nils-herrmann/clean_scirepeval huggingface dataset:
Vermitea Remediation of Hydrocarbon Contaminated Soil

Due to the global need for oil production and distribution, surrounding ecosystems have been negatively affected by oil spill externalities in individual health and community diversity. Conventional land remediation techniques run the risk of leaving chemical residues, and interacting with metals in the soil. The objective of this study was to test worm compost tea, also known as vermitea, as a bioremediation method to replace current techniques used on oil contaminated soils. To test the conditions that contributed to the efficacy of the teas, I examined different teas that looked into the mode and length of pollutant exposure. I examined oil emulsification activity, presence of biosurfactant-producing bacteria colonies, microbial diversity and abundance, and applicability of the teas to artificially contaminated soils. Overall, I found that the long-term direct oil tea had a 7.42% significant increase in biosurfactant producing microbes in comparison to the control tea. However, the long-term crude soil vermitea was found to be the best type of pollutant degrading tea in terms of emulsifying activity and general applicability towards reducing oil concentrations in the soil. These results will help broaden the scientific understanding towards stimulated microbial degradation of pollution, and broaden the approaches that can be taken in restoring polluted ecosystems.
Interventions to Promote Diabetes Self-Management in Children and Youth: A Scoping Review.

As children and youth with diabetes grow up, they become increasingly responsible for controlling and monitoring their condition. We conducted a scoping review to explore the research literature on self-management interventions for children and youth with diabetes. Eleven studies met the inclusion criteria. Some of the studies reviewed combined the participant population so that children with Type 1 as well as children with Type 2 diabetes were included. The majority of the studies focused on children age 14 yr or older and provided self-management education, self-management support, or both. Parent involvement was a key component of the majority of the interventions, and the use of technology was evident in 3 studies. The findings highlight factors that occupational therapy practitioners should consider when working with pediatric diabetes teams to select self-management interventions.
Two-Segments Compact Dielectric Resonator Antenna for UWB Application

A new compact two-segments dielectric resonator antenna (TSDR) for ultrawideband (UWB) application is presented and studied. The design consists of a thin monopole printed antenna loaded with two dielectric resonators with different dielectric constant. By applying a combination of U-shaped feedline and modified TSDR, proper radiation characteristics are achieved. The proposed antenna provides an ultrawide impedance bandwidth, high radiation efficiency, and compact antenna with an overall size of 18 × 36 × 11 mm . From the measurement results, it is found that the realized dielectric resonator antenna with good radiation characteristics provides an ultrawide bandwidth of about 110%, covering a range from 3.14 to 10.9 GHz, which covers UWB application.
A reinforcement learning approach to autonomous decision-making in smart electricity markets

The vision of a Smart Electric Grid relies critically on substantial advances in intelligent decentralized control mechanisms. We propose a novel class of autonomous broker agents for retail electricity trading that can operate in a wide range of Smart Electricity Markets, and that are capable of deriving long-term, profit-maximizing policies. Our brokers use Reinforcement Learning with function approximation, they can accommodate arbitrary economic signals from their environments, and they learn efficiently over the large state spaces resulting from these signals. We show how feature selection and regularization can be leveraged to automatically optimize brokers for particular market conditions, and demonstrate the performance of our design in extensive experiments using real-world energy market data.
Sequence-to-Sequence Voice Conversion with Similarity Metric Learned Using Generative Adversarial Networks

We propose a training framework for sequence-to-sequence voice conversion (SVC). A well-known problem regarding a conventional VC framework is that acoustic-feature sequences generated from a converter tend to be over-smoothed, resulting in buzzy-sounding speech. This is because a particular form of similarity metric or distribution for parameter training of the acoustic model is assumed so that the generated feature sequence that averagely fits the training target example is considered optimal. This over-smoothing occurs as long as a manually constructed similarity metric is used. To overcome this limitation, our proposed SVC framework uses a similarity metric implicitly derived from a generative adversarial network, enabling the measurement of the distance in the high-level abstract space. This would enable the model to mitigate the oversmoothing problem caused in the low-level data space. Furthermore, we use convolutional neural networks to model the long-range context-dependencies. This also enables the similarity metric to have a shift-invariant property; thus, making the model robust against misalignment errors involved in the parallel data. We tested our framework on a non-native-to-native VC task. The experimental results revealed that the use of the proposed framework had a certain effect in improving naturalness, clarity, and speaker individuality.
A Framework for Qualitative Performance Prediction

Performance prediction models at the source code level are crucial components in advanced optimizing compilers, programming environments, and tools for performance debugging. Compilers and programming environments use performance models to guide the selection of effective code improvement strategies. Tools for performance debugging may use performance prediction models to explain the performance behavior of a program to the user. Finding the best match between a performance prediction model and a specific source–level optimization task or performance explanation task is a challenging problem. The best performance prediction model for a given task is a model that satisfies the precision requirements while including as few performance factors as possible in order to minimize the cost of the performance predictions. In optimizing compilers, the lack of such a cost–effective performance model may make the application of an optimization prohibitively expensive. In the context of a programming environment, marginal performance factors should be avoided since they will obscure reasoning about the observed performance behavior. This paper discusses a new qualitative performance prediction framework at the program source level that automatically selects a minimal set of performance factors for a target system and performance precision requirement. In the context of this paper, a target system consists of a compiler, an operating system, and a machine architecture. The performance prediction framework identifies significant target system and application program parameters that have to be considered in order to achieve the requested precision. Such parameters may include application factors such as number and type of floating point operations, and machine characteristics such as L1 and L2 caches, TLB, and main memory. The reported performance factors can be used by a compiler writer to build or validate a quantitative performance model, and by a user to better understand the observed program performance. In addition, the failure of the framework to produce a model of the desired quality may be an indication that there exists a significant performance factor not considered within the performance framework. Such information is important to guiding a compiler writer or user in a more efficient search for crucial performance factors. Preliminary experimental results for a small computation kernel and a set of twelve target systems indicate the effectiveness of our framework. The target systems for the experiment consisted of four machine architectures (SuperSPARC I-II and UltraSPARC I-II running Solaris 2.5) and three compiler optimization levels (-none, -O3, -depend -fast). Our prototype framework determines different performance models (1) across different precision requirements for the same target ∗e-mail: chunghsu@cs.rutgers.edu, uli@cs.rutgers.edu; address: Department of Computer Science, Hill Center, Busch Campus, Rutgers University, Piscataway, NJ 08855
iLPR: an Indian license plate recognition system

One of the major concerns of Integrated Traffic Management System (ITMS) in India is the identification of vehicles violating the stop-line at a road crossing. A large number of Indian vehicles do not stop at the designated stop-line and pose serious threat to the pedestrians crossing the roads. The current work reports the technicalities of the i $$ i $$ LPR (Indian License Plate Recognition) system implemented at five busy road-junctions in one populous metro city in India. The designed system is capable of localizing single line and two-line license plates of various sizes and shapes, recognizing characters of standard/ non-standard fonts and performing seamlessly in varying weather conditions. The performance of the system is evaluated with a large database of images for different environmental conditions. We have published a limited database of Indian vehicle images in http://code.google.com/p/cmaterdb/ for non-commercial use by fellow researchers. Despite unparallel complexity in the Indian city-traffic scenario, we have achieved around 92 % plate localization accuracy and 92.75 % plate level recognition accuracy over the localized vehicle images.
Improved Measures of Integrated Information

Although there is growing interest in measuring integrated information in computational and cognitive systems, current methods for doing so in practice are computationally unfeasible. Existing and novel integration measures are investigated and classified by various desirable properties. A simple taxonomy of Φ-measures is presented where they are each characterized by their choice of factorization method (5 options), choice of probability distributions to compare (3 × 4 options) and choice of measure for comparing probability distributions (7 options). When requiring the Φ-measures to satisfy a minimum of attractive properties, these hundreds of options reduce to a mere handful, some of which turn out to be identical. Useful exact and approximate formulas are derived that can be applied to real-world data from laboratory experiments without posing unreasonable computational demands.
Near end listening enhancement optimized with respect to Speech Intelligibility Index

Signal processing algorithms for near end listening enhancement allow to improve the intelligibility of clean (far end) speech for the near end listener who perceives not only the far end speech but also ambient background noise. A typical scenario is mobile communication conducted in the presence of acoustical background noise such as traffic or babble noise. In this contribution we analyze the calculation rules of the Speech Intelligibility Index (SII) and derive a simple condition for the speech spectrum level of every subband that maximizes the SII for a given noise spectrum level. This rule is used to derive a theoretical bound for a maximum achievable SII as well as a new SII optimized algorithm for near end listening enhancement. The impact of ignoring masking effects in the algorithm is also investigated and seconds our SNR recovery algorithm proposed earlier. Instrumental evaluation shows that the new algorithm performs close to the established theoretical bound.
ASP for Minimal Entailment in a Rational Extension of SROEL

In this paper we exploit Answer Set Programming (ASP) for rea soning in a rational extension SROEL(⊓, ×)R T of the low complexity description logic SROEL(⊓,×), which underlies the OWL EL ontology language. In the extended language, a typicality operator T is allowed to define concepts T(C) (typical C’s) under a rational semantics. It has been proven that instance checking under rational entailment has a polynomial complexity. To strengthen rational entailment, in t his paper we consider a minimal model semantics. We show that, for arbitrary SROEL(⊓,×)RT knowledge bases, instance checking under minimal entailme nt is Π2 -complete. Relying on a Small Model result, where models cor respond to answer sets of a suitable ASP encoding, we exploit Answer Set Preferences (and, in par ticular, theasprin framework) for reasoning under minimal entailment. The paper is under consideration for acceptance in Theory and Practice of Logic Programming.
OS-Independent Live Migration Scheme for Bare-Metal Clouds

Bare-metal clouds are an emerging and attractive platform for cloud users who demand extreme computer performance. Bare-metal clouds lease physical machines rather than virtual machines, eliminating a virtualization overhead and providing maximum computer hardware performance. Therefore, bare-metal clouds are suitable for applications that require intensive, consistent, and predictable performance, such as big-data and high-performance computing applications. Unfortunately, existing bare-metal clouds do not support live migration because they lack virtualization layers. Live migration is an essential feature for bare-metal cloud vendors to perform proactive maintenance and fault tolerance that can avoid long user application downtime when underlying physical hardware is about to fail. Existing live migration approaches require either a virtualization overhead or OS-dependence and are therefore unsuitable for bare-metal clouds. This paper introduces an OS-independent live migration scheme for bare-metal clouds. We utilize a very thin hypervisor layer that does not virtualize hardware and directly exposes physical hardware to a guest OS. During live migration, the hypervisor carefully monitors and controls access to physical devices to capture, transfer, and restore the device states while the guest OS is still controlling the devices. After live migration, the hypervisor does almost nothing to eliminate the virtualization overhead and provide bare-metal performance for the guest OS. Experimental results confirmed that network performance of our system was comparable with that of bare-metal machines.
Neocognitron: A neural network model for a mechanism of visual pattern recognition

A recognition with a large-scale network is simulated on a PDP-11/34 minicomputer and is shown to have a great capability for visual pattern recognition. The model consists of nine layers of cells. The authors demonstrate that the model can be trained to recognize handwritten Arabic numerals even with considerable deformations in shape. A learning-with-a-teacher process is used for the reinforcement of the modifiable synapses in the new large-scale model, instead of the learning-without-a-teacher process applied to a previous model. The authors focus on the mechanism for pattern recognition rather than that for self-organization.
End-to-end Video-level Representation Learning for Action Recognition

From the frame/clip-level feature learning to the video-level representation building, deep learning methods in action recognition have developed rapidly in recent years. However, current methods suffer from the confusion caused by partial observation training, or without end-to-end learning, or restricted to single temporal scale modeling and so on. In this paper, we build upon two-stream ConvNets and propose Deep networks with Temporal Pyramid Pooling (DTPP), an end-to-end video-level representation learning approach, to address these problems. Specifically, at first, RGB images and optical flow stacks are sparsely sampled across the whole video. Then a temporal pyramid pooling layer is used to aggregate the frame-level features which consist of spatial and temporal cues. Lastly, the trained model has compact video-level representation with multiple temporal scales, which is both global and sequence-aware. Experimental results show that DTPP achieves the state-of-the-art performance on two challenging video action datasets: UCF101 and HMDB51, either by ImageNet pre-training or Kinetics pre-training.
ImageNet Training in Minutes

In this paper, we investigate large scale computers' capability of speeding up deep neural networks (DNN) training. Our approach is to use large batch size, powered by the Layer-wise Adaptive Rate Scaling (LARS) algorithm, for efficient usage of massive computing resources. Our approach is generic, as we empirically evaluate the effectiveness on two neural networks: AlexNet and ResNet-50 trained with the ImageNet-1k dataset while preserving the state-of-the-art test accuracy. Compared to the baseline of a previous study from a group of researchers at Facebook, our approach shows higher test accuracy on batch sizes that are larger than 16K. Using 2,048 Intel Xeon Platinum 8160 processors, we reduce the 100-epoch AlexNet training time from hours to 11 minutes. With 2,048 Intel Xeon Phi 7250 Processors, we reduce the 90-epoch ResNet-50 training time from hours to 20 minutes. Our implementation is open source and has been released in the Intel distribution of Caffe v1.0.7.
R-PackDroid: API package-based characterization and detection of mobile ransomware

Ransomware has become a serious and concrete threat for mobile platforms and in particular for Android. In this paper, we propose R-PackDroid, a machine learning system for the detection of Android ransomware. Differently to previous works, we leverage information extracted from system API packages, which allow to characterize applications without specific knowledge of user-defined content such as the application language or strings. Results attained on very recent data show that it is possible to detect Android ransomware and to distinguish it from generic malware with very high accuracy. Moreover, we used R-PackDroid to flag applications that were detected as ransomware with very low confidence by the VirusTotal service. In this way, we were able to correctly distinguish true ransomware from false positives, thus providing valuable help for the analysis of these malicious applications.
Optical absorption of small silver clusters: Ag(n), (n=4-22).

We present a joint theoretical and experimental investigation of the absorption spectra of silver clusters Ag(n) (4<or=n<or=22). The experimental spectra of clusters isolated in an Ar matrix are compared with the calculated ones in the framework of the time-dependent density functional theory. The analysis of the molecular transitions indicates that the s-electrons are responsible for the optical response of small clusters (n<or=8) while the d-electrons play a crucial role in the optical excitations for larger n values.
Neutronics Analysis of the TRIGA Vienna Mixed Core

The 250 kW TRIGA Mark II research reactor, Vienna, operates since 7 March 1962. The initial criticality was achieved with the first core loading of 57 fuel elements (FE) of same type (Aluminium clad fuel with 20% enrichment). Later on due to fuel consumption SST clad 20% enriched FE (s) have been added to compensate the reactor core burn-up. In 1975 high enriched (HEU) TRIGA fuel (FLIP fuel = Fuel Lifetime Improvement Program) was introduced into the core. The addition of this FLIP fuel resulted in the current completely mixed core. Therefore the current core of the TRIGA reactor Vienna is operating with a completely mixed core using three different types of fuels with two categories of enrichments. This makes the reactor physics calculations very complicated. To calculate the current core, a Monte Carlo based radiation transport computer code MCNP5 was employed to develop the current core of the TRIGA reactor. The present work presents the MCNP model of the current core and its validation through two experiments performed on the reactor. The experimental results of criticality and reactivity distribution experiments confirm the current core model. As the basis of this paper is based on the long-term cooperation with our colleague Dr. Matjaz Ravnik we therefore devote this paper in his memory.
D-flow: immersive virtual reality and real-time feedback for rehabilitation

D-Flow is a software system designed for the development of interactive and immersive virtual reality applications, for the purpose of clinical research and rehabilitation. Key concept of the D-Flow software system is that the subject is regarded as an integral part of a real-time feedback loop, in which multi-sensory input devices measure the behavior of the subject, while output devices return motor-sensory, visual and auditory feedback to the subject. The D-Flow software system allows an operator to define feedback strategies through a flexible and extensible application development framework, based on visual programming. We describe the requirements, architecture and design considerations of the D-Flow software system, as well as a number of applications that have been developed using D-Flow, both for clinical research and rehabilitation.
LTP-triggered cholesterol redistribution activates Cdc42 and drives AMPA receptor synaptic delivery

Neurotransmitter receptor trafficking during synaptic plasticity requires the concerted action of multiple signaling pathways and the protein transport machinery. However, little is known about the contribution of lipid metabolism during these processes. In this paper, we addressed the question of the role of cholesterol in synaptic changes during long-term potentiation (LTP). We found that N-methyl-d-aspartate-type glutamate receptor (NMDAR) activation during LTP induction leads to a rapid and sustained loss or redistribution of intracellular cholesterol in the neuron. A reduction in cholesterol, in turn, leads to the activation of Cdc42 and the mobilization of GluA1-containing α-amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid-type glutamate receptors (AMPARs) from Rab11-recycling endosomes into the synaptic membrane, leading to synaptic potentiation. This process is accompanied by an increase of NMDAR function and an enhancement of LTP. These results imply that cholesterol acts as a sensor of NMDAR activation and as a trigger of downstream signaling to engage small GTPase (guanosine triphosphatase) activation and AMPAR synaptic delivery during LTP.
Computational Origami of a Morley's Triangle

We present a computational origami construction of Morley’s triangles and automated proof of correctness of the generalized Morley’s theorem in a streamlined process of solving-computing-proving. The whole process is realized by a computational origami system being developed by us. During the computational origami construction, geometric constraints in symbolic and numeric representation are generated and accumulated. Those constraints are then transformed into algebraic relations, which in turn are used to prove the correctness of the construction. The automated proof required non-trivial amount of computer resources, and shows the necessity of networked services of mathematical software. This example is considered to be a case study for innovative mathematical knowledge management.
A Novel Design of a Low Cost Wideband Wilkinson Power Divider

This paper presents analysis and design of a wideband Wilkinson power divider for wireless applications. The design is accomplished by transforming the lengths and impedances of the quarter wavelength sections of the conventional Wilkinson power divider into U-shaped sections. The designed power divider is simulated by using ADS Agilent technologies and CST microwave studio software. It is shown that the proposed power divider has simple topology and good performances in terms of insertion loss, port matching and isolation at all operating frequencies (1.8 GHz, 2.45 GHz and 3.55 GHz).