nils-herrmann/clean_scirepeval huggingface dataset:
VeTrack: Real Time Vehicle Tracking in Uninstrumented Indoor Environments

Although location awareness and turn-by-turn instructions are prevalent outdoors due to GPS, we are back into the darkness in uninstrumented indoor environments such as underground parking structures. We get confused, disoriented when driving in these mazes, and frequently forget where we parked, ending up circling back and forth upon return.In this paper, we propose VeTrack, a smartphone-only system that tracks the vehicle's location in real time using the phone's inertial sensors. It does not require any environment instrumentation or cloud backend. It uses a novel "shadow" tracing method to accurately estimate the vehicle's trajectories despite arbitrary phone/vehicle poses and frequent disturbances. We develop algorithms in a Sequential Monte Carlo framework to represent vehicle states probabilistically, and harness constraints by the garage map and detected landmarks to robustly infer the vehicle location. We also find landmark (e.g., speed bumps, turns) recognition methods reliable against noises, disturbances from bumpy rides and even hand-held movements. We implement a highly efficient prototype and conduct extensive experiments in multiple parking structures of different sizes and structures, with multiple vehicles and drivers. We find that VeTrack can estimate the vehicle's real time location with almost negligible latency, with error of 2-4 parking spaces at 80-percentile.
A C/X/Ku-band dual polarized Cassegrain antenna system

There is an increasing demand for multiple frequency band capability in satellite Earth station antenna systems. This paper describes an 18.3 m shaped Cassegrain antenna with a single aperture, tri-band, six-port receive-only, feed system. The frequencies of operation are 3.4-4.2 GHz, 7.25-7.75 GHz, and 10.9-12.75 GHz. The C- and X-band feed ports offer dual circular polarization, and the Ku-band ports offer dual linear polarization with an internal polarization vector angle adjustment mechanism. Frequency and polarization multiplexing is performed by a novel OMJ structure.
A review of more electric aircraft technology

Air travel is the main focus of attention with respect to reducing greenhouse gas emissions in the transport sector. This creates the necessity for having environmentally friendly flights to facilitate local and global transport. To address this issue, several initiatives are being taken by the aircraft industry to promote the use of electrical power to drive most of the aircraft systems and subsystems that were conventionally driven by hydraulic, pneumatic and mechanical systems. The development of aircraft designs with improved performance to make them more efficient and environmentally-friendly is the motivation underlying the concept of More Electric Aircraft (MEA). In this context, this paper reviews some of the notable MEA initiatives undertaken by the aerospace industry.
Gated Recurrent Capsules for Visual Word Embeddings

The caption retrieval task can be defined as follows: given a set of images I and a set of describing sentences S, for each image i in I we ought to find the sentence in S that best describes i. The most commonly applied method to solve this problem is to build a multimodal space and to map each image and each sentence to that space, so that they can be compared easily. A non-conventional model called Word2VisualVec has been proposed recently: instead of mapping images and sentences to a multimodal space, they mapped sentences directly to a space of visual features. Advances in the computation of visual features let us infer that such an approach is promising. In this paper, we propose a new Recurrent Neural Network model following that unconventional approach based on Gated Recurrent Capsules (GRCs), designed as an extension of Gated Recurrent Units (GRUs). We show that GRCs outperform GRUs on the caption retrieval task. We also state that GRCs present a great potential for other applications.
Rubinstein Taybi Syndrome in an Indian Child due to EP300 Gene Mutation.

A 10-y-old girl was diagnosed with Rubinstein Taybi Syndrome (RTS) based on distinctive facial features (low hanging columella, high palate, grimacing smile, talon cusps), broad, angulated thumbs and great toes, short stature and moderate intellectual disability . Her younger sister, who died at 11 mo of age due to acute myeloid leukemia, did not have clinical features of RTS. Complete sequencing of CREBBP and EP300 genes was done for the proband as previously described [1, 2]. No pathogenic mutation was noted in CREBBP gene. She was found to be heterozygous for c.1282C > T or p.P428S mutation in exon five of EP300 gene . The mutation is predicted to affect the donor splicing . The patient’s mother was heterozygous for the same mutation. Prenatal diagnosis was offered to the couple and the fetus did not carry this mutation, and was confirmed to be unaffected postnatally. This mutation was not identified in 100 normal unrelated individuals. Deletion/duplication analysis of CREBBP and EP300 gene was not available. Our patient had horizontal palpebral fissures as described with EP300 related RTS, compared with CREBBP related RTS, who have anti-mongoloid slant . Although patients with EP300 mutation have broad thumbs or toes, radial deviation has not been reported  . This mutation was inherited from her mother who had mild hypertelorism, broad nasal bridge, normal intelligence and hands/feet. Negri et al. reported a patient with mutation in EP300 gene (p.N1511T) that was inherited from a healthy mother. Low penetrance in EP300 related RTS may present a challenge during genetic counseling and prenatal diagnosis . EP300 gene somatic mutations are reported in acute leukemia and RTS patients have an increased incidence of cancer [2, 5]. Thus, we report an Indian child with Rubinstein Taybi Syndrome with familial EP300 gene mutation.
SiC-MOSFET based single phase AC to DC PWM converter

Single phase AC to DC converters are essential parts of any modern electrical, electronic, computer, communication and automation systems. These units will be powering the systems all the day through the year. Millions of such units are in operation through the world. So these units must be compact, efficient and reliable. This research paper presents efficient AC to DC PWM converters based on modern devices like Silicon Carbide-MOSFET. The SiC-MOSFET is highly efficient and also allows higher switching frequency compared to Silicon-MOSFET. Novel closed loop vector control algorithm on synchronous rotating d-q axes is developed and presented. The proposed closed loop vector control AC to DC converter system is simulated and results are presented. Switching and conduction losses for the SiC-MOSFETs are estimated and compared with that of Silicon based device to demonstrate the improved efficiency.
Automatic segmentation and degree identification in burn color images

When burn injury occurs, the most important step is to provide treatment to the injury immediately by identifying degree of the burn which can only be diagnosed by specialists. However, specialists for burn trauma are still inadequate for some local hospitals. Hence, the invention of an automatic system that is able to help evaluating the burn would be extremely beneficial to those hospitals. The aim of this work is to develop an automatic system with the ability of providing the first assessment to burn injury from burn color images. The method used in this work can be divided into 2 parts, i.e., burn image segmentation and degree of burn identification. Burn image segmentation employs the Cr-transformation, Luv-transformation and fuzzy c-means clustering technique to separate the burn wound area from healthy skin and then mathematical morphology is applied to reduce segmentation errors. The segmentation algorithm performance is evaluated by the positive predictive value (PPV) and the sensitivity (S). Burn degree identification uses h-transformation and texture analysis to extract feature vectors and the support vector machine (SVM) is applied to identify the degree of burn. The classification results are compared with that of Bayes and K-nearest neighbor classifiers. The experimental results show that our proposed segmentation algorithm yields good results for the burn color images. The PPV and S are about 0.92 and 0.84, respectively. Degree of burn identification experiments show that SVM yields the best results of 89.29 % correct classification on the validation sets of the 4-fold cross validation. SVM also yields 75.33 % correct classification on the blind test experiment.
A Self-Reliant DC Microgrid: Sizing, Control, Adaptive Dynamic Power Management, and Experimental Analysis

This paper presents an adaptive power management and control for a self-reliant dc microgrid powered by photovoltaic (PV) array and fuel cell stack that is escorted with the supercapacitor- and electrolyzer-based hybrid energy storage device. The prime operational impediments associated with this dc microgrid are the unpredictable variations in the PV generation and load demand, fuel starvation phenomenon of the fuel cell, the time constant associated with the electrolyzer operation, and state-of-charge limitation of the supercapacitor. In this paper, the above-mentioned issues are addressed by employing an adaptive dynamic power management strategy (ADPMS) that supervises the overall power flow in the system. The ADPMS enables the co-existence of high power density and high energy density storage devices to deliver the power flow required by the loads. The expeditious support extended by the supercapacitor helps in swift regulation of the dc-link voltage during unforeseen transient load/source power variations. The electrolyzer–fuel cell combination employed in this system can supplement a battery bank and can equip high energy density and self-reliance to the system. The set-reset flip-flop based fixed frequency current controller is simple and effective in accurately tracking the reference currents established by the ADPMS. The proposed ADPMS along with the control is validated for transient unforeseen load profiles and renewable generation changes via simulation studies and its performance is also validated through an experimental analysis on a laboratory-scale dc microgrid testbed.
Graphite: Iterative Generative Modeling of Graphs

Graphs are a fundamental abstraction for modeling relational data. However, graphs are discrete and combinatorial in nature, and learning representations suitable for machine learning tasks poses statistical and computational challenges. In this work, we propose Graphite an algorithmic framework for unsupervised learning of representations over nodes in a graph using deep latent variable generative models. Our model is based on variational autoencoders (VAE), and uses graph neural networks for parameterizing both the generative model (i.e., decoder) and inference model (i.e., encoder). The use of graph neural networks directly incorporates inductive biases due to the spatial, local structure of graphs directly in the generative model. We draw novel connections of our framework with approximate inference via kernel embeddings. Empirically, Graphite outperforms competing approaches for the tasks of density estimation, link prediction, and node classification on synthetic and benchmark datasets.
Investigating reproducibility and tracking provenance – A genomic workflow case study

Computational bioinformatics workflows are extensively used to analyse genomics data, with different approaches available to support implementation and execution of these workflows. Reproducibility is one of the core principles for any scientific workflow and remains a challenge, which is not fully addressed. This is due to incomplete understanding of reproducibility requirements and assumptions of workflow definition approaches. Provenance information should be tracked and used to capture all these requirements supporting reusability of existing workflows. We have implemented a complex but widely deployed bioinformatics workflow using three representative approaches to workflow definition and execution. Through implementation, we identified assumptions implicit in these approaches that ultimately produce insufficient documentation of workflow requirements resulting in failed execution of the workflow. This study proposes a set of recommendations that aims to mitigate these assumptions and guides the scientific community to accomplish reproducible science, hence addressing reproducibility crisis. Reproducing, adapting or even repeating a bioinformatics workflow in any environment requires substantial technical knowledge of the workflow execution environment, resolving analysis assumptions and rigorous compliance with reproducibility requirements. Towards these goals, we propose conclusive recommendations that along with an explicit declaration of workflow specification would result in enhanced reproducibility of computational genomic analyses.
Engagement in Multimedia Training Systems

Two studies examined user engagement in two type multimedia training systems -a more passive medium, videotape, and a less passive medium, interactive softw Each study compared engagement in three formats: the Text format contained text and still images, the Audio format contained audio and still images, and the Video format contained audio and video images. In both studies, engagement was lower in the Text condition than in the Video condition. However, there were no differences in engagement between Text and Audio in the videotapebased training, and no differences between Audio and Video in the computer-based training.
Failure Prediction And Detection In Cloud Datacenters

Cloud computing is a novel technology in the field of distributed computing. Usage of Cloud computing is increasing rapidly day by day. In order to serve the customers and businesses satisfactorily, fault occurring in datacenters and servers must be detected and predicted efficiently in order to launch mechanisms to tolerate the failures occurred. Failure in one of the hosted datacenters may propagate to other datacenters and make the situation worse. In order to prevent such situations, one can predict a failure proliferating throughout the cloud computing system and launch mechanisms to deal with it proactively. One of the ways to predict failures is to train a machine to predict failure on the basis of messages or logs passed between various components of the cloud. In the training session, the machine can identify certain message patterns relating to failure of data centers. Later on, the machine can be used to check whether a certain group of message logs follow such patterns or not. Moreover, each cloud server can be defined by a state which indicates whether the cloud is running properly or is facing some failure. Parameters such as CPU usage, memory usage etc. can be maintained for each of the servers. Using this parameters, we can add a layer of detection where in we develop a decision tree based on these parameters which can classify whether the passed in parameters to the decision tree indicate failure state or proper state.
Efficient Subspace Segmentation via Quadratic Programming

We explore in this paper efficient algorithmic solutions to robust subspace segmentation. We propose the SSQP, namely Subspace Segmentation via Quadratic Programming, to partition data drawn from multiple subspaces into multiple clusters. The basic idea of SSQP is to express each datum as the linear combination of other data regularized by an overall term targeting zero reconstruction coefficients over vectors from different subspaces. The derived coefficient matrix by solving a quadratic programming problem is taken as an affinity matrix, upon which spectral clustering is applied to obtain the ultimate segmentation result. Similar to sparse subspace clustering (SCC) and low-rank representation (LRR), SSQP is robust to data noises as validated by experiments on toy data. Experiments on Hopkins 155 database show that SSQP can achieve competitive accuracy as SCC and LRR in segmenting affine subspaces, while experimental results on the Extended Yale Face Database B demonstrate SSQP’s superiority over SCC and LRR. Beyond segmentation accuracy, all experiments show that SSQP is much faster than both SSC and LRR in the practice of subspace segmentation.
Learning Graphical State Transitions

Graph-structured data is important in modeling relationships between multiple entities, and can be used to represent states of the world as well as many data structures. Li et al. (2016) describe a model known as a Gated Graph Sequence Neural Network (GGS-NN) that produces sequences from graph-structured input. In this work I introduce the Gated Graph Transformer Neural Network (GGTNN), an extension of GGS-NNs that uses graph-structured data as an intermediate representation. The model can learn to construct and modify graphs in sophisticated ways based on textual input, and also to use the graphs to produce a variety of outputs. For example, the model successfully learns to solve almost all of the bAbI tasks (Weston et al., 2016), and also discovers the rules governing graphical formulations of a simple cellular automaton and a family of Turing machines.
A Mobile-Cloud Collaborative Traffic Lights Detector for Blind Navigation

Context-awareness is a critical aspect of safe navigation especially for the blind and visually impaired in unfamiliar environments. Existing mobile devices for context-aware navigation fall short in many cases due to their dependence on specific infrastructure requirements as well as having limited access to resources that could provide a wealth of contextual clues. In this paper, we propose a mobile-cloud collaborative approach for context-aware navigation by exploiting the computational power of resources made available by Cloud Computing providers as well as the wealth of location-specific resources available on the Internet. We propose an extensible system architecture that minimizes reliance on infrastructure, thus allowing for wide usability. We present a traffic light detector that we developed as an initial application component of the proposed system. We present preliminary results of experiments performed to test the appropriateness for the real-time nature of the application.
Putting Gender Into Context : An Interactive Model of Gender-Related Behavior

A model that describes conditions influencing the display of gender-related behavior is presented as a supplement to existent models of sex differences. Whereas many previous models stress the importance of distal factors, our model emphasizes the degree to which gender-related behavior is variable, proximally caused, and context dependent. More specifically, we propose that gender-related behaviore are influenced by the expectations of perceivers, self-systems of the target, and situational cues. This model of gender-related behavior builds on theory and data in the areas of (a) expectancy confirmation processes and (b) self-verification and self-presentation strategies. Support for the model is presented, and suggestions are offered for its future development.
Enhanced End-of-Turn Detection for Speech to a Personal Assistant

Speech to personal assistants (e.g., reminders, calendar entries, messaging, voice search) is often uttered under cognitive load, causing nonfinal pausing that can result in premature recognition cut-offs. Prior research suggests that prepausal features can discriminate final from nonfinal pauses, but it does not reveal how speakers would behave if given longer to pause. To this end, we collected and compared two elicitation corpora differing in naturalness and task complexity. The Template Corpus (4409 nonfinal pauses) uses keyword-based prompts; the Freeform Corpus (8061 nonfinal pauses) elicits open-ended speech. While nonfinal pauses are longer and twice as frequent in the Freeform data, prepausal feature modelling is roughly equally effective in both corpora. At a response latency of 100 ms, prepausal features modelled by an SVM reduced cut-off rates from 100% to 20% for both corpora. Results have implications for enhancing turn-taking efficiency and naturalness in personal-assistant technology.
A brief survey of blackhole detection and avoidance for ZRP protocol in MANETs

Within Mobile Ad-Hoc Network(MANETs) unusual categories of routing protocols have been determined. It is a group of wireless system. MANET is other susceptible to a variety of attack than wired system. Black hole attack is further meticulous intimidation to MANETs. The routing protocols of MANET is less protected and therefore consequenced the system with malicious node. There are various routing protocols being used for MANETs. All routing protocols have been briefly discussed, however, Zone Routing Protocol (ZRP) is discussed in detail. Black hole is a major security threat for MANETs. Hence, in this paper, the various techniques used for detection and avoidance of Black hole attack in MANETs using ZRP routing protocol have been discussed.
AIDE: Fast and Communication Efficient Distributed Optimization

In this paper, we present two new communication-efficient methods for distributed minimization of an average of functions. The first algorithm is an inexact variant of the DANE algorithm [20] that allows any local algorithm to return an approximate solution to a local subproblem. We show that such a strategy does not affect the theoretical guarantees of DANE significantly. In fact, our approach can be viewed as a robustification strategy since the method is substantially better behaved than DANE on data partition arising in practice. It is well known that DANE algorithm does not match the communication complexity lower bounds. To bridge this gap, we propose an accelerated variant of the first method, called AIDE, that not only matches the communication lower bounds but can also be implemented using a purely first-order oracle. Our empirical results show that AIDE is superior to other communication efficient algorithms in settings that naturally arise in machine learning applications.
Surgical Management of Hidradenitis Suppurativa: Outcomes of 590 Consecutive Patients.

Hidradenitis suppurativa is a progressive, recurrent inflammatory disease. Surgical management is potentially curative with limited efficacy data.



To evaluate hidradenitis surgical patients.



Retrospective review of outcomes of 590 consecutive surgically treated patients.



Most patients were white (91.0% [435/478]), men (337 [57.1%]), smokers (57.7% [297/515]) with Hurley Stage III disease (476 [80.7%]). Procedure types were excision (405 [68.6%]), unroofing (168 [28.5%]), and drainage (17 [2.9%]) treating disease of perianal/perineum (294 [49.8%]), axilla (124 [21.0%]), gluteal cleft (76 [12.9%]), inframammary (12 [2.0%]), and multiple surgical sites (84 [14.2%]). Postoperative complications occurred in 15 patients (2.5%) and one-fourth (144 [24.4%]) suffered postoperative recurrence, which necessitated reoperation in one-tenth (69 [11.7%]) of patients. Recurrence risk was increased by younger age (hazard ratio [HR], 0.8; 95% confidence interval [CI], 0.7-0.9), multiple surgical sites (HR, 1.6; 95% CI, 1.1-2.5), and drainage-type procedures (HR, 3.5; 95% CI, 1.2-10.7). Operative location, disease severity, gender, and operative extent did not influence recurrence rate.



Excision and unroofing procedures were effective treatments with infrequent complications and low recurrence rates. Well-planned surgical treatment aiming to remove or unroof the area of intractable hidradenitis suppurativa was highly effective in the management of this challenging disease.