nils-herrmann/clean_scirepeval huggingface dataset:
Sharing Spectrum Through Spectrum Policy Reform and Cognitive Radio

Traditionally, interference protection is guaranteed through a policy of spectrum licensing, whereby wireless systems get exclusive access to spectrum. This is an effective way to prevent interference, but it leads to highly inefficient use of spectrum. Cognitive radio along with software radio, spectrum sensors, mesh networks, and other emerging technologies can facilitate new forms of spectrum sharing that greatly improve spectral efficiency and alleviate scarcity, if policies are in place that support these forms of sharing. On the other hand, new technology that is inconsistent with spectrum policy will have little impact. This paper discusses policies that can enable or facilitate use of many spectrum-sharing arrangements, where the arrangements are categorized as being based on coexistence or cooperation and as sharing among equals or primary-secondary sharing. A shared spectrum band may be managed directly by the regulator, or this responsibility may be delegated in large part to a license-holder. The type of sharing arrangement and the entity that manages it have a great impact on which technical approaches are viable and effective. The most efficient and cost-effective form of spectrum sharing will depend on the type of systems involved, where systems under current consideration are as diverse as television broadcasters, cellular carriers, public safety systems, point-to-point links, and personal and local-area networks. In addition, while cognitive radio offers policy-makers the opportunity to improve spectral efficiency, cognitive radio also provides new challenges for policy enforcement. A responsible regulator will not allow a device into the marketplace that might harm other systems. Thus, designers must seek innovative ways to assure regulators that new devices will comply with policy requirements and will not cause harmful interference.
A Divide-and-Conquer Solver for Kernel Support Vector Machines

The kernel support vector machine (SVM) is one of the most widely used classification methods; however, the amount of computation required becomes the bottleneck when facing millions of samples. In this paper, we propose and analyze a novel divide-andconquer solver for kernel SVMs (DC-SVM). In the division step, we partition the kernel SVM problem into smaller subproblems by clustering the data, so that each subproblem can be solved independently and efficiently. We show theoretically that the support vectors identified by the subproblem solution are likely to be support vectors of the entire kernel SVM problem, provided that the problem is partitioned appropriately by kernel clustering. In the conquer step, the local solutions from the subproblems are used to initialize a global coordinate descent solver, which converges quickly as suggested by our analysis. By extending this idea, we develop a multilevel Divide-and-Conquer SVM algorithm with adaptive clustering and early prediction strategy, which outperforms state-of-the-art methods in terms of training speed, testing accuracy, and memory usage. As an example, on the covtype dataset with half-a-million samples, DC-SVM is 7 times faster than LIBSVM in obtaining the exact SVM solution (to within 10−6 relative error) which achieves 96.15% prediction accuracy. Moreover, with our proposed early prediction strategy, DCSVM achieves about 96% accuracy in only 12 minutes, which is more than 100 times faster than LIBSVM.
Crowdsourced Delivery : A Dynamic Pickup and Delivery Problem with Ad-hoc drivers

The trend towards shorter delivery lead-times reduces operational efficiency and increases transportation costs for internet retailers. Mobile technology, however, creates new opportunities to organize the last-mile. In this paper, we study the concept of crowdsourced delivery that aims to use excess capacity on journeys that already take place to make deliveries. We consider a peer-to-peer platform that automatically creates matches between parcel delivery tasks and ad-hoc drivers. The platform also operates a fleet of backup vehicles to serve the tasks that cannot be served by the ad-hoc drivers. The matching of tasks, drivers and backup vehicles gives rise to a new variant of the dynamic pick-up and delivery problem. We propose a rolling horizon framework and develop an exact solution approach to solve the various subproblems. In order to investigate the potential benefit of crowdsourced delivery, we conduct a wide range of computational experiments. The experiments provide insights into the viability of crowdsourced delivery under various assumptions about the environment and the behavior of the ad-hoc drivers. The results suggest that the use of ad-hoc drivers has the potential to make the last-mile more cost-efficient and can reduce the system-wide vehicle-miles.
UNLocBoX A matlab convex optimization toolbox using proximal splitting methods

Nowadays the trend to solve optimization problems is to use s pecific algorithms rather than very general ones. The UNLocBoX provides a general framework allowing the user to design his own algorithms. To do so, the framework try to stay as close from the mathematical problem as possible. M ore precisely, the UNLocBoX is a Matlab toolbox designed to solve convex optimi zation problem of the form
Energy and Memory Efficient Mapping of Bitonic Sorting on FPGA

Parallel sorting networks are widely employed in hardware implementations for sorting due to their high data parallelism and low control overhead. In this paper, we propose an energy and memory efficient mapping methodology for implementing bitonic sorting network on FPGA. Using this methodology, the proposed sorting architecture can be built for a given data parallelism while supporting continuous data streams. We propose a streaming permutation network (SPN) by "folding" the classic Clos network. We prove that the SPN is programmable to realize all the interconnection patterns in the bitonic sorting network. A low cost design for sorting with minimal resource usage is obtained by reusing one SPN . We also demonstrate a high throughput design by trading off area for performance. With a data parallelism of p (2 ≤ p ≤ N/ log2 N), the high throughput design sorts an N-key sequence with latency O(N/p), throughput (# of keys sorted per cycle) O(p) and uses O(N) memory. This achieves optimal memory efficiency (defined as the ratio of throughput to the amount of on-chip memory used by the design) of O(p/N). Another noteworthy feature of the high throughput design is that only single-port memory rather than dual-port memory is required for processing continuous data streams. This results in 50% reduction in memory consumption. Post place-and-route results show that our architecture demonstrates 1.3x ∼1.6x improvment in energy efficiency and 1.5x ∼ 5.3x better memory efficiency compared with the state-of-the-art designs.
Response Surface Methodology 3.1 Introduction

Response surface methodology (RSM) is a collection of mathematical and statistical techniques for empirical model building. By careful design of experiments, the objective is to optimize a response (output variable) which is influenced by several independent variables (input variables). An experiment is a series of tests, called runs, in which changes are made in the input variables in order to identify the reasons for changes in the output response. Originally, RSM was developed to model experimental responses (Box and Draper, 1987), and then migrated into the modelling of numerical experiments. The difference is in the type of error generated by the response. In physical experiments, inaccuracy can be due, for example, to measurement errors while, in computer experiments, numerical noise is a result of incomplete convergence of iterative processes, round-off errors or the discrete representation of continuous physical RSM, the errors are assumed to be random.
An omnidirectional mobile robot: Concept and analysis

This paper presents a novel omnidirectional wheel mechanism, referred to as MY wheel-II, based on a sliced ball structure. The wheel consists of two balls of equal diameter on a common shaft and both balls are sliced into four spherical crowns. The two sets of spherical crowns are mounted at 45° from each other to produce a combined circular profile. Compared with previous MY wheel mechanism, this improved wheel mechanism not only is more insensitive to fragments and irregularities on the floor but also has a higher payload capacity. A kinematic model of a three-wheeled prototype platform is also derived, and the problem of wheel angular velocity fluctuations caused by the specific mechanical structure is studied. The optimal scale factor (OSF) leading to a minimum of trajectory error is adopted to solve this problem. The factors influencing the OSF are investigated through simulation. In addition, the methods used for determining the OSF are discussed briefly.
Confidence Based Dual Reinforcement Q-Routing: An adaptive online network routing algorithm

This paper describes and evaluates the Con dence-based Dual Reinforcement QRouting algorithm (CDRQ-Routing) for adaptive packet routing in communication networks. CDRQ-Routing is based on an application of the Q-learning framework to network routing, as rst proposed by Littman and Boyan (1993). The main contribution of CDRQ-routing is an increased quantity and an improved quality of exploration. Compared to Q-Routing, the state-of-the-art adaptive Bellman-Ford Routing algorithm, and the non-adaptive shortest path method, CDRQ-Routing learns superior policies signi cantly faster. Moreover, the overhead due to exploration is shown to be insigni cant compared to the improvements achieved, which makes CDRQ-Routing a practical method for real communication networks.
Real-time Dynamic MRI Reconstruction using Stacked Denoising Autoencoder

In this work we address the problem of real-time dynamic MRI reconstruction. There are a handful of studies on this topic; these techniques are either based on compressed sensing or employ Kalman Filtering. These techniques cannot achieve the reconstruction speed necessary for real-time reconstruction. In this work, we propose a new approach to MRI reconstruction. We learn a non-linear mapping from the unstructured aliased images to the corresponding clean images using a stacked denoising autoencoder (SDAE). The training for SDAE is slow, but the reconstruction is very fast only requiring a few matrix vector multiplications. In this work, we have shown that using SDAE one can reconstruct the MRI frame faster than the data acquisition rate, thereby achieving real-time reconstruction. The quality of reconstruction is of the same order as a previous compressed sensing based online reconstruction technique.
Elementary gates for quantum computation.

We show that a set of gates that consists of all one-bit quantum gates (U(2)) and the two-bit exclusive-or gate (that maps Boolean values (x, y) to (x, x⊕y)) is universal in the sense that all unitary operations on arbitrarily many bits n (U(2n)) can be expressed as compositions of these gates. We investigate the number of the above gates required to implement other gates, such as generalized Deutsch-Toffoli gates, that apply a specific U(2) transformation to one input bit if and only if the logical AND of all remaining input bits is satisfied. These gates play a central role in many proposed constructions of quantum computational networks. We derive upper and lower bounds on the exact number of elementary gates required to build up a variety of twoand three-bit quantum gates, the asymptotic number required for n-bit Deutsch-Toffoli gates, and make some observations about the number required for arbitrary n-bit unitary operations. PACS numbers: 03.65.Ca, 07.05.Bx, 02.70.Rw, 89.80.+h ∗ Clarendon Laboratory, Oxford OX1 3PU, UK; a.barenco@mildred.physics.ox.ac.uk. † Yorktown Heights, New York, NY 10598, USA; bennetc/divince@watson.ibm.com. ‡ Department of Computer Science, Calgary, Alberta, Canada T2N 1N4; cleve@cpsc.ucalgary.ca. § Laboratory for Computer Science, Cambridge MA 02139 USA;  USA; shor@research.att.com.  New York, NY 10003 USA; tycho@sleator.physics.nyu.edu. ∗∗ Physics Dept., Los Angeles, CA 90024; smolin@vesta.physics.ucla.edu. (and IBM Research.
Supervised Sequential Classification Under Budget Constraints

In this paper we develop a framework for a sequential decision making under budget constraints for multi-class classification. In many classification systems, such as medical diagnosis and homeland security, sequential decisions are often warranted. For each instance, a sensor is first chosen for acquiring measurements and then based on the available information one decides (rejects) to seek more measurements from a new sensor/modality or to terminate by classifying the example based on the available information. Different sensors have varying costs for acquisition, and these costs account for delay, throughput or monetary value. Consequently, we seek methods for maximizing performance of the system subject to budget constraints. We formulate a multi-stage multi-class empirical risk objective and learn sequential decision functions from training data. We show that reject decision at each stage can be posed as supervised binary classification. We derive bounds for the VC dimension of the multi-stage system to quantify the generalization error. We compare our approach to alternative strategies on several multi-class real world datasets.
The Resource-Based View Within the Conversation of Strategic Management

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A design of quad-ridged horn antenna with dielectric loading

A dielectric-loaded quad-ridged horn antenna (DQRHA) that operates at 14-38GHz frequency band with dual-polarized character is analysed and simulated for ultra-wideband applications. The dielectric loading method using a perpendicular circular dielectric above the aperture can increase the gain of the quad-ridged horn antenna by 1-1.5dB from 17GHz to 38GHz. Simulated results of VSWR and gain prove that the DQRHA can achieve high gain from 14GHz to 38GHz with VSWR<;2 for the two ports.
Multi-Perspective Sentence Similarity Modeling with Convolutional Neural Networks

• J. Wieting, M. Bansal, K. Gimpel, K. Livescu, and D. Roth. 2015. From paraphrase database to compositional paraphrase model and back. TACL. • K. S. Tai, R. Socher, and C. D. Manning. 2015. Improved semantic representations from treestructured long short-term memory networks. ACL. • W. Yin and H. Schutze. 2015. Convolutional neural network for paraphrase identification. NAACL. The product also streams internet radio and comes with a 30-day free trial for realnetworks' rhapsody music subscription. The device plays internet radio streams and comes with a 30-day trial of realnetworks rhapsody music service. Given two sentences, measure their similarity:
A Duality Based Approach for Realtime TV-L1 Optical Flow

Variational methods are among the most successful approaches to calculate the optical flow between two image frames. A particularly appealing formulation is based on total variation (TV) regularization and the robust L norm in the data fidelity term. This formulation can preserve discontinuities in the flow field and offers an increased robustness against illumination changes, occlusions and noise. In this work we present a novel approach to solve the TV-L formulation. Our method results in a very efficient numerical scheme, which is based on a dual formulation of the TV energy and employs an efficient point-wise thresholding step. Additionally, our approach can be accelerated by modern graphics processing units. We demonstrate the real-time performance (30 fps) of our approach for video inputs at a resolution of 320 × 240 pixels.
Leveraging existing tests in automated test generation for web applications

To test web applications, developers currently write test cases in frameworks such as Selenium. On the other hand, most web test generation techniques rely on a crawler to explore the dynamic states of the application. The first approach requires much manual effort, but benefits from the domain knowledge of the developer writing the test cases. The second one is automated and systematic, but lacks the domain knowledge required to be as effective. We believe combining the two can be advantageous. In this paper, we propose to (1) mine the human knowledge present in the form of input values, event sequences, and assertions, in the human-written test suites, (2) combine that inferred knowledge with the power of automated crawling, and (3) extend the test suite for uncovered/unchecked portions of the web application under test. Our approach is implemented in a tool called Testilizer. An evaluation of our approach indicates that Testilizer (1) outperforms a random test generator, and (2) on average, can generate test suites with improvements of up to 150% in fault detection rate and up to 30% in code coverage, compared to the original test suite.
Learning Summary Prior Representation for Extractive Summarization

In this paper, we propose the concept of summary prior to define how much a sentence is appropriate to be selected into summary without consideration of its context. Different from previous work using manually compiled documentindependent features, we develop a novel summary system called PriorSum, which applies the enhanced convolutional neural networks to capture the summary prior features derived from length-variable phrases. Under a regression framework, the learned prior features are concatenated with document-dependent features for sentence ranking. Experiments on the DUC generic summarization benchmarks show that PriorSum can discover different aspects supporting the summary prior and outperform state-of-the-art baselines.
Infusion monitoring using WiFi (802.11) through MQTT protocol

Infusion is part of treatment to get drugs or vitamins into body. This is efficient way to accelerate treatment because it faster while absorbed in body and can avoid impact on digestion. If the dosage given does not match or fluids into the body getting too much, it causing disruption to the patient's health. The main objective of this paper is to provide information on the speed and volume of the infusion that being used by each patient using a photodiode sensor and node.js server can distinguish each incoming data by utilizing topic features on MQTT. Topic feature used to exchange data using ESP8266 identity and the data being sent is the volume and velocity of the infusion. Topics, one of features on MQTT, can be used to manage the data from multiple infusion into the server. Additionally, the system provides warning information of the residual volume and velocity limit when the infusion rate exceeds the normal limit that has been specified by the user.
Discovering Different Types of Topics: Factored Topic Models

In traditional topic models such as LDA, a word is generated by choosing a topic from a collection. However, existing topic models do not identify different types of topics in a document, such as topics that represent the content and topics that represent the sentiment. In this paper, our goal is to discover such different types of topics, if they exist. We represent our model as several parallel topic models (called topic factors), where each word is generated from topics from these factors jointly. Since the latent membership of the word is now a vector, the learning algorithms become challenging. We show that using a variational approximation still allows us to keep the algorithm tractable. Our experiments over several datasets show that our approach consistently outperforms many classic topic models while also discovering fewer, more meaningful, topics.
A comprehensive study of failure mode in IGBT applications due to freewheeling diode snappy recovery

In modern power converter circuits, freewheeling diode snappy recovery phenomenon (voltage snap-off) can ultimately destroy the insulated gate bipolar transistor (IGBT) during turn-on and cause a subsequent circuit failure. In this paper, snappy recovery of modern fast power diodes is investigated with the aid of semiconductor device simulation tools, and experimental test results. The work presented here confirms that the reverse recovery process can by expressed by means of diode capacitive effects which influence the reverse recovery characteristics and determine if the diode exhibits soft or snappy recovery behavior. From the experimental and simulation results, a clear view is obtained for the physical process, causes and device/circuit conditions at which snap-off occurs. The analysis is based on the effect of both device and external operating parameters on the excess minority carrier distributions before and during the reverse recovery transient period.
From the Internet of Things to the Internet of People

There's growing interest in developing applications for the Internet of Things. Such applications' main objective is to integrate technology into people's everyday lives, to be of service to them en masse. The form in which this integration is implemented, however, still leaves much room for improvement. Usually, the user must set parameters within the application. When the person's context changes, they have to manually reconfigure the parameters. What was meant to be a commodity in an unforeseen situation then becomes extra noise. This article describes a reference architecture that improves how people are integrated with the IoT, with smartphones doing the connecting. The resulting integration opens the way to new IoT scenarios supporting evolution towards the Internet of People.
Joint Channel Assignment and Routing for Throughput Optimization in Multiradio Wireless Mesh Networks

Multi-hop infrastructure wireless mesh networks offer increased reliability, coverage and reduced equipment costs over their single-hop counterpart, wireless LANs. Equipping wireless routers with multiple radios further improves the capacity by transmitting over multiple radios simultaneously using orthogonal channels. Efficient channel assignment and routing is essential for throughput optimization of mesh clients. Efficient channel assignment schemes can greatly relieve the interference effect of close-by transmissions; effective routing schemes can alleviate potential congestion on any gateways to the Internet, thereby improving per-client throughput. Unlike previous heuristic approaches, we mathematically formulate the joint channel assignment and routing problem, taking into account the interference constraints, the number of channels in the network and the number of radios available at each mesh router. We then use this formulation to develop a solution for our problem that optimizes the overall network throughput subject to fairness constraints on allocation of scarce wireless capacity among mobile clients. We show that the performance of our algorithms is within a constant factor of that of any optimal algorithm for the joint channel assignment and routing problem. Our evaluation demonstrates that our algorithm can effectively exploit the increased number of channels and radios, and it performs much better than the theoretical worst case bounds.