nils-herrmann/clean_scirepeval huggingface dataset:
Zebrafish as a model to study cardiac development and human cardiac disease

Over the last decade, the zebrafish has entered the field of cardiovascular research as a new model organism. This is largely due to a number of highly successful small- and large-scale forward genetic screens, which have led to the identification of zebrafish mutants with cardiovascular defects. Genetic mapping and identification of the affected genes have resulted in novel insights into the molecular regulation of vertebrate cardiac development. More recently, the zebrafish has become an attractive model to study the effect of genetic variations identified in patients with cardiovascular defects by candidate gene or whole-genome-association studies. Thanks to an almost entirely sequenced genome and high conservation of gene function compared with humans, the zebrafish has proved highly informative to express and study human disease-related gene variants, providing novel insights into human cardiovascular disease mechanisms, and highlighting the suitability of the zebrafish as an excellent model to study human cardiovascular diseases. In this review, I discuss recent discoveries in the field of cardiac development and specific cases in which the zebrafish has been used to model human congenital and acquired cardiac diseases.
Database auditing and forensics: Exploration and evaluation

Database auditing is a prerequisite in the process of database forensics. Log files of different types and purposes are used in correlating evidence related to forensic investigation. In this paper, a new framework is proposed to explore and implement auditing features and DBMS-specific built-in utilities to aid in carrying out database forensics. The new framework is implemented in three phases, where ideal forensic auditing settings are suggested, techniques and approaches to conduct forensics are evaluated, and finally database forensic tools are investigated and evaluated. The research findings serve as guidelines toward focusing on database forensics. There is a crucial need to fill in the gap where forensic tools are few and not database specific.
General systems theory: applications for organization and management.

General systems theory has been proposed as a basis for the unification of science. The open systems model has stimulated many new conceptualizations in organization theory and management practice. However, experience in utilizing these concepts suggests many unresolved dilemmas. Contingency views represent a step toward less abstraction, more explicit patterns of relationships, and more applicable theory. Sophistication will come when we have a more complete understanding of organizations as total systems (configurations of subsystems) so that we can prescribe more appropriate organizational designs and managerial systems. Ultimately, organization theory should serve as the foundation for more effective management practice.
A passive approach to wireless device fingerprinting

We propose a passive blackbox-based technique for determining the type of access point (AP) connected to a network. Essentially, a stimulant (i.e., packet train) that emulates normal data transmission is sent through the access point. Since access points from different vendors are architecturally heterogeneous (e.g., chipset, firmware, driver), each AP will act upon the packet train differently. By applying wavelet analysis to the resultant packet train, a distinct but reproducible pattern is extracted allowing a clear classification of different AP types. This has two important applications: (1) as a system administrator, this technique can be used to determine if a rogue access point has connected to the network; and (2) as an attacker, fingerprinting the access point is necessary to launch driver/firmware specific attacks. Extensive experiments were conducted (over 60GB of data was collected) to differentiate 6 APs. We show that this technique can classify APs with a high accuracy (in some cases, we can classify successfully 100% of the time) with as little as 100000 packets. Further, we illustrate that this technique is independent of the stimulant traffic type (e.g., TCP or UDP). Finally, we show that the AP profile is stable across multiple models of the same AP.
Closed-loop control of mechanical ventilation: description and classification of targeting schemes.

There has been a dramatic increase in the number and complexity of new ventilation modes over the last 30 years. The impetus for this has been the desire to improve the safety, efficiency, and synchrony of ventilator-patient interaction. Unfortunately, the proliferation of names for ventilation modes has made understanding mode capabilities problematic. New modes are generally based on increasingly sophisticated closed-loop control systems or targeting schemes. We describe the 6 basic targeting schemes used in commercially available ventilators today: set-point, dual, servo, adaptive, optimal, and intelligent. These control systems are designed to serve the 3 primary goals of mechanical ventilation: safety, comfort, and liberation. The basic operations of these schemes may be understood by clinicians without any engineering background, and they provide the basis for understanding the wide variety of ventilation modes and their relative advantages for improving patient-ventilator synchrony. Conversely, their descriptions may provide engineers with a means to better communicate to end users.
Management and outcome of 35 cases with foetal/neonatal alloimmune neutropenia.

The aim of this study was to provide an overview of foetal/neonatal alloimmune neutropenia (FNAIN), together with advice on the clinical management.



Neutrophil serology in the Netherlands is centralised at Sanquin Diagnostic Services. We examined FNAIN cases between January 1, 1991, and July 1, 2013, to determine the number of cases diagnosed, the relationship with human neutrophil antigen (HNA) antibody, the clinical presentation and therapeutic interventions.



We identified 35 FNAIN cases. The detected HNA antibodies were as follows: anti-HNA-1a (n = 7), anti-HNA-1b (n = 12), anti-HNA-1c (n = 2), anti-HNA-2 (n = 8), anti-HNA-3a (n = 1), anti-HNA-5a (n = 1) and anti-FcγRIIIb (n = 4). No infections were diagnosed in 14 neonates, and the other 21 neonates suffered from omphalitis (n = 6), urinary tract infection (n = 1), candida mucositis (n = 1), fever of unknown origin (n = 6) and sepsis (n = 7, 20%). Parity, gestational age, birthweight, neutrophil counts and antibody specificity were not significantly different for cases with, and without, infections. All the infected children were treated with antibiotics. No children died.



More than half (21) of the 35 cases of FNAIN presented with infections and most implicated were HNA-1a, HNA-1b and HNA-2. Treatment with antibiotics seemed adequate. A neonatal neutropenia workflow model for use in neonatal intensive care units is presented.
Learning Word Meta-Embeddings by Autoencoding

Distributed word embeddings have shown superior performances in numerous Natural Language Processing (NLP) tasks. However, their performances vary significantly across different tasks, implying that the word embeddings learnt by those methods capture complementary aspects of lexical semantics. Therefore, we believe that it is important to combine the existing word embeddings to produce more accurate and complete meta-embeddings of words. We model the meta-embedding learning problem as an autoencoding problem, where we would like to learn a meta-embedding space that can accurately reconstruct all source embeddings simultaneously. Thereby, the meta-embedding space is enforced to capture complementary information in different source embeddings via a coherent common embedding space. We propose three flavours of autoencoded meta-embeddings motivated by different requirements that must be satisfied by a meta-embedding. Our experimental results on a series of benchmark evaluations show that the proposed autoencoded meta-embeddings outperform the existing state-of-the-art metaembeddings in multiple tasks.
agriCulTure aT The urban inTerfaCe : aTTiTuDes of neW rural resiDenTs

As the buffer between farming and residential areas shrinks, pesticide related conflicts increase. Dan Levi and Kathryn Sperry discuss their study examining environmental attitudes and health impacts of living at the agricultural / urban interface in Oceano, California. The discussion illustrates the importance of including these potential environmental impacts and their mitigation in the planning agenda. Daniel Levi is a Professor at the Psychology and Child Development Department, Cal Poly San Luis Obispo.
Brace technology thematic series - The Sforzesco and Sibilla braces, and the SPoRT (Symmetric, Patient oriented, Rigid, Three-dimensional, active) concept

Bracing is an effective strategy for scoliosis treatment, but there is no consensus on the best type of brace, nor on the way in which it should act on the spine to achieve good correction. The aim of this paper is to present the family of SPoRT (Symmetric, Patient-oriented, Rigid, Three-dimensional, active) braces: Sforzesco (the first introduced), Sibilla and Lapadula.



The Sforzesco brace was developed following specific principles of correction. Due to its overall symmetry, the brace provides space over pathological depressions and pushes over elevations. Correction is reached through construction of the envelope, pushes, escapes, stops, and drivers. The real novelty is the drivers, introduced for the first time with the Sforzesco brace; they allow to achieve the main action of the brace: a three-dimensional elongation pushing the spine in a down-up direction.Brace prescription is made plane by plane: frontal (on the "slopes", another novelty of this concept, i.e. the laterally flexed sections of the spine), horizontal, and sagittal. The brace is built modelling the trunk shape obtained either by a plaster cast mould or by CAD-CAM construction. Brace checking is essential, since SPoRT braces are adjustable and customisable according to each individual curve pattern.Treatment time and duration is individually tailored (18-23 hours per day until Risser 3, then gradual reduction). SEAS (Scientific Exercises Approach to Scoliosis) exercises are a key factor to achieve success.



The Sforzesco brace has shown to be more effective than the Lyon brace (matched case/control), equally effective as the Risser plaster cast (prospective cohort with retrospective controls), more effective than the Risser cast + Lyon brace in treating curves over 45 degrees Cobb (prospective cohort), and is able to improve aesthetic appearance (prospective cohort).



The SPoRT concept of bracing (three-dimensional elongation pushing in a down-up direction) is different from the other corrective systems: 3-point, traction, postural, and movement-based. The Sforzesco brace, being comparable to casting, may be the best brace for the worst cases.
Addressing Behavioral Uncertainty in Security Games: An Efficient Robust Strategic Solution for Defender Patrols

Stackelberg Security Games (SSG) have been widely applied for solving real-world security problems - with a significant research emphasis on modeling attackers' behaviors to handle their bounded rationality. However, access to real-world data (used for learning an accurate behavioral model) is often limited, leading to uncertainty in attacker's behaviors while modeling. This paper therefore focuses on addressing behavioral uncertainty in SSG with the following main contributions: 1) we present a new uncertainty game model that integrates uncertainty intervals into a behavioral model to capture behavioral uncertainty, and 2) based on this game model, we propose a novel robust algorithm that approximately computes the defender's optimal strategy in the worst-case scenario of uncertainty. We show that our algorithm guarantees an additive bound on its solution quality.
Measuring behavioral and endocrine responses to novelty stress in adult zebrafish

Several behavioral assays are currently used for high-throughput neurophenotyping and screening of genetic mutations and psychotropic drugs in zebrafish (Danio rerio). In this protocol, we describe a battery of two assays to characterize anxiety-related behavioral and endocrine phenotypes in adult zebrafish. Here, we detail how to use the 'novel tank' test to assess behavioral indices of anxiety (including reduced exploration, increased freezing behavior and erratic movement), which are quantifiable using manual registration and computer-aided video-tracking analyses. In addition, we describe how to analyze whole-body zebrafish cortisol concentrations that correspond to their behavior in the novel tank test. This protocol is an easy, inexpensive and effective alternative to other methods of measuring stress responses in zebrafish, thus enabling the rapid acquisition and analysis of large amounts of data. As will be shown here, fish anxiety-like behavior can be either attenuated or exaggerated depending on stress or drug exposure, with cortisol levels generally expected to parallel anxiety behaviors. This protocol can be completed over the course of 2 d, with a variable testing duration depending on the number of fish used.
Talk more listen less: Energy-efficient neighbor discovery in wireless sensor networks

Neighbor discovery is a fundamental service for initialization and managing network dynamics in wireless sensor networks and mobile sensing applications. In this paper, we present a novel design principle named Talk More Listen Less (TMLL) to reduce idle-listening in neighbor discovery protocols by learning the fact that more beacons lead to fewer wakeups. We propose an extended neighbor discovery model for analyzing wakeup schedules in which beacons are not necessarily placed in the wakeup slots. Furthermore, we are the first to consider channel occupancy rate in discovery protocols by introducing a new metric to trade off among duty-cycle, latency and channel occupancy rate. Guided by the TMLL principle, we have designed Nihao, a family of energy-efficient asynchronous neighbor discovery protocols for symmetric and asymmetric cases. We compared Nihao with existing state of the art protocols via analysis and real-world testbed experiments. The result shows that Nihao significantly outperforms the others both in theory and practice.
Calibration of the Minolta SPAD-502 leaf chlorophyll meter

Use of leaf meters to provide an instantaneous assessment of leaf chlorophyll has become common, but calibration of meter output into direct units of leaf chlorophyll concentration has been difficult and an understanding of the relationship between these two parameters has remained elusive. We examined the correlation of soybean (Glycine max) and maize (Zea mays L.) leaf chlorophyll concentration, as measured by organic extraction and spectrophotometric analysis, with output (M) of the Minolta SPAD-502 leaf chlorophyll meter. The relationship is non-linear and can be described by the equation chlorophyll (μmol m−2)=10(M0.265), r 2=0.94. Use of such an exponential equation is theoretically justified and forces a more appropriate fit to a limited data set than polynomial equations. The exact relationship will vary from meter to meter, but will be similar and can be readily determined by empirical methods. The ability to rapidly determine leaf chlorophyll concentrations by use of the calibration method reported herein should be useful in studies on photosynthesis and crop physiology.
Designing and Evaluating a Social Gaze-Control System for a Humanoid Robot

This paper describes a context-dependent social gaze-control system implemented as part of a humanoid social robot. The system enables the robot to direct its gaze at multiple humans who are interacting with each other and with the robot. The attention mechanism of the gaze-control system is based on features that have been proven to guide human attention: nonverbal and verbal cues, proxemics, the visual field of view, and the habituation effect. Our gaze-control system uses Kinect skeleton tracking together with speech recognition and SHORE-based facial expression recognition to implement the same features. As part of a pilot evaluation, we collected the gaze behavior of 11 participants in an eye-tracking study. We showed participants videos of two-person interactions and tracked their gaze behavior. A comparison of the human gaze behavior with the behavior of our gaze-control system running on the same videos shows that it replicated human gaze behavior 89% of the time.
Multi-Track Programming Competitions with Scratch

High school programming competitions often follow the traditional model of collegiate competitions, exemplified by the ACM International Collegiate Programming Contest (ICPC). This tradition has been reinforced by the nature of Advanced Placement Computer Science (AP CS A), for which ICPC-style problems are considered an excellent practice regimen. As more and more students in high school computer science courses approach the field from broader starting points, such as Exploring Computer Science (ECS), or the new AP CS Principles course, an analogous structure for high school outreach events becomes of greater importance.
 This paper describes our work on developing a Scratch-based alternative competition for high school students, that can be run in parallel with a traditional morning of ICPC-style problems.
L OSS-AWARE B INARIZATION OF D EEP N ETWORKS

Deep neural network models, though very powerful and highly successful, are computationally expensive in terms of space and time. Recently, there have been a number of attempts on binarizing the network weights and activations. This greatly reduces the network size, and replaces the underlying multiplications to additions or even XNOR bit operations. However, existing binarization schemes are based on simple matrix approximations and ignore the effect of binarization on the loss. In this paper, we propose a proximal Newton algorithm with diagonal Hessian approximation that directly minimizes the loss w.r.t. the binarized weights. The underlying proximal step has an efficient closed-form solution, and the second-order information can be efficiently obtained from the second moments already computed by the Adam optimizer. Experiments on both feedforward and recurrent networks show that the proposed loss-aware binarization algorithm outperforms existing binarization schemes, and is also more robust for wide and deep networks.
An energy-efficient pulse position modulation transmitter for galvanic intrabody communications

Intrabody communications (IBC) is a novel communication technique which uses the human body itself as the signal propagation medium. This communication method is categorized as a physical layer of IEEE 802.15.6 or Wireless Body Area Network (WBAN) standard. It is significant to investigate the IBC systems to improve the transceiver design characteristics such as data rate and power consumption. In this paper, we propose a new IBC transmitter implementing pulse position modulation (PPM) scheme based on impulse radio. A FPGA is employed to implement the architecture of a carrier-free PPM transmission. Results demonstrate the data rate of 1.56 Mb/s which is suitable for the galvanic coupling IBC method. The PPM transmitter power consumption is 2.0 mW with 3.3 V supply voltage. Having energy efficiency as low as 1.28 nJ/bit provides an enhanced solution for portable biomedical applications based on body area networks.
Learning to gather information via imitation

The budgeted information gathering problem — where a robot with a fixed fuel budget is required to maximize the amount of information gathered from the world — appears in practice across a wide range of applications in autonomous exploration and inspection with mobile robots. Although there is an extensive amount of prior work investigating effective approximations of the problem, these methods do not address the fact that their performance is heavily dependent on distribution of objects in the world. In this paper, we attempt to address this issue by proposing a novel data-driven imitation learning framework. We present an efficient algorithm, EXPLORE, that trains a policy on the target distribution to imitate a clairvoyant oracle — an oracle that has full information about the world and computes non-myopic solutions to maximize information gathered. We validate the approach on a spectrum of results on a number of 2D and 3D exploration problems that demonstrates the ability of EXPLORE to adapt to different object distributions. Additionally, our analysis provides theoretical insight into the behavior of EXPLORE. Our approach paves the way forward for efficiently applying data-driven methods to the domain of information gathering.
End-to-end speech recognition accuracy metric for voice-search tasks

We introduce a novel metric for speech recognition success in voice search tasks, designed to reflect the impact of speech recognition errors on user's overall experience with the system. The computation of the metric is seeded using intuitive labels from human subjects and subsequently automated by replacing human annotations with a machine learning algorithm. The results show that search-based recognition accuracy is significantly higher than accuracy based on sentence error rate computation, and that the automated system is very successful in replicating human judgments regarding search quality results.
Integration of Vision and Inertial Sensors for 3D Arm Motion Tracking in Home-based Rehabilitation

The integration of visual and inertial sensors for human motion tracking has attracted significant attention recently, due to its robust performance and wide potential application. This paper introduces a real-time hybrid solution to articulated 3D arm motion tracking for home-based rehabilitation by combining visual and inertial sensors. Data fusion is a key issue in this hybrid system and two different data fusion methods are proposed. The first is a deterministic method based on arm structure and geometry information, which is suitable for simple rehabilitation motions. The second is a probabilistic method based on an Extended Kalman Filter (EKF) in which data from two sensors is fused in a predict-correct manner in order to deal with sensor noise and model inaccuracy. Experimental results are presented and compared with commercial marker-based systems, CODA and Qualysis. They show good performance for the proposed
Named Entity Recognition for the Agricultural Domain

Agricultural data have a major role in the planning and success of rural development activities. Agriculturalists, planners, policy makers, government officials, farmers and researchers require relevant information to trigger decision making processes. This paper presents our approach towards extracting named entities from real-world agricultural data from different areas of agriculture using Conditional Random Fields (CRFs). Specifically, we have created a Named Entity tagset consisting of 19 fine grained tags. To the best of our knowledge, there is no specific tag set and annotated corpus available for the agricultural domain. We have performed several experiments using different combination of features and obtained encouraging results. Most of the issues observed in an error analysis have been addressed by post-processing heuristic rules, which resulted in a significant improvement of our system’s accuracy.