nils-herrmann/clean_scirepeval huggingface dataset:
On Learning and Learned Representation with Dynamic Routing in Capsule Networks

Capsule Networks (CapsNet) are recently proposed multi-stage computational models specialized for entity representation and discovery in image data. CapsNet employs iterative routing that shapes how the information cascades through different levels of interpretations. In this work, we investigate i) how the routing affects the CapsNet model fitting, ii) how the representation by capsules helps discover global structures in data distribution and iii) how learned data representation adapts and generalizes to new tasks. Our investigation shows: i) routing operation determines the certainty with which one layer of capsules pass information to the layer above, and the appropriate level of certainty is related to the model fitness, ii) in a designed experiment using data with a known 2D structure, capsule representations allow more meaningful 2D manifold embedding than neurons in a standard CNN do and iii) compared to neurons of standard CNN, capsules of successive layers are less coupled and more adaptive to new data distribution.
The Tower of Hanoi with Forbidden Moves

We consider a variant of the classical three-peg Tower of Hanoi problem, where limitations on the possible moves among the pegs are imposed. Each variant corresponds to a di-graph whose vertices are the pegs, and an edge from one vertex to another designates the ability of moving a disk from the first peg to the other, provided that the rules concerning the disk sizes are obeyed. There are five non-isomorphic graphs on three vertices, which are strongly connected—a sufficient condition for the existence of a solution to the problem. We provide optimal algorithms for the problem for all these graphs, and find the number of moves each requires.
Eosinophilic Esophagitis: Pathophysiology and Definition.

Eosinophilic esophagitis is an adaptive immune response to patient-specific antigens, mostly foods. Eosinophilic esophagitis is not solely IgE-mediated and is likely characterized by Th2 lymphocytes with an impaired esophageal barrier function. The key cytokines and chemokines are thymic stromal lymphopoeitin, interleukin-13, CCL26/eotaxin-3, and transforming growth factor-β, all involved in eosinophil recruitment and remodeling. Chronic food dysphagia and food impactions, the feared late complications, are related in part to dense subepithelial fibrosis, likely induced by interleukin-13 and transforming growth factor-β.
Redefining the Vascular Classifications of the Lateral Supramalleolar Flap.

The blood supply of the lateral supramalleolar flap (LSMF) generally comes from the perforating branch of the peroneal artery. However, the cutaneous branch may also receive blood from the anterior tibial artery. The main objective of the present study was to clarify the vascular anatomy of the LSMF.



Anatomical dissections were performed on 28 perfused fresh cadaver legs. The cutaneous branches of LSMF were identified, and the anatomic relationship between the cutaneous branches and the peroneal and anterior tibial arteries was analyzed.



The vascular supply for LSMF was divided into 2 main types. A collateral inferolateral branch from the anterior tibial artery anastomosed with the perforating branch of the peroneal artery around the inferior tibiofibular angle, and the main cutaneous branch of the flap arose from this arterial anastomosis in 20 of 28 limbs (71.4%). The collateral inferolateral branch was absent or very small in the other 8 of 28 dissections (28.6%), and the cutaneous branches solely arose from the perforating branch of the peroneal artery. The anastomosis of the descending branch of the peroneal artery and anterior lateral malleolar artery was always (100%) found around the tibiotalar joint.



In addition to the perforating branch of the peroneal artery, the LSMF may also receive blood from the anterior tibial artery through the collateral inferolateral branch. New modified proximally based flaps could be designed, and caution is warranted for these variations when a distally based flap is performed.
A compact 0.135-mW/channel LNA array for piezoelectric ultrasound transducers

This paper presents a power- and area-efficient 9-channel LNA array for piezoelectric ultrasound transducers to enable real-time 3D imaging with miniature endoscopic and catheter-based probes. In view of the relatively low impedance of piezoelectric transducers, the LNA is implemented as a capacitive feedback voltage amplifier, rather than a trans-impedance amplifier, to achieve a better noise-power trade-off. The use of a current-efficient inverter-based OTA with optimized bias scheme and dual-rail regulation further improves the power efficiency of the LNA while keeping the area compact: 0.01 mm2 per channel. Electrical and acoustic measurement results show that the proposed LNA achieves a 0.6 mPa/√Hz input-referred noise at 4 MHz while consuming only 0.135 mW, which represents a noise efficiency 2.5 × better than the state-of-the-art.
AN INVESTIGATION OF SELF-REPORTED COMPUTER LITERACY: IS IT RELIABLE?

Research in management information systems often examines computer literacy as an independent variable. Study subjects may be asked to self-report their computer literacy and that literacy is then utilized as a research variable. However, it is not known whether self-reported computer literacy is a valid measure of a subject’s actual computer literacy. The research presented in this paper examined the question of whether self-reported computer literacy can be a reliable indication of actual computer literacy and therefore valid for use in empirical research. Study participants were surveyed and asked to self-report their level of computer literacy. Following, subjects were tested to determine an objective measure of computer literacy. The data analysis determined that self-reported computer literacy is not reliable. Results of this research are important for academic programs, for businesses, and for future empirical studies in management information systems.
Shape Measures for Content Based Image Retrieval: A Comparison

A great deal of work has been done on the evaluation of information retrieval systems for alphanumeric data. The same thing can not be said about the newly emerging multimedia and image database systems. One of the central concerns in these systems is the automatic characterization of image content and retrieval of images based on similarity of image content. In this paper, we discuss effectiveness of several shape measures for content based similarity retrieval of images. The different shape measures we have implemented include outline based features (chain code based string features, Fourier descriptors, UNL Fourier features), region based features (invariant moments, Zemike moments, pseudoZemike moments), and combined features (invariant moments & Fourier descriptors, invariant moments & UNL Fourier features). Given an image, all these shape feature measures (vectors) are computed automatically, and the feature vector can either be used for the retrieval purpose or can be stored in the database for future queries. We have tested all of the above shape features for image retrieval on a database of 500 trademark images. The average retrieval efficiency values computed over a set of fifteen representative queries for all the methods is presented. The output of a sample shape similarity query using all the features is also shown.
Catching Dynamic Heterogeneous User Data for Identity Linkage Learning

Benefitting from the development of social platforms, more and more users tend to register multiple accounts on different social networks. Linking user identities across multiple online social networks based on user behavior patterns is considerable for network supervision and information tracking. However, a user’s online behavior in a social network is dynamic. The user profile may be changed due to some specific reasons such as user migration or job changes. Thus, catching the dynamics of evolutionary user data and collecting the latest user features are important and challenging issues in the area of user identity linkage. Inspired by deep learning models such as word2vec and Deep Walk, this paper proposes an integrated framework to catch the dynamic user data by supplementing vacant features and updating outdated features in data sources. The framework firstly represents all textual and structural user data into Iow- dimensional latent spaces by utilizing word2vec and DeepWalk, then, integrates different user features and predicts vacant data fields based on late fusion approach and cosine similarity computation. We then explore and evaluate the application of our proposed method in a user identity mapping task. The results proved that our framework can successfully catch the dynamic user data and enhance the performance of identity linkage models by supplementing and updating data sources advance with the times.
Access to Pictorial Material: A Review of Current Research and Future Prospects

Rapid expansion in the digitization of image and image collections has vastly increased the numbers of images available to scholars and researchers through electronic means. This research review will familiarize the reader with current research applicable to the development of image retrieval systems and provides additional material for exploring the topic further, both in print and online. The discussion will cover several broad areas, among them classification and indexing systems used for describing image collections and research initiatives into image access focusing on image attributes, users, queries, tasks, and cognitive aspects of searching. Prospects for the future of image access, including an outline of future research initiatives, are discussed. Further research in each of these areas will provide basic data which will inform and enrich image access system design and will hopefully provide a richer, more flexible, and satisfactory environment for searching for and discovering images. Harnessing the true power of the digital image environment will only be possible when image retrieval systems are coherently designed from principles derived from the fullest range of applicable disciplines, rather than from isolated or fragmented perspectives.
ADaPT: optimizing CNN inference on IoT and mobile devices using approximately separable 1-D kernels

Breakthroughs from the field of deep learning are radically changing how sensor data are interpreted to extract important information to help advance healthcare, make our cities smarter, and innovate in smart home technology. Deep convolutional neural networks, which are at the heart of many emerging Internet-of-Things (IoT) applications, achieve remarkable performance in audio and visual recognition tasks, at the expense of high computational complexity in convolutional layers, limiting their deployability. In this paper, we present an easy-to-implement acceleration scheme, named ADaPT, which can be applied to already available pre-trained networks. Our proposed technique exploits redundancy present in the convolutional layers to reduce computation and storage requirements. Additionally, we also decompose each convolution layer into two consecutive one-dimensional stages to make full use of the approximate model. This technique can easily be applied to existing low power processors, GPUs or new accelerators. We evaluated this technique using four diverse and widely used benchmarks, on hardware ranging from embedded CPUs to server GPUs. Our experiments show an average 3-5x speed-up in all deep models and a maximum 8-9x speed-up on many individual convolutional layers. We demonstrate that unlike iterative pruning based methodology, our approximation technique is mathematically well grounded, robust, does not require any time-consuming retraining, and still achieves speed-ups solely from convolutional layers with no loss in baseline accuracy.
Virtual spatial registration of stand-alone fNIRS data to MNI space

The registration of functional brain data to common stereotaxic brain space facilitates data sharing and integration across different subjects, studies, and even imaging modalities. Thus, we previously described a method for the probabilistic registration of functional near-infrared spectroscopy (fNIRS) data onto Montreal Neurological Institute (MNI) coordinate space that can be used even when magnetic resonance images of the subjects are not available. This method, however, requires the careful measurement of scalp landmarks and fNIRS optode positions using a 3D-digitizer. Here we present a novel registration method, based on simulations in place of physical measurements for optode positioning. First, we constructed a holder deformation algorithm and examined its validity by comparing virtual and actual deformation of holders on spherical phantoms and real head surfaces. The discrepancies were negligible. Next, we registered virtual holders on synthetic heads and brains that represent size and shape variations among the population. The registered positions were normalized to MNI space. By repeating this process across synthetic heads and brains, we statistically estimated the most probable MNI coordinate values, and clarified errors, which were in the order of several millimeters across the scalp, associated with this estimation. In essence, the current method allowed the spatial registration of completely stand-alone fNIRS data onto MNI space without the use of supplementary measurements. This method will not only provide a practical solution to the spatial registration issues in fNIRS studies, but will also enhance cross-modal communications within the neuroimaging community.
Towards Self-organized Large-Scale Shape Formation: A Cognitive Agent-Based Computing Approach

Swarm robotic systems are currently being used to address many real-world problems. One interesting application of swarm robotics is the self-organized formation of structures and shapes. Some of the key challenges in the swarm robotic systems include swarm size constraint, random motion, coordination among robots, localization, and adaptability in a decentralized environment. Rubenstein et al. presented a system (“Programmable self-assembly in a thousand-robot swarm”, Science, 2014) for thousand-robot swarm able to form only solid shapes with the robots in aggregated form by applying the collective behavior algorithm. Even though agent-based approaches have been presented in various studies for self-organized formation, however these studies lack agent-based modeling (ABM) approach along with the constraints in term of structure complexity and heterogeneity in large swarms with dynamic localization. The cognitive agent-based computing (CABC) approach is capable of modeling such self-organization based multi-agents systems (MAS). In this paper, we develop a simulation model using ABM under CABC approach for self-organized shape formation in swarm robots. We propose a shape formation algorithm for validating our model and perform simulation-based experiments for six different shapes including hole-based shapes. We also demonstrate the formal specification for our model. The simulation result shows the robustness of the proposed approach having the emergent behavior of robots for the self-organized shape formation. The performance of the proposed approach is evaluated by robots convergence rate.
Light scattering from human hair fibers

Light scattering from hair is normally simulated in computer graphics using Kajiya and Kay’s classic phenomenological model. We have made new measurements that exhibit visually significant effects not predicted by Kajiya and Kay’s model. Our measurements go beyond previous hair measurements by examining out-of-plane scattering, and together with this previous work they show a multiple specular highlight and variation in scattering with rotation about the fiber axis. We explain the sources of these effects using a model of a hair fiber as a transparent elliptical cylinder with an absorbing interior and a surface covered with tilted scales. Based on an analytical scattering function for a circular cylinder, we propose a practical shading model for hair that qualitatively matches the scattering behavior shown in the measurements. In a comparison between a photograph and rendered images, we demonstrate the new model’s ability to match the appearance of real hair. CR Categories: I.3.7 [Computer Graphics]: Three-Dimensional Graphics and Realism—Shading
Migrate or not? Exploring virtual machine migration in roadside cloudlet-based vehicular cloud

Vehicle Ad-Hoc Networks (VANET) enable all components in intelligent transportation systems to be connected so as to improve transport safety, relieve traffic congestion, reduce air pollution, and enhance driving comfort. The vision of all vehicles connected poses a significant challenge to the collection, storage, and analysis of big traffic-related data. Vehicular cloud computing, which incorporates cloud computing into vehicular networks, emerges as a promising solution. Different from conventional cloud computing platform, the vehicle mobility poses new challenges to the allocation and management of cloud resources in roadside cloudlet. In this paper, we study a virtual machine (VM) migration problem in roadside cloudletbased vehicular network and unfold that (1) whether a VM shall be migrated or not along with the vehicle moving and (2) where a VM shall be migrated, in order to minimize the overall network cost for both VM migration and normal data traffic. We first treat the problem as a static off-line VM placement problem and formulate it into a mixed-integer quadratic programming problem. A heuristic algorithm with polynomial time is then proposed to tackle the complexity of solving mixed-integer quadratic programming. Extensive simulation results show that it produces near-optimal performance and outperforms other related algorithms significantly.
Methods of Third-Party Intervention

Third party intervention is a typical response to destructive and persistent social conflict and comes in a number of different forms attended by a variety of issues. Mediation is a common form of intervention designed to facilitate a negotiated settlement on substantive issues between conflicting parties. Mediators are usually external to the parties and carry an identity, motives and competencies required to play a useful role in addressing the dispute. While impartiality is generally seen as an important prerequisite for effective intervention, biased mediators also appear to have a role to play. This article lays out the different forms of third-party intervention in a taxonomy of six methods, and proposes a contingency model which matches each type of intervention to the appropriate stage of conflict escalation. Interventions are then sequenced, in order to assist the parties in de-escalating and resolving the conflict. It must be pointed out, however, that the mixing of interventions with different power bases raises a number of ethical and moral questions about the use of reward and coercive power by third parties. The article then discusses several issues around the practice of intervention. It is essential to give these issues careful consideration if third-party methods are to play their proper and useful role in the wider process of conflict transformation. Psychology from the University of Saskatchewan and a Ph.D. in Social Psychology from the University of Michigan. He has provided training and consulting services to various organizations and international institutes in conflict management. His current interests include third party intervention, interactive conflict resolution, and reconciliation in situations of ethnopolitical conflict. A b s t r a c t A b o u t t h e C o n t r i b u t o r
Omega-3 Polyunsaturated Fatty Acid ( Fish Oil ) Supplementation and the Prevention of Clinical Cardiovascular Disease

Circulation. 2017;135:e867–e884.  Multiple randomized controlled trials (RCTs) have assessed the effects of supplementation with eicosapentaenoic acid plus docosahexaenoic acid (omega-3 polyunsaturated fatty acids, commonly called fish oils) on the occurrence of clinical cardiovascular diseases. Although the effects of supplementation for the primary prevention of clinical cardiovascular events in the general population have not been examined, RCTs have assessed the role of supplementation in secondary prevention among patients with diabetes mellitus and prediabetes, patients at high risk of cardiovascular disease, and those with prevalent coronary heart disease. In this scientific advisory, we take a clinical approach and focus on common indications for omega-3 polyunsaturated fatty acid supplements related to the prevention of clinical cardiovascular events. We limited the scope of our review to large RCTs of supplementation with major clinical cardiovascular disease end points; meta-analyses were considered secondarily. We discuss the features of available RCTs and provide the rationale for our recommendations. We then use existing American Heart Association criteria to assess the strength of the recommendation and the level of evidence. On the basis of our review of the cumulative evidence from RCTs designed to assess the effect of omega-3 polyunsaturated fatty acid supplementation on clinical cardiovascular events, we update prior recommendations for patients with prevalent coronary heart disease, and we offer recommendations, when data are available, for patients with other clinical indications, including patients with diabetes mellitus and prediabetes and those with high risk of cardiovascular disease, stroke, heart failure, and atrial fibrillation. David S. Siscovick, MD, MPH, FAHA, Chair Thomas A. Barringer, MD, FAHA Amanda M. Fretts, PhD, MPH Jason H.Y. Wu, PhD, MSc, FAHA Alice H. Lichtenstein, DSc, FAHA Rebecca B. Costello, PhD, FAHA Penny M. Kris-Etherton, PhD, RD, FAHA Terry A. Jacobson, MD, FAHA Mary B. Engler, PhD, RN, MS, FAHA Heather M. Alger, PhD Lawrence J. Appel, MD, MPH, FAHA Dariush Mozaffarian, MD, DrPH, FAHA On behalf of the American Heart Association Nutrition Committee of the Council on Lifestyle and Cardiometabolic Health; Council on Epidemiology and Prevention; Council on Cardiovascular Disease in the Young; Council on Cardiovascular and Stroke Nursing; and Council on Clinical Cardiology Omega-3 Polyunsaturated Fatty Acid (Fish Oil) Supplementation and the Prevention of Clinical Cardiovascular Disease
Job Satisfaction , Organizational Commitment , and Turnover Intention : A Case Study on Employees of a Retail Company in Malaysia

High employee turnover rate in Malaysia’s retail industry has become a major issue that needs to be addressed. This study determines the levels of job satisfaction, organizational commitment, and turnover intention of employees in a retail company in Malaysia. The relationships between job satisfaction and organizational commitment on turnover intention are also investigated. A questionnaire was developed using Job Descriptive Index, Organizational Commitment Questionnaire, and Lee and Mowday’s turnover intention items and data were collected from 62 respondents. The findings suggested that the respondents were moderately satisfied with job satisfaction facets such as promotion, work itself, co-workers, and supervisors but were unsatisfied with salary. They also had moderate commitment level with considerably high intention to leave the organization. All satisfaction facets (except for co-workers) and organizational commitment were significantly and negatively related to turnover intention. Based on the findings, retention strategies of retail employees were proposed. Job satisfaction, organizational commitment, retail employees, turnover intention.