nils-herrmann/clean_scirepeval huggingface dataset:
Mapping virtual and physical reality

Real walking offers higher immersive presence for virtual reality (VR) applications than alternative locomotive means such as walking-in-place and external control gadgets, but needs to take into consideration different room sizes, wall shapes, and surrounding objects in the virtual and real worlds. Despite perceptual study of impossible spaces and redirected walking, there are no general methods to match a given pair of virtual and real scenes.
 We propose a system to match a given pair of virtual and physical worlds for immersive VR navigation. We first compute a planar map between the virtual and physical floor plans that minimizes angular and distal distortions while conforming to the virtual environment goals and physical environment constraints. Our key idea is to design maps that are globally surjective to allow proper folding of large virtual scenes into smaller real scenes but locally injective to avoid locomotion ambiguity and intersecting virtual objects. From these maps we derive altered rendering to guide user navigation within the physical environment while retaining visual fidelity to the virtual environment. Our key idea is to properly warp the virtual world appearance into real world geometry with sufficient quality and performance. We evaluate our method through a formative user study, and demonstrate applications in gaming, architecture walkthrough, and medical imaging.
[Circumcision affects glans penis vibration perception threshold].

To evaluate the effect of circumcision on the glans penis sensitivity by comparing the changes of the glans penis vibrotactile threshold between normal men and patients with simple redundant prepuce and among the patients before and after the operation.



The vibrotactile thresholds were measured at the forefinger and glans penis in 73 normal volunteer controls and 96 patients with simple redundant prepuce before and after circumcision by biological vibration measurement instrument, and the changes in the perception sensitivity of the body surface were analyzed.



The G/F (glans/finger) indexes in the control and the test group were respectively 2.39 +/- 1.72 and 1.97 +/- 0.71, with no significant difference in between (P > 0.05). And those of the test group were 1.97 +/- 0.71, 2.64 +/- 1.38, 3.09 +/-1.46 and 2.97 +/- 1.20 respectively before and 1, 2 and 3 months after circumcision, with significant difference between pre- and post-operation (P < 0.05).



There is a statistic difference in the glans penis vibration perception threshold between normal men and patients with simple redundant prepuce. The glans penis perception sensitivity decreases after circumcision.
Robot Learning From Demonstration

The goal of robot learning from demonstra tion is to have a robot learn from watching a demonstration of the task to be performed In our approach to learning from demon stration the robot learns a reward function from the demonstration and a task model from repeated attempts to perform the task A policy is computed based on the learned reward function and task model Lessons learned from an implementation on an an thropomorphic robot arm using a pendulum swing up task include simply mimicking demonstrated motions is not adequate to per form this task a task planner can use a learned model and reward function to com pute an appropriate policy this model based planning process supports rapid learn ing both parametric and nonparametric models can be learned and used and in corporating a task level direct learning com ponent which is non model based in addi tion to the model based planner is useful in compensating for structural modeling errors and slow model learning
Explaining Data-Driven Document Classifications

To demonstrate generality and to illustrate some additional properties of the method, we also apply the explanation method to a second domain: classifying news stories. The 20 newsgroups data set is a benchmark data set used in document classification research. It contains about 20,000 news items from 20 newsgroups representing different topics, and has a vocabulary of 26,214 different words (after stemming) (Lang 1995). The 20 topics can be categorized into seven top-level usenet categories with related news items: alternative (alt), computers (comp), miscellaneous (misc), recreation (rec), science (sci), society (soc), and talk (talk). One typical problem studied with this data set is to build classifiers to identify stories from these seven high-level news categories, which for our purposes gives a wide variety of different topics across which to provide document classification explanations. Looking at the seven high-level categories also provides realistic richness to the task: in many real document classification tasks, the class of interest is actually a collection (disjunction) of related concepts (consider, for example, “hate speech” in the safe-advertising domain).
Multi-directional Recurrent Neural Networks : A Novel Method for Estimating Missing Data

Most time-series datasets with multiple data streams have (many) missing measurements that need to be estimated. Most existing methods address this estimation problem either by interpolating within data streams or imputing across data streams; we develop a novel approach that does both. Our approach is based on a deep learning architecture that we call a Multidirectional Recurrent Neural Network (M-RNN). An M-RNN differs from a bi-directional RNN in that it operates across streams in addition to within streams, and because the timing of inputs into the hidden layers is both lagged and advanced. To demonstrate the power of our approach we apply it to a familiar real-world medical dataset and demonstrate significantly improved performance.
Inhibition of Glycine Transporter-I as a Novel Mechanism for the Treatment of Depression

Antidepressants, aiming at monoaminergic neurotransmission, exhibit delayed onset of action, limited efficacy, and poor compliance. Glutamatergic neurotransmission is involved in depression. However, it is unclear whether enhancement of the N-methyl-D-aspartate (NMDA) subtype glutamate receptor can be a treatment for depression.



We studied sarcosine, a glycine transporter-I inhibitor that potentiates NMDA function, in animal models and in depressed patients. We investigated its effects in forced swim test, tail suspension test, elevated plus maze test, novelty-suppressed feeding test, and chronic unpredictable stress test in rats and conducted a 6-week randomized, double-blinded, citalopram-controlled trial in 40 patients with major depressive disorder. Clinical efficacy and side effects were assessed biweekly, with the main outcomes of Hamilton Depression Rating Scale, Global Assessment of Function, and remission rate. The time course of response and dropout rates was also compared.



Sarcosine decreased immobility in the forced swim test and tail suspension test, reduced the latency to feed in the novelty-suppressed feeding test, and reversed behavioral deficits caused by chronic unpredictable stress test, which are characteristics for an antidepressant. In the clinical study, sarcosine substantially improved scores of Hamilton Depression Rating Scale, Clinical Global Impression, and Global Assessment of Function more than citalopram treatment. Sarcosine-treated patients were much more likely and quicker to remit and less likely to drop out. Sarcosine was well tolerated without significant side effects.



Our preliminary findings suggest that enhancing NMDA function can improve depression-like behaviors in rodent models and in human depression. Establishment of glycine transporter-I inhibition as a novel treatment for depression waits for confirmation by further proof-of-principle studies.
AHA/ACCF/HRS recommendations for the standardization and interpretation of the electrocardiogram: part VI: acute ischemia/infarction: a scientific statement from the American Heart Association Electrocardiography and Arrhythmias Committee, Council on Clinical Cardiology; the American College of Card

2009;53;992-1002; originally published online Feb 19, 2009;  Deal, David M. Mirvis, Peter Okin, Paul Kligfield, International Society for Computerized Electrocardiology Endorsed by the Cardiology Foundation; and the Heart Rhythm Society Committee, Council on Clinical Cardiology; the American College of the American Heart Association Electrocardiography and Arrhythmias Associated With Cardiac Chamber Hypertrophy A Scientific Statement From Interpretation of the Electrocardiogram: Part V: Electrocardiogram Changes AHA/ACCF/HRS Recommendations for the Standardization and This information is current as of August 2, 2011 http://content.onlinejacc.org/cgi/content/full/53/11/992 located on the World Wide Web at: The online version of this article, along with updated information and services, is
Hedging Deep Features for Visual Tracking.

Convolutional Neural Networks (CNNs) have been applied to visual tracking with demonstrated success in recent years. Most CNN-based trackers utilize hierarchical features extracted from a certain layer to represent the target. However, features from a certain layer are not always effective for distinguishing the target object from the backgrounds especially in the presence of complicated interfering factors (e.g., heavy occlusion, background clutter, illumination variation, and shape deformation). In this work, we propose a CNN-based tracking algorithm which hedges deep features from different CNN layers to better distinguish target objects and background clutters. Correlation filters are applied to feature maps of each CNN layer to construct a weak tracker, and all weak trackers are hedged into a strong one. For robust visual tracking, we propose a hedge method to adaptively determine weights of weak classifiers by considering both the difference between the historical as well as instantaneous performance, and the difference among all weak trackers over time. In addition, we design a siamese network to define the loss of each weak tracker for the proposed hedge method. Extensive experiments on large benchmark datasets demonstrate the effectiveness of the proposed algorithm against the state-of-the-art tracking methods.
CnC-CUDA: Declarative Programming for GPUs

The computer industry is at a major inflection point in its hardware roadmap due to the end of a decades-long trend of exponentially increasing clock frequencies. Instead, future computer systems are expected to be built using homogeneous and heterogeneous many-core processors with 10’s to 100’s of cores per chip, and complex hardware designs to address the challenges of concurrency, energy efficiency and resiliency. Unlike previous generations of hardware evolution, this shift towards many-core computing will have a profound impact on software. These software challenges are further compounded by the need to enable parallelism in workloads and application domains that traditionally did not have to worry about multiprocessor parallelism in the past. A recent trend in mainstream desktop systems is the use of graphics processor units (GPUs) to obtain order-of-magnitude performance improvements relative to general-purpose CPUs. Unfortunately, hybrid programming models that support multithreaded execution on CPUs in parallel with CUDA execution on GPUs prove to be too complex for use by mainstream programmers and domain experts, especially when targeting platforms with multiple CPU cores and multiple GPU devices. In this paper, we extend past work on Intel’s Concurrent Collections (CnC) programming model to address the hybrid programming challenge using a model called CnC-CUDA. CnC is a declarative and implicitly parallel coordination language that supports flexible combinations of task and data parallelism while retaining determinism. CnC computations are built using steps that are related by data and control dependence edges, which are represented by a CnC graph. The CnC-CUDA extensions in this paper include the definition of multithreaded steps for execution on GPUs, and automatic generation of data and control flow between CPU steps and GPU steps. Experimental results show that this approach can yield significant performance benefits with both GPU execution and hybrid CPU/GPU execution.
A Perspective for Supply Chain Management : Building a Conceptual Framework

Supply chain relationships play a significant role in supply chain management to respond to dynamic export market changes. If the dyadic exporter-producer relationships are still weak, they impede the emergence of a high performance supply chain within an export market. This paper develops a conceptual framework for understanding how exporter-producer relationships include not only the relationship system but also network and transaction systems; and thus introduces a more integrated way of looking at supply chain management based on information sharing as a key process between exporters and producers. To achieve this aim, supply chain relationships are reviewed from the perspectives of relationship marketing theory, network theory and transaction cost theory. Findings from previous research are discussed to provide a better understanding of how these relationships have evolved. A conceptual framework is built by offering a central proposition that specific dimensions of relationships, networks and transactions are the key antecedents of information sharing, which in turn influences export performance in supply chain management.
Visualizing Unstructured Patient Data for Assessing Diagnostic and Therapeutic History

Having access to relevant patient data is crucial for clinical decision making. The data is often documented in unstructured texts and collected in the electronic health record. In this paper, we evaluate an approach to visualize information extracted from clinical documents by means of tag cloud. Tag clouds will be generated using a bag of word approach and by exploiting part of speech tags. For a real word data set comprising radiological reports, pathological reports and surgical operation reports, tag clouds are generated and a questionnaire-based study is conducted as evaluation. Feedback from the physicians shows that the tag cloud visualization is an effective and rapid approach to represent relevant parts of unstructured patient data. To handle the different medical narratives, we have summarized several possible improvements according to the user feedback and evaluation results.
FieldSAFE: Dataset for Obstacle Detection in Agriculture

In this paper, we present a multi-modal dataset for obstacle detection in agriculture. The dataset comprises approximately 2 h of raw sensor data from a tractor-mounted sensor system in a grass mowing scenario in Denmark, October 2016. Sensing modalities include stereo camera, thermal camera, web camera, 360 ∘ camera, LiDAR and radar, while precise localization is available from fused IMU and GNSS. Both static and moving obstacles are present, including humans, mannequin dolls, rocks, barrels, buildings, vehicles and vegetation. All obstacles have ground truth object labels and geographic coordinates.
Fabrication of silver nanoparticles from leaf extract of Butea monosperma (Flame of Forest) and their inhibitory effect on bloom-forming cyanobacteria

Silver nanoparticles (SNPs) are used extensively in areas such as medicine, catalysis, electronics, environmental science, and biotechnology. Therefore, facile synthesis of SNPs from an eco-friendly, inexpensive source is a prerequisite. In the present study, fabrication of SNPs from the leaf extract of Butea monosperma (Flame of Forest) has been performed. SNPs were synthesized from 1% leaf extract solution and characterized by ultraviolet-visible (UV-vis) spectroscopy and transmission electron microscopy (TEM). The mechanism of SNP formation was studied by Fourier transform infrared (FTIR), and anti-algal properties of SNPs on selected toxic cyanobacteria were evaluated.  TEM analysis indicated that size distribution of SNPs was under 5 to 30 nm. FTIR analysis indicated the role of amide I and II linkages present in protein in the reduction of silver ions. SNPs showed potent anti-algal properties on two cyanobacteria, namely, Anabaena spp. and Cylindrospermum spp. At a concentration of 800 μg/ml of SNPs, maximum anti-algal activity was observed in both cyanobacteria.  This study clearly demonstrates that small-sized, stable SNPs can be synthesized from the leaf extract of B. monosperma. SNPs can be effectively employed for removal of toxic cyanobacteria.
Assessing the evidence of Six Sigma and Lean in the health care industry.

Popular quality improvement tools such as Six Sigma and Lean Systems (SS/L) claim to provide health care managers the opportunity to improve health care quality on the basis of sound methodology and data. However, it is unclear whether these 2 quality improvement tools actually improve health care quality.



The authors conducted a comprehensive literature review to assess the empirical evidence relating SS/L to improved clinical outcomes, processes of care, and financial performance of health care organizations.



The authors identified 177 articles on SS/L published in the last 10 years. However, only 34 of them reported any outcomes of the SS/L projects studied, and less than one-third of these articles included statistical analyses to test for significant changes in outcomes.



This review demonstrates that there are significant gaps in the SS/L health care quality improvement literature and very weak evidence that SS/L improve health care quality.
Knowledge , Motivation , and Adaptive Behavior : A Framework for Improving Selling Effectiveness

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Multiparameter Estimation in Networked Quantum Sensors.

We introduce a general model for a network of quantum sensors, and we use this model to consider the following question: When can entanglement between the sensors, and/or global measurements, enhance the precision with which the network can measure a set of unknown parameters? We rigorously answer this question by presenting precise theorems proving that for a broad class of problems there is, at most, a very limited intrinsic advantage to using entangled states or global measurements. Moreover, for many estimation problems separable states and local measurements are optimal, and can achieve the ultimate quantum limit on the estimation uncertainty. This immediately implies that there are broad conditions under which simultaneous estimation of multiple parameters cannot outperform individual, independent estimations. Our results apply to any situation in which spatially localized sensors are unitarily encoded with independent parameters, such as when estimating multiple linear or nonlinear optical phase shifts in quantum imaging, or when mapping out the spatial profile of an unknown magnetic field. We conclude by showing that entangling the sensors can enhance the estimation precision when the parameters of interest are global properties of the entire network.
Elascale: Autoscaling and Monitoring as a Service

Auto-scalability has become an evident feature for cloud software systems including but not limited to big data and IoT applications. Cloud application providers now are in full control over their applications’ microservices and macroservices; virtual machines and containers can be provisioned or deprovisioned on demand at runtime. Elascale strives to adjust both micro/macro resources with respect to workload and changes in the internal state of the whole application stack. Elascale leverages Elasticsearch stack for collection, analysis and storage of performancemetrics. Elascale then uses its default scaling engine to elastically adapt the managed application. Extendibility is guaranteed through provider, schema, plug-in and policy elements in the Elascale by which flexible scalability algorithms, including both reactive and proactive techniques, can be designed and implemented for various technologies, infrastructures and software stacks. In this paper, we present the architecture and initial implementation of Elascale; an instance will be leveraged to add auto-scalability to a generic IoT application. Due to zero dependency to the target software system, Elascale can be leveraged to provide auto-scalability and monitoring as-a-service for any type of cloud software system.
A robust shape model for multi-view car alignment

We present a robust shape model for localizing a set of feature points on a 2D image. Previous shape alignment models assume Gaussian observation noise and attempt to fit a regularized shape using all the observed data. However, such an assumption is vulnerable to gross feature detection errors resulted from partial occlusions or spurious background features. We address this problem by using a hypothesis-and-test approach. First, a Bayesian inference algorithm is developed to generate object shape and pose hypotheses from randomly sampled partial shapes - subsets of feature points. The hypotheses are then evaluated to find the one that minimizes the shape prediction error. The proposed model can effectively handle outliers and recover the object shape. We evaluate our approach on a challenging dataset which contains over 2,000 multi-view car images and spans a wide variety of types, lightings, background scenes, and partial occlusions. Experimental results demonstrate favorable improvements over previous methods on both accuracy and robustness.
Expansion of the clinical phenotype of the distal 10q26.3 deletion syndrome to include ataxia and hyperemia of the hands and feet.

Distal deletion of the long arm of chromosome 10 is associated with a dysmorphic craniofacial appearance, microcephaly, behavioral issues, developmental delay, intellectual disability, and ocular, urogenital, and limb abnormalities. Herein, we present clinical, molecular, and cytogenetic investigations of four patients, including two siblings, with nearly identical terminal deletions of 10q26.3, all of whom have an atypical presentation of this syndrome. Their prominent features include ataxia, mild-to-moderate intellectual disability, and hyperemia of the hands and feet, and they do not display many of the other features commonly associated with deletions of this region. These results point to a novel gene locus associated with ataxia and highlight the variability of the clinical presentation of patients with deletions of this region.