nils-herrmann/clean_scirepeval huggingface dataset:
Human mini guts: new insights into intestinal physiology and host pathogen interactions
Venue: Nature Reviews Gastroenterology &Hepatology

The development of indefinitely propagating human 'mini guts' has led to a rapid advance in gastrointestinal research related to transport physiology, developmental biology, pharmacology, and pathophysiology. These mini guts, also called enteroids or colonoids, are derived from LGR5+ intestinal stem cells isolated from the small intestine or colon. Addition of WNT3A and other growth factors promotes stemness and results in viable, physiologically functional human intestinal or colonic cultures that develop a crypt villus axis and can be differentiated into all intestinal epithelial cell types. The success of research using human enteroids has highlighted the limitations of using animals or in vitro, cancer derived cell lines to model transport physiology and pathophysiology. For example, curative or preventive therapies for acute enteric infections have been limited, mostly due to the lack of a physiological human intestinal model. However, the human enteroid model enables specific functional studies of secretion and absorption in each intestinal segment as well as observations of the earliest molecular events that occur during enteric infections. This Review describes studies characterizing these human mini guts as a physiological model to investigate intestinal transport and host pathogen interactions.
Design and fabrication of a tip on aperture probe for resolution enhancement of optical patterning

A variety of near field scanning optical microscope (NSOM) based lithography techniques have been developed for different applications through the use of a sub wavelength aperture probe. The light transmission efficiency of an NSOM probe decreases markedly as the aperture diameter decreases. Apertureless NSOM yields a much higher resolution by concentrating the light field near the tip apex. However, far field illumination by a focused laser beam deteriorates the resolution in optical patterning process. In this report, a tip on aperture (TOA) probe was fabricated to achieve high pattern resolution beyond that obtained from conventional NSOM probes. The shape of the probes was designed using the results of numerical analysis based on a finite difference time domain (FDTD) algorithm. The results showed that a probe with a triangular shape tip confined the electromagnetic energy with a 6.7 times larger intensity than the value obtained from a probe with a cylindrical shape tip. The TOA probes were fabricated by generating a triangular tip on a metal coated NSOM probe using a focused ion beam (FIB) process. The resolution enhancement was experimentally demonstrated by the formation of the developed pattern on photo resist. Display Omitted We proposed a plasmonic probe with a specially shaped tip on an aperture NSOM probe.A right triangle tip was selected as the optimal tip shape for optical resolution enhancement.The designed probe was effectively fabricated by FIB milling at different angles.Isolated line segments as narrow as 50nm were produced on photoresist film.
Cognitive Computing: Building a Smarter Planet

Cognitive co mputing is the simulat ion of human thought processes in a co mputerized model. Cognitive systems are co mplex informat ion processing ones, capable of acquiring info rmation, putting it in to action and transmitting knowledge. Cognitiv e computing involves self learning systems that use data min ing, pattern recognition and natural language processing to mimic the way the human brain works. Cognitive Systems will require innovation breakthroughs at every layer of info rmation technology, starting with nanotechnology and progressing through computing systems design, information management, programming and mach ine learning, and, finally, the interfaces between mach ines and humans. Advances on this scale will require remarkable efforts and collaboration, calling forth the best minds and the combined resources of academia, government and industry.
Exploring differences in how learners navigate in MOOCs based on self regulated learning and learning styles: A process mining approach
Venue: 2016 XLII Latin American Computing Conference (CLEI)

Study in a Massive Open and Online Courses (MOOCs) is challenging, since participants take the course without the support of a teacher. Taking a MOOC require the students to have the ability to self regulate their learning. However, every person has its own learning style and the way each one interacts and self regulate in a MOOC varies. In this work we present an exploratory study from a process oriented perspective to study whether students with different learning styles and SRL profiles show differences in navigating through a MOOC. Specifically, we investigate using Process Mining Techniques to analyze log files recording the course behavior of 99 learners across an Open edX MOOC combined with data from self reported surveys. Our findings show that learners with different SRL profiles follow similar navigation paths, but there are differences when differentiating students by their learning styles.
Experimenting Machine Learning Techniques to Predict Vulnerabilities
Venue: 2016 Seventh Latin American Symposium on Dependable Computing (LADC)

Software metrics can be used as a indicator of the presence of software vulnerabilities. These metrics have been used with machine learning to predict source code prone to contain vulnerabilities. Although it is not possible to find the exact location of the flaws, the models can show which components require more attention during inspections and testing. Each new technique uses his own evaluation dataset, which many times has limited size and representativeness. In this experience report, we use a large and representative dataset to evaluate several state of the art vulnerability prediction techniques. This dataset was built with information of 2186 vulnerabilities from five widely used open source projects. Results show that the dataset can be used to distinguish which are the best techniques. It is also shown that some of the techniques can predict nearly all of the vulnerabilities present in the dataset, although with very low precisions. Finally, accuracy, precision and recall are not the most effective to characterize the effectiveness of this tools.
A study on the variations of arterial supply to adrenal gland

Adrenal glands are richly vascular and get their arterial supply by means of three arteries namely superior, middle, inferior suprarenal arteries. The inferior phrenic artery gives off the superior branch, while middle branch arises directly from the abdominal aorta, and the inferior suprarenal branch is given off by the renal artery.  The study was conducted in 15 formalin fixed cadavers obtained from the department of Anatomy and are carefully dissected to observe the arterial supply of both right and left adrenal glands.  It has been noted that variations were observed in four cases out of 30 adrenal glands, two cases showed variations on right side and in two cases variation is seen on left side. First case on the right side, both MSA and ISA are given off by ARA. In the second case on right side IPA is given off by RA and from the junction of these two arteries MSA was given .In third case on left side MSA is given by the coeliac trunk and ISA is from ARA, in fourth case on left side ISA is originated from ARA.  A good understanding in the variation of adrenal gland vasculature is obligatory for a surgeon while performing adrenalectomy, kidney transplantations etc. and also for radiological arbitrations.
Vibration parameter estimation using FMCW radar
Venue: 2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

Vibration sensing is essential in many applications. Traditional vibration sensors are contact based. With the advance of low cost and highly integrated CMOS radars, another class of non contact vibration sensors is emerging. In this paper, we present detailed analysis on obtaining vibration parameters using frequency modulated continuous wave (FMCW) radars. We establish the Cramer Rao lower bounds (CRLB) of the parameter estimation problem and propose an estimation algorithm that achieves the bounds in simulations. These analysis show that vibration sensing using FMCW radars can easily achieve sub Hertz frequency accuracy and micrometer level amplitude accuracy.
Sampled and Continuous Time 1 Bit Signal Processing in CMOS for Wireless Sensor Networks

This thesis investigates theoretical aspects of 1 bit signal processing and contains published papers describing many different CMOS implementations that utilize such processing. So called Suprathreshold Stochastic Resonance (SSR) systems are studied in detail. These systems take a noisy signal as input and then 1 bit quantize it. As it turns out, the combination of noise, coarse quantization, and also integration, results in systems that, perhaps a bit unexpectedly, have quite good performance. This is known as the Stochastic Resonance (SR) phenomenon. Many interesting plots explaining different aspects of the behavior of SSR systems are shown. Detailed explanations of when SSR systems should and should not be used in, are given. This includes analysis of their power consumption. One of the most interesting results in this thesis, is that when the input to the SSR system has flicker noise, the SNR loss in the system will be negligible. With white Gaussian noise, the SNR loss will be much larger, namely 1.96 dB. The flicker noise result does not seem to have been published before. In addition to using 1 bit signal processing, most of the CMOS implementations also utilize Impulse Radio UltraWideBand (IR UWB) and yet another commonality is that they can be used for Wireless Sensor Network (WSN) applications. Many of the implementations base much of their signal processing on Continuous Time Binary Value (CTBV) signals, which is an unusual way of doing things and therefore an interesting experiment. Some of the CMOS implementations are as follows: Radar, localization, communication, beamforming, bitstream processing, n path band pass filter, and power harvesting.
Teaching project: a low cost swine model for chest tube insertion training.
Venue: Revista do Colegio Brasileiro de Cirurgioes

to describe and evaluate the acceptance of a low cost chest tube insertion porcine model in a medical education project in the southwest of Parana, Brazil. METHODS we developed a low cost and low technology porcine model for teaching chest tube insertion and used it in a teaching project. Medical trainees students and residents received theoretical instructions about the procedure and performed thoracic drainage in this porcine model. After performing the procedure, the participants filled a feedback questionnaire about the proposed experimental model. This study presents the model and analyzes the questionnaire responses. RESULTS seventy nine medical trainees used and evaluated the model. The anatomical correlation between the porcine model and human anatomy was considered high and averaged 8.1+ 1.0 among trainees. All study participants approved the low cost porcine model for chest tube insertion.  the presented low cost porcine model for chest tube insertion training was feasible and had good acceptability among trainees. This model has potential use as a teaching tool in medical education.
Impact of simulation based training in surgical chest tube insertion on a model of traumatic pneumothorax
Venue: Advances in Simulation

BackgroundChest tube insertion is required for most cases of traumatic pneumothorax. However, this procedure entails risks of potentially life threatening complications. A "surgical" approach is widely recommended to minimize these risks. Simulation based education has previously been used in surgical chest tube insertion, but not been subjected to rigorous evaluation.MethodsThe primary objective was to evaluate the success rate of surgical chest tube insertion in a task trainer (previously published) Secondary objectives were to assess performance with a performance assessment scale (previously designed) to measure the time of insertion, and to seek out a correlation between the learner's status, experience, and performance and success rate. Participants were surveyed for realism of the model and satisfaction; 65 participants (18 residents, 47 senior physicians) were randomized into SIM+ or SIM groups. Both groups received didactic lessons. The SIM+ group was assigned deliberate practice on the model under supervision. Both groups were assessed on the model 1 month later.ResultsThere was no difference between the SIM+ (n 34) and SIM (n 31) groups regarding status (p 0.44) or previous surgical insertion (p 0.12) Success rate was 97 (SIM+ and 58 (SIM p 0.0002. Performance score was 16.29 1.82 (SIM+ and 11.39 3.67 (SIM p 3.13 x 10 8. SIM+ presented shorter dissection time than SIM (p 0.047) but procedure time was similar (p 0.71) Status or experience was not correlated with success rate, performance score, procedure time, or dissection time. SIM+ gained more self confidence, judged the model more realistic, and were more satisfied than SIM .ConclusionsSimulation based education significantly improved the success rate and performance of surgical chest tube insertion on a traumatic pneumothorax model.
A click chemistry based microRNA maturation assay optimized for high throughput screening.
Venue: Chemical communications

Catalytic enzyme linked click chemistry assays (cat ELCCA) are an emerging class of biochemical assay. Herein we report on expanding the toolkit of cat ELCCA to include the kinetically superior inverse electron demand Diels Alder (IEDDA) reaction. The result is a technology with improved sensitivity and reproducibility, enabling automated high throughput screening.
InAs based terahertz quantum cascade lasers

We demonstrate terahertz lasing emission from a quantum cascade structure, realized with InAs/AlAs0.16Sb0.84 heterostructures. Due to the lower effective electron mass, InAs based active regions are expected to provide a higher optical gain compared to structures consisting of GaAs or InGaAs. The growth by molecular beam epitaxy enabled the fabrication of monolayer thick barriers, required for the active region, which is based on a 3 well resonant phonon depletion design. Devices were processed in a double metal waveguide geometry to ensure high mode confinement and low optical losses. Lasing emission at 3.8 THz was observed at liquid helium temperatures by applying a magnetic field perpendicular to the layered structure in order to suppress parasitic scattering channels. These results demonstrate the feasibility of InAs based active regions for terahertz quantum cascade lasers, potentially enabling higher operating temperatures.
Conditional Deep Learning for energy efficient and enhanced pattern recognition
Venue: 2016 Design, Automation Test in Europe Conference Exhibition (DATE)

Deep learning neural networks have emerged as one of the most powerful classification tools for vision related applications. However, the computational and energy requirements associated with such deep nets can be quite high, and hence their energy efficient implementation is of great interest. Although traditionally the entire network is utilized for the recognition of all inputs, we observe that the classification difficulty varies widely across inputs in real world datasets; only a small fraction of inputs require the full computational effort of a network, while a large majority can be classified correctly with very low effort. In this paper, we propose Conditional Deep Learning (CDL) where the convolutional layer features are used to identify the variability in the difficulty of input instances and conditionally activate the deeper layers of the network. We achieve this by cascading a linear network of output neurons for each convolutional layer and monitoring the output of the linear network to decide whether classification can be terminated at the current stage or not. The proposed methodology thus enables the network to dynamically adjust the computational effort depending upon the difficulty of the input data while maintaining competitive classification accuracy. We evaluate our approach on the MNIST dataset. Our experiments demonstrate that our proposed CDL yields 1.91x reduction in average number of operations per input, which translates to 1.84x improvement in energy. In addition, our results show an improvement in classification accuracy from 97.5% to 98.9% as compared to the original network.
DeepVessel: Retinal Vessel Segmentation via Deep Learning and Conditional Random Field
Venue: MICCAI

Retinal vessel segmentation is a fundamental step for various ocular imaging applications. In this paper, we formulate the retinal vessel segmentation problem as a boundary detection task and solve it using a novel deep learning architecture. Our method is based on two key ideas: (1) applying a multi scale and multi level Convolutional Neural Network (CNN) with a side output layer to learn a rich hierarchical representation, and (2) utilizing a Conditional Random Field (CRF) to model the long range interactions between pixels. We combine the CNN and CRF layers into an integrated deep network called DeepVessel. Our experiments show that the DeepVessel system achieves state of the art retinal vessel segmentation performance on the DRIVE, STARE, and CHASE_DB1 datasets with an efficient running time.
Retinal vessel segmentation via deep learning network and fully connected conditional random fields
Venue: 2016 IEEE 13th International Symposium on Biomedical Imaging (ISBI)

Vessel segmentation is a key step for various medical applications. This paper introduces the deep learning architecture to improve the performance of retinal vessel segmentation. Deep learning architecture has been demonstrated having the powerful ability in automatically learning the rich hierarchical representations. In this paper, we formulate the vessel segmentation to a boundary detection problem, and utilize the fully convolutional neural networks (CNNs) to generate a vessel probability map. Our vessel probability map distinguishes the vessels and background in the inadequate contrast region, and has robustness to the pathological regions in the fundus image. Moreover, a fully connected Conditional Random Fields (CRFs) is also employed to combine the discriminative vessel probability map and long range interactions between pixels. Finally, a binary vessel segmentation result is obtained by our method. We show that our proposed method achieve a state of the art vessel segmentation performance on the DRIVE and STARE datasets.
Deep Conditional Multi Task Learning in Atari

Deep Q Networks (DQN) have become one of the most successful architectures for the domain of Atari as it has been shown to outperform humans on most games. The problem at hand is that the training time for DQN is immense and the network must be retrained for each game. Through conditional neural networks (Condnets) we break our network up into blocks that can each specialize in solving a particular sub problem of a particular game. We then look one level further and consider approaching the problem of learning a shared representation across multiple games.
Deep Learning for Limit Order Books

This paper develops a new neural network architecture for modeling spatial distributions (i.e. distributions on R^d) which is computationally efficient and specifically designed to take advantage of the spatial structure of limit order books. The new architecture yields a low dimensional model of price movements deep into the limit order book, allowing more effective use of information from deep in the limit order book (i.e. many levels beyond the best bid and best ask) This "spatial neural network" models the joint distribution of the state of the limit order book at a future time conditional on the current state of the limit order book. The spatial neural network outperforms other models such as the naive empirical model, logistic regression (with nonlinear features) and a standard neural network architecture. Both neural networks strongly outperform the logistic regression model. Due to its more effective use of information deep in the limit order book, the spatial neural network especially outperforms the standard neural network in the tail of the distribution, which is important for risk management applications. The models are trained and tested on nearly 500 stocks. Techniques from deep learning such as dropout are employed to improve performance. Due to the significant computational challenges associated with the large amount of data, models are trained with a cluster of 50 GPUs.
Probabilistic Reasoning via Deep Learning: Neural Association Models
Venue: ArXiv

In this paper, we propose a new deep learning approach, called neural association model (NAM) for probabilistic reasoning in artificial intelligence. We propose to use neural networks to model association between any two events in a domain. Neural networks take one event as input and compute a conditional probability of the other event to model how likely these two events are to be associated. The actual meaning of the conditional probabilities varies between applications and depends on how the models are trained. In this work, as two case studies, we have investigated two NAM structures, namely deep neural networks (DNN) and relation modulated neural nets (RMNN) on several probabilistic reasoning tasks in AI, including recognizing textual entailment, triple classification in multi relational knowledge bases and commonsense reasoning. Experimental results on several popular datasets derived from WordNet, FreeBase and ConceptNet have all demonstrated that both DNNs and RMNNs perform equally well and they can significantly outperform the conventional methods available for these reasoning tasks. Moreover, compared with DNNs, RMNNs are superior in knowledge transfer, where a pre trained model can be quickly extended to an unseen relation after observing only a few training samples. To further prove the effectiveness of the proposed models, in this work, we have applied NAMs to solving challenging Winograd Schema (WS) problems. Experiments conducted on a set of WS problems prove that the proposed models have the potential for commonsense reasoning.
Simultaneous load and temperature measurement using Lophine coated fiber Bragg gratings

This paper presents a single fiber Bragg grating (FBG) sensor that is capable of measuring the axial load and temperature simultaneously. The FBG sensor is coated with a block of recrystallized Lophine, which introduces the bandwidth modulation of the FBG reflectance spectrum. Thermal mechanical testing of the Lophine coated FBG sensor under different load and temperature combinations was carried out. It was discovered that the FBG bandwidth is linearly proportional to the applied load and temperature. By fitting the measured FBG bandwidths and central Bragg wavelengths as linear functions of the axial load and temperature, we demonstrated that the axial load and temperature can be inversely determined from the measured reflectance spectrum of a single Lophine coated FBG sensor. The measurement errors for the axial load and the temperature were found to be 4 gm and 4 degC, respectively.
Entropy Based Pruning for Non Negative Matrix Based Language Models with Contextual Features
Venue: INTERSPEECH

Non negative matrix based language models have been recently introduced  as a computationally efficient alternative to other feature based models such as maximum entropy models. We present a new entropy based pruning algorithm for this class of language models, which is fast and scalable. We present perplexity and word error rate results and compare these against regular n gram pruning. We also train models with location and personalization features and report results at various pruning thresholds. We demonstrate that contextual features are helpful over the vanilla model even after pruning to a similar size.