nils-herrmann/clean_scirepeval huggingface dataset:
Network Monitoring as a Streaming Analytics Problem

Programmable switches potentially make it easier to perform flexible network monitoring queries at line rate, and scalable stream processors make it possible to fuse data streams to answer more sophisticated queries about the network in real-time. However, processing such network monitoring queries at high traffic rates requires both the switches and the stream processors to filter the traffic iteratively and adaptively so as to extract only that traffic that is of interest to the query at hand. While the realization that network monitoring is a streaming analytics problem has been made earlier, our main contribution in this paper is the design and implementation of Sonata, a closed-loop system that enables network operators to perform streaming analytics for network monitoring applications at scale. To achieve this objective, Sonata allows operators to express a network monitoring query by considering each packet as a tuple. More importantly, Sonata allows them to partition the query across both the switches and the stream processor, and through iterative refinement, Sonata's runtime attempts to extract only the traffic that pertains to the query, thus ensuring that the stream processor can scale to satisfy a large number of queries for traffic at very high rates. We show with a simple example query involving DNS reflection attacks and traffic traces from one of the world's largest IXPs that Sonata can capture 95% of all traffic pertaining to the query, while reducing the overall data rate by a factor of about 400 and the number of required counters by four orders of magnitude.
The compressive response of carbon fiber composite pyramidal truss sandwich cores Dedicated to Professor

Pyramidal truss sandwich cores with relative densities 4q in the range 1 – 10 % have been made from carbon fiber reinforced polymer laminates using a snap-fitting method. The measured quasi-static uniaxial compressive strength increased with increasing 4q from 1 to 11 MPa over the relative density range investigated here. A robust face-sheet/ truss joint design was developed to suppress truss– face sheet node fracture. Core failure then occurred by either (i) Euler buckling (4q < 2%) or (ii) delamination failure (4q > 2%) of the struts. Micro-buckling failure of the struts was not observed in the experiments reported here. Analytical models for the collapse of the composite cores by Euler bucking, delamination failure and micro-buckling of the struts have been developed. Good agreement between the measurements and predictions based on the Euler buckling and delamination failure of the struts is obtained. The micro-buckling analysis indicates this mechanism of failure is not activated until delamination is suppressed. The measurements and predictions reported here indicate that composite cellular materials with a pyramidal micro-structure reside in a gap in the strength versus density material property space, providing new opportunities for lightweight, high strength structural design.
Factors affecting the acceptance of information systems supporting emergency operations centres

Despite the recognition that information system acceptance is an important antecedent of effective emergency management, there has been comparatively very little research examining this aspect of technology acceptance. The current research responded to this gap in literature by adapting and integrating existing models of technology acceptance. This was done in order to examine how a range of technology acceptance factors could affect the acceptance of emergency operations centre information systems. Relationships between several of these factors were also examined. Questionnaire data from 383 end-users of four different emergency operations centre information systems were analysed using structural equation modelling. This analysis concluded that technology acceptance factors of performance expectancy, effort expectancy, social influence and information quality explained 65 percent of variance in symbolic adoption, which is a combination of mental acceptance and psychological attachment towards an information system. A number of moderating effects of age, gender, experience of use and domain experience were also identified. A mediating component, of performance expectancy, explained 49 percent of variance between facilitating conditions, information quality, effort expectancy, and resulting symbolic adoption. These findings highlight a need to re-focus technology acceptance research on both mediating and moderating effects and the importance of considering domain specific factors. Applied recommendations are also made, for successfully implementing relevant information
Actuator fault detection and isolation system for an hexacopter

The problem of detection and isolation of actuator faults in hexacopter vehicles is address in this article. The aim is to detect and isolate possible faults which can occur on the vehicle actuation system (i.e. rotors and propellers). The dynamic nonlinear model of an hexarotor vehicle is first derived to build a model-based diagnosis system. Then a Thau observer is developed to estimate the states of the hexarotor and, on the usage of this information, to generate the needed set of residuals. The developed fault detection and isolation system is finally tested in a high fidelity hexacopter simulator in which different fault situations are considered.
Improving efficiency in convolutional neural networks with multilinear filters

The excellent performance of deep neural networks has enabled us to solve several automatization problems, opening an era of autonomous devices. However, current deep net architectures are heavy with millions of parameters and require billions of floating point operations. Several works have been developed to compress a pre-trained deep network to reduce memory footprint and, possibly, computation. Instead of compressing a pre-trained network, in this work, we propose a generic neural network layer structure employing multilinear projection as the primary feature extractor. The proposed architecture requires several times less memory as compared to the traditional Convolutional Neural Networks (CNN), while inherits the similar design principles of a CNN. In addition, the proposed architecture is equipped with two computation schemes that enable computation reduction or scalability. Experimental results show the effectiveness of our compact projection that outperforms traditional CNN, while requiring far fewer parameters.
DRAFT : DESIGN AND FABRICATION OF A SOFT ROBOTIC DIRECT CARDIAC COMPRESSION DEVICE

A direct cardiac compression (DCC) device is an active sleeve that is surgically placed around the heart to help the failing heart to pump without contacting blood. Soft robotic techniques enable fabrication of a conformable DCC device containing modular actuators oriented in a biomimetic manner that can restore the natural motion of the heart and provide tunable active assistance. In this paper we describe the fabrication of a DCC device; the optimization of pneumatic actuators, their integration into a matrix with a modulus in the range of cardiac tissue and methods to affix this device to the heart wall. Pneumatic air muscles (PAMs) were fabricated using a modified McKibben technique and four types of internal bladders; low durometer silicone tubes molded inhouse, polyester terephthalate (PET) heat shrink tubing, nylon medical balloons and thermoplastic urethane (TPU) balloons thermally formed in-house. Balloons were bonded to air supply lines, placed inside a braided nylon mesh with a 6.35mm resting diameter and bonded at one end. When pressurized to 145kPa silicone tubes failed and PET, nylon and TPU actuators generated isometric axial forces of 14.28, 19.65 and 19.05N respectively, with axial contractions of 33.11, 28.69 and 37.54%. Circumferential actuators placed around the heart
RAMSYS: Resource-Aware Asynchronous Data Transfer with Multicore SYStems

High-speed data transfer is vital to data-intensive computing that often requires moving large data volumes efficiently within a local data center and among geographically dispersed facilities. Effective utilization of the abundant resources in modern multicore environments for data transfer remains a persistent challenge, particularly, for Non-Uniform Memory Access (NUMA) systems wherein the locality of data accessing is an important factor. This requires rethinking how to exploit parallel access to data and to optimize the storage and network I/Os. We address this challenge and present a novel design of asynchronous processing and resource-aware task scheduling in the context of high-throughput data replication. Our software allocates multiple sets of threads to different stages of the processing pipeline, including storage I/O and network communication, based on their capacities. Threads belonging to each stage follow an asynchronous model, and attain high performance via multiple locality-aware and peer-aware mechanisms, such as task grouping, buffer sharing, affinity control and communication protocols. Our design also integrates high performance features to enhance the scalability of data transfer in several scenarios, e.g., file-level sorting, block-level asynchrony, and thread-level pipelining. Our experiments confirm the advantages of our software under different types of workloads and dynamic environments with contention for shared resources, including a 28-160 percent increase in bandwidth for transferring large files, 1.7-66 times speed-up for small files, and up to 108 percent larger throughput for mixed workloads compared with three state of the art alternatives, GridFTP , BBCP and Aspera.
Analysis and design of a high-efficiency multistage Doherty power amplifier for wireless communications

A comprehensive analysis of a multistage Doherty amplifier, which can be used to achieve higher efficiency at a lower output power level compared to the classical Doherty amplifier, is presented. Generalized design equations that explain the operation of a three-stage Doherty amplifier, which can be easily extended to an N-stage Doherty amplifier, are derived. In addition, the optimum device periphery, which minimizes AM-AM distortion for perfect Doherty amplifier operation, is analyzed. For the first time, a multistage Doherty power amplifier that meets wide-band code-division multiple-access (WCDMA) requirements is demonstrated. The designed power amplifier exhibits a power-added efficiency (PAE) of 42% at 6-dB output power backoff and 27% at 12-dB output power backoff. These PAEs are more than 2/spl times/ and 7/spl times/ better, respectively, than that of a single-stage linear power amplifier at the same output power backoff levels. The power amplifier is capable of delivering up to 33 dBm of output power, and has a maximum adjacent channel power leakage ratio of -35 and -47 dBc at 5- and 10-MHz offset, respectively. To the best of the authors' knowledge, these represent the best reported results of a Doherty amplifier for WCDMA application in the 1.95-GHz band to date.
Characterizing Horn Antenna Signals for Breast Cancer Detection

An in-depth analysis of signals from horn antennas used in breast cancer detection research has been carried out. It was found that following the excitation signal, both the double-ridged and the quad-ridged ultra-wideband horn antennas radiate additional unwanted signals that persistently oscillate. Undesirable signal oscillations sources were identified as the horn antenna cavity resonance and inherent antenna LC resonance. These signals interfere with the tumor’s signal response and need to be eliminated for successful detection of the cancerous growth. This paper proposes solutions to remove or minimize these signals without affecting antenna parameters such as bandwidth, gain, ports isolation, and polarization isolation. Modification of the antenna cavity successfully suppressed the unwanted cavity oscillation. Modification of the antenna waveguide reduced inductance and consequently mitigated LC oscillation. The resulting time and frequency domain horn antenna signals demonstrate the effectiveness of the proposed methods. Finally, a breast phantom with a tumor is simulated using signals from the original and a modified horn antenna. The delay and sum method is used to create images. The breast images demonstrate enhanced image quality through the reduction of clutter using the proposed techniques. The signal-to-clutter ratios are 0.448 and 1.6823 dB for the images produced by using the original and modified antennas, respectively.
Lyapunov-function-based flux and speed observer for AC induction motor sensorless control and parameters estimation

AC induction motors have become very popular for motion-control applications due to their simple and reliable construction. Control of drives based on ac induction motors is a quite complex task. Provided the vector-control algorithm is used, not only the rotor speed but also the position of the magnetic flux inside the motor during the control process should be known. In most applications, the flux sensors are omitted and the magnetic-flux phasor position has to be calculated. However, there are also applications in which even speed sensors should be omitted. In such a situation, the task of state reconstruction can be solved only from voltage and current measurements. In the current paper, a method based on deterministic evaluation of measurement using the state observer based on the Lyapunov function is presented. The method has been proven in testing on a real ac induction machine.
A Deep Convolutional Neural Network-Based Framework for Automatic Fetal Facial Standard Plane Recognition

Ultrasound imaging has become a prevalent examination method in prenatal diagnosis. Accurate acquisition of fetal facial standard plane (FFSP) is the most important precondition for subsequent diagnosis and measurement. In the past few years, considerable effort has been devoted to FFSP recognition using various hand-crafted features, but the recognition performance is still unsatisfactory due to the high intraclass variation of FFSPs and the high degree of visual similarity between FFSPs and other non-FFSPs. To improve the recognition performance, we propose a method to automatically recognize FFSP via a deep convolutional neural network (DCNN) architecture. The proposed DCNN consists of 16 convolutional layers with small 3 × 3 size kernels and three fully connected layers. A global average pooling is adopted in the last pooling layer to significantly reduce network parameters, which alleviates the overfitting problems and improves the performance under limited training data. Both the transfer learning strategy and a data augmentation technique tailored for FFSP are implemented to further boost the recognition performance. Extensive experiments demonstrate the advantage of our proposed method over traditional approaches and the effectiveness of DCNN to recognize FFSP for clinical diagnosis.
SVM classifier on chip for melanoma detection

Support Vector Machine (SVM) is a common classifier used for efficient classification with high accuracy. SVM shows high accuracy for classifying melanoma (skin cancer) clinical images within computer-aided diagnosis systems used by skin cancer specialists to detect melanoma early and save lives. We aim to develop a medical low-cost handheld device that runs a real-time embedded SVM-based diagnosis system for use in primary care for early detection of melanoma. In this paper, an optimized SVM classifier is implemented onto a recent FPGA platform using the latest design methodology to be embedded into the proposed device for realizing online efficient melanoma detection on a single system on chip/device. The hardware implementation results demonstrate a high classification accuracy of 97.9% and a significant acceleration factor of 26 from equivalent software implementation on an embedded processor, with 34% of resources utilization and 2 watts for power consumption. Consequently, the implemented system meets crucial embedded systems constraints of high performance and low cost, resources utilization and power consumption, while achieving high classification accuracy.
A quick MST-based algorithm to obtain Pathfinder networks (∞,n-1)

Network scaling algorithms such as the Pathfinder algorithm are used to prunemany different kinds of networks, including citation networks, randomnetworks, and social networks. However, this algorithm suffers from run time problems for large networks and online processing due to its O(n4) time complexity. In this article, we introduce a new alternative, the MST-Pathfinder algorithm, which will allow us to prune the original network to get its PFNET(∞, n −1) in justO(n2 · logn) time.Theunderlying idea comes from the fact that the union (superposition) of all the Minimum Spanning Trees extracted from a given network is equivalent to the PFNET resulting from the Pathfinder algorithmparameterized by a specific set of values (r = ∞ and q = n −1), those usually considered in many different applications. Although this property is well-known in the literature, it seems that no algorithm based on it has been proposed, up to now, to decrease the high computational cost of the original Pathfinder algorithm.We also present a mathematical proof of the correctness of this new alternative and test its good efficiency in two different case studies: one dedicated to the post-processing of large random graphs, and the other one to a real world case in which medium networks obtained by a cocitation analysis of the scientific domains in different countries are pruned.
A theoretical framework for simulated annealing

Simulated Annealing has been a very successful general algorithm for the solution of large, complex combinatorial optimization problems. Since its introduction, several applications in different fields of engineering, such as integrated circuit placement, optimal encoding, resource allocation, logic synthesis, have been developed. In parallel, theoretical studies have been focusing on the reasons for the excellent behavior of the algorithm. This paper reviews most of the important results on the theory of Simulated Annealing, placing them in a unified framework. New results are reported as well.
Looking for crossmodal correspondences between classical music and fine wine

Wine writers sometimes compare wines to pieces of music, a particular musical style or artist, or even to specific musical parameters. To date, though, it is unclear whether such comparisons merely reflect the idiosyncratic matches of the writers concerned or whether instead they reflect more general crossmodal matching tendencies that would also be shared by others (e.g., social drinkers). In our first experiment, we looked for any consensual patterns of crossmodal matching across a group of 24 participants who were presented with four distinctive wines to taste. In our second experiment, three of the wines were presented with and without music and 26 participants were asked to rate the perceived sweetness, acidity, alcohol level, fruitiness, tannin level, and their own enjoyment of the wines. The results of experiment 1 revealed the existence of a significant agreement amongst the participants in terms of specific classical music - fine wine pairings that appeared to go particularly well (or badly) together. For example, Tchaikovsky’s String Quartet No 1 in D major turned out to be a very good match for the Château Margaux 2004 (red wine). Meanwhile, Mozart’s Flute Quartet in D major, K285 was found to be a good match for the Pouilly Fumé (white wine). The results of experiment 2 revealed that participants perceived the wine as tasting sweeter and enjoyed the experience more while listening to the matching music than while tasting the wine in silence. Taken together, the results of the two experiments reported here suggest that people (social drinkers) share a number of crossmodal associations when it comes to pairing wines and music. Furthermore, listening to the appropriate classical music can enhance the overall experience associated with drinking wine. As such, our findings provide prima facie evidence to support the claim that comparing a wine to a particular style of music (as documented in the work of a number of wine writers) might provide the social drinker with useful clues about the sensory properties that they should expect to perceive in a wine should they eventually get to taste it.
THE SERIAL POSITION EFFECT OF FREE RECALL

Recently Murdock (1960) has shown that in free recall RI, the total number of words recalled after one presentation, is a linear function of t, total presentation time. Nothing was said about the serial position effect, though this is a well-known phenomenon of free recall (e.g., Deese & Kaufman, 1957). However, given that there is a serial position effect, the simple linear relationship between RI and t is rather surprising. In the customary serial position curve of free recall, probability of recall is plotted as a function of serial position. This means, then, that the area under the serial position curve is equal to RI, the number of words recalled after one presentation. If R j is a linear function of t then it must follow that the area under the serial position curve is also a linear function of t. However, it is not immediately apparent how the serial position curve varies with t in such a way as to maintain this simple linear relationship. The present experiment was designed as an attempt to determine how the serial position curve varied with list length and presentation rate while still maintaining this linear relationship. Unfortunately, at the end of the experiment it was still not clear how this relationship came about or, for that matter, whether the relationship was even linear after all. The basic reason for this failure was
Fast Optimal Transport Averaging of Neuroimaging Data

Knowing how the Human brain is anatomically and functionally organized at the level of a group of healthy individuals or patients is the primary goal of neuroimaging research. Yet computing an average of brain imaging data defined over a voxel grid or a triangulation remains a challenge. Data are large, the geometry of the brain is complex and the between subjects variability leads to spatially or temporally non-overlapping effects of interest. To address the problem of variability, data are commonly smoothed before performing a linear group averaging. In this work we build on ideas originally introduced by Kantorovich to propose a new algorithm that can average efficiently non-normalized data defined over arbitrary discrete domains using transportation metrics. We show how Kantorovich means can be linked to Wasserstein barycenters in order to take advantage of the entropic smoothing approach used by. It leads to a smooth convex optimization problem and an algorithm with strong convergence guarantees. We illustrate the versatility of this tool and its empirical behavior on functional neuroimaging data, functional MRI and magnetoencephalography (MEG) source estimates, defined on voxel grids and triangulations of the folded cortical surface.
Spark gap synchronization using subnanosecond pulsur based on avalanche transistor

Design and implementation of a 20kV subnanosecond pulse generator using Avalanche transistor as a switch. The circuit with avalanche transistor as a switch and connected in a form of string could generate a very short pulse with high peak voltage. The avalanche effect of the transistor is responsible for the ultra-fast falling edge of the output pulse. The pulse hence generated can be used for synchronization of two or more spark gap. Because of the fast rise time and the high output voltage the reliability of the synchronization is increased. The Experimental output waveforms are shown.