nils-herrmann/clean_scirepeval huggingface dataset:
Turbocharging ambient backscatter communication

Communication primitives such as coding and multiple antenna processing have provided significant benefits for traditional wireless systems. Existing designs, however, consume significant power and computational resources, and hence cannot be run on low complexity, power constrained backscatter devices. This paper makes two main contributions: (1) we introduce the first multi-antenna cancellation design that operates on backscatter devices while retaining a small form factor and power footprint, (2) we introduce a novel coding mechanism that enables long range communication as well as concurrent transmissions and can be decoded on backscatter devices. We build hardware prototypes of the above designs that can be powered solely using harvested energy from TV and solar sources. The results show that our designs provide benefits for both RFID and ambient backscatter systems: they enable RFID tags to communicate directly with each other at distances of tens of meters and through multiple walls. They also increase the communication rate and range achieved by ambient backscatter systems by 100X and 40X respectively. We believe that this paper represents a substantial leap in the capabilities of backscatter communication.
Stabilization of the ulna stump after Darrach's procedure at the wrist

We studied the outcome of patients with chronic distal radioulnar joint symptoms who were treated with excision of the distal ulna and reconstruction using the flexor carpi ulnaris tendon. Twelve patients with 14 wrists were assessed. Ten patients were posttraumatic, and two had bilateral surgery for rheumatoid arthritis. Average age was 37 years at time of operation. Follow-up averaged 20 months (9 months to 4 years). All patients except one reported improvement in pain symptoms. Grip strength of the operated hand as measured by dynamometer readings was 67% of the strength of the normal hand with five wrists achieving an excellent result in grip strength. Eleven patients reported a subjective improvement in functional activities. An improved range of motion was obtained in all patients. We discuss the importance and basis for ligamentous reconstruction following excision of the distal ulna and review the literature for other previously described procedures. Nous avons étudié le résultat de malades souffrant de symptômes chroniques de l'articulation radio-ulnaire distale qui ont été traité par excision du cubitus distal et reconstruction avec le tendon du fléchisseur carpien ulnaire. Un total de douze malades avec quatorze poignets ont été étudiés. Dix étaient post traumatiques et deux ont eu une chirurgie bilatérale pour polyarthrite rhumatoïde. L'âge moyen était de 37 ans à l'opération. Le suivi moyen était de 20 mois (neuf mois à quatre années). Tous les malades sauf un ont eu une amélioration de la douleur. La force de préhension, mesurée par dynamomètre, était de 67% de celle de la main normale et 5 des 14 poignets ont obtenus une force de très bonne qualité. Une amélioration de la mobilité a été obtenue chez tous les patients. Onze des douze malades ont rapporté une amélioration subjective dans les activités quotidiennes. Les auteurs discutent l'importance de la reconstruction ligamentaire après résection distale du cubitus.
Automating the production planning of a 3D printing factory

To increase a 3D printer's throughput and decrease the print objects' lead times, composing good batches for 3D printers in high-volume 3D printing environments is of great importance. Since manual planners cannot oversee the whole production planning, they tend to make sub-optimal decisions. This paper presents a two-stage procedure that automatically generates batches for multiple 3D printers. The first stage consists of allocating objects to a batch. This problem is an extension of the classical one-dimensional bin packing problem by including lateness and object size mixture requirements. In the second stage, the positions of the print objects in a tray are determined by using third party three-dimensional packing software. By simulating the production planning of Shapeways, a popular 3D printing marketplace, model parameters are calibrated and the performance of the two-stage procedure is evaluated. The results show an increase of the throughput of the 3D-printers by approximately 10% in comparison with manually created batches.
Advanced tutorial: Input uncertainty quantification

“Input uncertainty” refers to the (often unmeasured) effect of not knowing the true, correct distributions of the basic stochastic processes that drive the simulation. These include, for instance, interarrival-time and service-time distributions in queueing models; bed-occupancy distributions in health care models; distributions for the values of underlying assets in financial models; and time-to-failure and time-to-repair distributions in reliability models. When the input distributions are obtained by fitting to observed real-world data, then it is possible to quantify the impact of input uncertainty on the output results. In this tutorial we carefully define input uncertainty, describe various proposals for measuring it, contrast input uncertainty with input sensitivity, and provide and illustrate a practical approach for quantifying overall input uncertainty and the relative contribution of each input model to overall input uncertainty.
Fractional calculus in pharmacokinetics

We are witnessing the birth of a new variety of pharmacokinetics where non-integer-order differential equations are employed to study the time course of drugs in the body: this is dubbed “fractional pharmacokinetics”. The presence of fractional kinetics has important clinical implications such as the lack of a half-life, observed, for example with the drug amiodarone and the associated irregular accumulation patterns following constant and multiple-dose administration. Building models that accurately reflect this behaviour is essential for the design of less toxic and more effective drug administration protocols and devices. This article introduces the readers to the theory of fractional pharmacokinetics and the research challenges that arise. After a short introduction to the concepts of fractional calculus, and the main applications that have appeared in literature up to date, we address two important aspects. First, numerical methods that allow us to simulate fractional order systems accurately and second, optimal control methodologies that can be used to design dosing regimens to individuals and populations.
Broadband microstrip-line-fed circularly-polarized circular slot antenna

A broadband microstrip-line-fed circularly-polarized circular slot antenna is presented in this paper. The circularly polarized (CP) operation can be designed by embedding a spur slot into the circular slot and the broadband CP operation can be simply achieved by an open slot. From the experimental results, the 3-dB axial-ratio (AR) bandwidth can reach as large as 350 MHz (14.5% relative to the center frequency of 2415 MHz) which is about 3 times of that of the proposed antenna without the open slot to cover the WLAN band.
Building better biomarkers: brain models in translational neuroimaging

Despite its great promise, neuroimaging has yet to substantially impact clinical practice and public health. However, a developing synergy between emerging analysis techniques and data-sharing initiatives has the potential to transform the role of neuroimaging in clinical applications. We review the state of translational neuroimaging and outline an approach to developing brain signatures that can be shared, tested in multiple contexts and applied in clinical settings. The approach rests on three pillars: (i) the use of multivariate pattern-recognition techniques to develop brain signatures for clinical outcomes and relevant mental processes; (ii) assessment and optimization of their diagnostic value; and (iii) a program of broad exploration followed by increasingly rigorous assessment of generalizability across samples, research contexts and populations. Increasingly sophisticated models based on these principles will help to overcome some of the obstacles on the road from basic neuroscience to better health and will ultimately serve both basic and applied goals.
The prevalence and socio-demographic correlations of depression, anxiety and stress among a group of university students

The mental health of university students is an area of increasing concern worldwide. The objective of this study is to examine the prevalence of depression, anxiety and stress among a group of Turkish university students. Depression Anxiety and Stress Scale (DASS-42) completed anonymously in the students’ respective classrooms by 1,617 students. Depression, anxiety and stress levels of moderate severity or above were found in 27.1, 47.1 and 27% of our respondents, respectively. Anxiety and stress scores were higher among female students. First- and second-year students had higher depression, anxiety and stress scores than the others. Students who were satisfied with their education had lower depression, anxiety and stress scores than those who were not satisfied. The high prevalence of depression, anxiety and stress symptoms among university students is alarming. This shows the need for primary and secondary prevention measures, with the development of adequate and appropriate support services for this group.
Information Retrieval and Folksonomies together for Recommender Systems

The powerful and democratic activity of social tagging allows the wide set of Web users to add free annotations on resources. Tags express user interests, preferences and needs, but also automatically generate folksonomies. They can be considered as gold mine, especially for e-commerce applications, in order to provide effective recommendations. Thus, several recommender systems exploit folksonomies in this context. Folksonomies have also been involved in many information retrieval approaches. In considering that information retrieval and recommender systems are siblings, we notice that few works deal with the integration of their approaches, concepts and techniques to improve recommendation. This paper is a first attempt in this direction. We propose a trail through recommender systems, social Web, e-commerce and social commerce, tags and information retrieval: an overview on the methodologies, and a survey on folksonomy-based information retrieval from recommender systems point of view, delineating a set of open and new perspectives.
A DESIGN OF PARITY CHECK MATRIX FOR SHORT IRREGULAR LDPC CODES VIA MAGIC SQUARE BASED ALGORITHM

This paper presents a construction algorithm for the short block irregular low-density parity-check (LDPC) codes. By applying a magic square theorem as a part of the matrix construction, a newly developed algorithm, the so-called Magic Square Based Algorithm (MSBA), is obtained. The modified array codes are focused on in this study since the reduction of 1s can lead to simple encoding and decoding schemes. Simulation results based on AWGN channels show that with the code rate of 0.8 and SNR 5 dB, the BER of 10 can be obtained whilst the number of decoding iteration is relatively low.
Inferring Parameters for an Elementary Step Model of DNA Structure Kinetics with Locally Context-Dependent Arrhenius Rates

Models of nucleic acid thermal stability are calibrated to a wide range of experimental observations, and typically predict equilibrium probabilities of nucleic acid secondary structures with reasonable accuracy. By comparison, a similar calibration and evaluation of nucleic acid kinetic models to a broad range of measurements has not been attempted so far. We introduce an Arrhenius model of interacting nucleic acid kinetics that relates the activation energy of a state transition with the immediate local environment of the affected base pair. Our model can be used in stochastic simulations to estimate kinetic properties and is consistent with existing thermodynamic models. We infer parameters for our model using an ensemble Markov chain Monte Carlo (MCMC) approach on a training dataset with 320 kinetic measurements of hairpin closing and opening, helix association and dissociation, bubble closing and toehold-mediated strand exchange. Our new model surpasses the performance of the previously established Metropolis model both on the training set and on a testing set of size 56 composed of toehold-mediated 3-way strand displacement with mismatches and hairpin opening and closing rates: reaction rates are predicted to within a factor of three for 93.4% and 78.5% of reactions for the training and testing sets, respectively.
Short time fourier transform with coefficient optimization for detecting salient regions in stereoscopic 3D images: GSDU

In multimedia scenario, number of saliency detection designs has been portrayed for different intelligent applications regarding the accurate saliency detection like human visual system. More challenges exist regarding complexity in natural images and lesser scale prototypes in salient objects. In lots of the prevailing methods, the competency of identifying objects’ instances in the discovered salient regions is still not up to the mark. Hence it is planned to assist a new strategy by considering certain parameters of feature evaluation under optimization algorithms which diverts the new method of capturing the optimal parameters to acquire better outcomes. The given paper proposes a new saliency detection design that encompasses 2 phases like Feature extraction and depth saliency detection. In which Gaussian kernel model is processed for extracting the STFT features (Short-Time Fourier Transform), Texture features, and Depth features; and Gabor filter is used to get the depth saliency map. Here, the color space information is progressed under STFT model for extracting the STFT features. This is the major contribution, where all the STFT feature, Texture feature and Depth features are taken out to gain the depth saliency map. Additionally, this paper proffers a new optimization prototype that optimizes 2 coefficients namely feature difference amongst image patches from feature evaluation, and fine scale, by which more precise saliency detection outcomes can be attained through the proposed model. Subsequently, the proposed GSDU (Glowworm Swarm optimization with Dragonfly Update) contrasts its performance over other conventional methodologies concerning i) ROC (Receiver Operator Curve), ii) PCC (Pearson Correlation Coefficient), iii) KLD (Kullback Leibler Divergence) as well as iv) AUC (Area Under the Curve), and the efficiency of proposed model is proven grounded on higher accuracy rate.
The Inside-Outside Recursive Neural Network model for Dependency Parsing

We propose the first implementation of an infinite-order generative dependency model. The model is based on a new recursive neural network architecture, the Inside-Outside Recursive Neural Network. This architecture allows information to flow not only bottom-up, as in traditional recursive neural networks, but also topdown. This is achieved by computing content as well as context representations for any constituent, and letting these representations interact. Experimental results on the English section of the Universal Dependency Treebank show that the infinite-order model achieves a perplexity seven times lower than the traditional third-order model using counting, and tends to choose more accurate parses in k-best lists. In addition, reranking with this model achieves state-of-the-art unlabelled attachment scores and unlabelled exact match scores.
Early failure mechanisms of constrained tripolar acetabular sockets used in revision total hip arthroplasty.

Fifty-eight patients received an Osteonics constrained acetabular implant for recurrent instability (46), girdlestone reimplant (8), correction of leg lengthening (3), and periprosthetic fracture (1). The constrained liner was inserted into a cementless shell (49), cemented into a pre-existing cementless shell (6), cemented into a cage (2), and cemented directly into the acetabular bone (1). Eight patients (13.8%) required reoperation for failure of the constrained implant. Type I failure (bone-prosthesis interface) occurred in 3 cases. Two cementless shells became loose, and in 1 patient, the constrained liner was cemented into an acetabular cage, which then failed by pivoting laterally about the superior fixation screws. Type II failure (liner locking mechanism) occurred in 2 cases. Type III failure (femoral head locking mechanism) occurred in 3 patients. Seven of the 8 failures occurred in patients with recurrent instability. Constrained liners are an effective method for treatment during revision total hip arthroplasty but should be used in select cases only.
Design, stress analysis and determination of center of gravity on stair climber wheelchair

Not all facilities in Indonesia have supporting facilities for wheelchairs in order to access these facilities freely. Obstacles encountered especially not their specific path to enter, so had to climb the stairs. The problem is the absence of a special path to enter the access, so it must climb the stairs. The movement of the wheels to climb the stairs and sitting balance system of the patient become a major problem in this study. The purpose of this research is to make prototype of mechanical system in wheelchair so that can climb stairs easily and safely. In mechanical systems, the design of the wheel using three-wheel system which is arranged in parallel form a triangle. To balance the user's position, under the seats mounted screw system as a regulator of the seat tilt angle. This mechanical design planning takes into account the determination of the Center of Gravity (CoG) to ensure stability. Hopefully, this wheelchair prototype becomes a means of supporting mobility for the disabled to move to public facilities in Indonesia.
A New Technique for Combining Multiple Classifiers using The Dempster-Shafer Theory of Evidence

This paper presents a new classifier combination technique based on the DempsterShafer theory of evidence. The Dempster-Shafer theory of evidence is a powerful method for combining measures of evidence from different classifiers. However, since each of the available methods that estimates the evidence of classifiers has its own limitations, we propose here a new implementation which adapts to training data so that the overall mean square error is minimized. The proposed technique is shown to outperform most available classifier combination methods when tested on three different classification problems.
Evaluation of Real-time Deep Learning Turn-taking Models for Multiple Dialogue Scenarios

The task of identifying when to take a conversational turn is an important function of spoken dialogue systems. The turn-taking system should also ideally be able to handle many types of dialogue, from structured conversation to spontaneous and unstructured discourse. Our goal is to determine how much a generalized model trained on many types of dialogue scenarios would improve on a model trained only for a specific scenario. To achieve this goal we created a large corpus of Wizard-of-Oz conversation data which consisted of several different types of dialogue sessions, and then compared a generalized model with scenario-specific models. For our evaluation we go further than simply reporting conventional metrics, which we show are not informative enough to evaluate turn-taking in a real-time system. Instead, we process results using a performance curve of latency and false cut-in rate, and further improve our model's real-time performance using a finite-state turn-taking machine. Our results show that the generalized model greatly outperformed the individual model for attentive listening scenarios but was worse in job interview scenarios. This implies that a model based on a large corpus is better suited to conversation which is more user-initiated and unstructured. We also propose that our method of evaluation leads to more informative performance metrics in a real-time system.
Alcoholic liver disease is a strong predictor of colorectal polyps in liver transplant recipients

Colorectal cancer (CRC) is associated with a significantly reduced survival rate in transplant recipients. The prevalence and risk factors of CRC and of colorectal polyps after orthotopic liver transplant (OLT) remain unclear. The study aim was to determine the prevalence of colorectal polyps in OLT recipients. A secondary objective was to explore possible risk factors of polyps.


This was a retrospective single center study of all OLT recipients transplanted between 2007 and 2009. All patients who underwent a colonoscopy 5 ± 5 years after OLT were included. The outcome was colorectal polyps, as identified on colonoscopy. A logistic regression model was performed to identify potential predictors of polyps.


Of 164 OLT recipients, 80 were included in this study. Polyps were diagnosed in 37 % of patients before transplant and in 33 % afterwards. With regard to post-transplant lesions, 22 % were advanced adenomas or cancerous. In the regression analysis, the odds of post-transplant polyps were 11 times higher in patients with alcoholic liver disease (OR 11.3, 95 %CI 3.2 - 39.4; P  < 0.001).


Patients with end-stage liver disease may be at high risk of colorectal polyps before and after liver transplant, and screening should be continued in both contexts. Those with alcoholic liver disease are particularly at risk for post-OLT polyps and may benefit from more intensive screening.
Recognition of continuous probability models

It is well known that randomness is present in daily life and that often it is desirable to recognize inherent characteristics of this randomness. Probability theory describes a quantification of the uncertainty associated with this randomness. Based on probability theory, the present research describes an alternative methodology to the traditional statistical method of the recognition of the probabilistic models that best represent randomness. The main motivation of the methodology is to keep the largest possible amount of information present in the data. This methodology differs from the traditional statistical method, mainly in aspects related to the division of the data into classes when the data are continuous.
Vision-aided inertial navigation for resource-constrained systems

In this paper we present a resource-adaptive framework for real-time vision-aided inertial navigation. Specifically, we focus on the problem of visual-inertial odometry (VIO), in which the objective is to track the motion of a mobile platform in an unknown environment. Our primary interest is navigation using miniature devices with limited computational resources, similar for example to a mobile phone. Our proposed estimation framework consists of two main components: (i) a hybrid EKF estimator that integrates two algorithms with complementary computational characteristics, namely a sliding-window EKF and EKF-based SLAM, and (ii) an adaptive image-processing module that adjusts the number of detected image features based oadaptive image-processing module that adjusts the number of detected image features based on the availability of resources. By combining the hybrid EKF estimator, which optimally utilizes the feature measurements, with the adaptive image-processing algorithm, the proposed estimation architecture fully utilizes the system's computational resources. We present experimental results showing that the proposed estimation framework isn the availability of resources. By combining the hybrid EKF estimator, which optimally utilizes the feature measurements, with the adaptive image-processing algorithm, the proposed estimation architecture fully utilizes the system's computational resources. We present experimental results showing that the proposed estimation framework is capable of real-time processing of image and inertial data on the processor of a mobile phone.