COVID 19, covering the general public affected by COVID 19 prevention strategies. The Insomnia Severity Index (ISI) Patient Health Questionnaire (PHQ 9) Generalized Anxiety Disorder Scale (GAD 7) and Acute Stress Disorder Scale (ASDS) were used.  Threat degree of COVID 19 (groups) had significant correlations with insomnia, depression, anxiety, and stress (p 0.05, p 0.01) Age, gender, and area (Hubei province or other provinces) had significant correlations with insomnia (p 0.01) A total of 1380 (24.46% participants were suspected of having major depression based on the PHQ 9. Additionally, 1042 (18.47% participants were suspected of having generalized anxiety disorder based on the GAD 7. A total of 892 (15.8% of the participants had Acute Stress Disorder (ASD) according to the ASDS. The prevalence of clinical insomnia during the outbreak was 20.05% (1131) according to the ISI. The factors of satisfaction with the current sleep pattern and how perceptible the symptoms of the current sleep pattern are to other people (p 0.05) and the middle (difficulty staying asleep) and terminal (waking up too early) (p 0.01) factors of the ISI were significantly different across groups. A total of 1129 (20.01% participants spent more than one hour awake in bed.  The results indicated that insomnia is more severe in people who are female, young, living in the epicenter and experiencing a high degree of threat from COVID 19. As prevention and treatment efforts continue with regard to COVID 19, the general public has developed poor sleep hygiene habits, which deserve attention.
Synthesis of Distinct Iron Oxide Nanomaterial Shapes Using Lyotropic Liquid Crystal Solvents
Venue: Nanomaterials
Year: 2017

A room temperature reduction hydrolysis of Fe(III) precursors such as FeCl3 or Fe(acac)3 in various lyotropic liquid crystal phases (lamellar, hexagonal columnar, or micellar) formed by a range of ionic or neutral surfactants in H2O is shown to be an effective and mild approach for the preparation of iron oxide (IO) nanomaterials with several morphologies (shapes and dimensions) such as extended thin nanosheets with lateral dimensions of several hundred nanometers as well as smaller nanoflakes and nanodiscs in the tens of nanometers size regime. We will discuss the role of the used surfactants and lyotropic liquid crystal phases as well as the shape and size differences depending upon when and how the resulting nanomaterials were isolated from the reaction mixture. The presented synthetic methodology using lyotropic liquid crystal solvents should be widely applicable to several other transition metal oxides for which the described reduction hydrolysis reaction sequence is a suitable pathway to obtain nanoscale particles.
Design and Governance of Platform Ecosystems Key Concepts and Issues for Future Research
Venue: ECIS
Year: 2016

The purpose of this paper is to give an overview of current research in IS on the design and govern ance of platform ecosystems. To this end, we conduct a literature review of relevant journals and con ferences. We show that platform ecosystems have been analysed from two different perspectives: tech nology and market oriented. Thereby, most studies take on the viewpoint of the platform owner. Fur thermore, we summarize key concepts on the design and governance of platform ecosystems that have been discussed in literature. As most relevant concepts we identify the definition of roles, pricing, boundary resources and openness. Based on this analysis, we derive issues for future research: the integration of market and technology oriented perspectives, an individual level of analysis to include complementors and end users and the role of data as boundary resource in platform ecosystems. This paper contributes to the understanding of platform ecosystems in IS literature by structuring existing research with regard to different perspectives and concepts and by providing starting points for future work. In addition, it lays out which concepts practitioners need to consider when designing and gov erning platform ecosystems.
Probiotics and Obesity
Venue: Annals of Nutrition and Metabolism
Year: 2010

from the group of Gordon et al. [1, 2] who characterized the composition of the fecal microbiota by extensive 16S rRNA gene sequence analysis, the authors of the 2 recent human studies [4, 5] applied 16S rRNA targeted oligonucleotide probes for the quantification of major bacterial groups. However, the probes used in these trials do not detect members of the Erysipelotrichaceae, a bacterial family within the Firmicutes, which was shown in mice to be largely responsible for the increase in Firmicutes observed in response to a high fat diet [6]
Frequency offset compensation for satellite communication system with CE OFDM
Venue: China Communications
Year: 2017

Constant envelope orthogonal frequency division multiplexing (CE OFDM) is a waveform that can achieve 0dB peak to average power ratio and avoid the signal distortion caused by the nonlinear power amplifier. However, the carrier frequency offset (CFO) in CE OFDM systems can cause errors at phase unwrapper module. In this paper, a CFO estimation scheme is proposed for CE OFDM in satellite communication system. As the null subcarrier is inherent in the conjugate symmetric symbol structure at the transmitter, the proposed scheme uses the null subcarrier as prior information to estimate the CFO at the receiver. The ideal estimation range of normalized CFO is obtained by mathematical analysis. Simulation results show that the proposed scheme can estimate the CFO accurately under additive white Gaussian noise (AWGN) channel and multipath fading channel, especially for moderate and high signal to noise ratio (SNR)