nils-herrmann/clean_scirepeval huggingface dataset:
The Essential Nature of Product Traceability and its Relation to Agile Approaches

This is a discussion of the essential features of product development traceability maps in relation to requirements, architecture, functional models, components, and tests as a set of order type hierarchies and their cross-links. This paper lays out the structure of these ideal traceability relationships that define the essence of the product under development. The importance of the trace relationships to the product is clarified and then the abandonment of traceability in the Agile approach is discussed. Following that, a way to transform between synthetic canonical narrative (story) representations that appear in the product backlog and the traditionally separate hierarchical form of the trace structure of the product will be examined. The fact that it is possible to transform back and forth between the canonic narrative and traditional hierarchical representations of trace structures, and the fact that trace structures can be produced in a ‘just in time’ fashion that evolves during product development demonstrate that these trace structures can be used in both an Agile and Lean fashion within the development process. Also, we can show that when the trace structure is produced outside the narrative representation it can have the additional benefit of helping to determine the precedent order of development so that rework can be avoided. The lack of the extrinsic external trace structure of the product that gives access to its intelligibility is, in fact, a form of technical debt. Thus, traditional trace structures using this model can be seen as an essential tool for product owners to produce sound and coherent development narratives and for structuring and prioritizing the backlog in the Agile and Lean approaches to software and systems development.  Selection and peer-review under responsibility of the University of Southern California.
Data visualizations: A literature review and opportunities for technical and professional communication

This paper discusses the need for an integrative literature review on data visualizations, particularly in health and medical contexts. The paper analyzes 25 studies across disciplines. The findings suggest there is little agreement on the best way to visualize complex data for lay audiences, but some emerging effective practices are being develop. Pictographs, icon arrays, and bar charts seem to hold promise for comprehension by users, and visualizations need to be kept as simple as possible with attention to integrating other design features such as headings and legends. The review ends with five specific research areas in which technical and professional communicators should focus their attention on empirical studies that examine: interactive displays, merge attention and comprehension, look at numeracy and risk and finally, cross health and medical subjects.
Coronary sinus ostium in the lateral right atrium: Implications for the electrophysiologist

The coronary sinus (CS) is a critical structure that has helped aid electrophysiologists by providing information about epicardial activation along the mitral annulus. Additionally, the advent of biventricular pacing utilizes the CS venous system for lead placement. The CS collects the venous drainage of the atrial and ventricular venous branches and drains into the right atrium (RA). CS anomalies have been recognized, namely atresia of the CS ostium, unroofed CS, connection with the left atrium, and even hypoplasia of the CS. We report 3 cases of CS ostium opening in the midlateral RA. This anomaly may provide new insight into the development of the CS as well as provide an explanation for difficult cannulation of the CS.
Amazon Aurora: On Avoiding Distributed Consensus for I/Os, Commits, and Membership Changes

Amazon Aurora is a high-throughput cloud-native relational database offered as part of Amazon Web Services (AWS). One of the more novel differences between Aurora and other relational databases is how it pushes redo processing to a multi-tenant scale-out storage service, purpose-built for Aurora. Doing so reduces networking traffic, avoids checkpoints and crash recovery, enables failovers to replicas without loss of data, and enables fault-tolerant storage that heals without database involvement. Traditional implementations that leverage distributed storage would use distributed consensus algorithms for commits, reads, replication, and membership changes and amplify cost of underlying storage. In this paper, we describe how Aurora avoids distributed consensus under most circumstances by establishing invariants and leveraging local transient state. Doing so improves performance, reduces variability, and lowers costs.
Barriers to timely discharge from the general medicine service at an academic teaching hospital.

Reducing delays for patients who are safe to be discharged is important for minimising complications, managing costs and improving quality. Barriers to discharge include placement, multispecialty coordination of care and ineffective communication. There are a few recent studies that describe barriers from the perspective of all members of the multidisciplinary team.



To identify the barriers to discharge for patients from our medicine service who had a discharge delay of over 24 hours.



We developed and implemented a biweekly survey that was reviewed with attending physicians on each of the five medicine services to identify patients with an unnecessary delay. Separately, we conducted interviews with staff members involved in the discharge process to identify common barriers they observed on the wards.



Over the study period from 28 October to 22 November 2013, out of 259 total discharges, 87 patients had a delay of over 24 hours (33.6%) and experienced a total of 181 barriers. The top barriers from the survey included patient readiness, prolonged wait times for procedures or results, consult recommendations and facility placement. A total of 20 interviews were conducted, from which the top barriers included communication both between staff members and with the patient, timely notification of discharge and lack of discharge standardisation.



There are a number of frequent barriers to discharge encountered in our hospital that may be avoidable with planning, effective communication methods, more timely preparation and tools to standardise the discharge process.
Design of minimum multiplier fractional order differentiator based on lattice wave digital filter.

In this paper, a novel design of fractional order differentiator (FOD) based on lattice wave digital filter (LWDF) is proposed which requires minimum number of multiplier for its structural realization. Firstly, the FOD design problem is formulated as an optimization problem using the transfer function of lattice wave digital filter. Then, three optimization algorithms, namely, genetic algorithm (GA), particle swarm optimization (PSO) and cuckoo search algorithm (CSA) are applied to determine the optimal LWDF coefficients. The realization of FOD using LWD structure increases the design accuracy, as only N number of coefficients are to be optimized for Nth order FOD. Finally, two design examples of 3rd and 5th order lattice wave digital fractional order differentiator (LWDFOD) are demonstrated to justify the design accuracy. The performance analysis of the proposed design is carried out based on magnitude response, absolute magnitude error (dB), root mean square (RMS) magnitude error, arithmetic complexity, convergence profile and computation time. Simulation results are attained to show the comparison of the proposed LWDFOD with the published works and it is observed that an improvement of 29% is obtained in the proposed design. The proposed LWDFOD approximates the ideal FOD and surpasses the existing ones reasonably well in mid and high frequency range, thereby making the proposed LWDFOD a promising technique for the design of digital FODs.
# jazz : Automatic Music Genre Detection

Automatic genre classification of music is an important topic in Music Information Retrieval with many interesting applications. A solution to genre classification would allow for machine tagging of songs, which could serve as metadata for building song recommenders. In this paper, we investigate the following question: Given a song, can we automatically detect its genre? We look at three characteristics of a song to determine its genre: timbre, chord transitions, and lyrics. For each method, we develop multiple data models and apply supervised machine learning algorithms including k-means, k-NN, multi-class SVM and Naive Bayes. We are able to accurately classify 65− 75% of the songs from each genre in a 5-genre classification problem between Rock, Jazz, Pop, Hip-Hop, and Metal music.
Outcome from mild traumatic brain injury.

The focus of this review is outcome from mild traumatic brain injury. Recent literature relating to pathophysiology, neuropsychological outcome, and the persistent postconcussion syndrome will be integrated into the existing literature.



The MTBI literature is enormous, complex, methodologically flawed, and controversial. There have been dozens of studies relating to pathophysiology, neuropsychological outcome, and the postconcussion syndrome during the past year. Two major reviews have been published. Some of the most interesting prospective research has been done with athletes.



The cognitive and neurobehavioral sequelae are self-limiting and reasonably predictable. Mild traumatic brain injuries are characterized by immediate physiological changes conceptualized as a multilayered neurometabolic cascade in which affected cells typically recover, although under certain circumstances a small number might degenerate and die. The primary pathophysiologies include ionic shifts, abnormal energy metabolism, diminished cerebral blood flow, and impaired neurotransmission. During the first week after injury the brain undergoes a dynamic restorative process. Athletes typically return to pre-injury functioning (assessed using symptom ratings or brief neuropsychological measures) within 2-14 days. Trauma patients usually take longer to return to their pre-injury functioning. In these patients recovery can be incomplete and can be complicated by preexisting psychiatric or substance abuse problems, poor general health, concurrent orthopedic injuries, or comorbid problems (e.g. chronic pain, depression, substance abuse, life stress, unemployment, and protracted litigation).
Efficient Selection of Geospatial Data on Maps for Interactive and Visualized Exploration

With the proliferation of mobile devices, large collections of geospatial data are becoming available, such as geo-tagged photos. Map rendering systems play an important role in presenting such large geospatial datasets to end users. We propose that such systems should support the following desirable features: representativeness, visibility constraint, zooming consistency, and panning consistency. The first two constraints are fundamental challenges to a map exploration system, which aims to efficiently select a small set of representative objects from the current region of user's interest, and any two selected objects should not be too close to each other for users to distinguish in the limited space of a screen. We formalize it as the Spatial Object Selection (SOS) problem, prove that it is an NP-hard problem, and develop a novel approximation algorithm with performance guarantees. % To further support interactive exploration of geospatial data on maps, we propose the Interactive SOS (ISOS) problem, in which we enrich the SOS problem with the zooming consistency and panning consistency constraints. The objective of ISOS is to provide seamless experience for end-users to interactively explore the data by navigating the map. We extend our algorithm for the SOS problem to solve the ISOS problem, and propose a new strategy based on pre-fetching to significantly enhance the efficiency. Finally we have conducted extensive experiments to show the efficiency and scalability of our approach.
A Review of Apps and Websites for Promoting Mental Wellbeing: Findings and Initial Recommendations for Design

Mental wellbeing is the combination of feeling good and functioning well. Digital technology widens the opportunities for promoting mental wellbeing, particularly among those young people for whom technology is an ordinary part of life. This paper presents an initial review of publicly available apps and websites that have a primary purpose of promoting mental wellbeing. The review was in two stages: first, the interdisciplinary research team identified and reviewed 14 apps/websites, then 13 young people (7 female, 6 male) aged 12–18 years reviewed 11 of the apps/websites. Overall, the reviewers’ views were positive, although some significant criticisms were made. Based on the findings of the study, initial recommendations are offered to improve the design of apps/websites for promoting mental wellbeing among young people aged 12–18 years: highlight any age limits, provide information on mental wellbeing, improve findability, ensure accessibility on school computers, and highlight if young people were involved in design.
DGPose: Disentangled Semi-supervised Deep Generative Models for Human Body Analysis

Deep generative modelling for robust human body analysis is an emerging problem with many interesting applications, since it enables analysis-by-synthesis and unsupervised learning. However, the latent space learned by such models is typically not human-interpretable, resulting in less flexible models. In this work, we adopt a structured semi-supervised variational auto-encoder approach and present a deep generative model for human body analysis where the pose and appearance are disentangled in the latent space, allowing for pose estimation. Such a disentanglement allows independent manipulation of pose and appearance and hence enables applications such as pose-transfer without being explicitly trained for such a task. In addition, the ability to train in a semi-supervised setting relaxes the need for labelled data. We demonstrate the merits of our generative model on the Human3.6M and ChictopiaPlus datasets.
A CFD-Based Tool for Studying Temperature in Rack-Mounted Servers

Temperature-aware computing is becoming more important in design of computer systems as power densities are increasing and the implications of high operating temperatures result in higher failure rates of components and increased demand for cooling capability. Computer architects and system software designers need to understand the thermal consequences of their proposals, and develop techniques to lower operating temperatures to reduce both transient and permanent component failures. Recognizing the need for thermal modeling tools to support those researches, there has been work on modeling temperatures of processors at the micro-architectural level which can be easily understood and employed by computer architects for processor designs. However, there is a dearth of such tools in the academic/research community for undertaking architectural/systems studies beyond a processor - a server box, rack or even a machine room. In this paper we presents a detailed 3-dimensional computational fluid dynamics based thermal modeling tool, called ThermoStat, for rack-mounted server systems. We conduct several experiments with this tool to show how different load conditions affect the thermal profile, and also illustrate how this tool can help design dynamic thermal management techniques. We propose reactive and proactive thermal management for rack mounted server and isothermal workload distribution for rack.
An Open-Source Implementation of a Unit Quaternion based Attitude and Trajectory Tracking for Quadrotors

In this paper, we present results on the implementation of a hierarchical quaternion based attitude and trajectory controller for manual and autonomous flights of quadrotors. Unlike previous papers on using quaternion representation, we use the nonlinear complementary filter that estimates the attitude in quaternions and as such does not involve Euler angles or rotation matrices. We show that for precise trajectory tracking, the resulting attitude error dynamics of the system is non-autonomous and is almost globally asymptotically and locally exponentially stable under the proposed control law. We also show local exponential stability of the translational dynamics under the proposed trajectory tracking controller which sits at the highest level of the hierarchy. Thus by input-to-state stability, the entire system is locally exponentially stable. The quaternion based observer and controllers are available as open-source.
A segmentation method and classification of diagnosis for thyroid nodules

Heterogeneous features of thyroid nodules in ultrasound images is very difficult task when radiologists and physicians manually draw a complete shape of nodule, size and shape, image or distinguish what type of nodule is exist. Segmentation and classification is important methods for medical image processing. Ultrasound imaging is the best way to prediction of which type of thyroid is there. In this paper, uses the groups Benign (non-cancerous) and Malignant (cancerous) Thyroid Nodules images were used. The texture feature method like GLCM are very useful for classifying texture of images and these features are used to train the classifiers such as SVM, KNN and Bayesian. The experimental result shows the performance of the classifiers and shows the best predictive value and positively identify the percentage of the non-cancerous or cancerous people and shows the best performance accuracy using the SVM classifier as compare to the KNN and Bayesian classifier. KeywordsThyroid Ultrasound (US) images, Feature extraction, GLCM, RBAC, SVM, KNN and Bayesian.
Dragon's blood: botany, chemistry and therapeutic uses.

Dragon's blood is one of the renowned traditional medicines used in different cultures of world. It has got several therapeutic uses: haemostatic, antidiarrhetic, antiulcer, antimicrobial, antiviral, wound healing, antitumor, anti-inflammatory, antioxidant, etc. Besides these medicinal applications, it is used as a coloring material, varnish and also has got applications in folk magic. These red saps and resins are derived from a number of disparate taxa. Despite its wide uses, little research has been done to know about its true source, quality control and clinical applications. In this review, we have tried to overview different sources of Dragon's blood, its source wise chemical constituents and therapeutic uses. As well as, a little attempt has been done to review the techniques used for its quality control and safety.
Energy Efficiency Optimization of 5G Radio Frequency Chain Systems

With the massive multi-input multi-output (MIMO) antennas technology adopted for the fifth generation (5G) wireless communication systems, a large number of radio frequency (RF) chains have to be employed for RF circuits. However, a large number of RF chains not only increase the cost of RF circuits but also consume additional energy in 5G wireless communication systems. In this paper, we investigate energy and cost efficiency optimization solutions for 5G wireless communication systems with a large number of antennas and RF chains. An energy efficiency optimization problem is formulated for 5G wireless communication systems using massive MIMO antennas and millimeter wave technology. Considering the nonconcave feature of the objective function, a suboptimal iterative algorithm, i.e., the energy efficient hybrid precoding (EEHP) algorithm is developed for maximizing the energy efficiency of 5G wireless communication systems. To reduce the cost of RF circuits, the energy efficient hybrid precoding with the minimum number of RF chains (EEHP-MRFC) algorithm is also proposed. Moreover, the critical number of antennas searching (CNAS) and user equipment number optimization (UENO) algorithms are further developed to optimize the energy efficiency of 5G wireless communication systems by the number of transmit antennas and UEs. Compared with the maximum energy efficiency of conventional zero-forcing (ZF) precoding algorithm, numerical results indicate that the maximum energy efficiency of the proposed EEHP and EEHP-MRFC algorithms are improved by 220% and 171%, respectively.
Delivery of Loperamide Across the Blood-Brain Barrier with Polysorbate 80-Coated Polybutylcyanoacrylate Nanoparticles

The possibility of using polysorbate 80-coated nanoparticles for the delivery of the water insoluble opioid agonist loperamide across the blood-brain barrier was investigated. The analgesic effect after i.v. injection of the preparations was used to indicate drug transport through this barrier.  Loperamide was incorporated into PBCA nanoparticles. Drug-containing nanoparticles were coated with polysorbate 80 and injected intravenously into mice. Analgesia was then measured by the tail-flick test.  Intravenous injection of the particulate formulation resulted in a long and significant analgesic effect. A polysorbate 80 loperamide solution induced a much less pronounced and very short analgesia. Uncoated nanoparticles loaded with loperamide were unable to produce analgesia.  Polysorbate 80-coated PBCA nanoparticles loaded with loperamide enabled the transport of loperamide to the brain.
Neural Conversational Model with Mutual Information Ranking

We build a neural conversation system using a deep LST Seq2Seq model with an attention mechanism applied on the decoder. We further improve our system by introducing beam search and re-ranking with a Mutual Information objective function method to search for relevant and coherent responses. We find that both models achieve reasonable results after being trained on a domain-specific dataset and are able to pick up contextual information specific to the dataset. The second model, in particular, has promise with addressing the ”I don’t know” problem and de-prioritizing over-generic responses.
Graphene photonics, plasmonics, and broadband optoelectronic devices.

Graphene has been hailed as a wonderful material in electronics, and recently, it is the rising star in photonics, as well. The wonderful optical properties of graphene afford multiple functions of signal emitting, transmitting, modulating, and detection to be realized in one material. In this paper, the latest progress in graphene photonics, plasmonics, and broadband optoelectronic devices is reviewed. Particular emphasis is placed on the ability to integrate graphene photonics onto the silicon platform to afford broadband operation in light routing and amplification, which involves components like polarizer, modulator, and photodetector. Other functions like saturable absorber and optical limiter are also reviewed.
Explaining the Ambiguity of Object Detection and 6D Pose from Visual Data

3D object detection and pose estimation from a single image are two inherently ambiguous problems. Oftentimes, objects appear similar from different viewpoints due to shape symmetries, occlusion and repetitive textures. This ambiguity in both detection and pose estimation means that an object instance can be perfectly described by several different poses and even classes. In this work we propose to explicitly deal with this uncertainty. For each object instance we predict multiple pose and class outcomes to estimate the specific pose distribution generated by symmetries and repetitive textures. The distribution collapses to a single outcome when the visual appearance uniquely identifies just one valid pose. We show the benefits of our approach which provides not only a better explanation for pose ambiguity, but also a higher accuracy in terms of pose estimation.