nils-herrmann/clean_scirepeval huggingface dataset:
Direct path and multipath cancellation with discrete distribution structure in passive radar

The Direct path and multipath interference (DPI and MPI) Cancellation is a crucial issue in the passive radar. In this paper, two improvements have been done in NLMS algorithm. One is to estimate the values and distribution of DPI and MPI roughly, and then remove them from the echo signal before filtering. The other one is to vary the step size of NLMS according to the cross correlation function of the reference signal and the output signal of the adaptive filter. The performance of the improved algorithm in passive radar DPI and MPI Cancellation is reported. The computer simulation results show that the algorithm has a faster convergence rate and a smaller steady state error.
Understanding the influence of dead-time on GaN based synchronous boost converter

Gallium Nitride (GaN) based power switching devices are known to be superior to conventional Si devices due to properties such as low loss and fast switching speed. However, as a new technology, some special characteristics of GaN-based power devices still remain unknown to the public. This paper tries to investigate the effect of dead-time on a GaN-based synchronous boost converter, as compared to its Si counterpart. It is found out that GaN-based converter is more sensitive to dead-time related loss as a result of fast switching transition and high reverse voltage drop. Improper selection of dead-time value can offset its advantage over Si converter. Analyses also show that GaN HEMTs have different switching characteristics compared to Si MOSFET due to low Cgd to Cds ratio and lack of reverse recovery. These critical findings will help power electronic engineers take better advantage of GaN technology in synchronous rectification and inverter applications.
Internal Model Control . 4 . PID Controller Design

For a large number of single input-single output (SISO) models typically used in the process industries, the Internal Model Control (IMC) design procedure is shown to lead to PID controllers, occaslonally augmented with a first-order lag. These PID controllers have as their only tuning parameter the closedloop time constant or, equivalently, the closed-loop bandwidth. On-line adjustments are therefore much simpler than for general PID controllers. As a special case, PIand PID-tuning rules for systems modeled by a first-order lag with dead time are derived analytically. The superiority of these rules in terms of both closed-loop performance and robustness is demonstrated.
Lessons Learned: The Complexity of Accurate Identification of in-Text Citations

The importance of citations is widely recognized by the scientific community. Citations are being used in making a number of vital decisions such as calculating impact factor of journals, calculating impact of a researcher (H-Index), ranking universities and research organizations. Furthermore, citation indexes, along with other criteria, employ citation counts to retrieve and rank relevant research papers. However, citing patterns and in-text citation frequency are not used for such important decisions. The identification of in-text citation from a scientific document is an important problem. However, identification of in-text citation is a tough ask due to the ambiguity between citation tag and content. This research focuses on in-text citation analysis and makes the following specific contributions such as: Provides detailed in-text citation analysis on 16,000 citations of an online journal, reports different pattern of citations-tags and its in-text citations and highlights the problems (mathematical ambiguities, wrong allotments, commonality in content and string variation) in identifying in-text citations from scientific documents. The accurate identification of in-text citations will help information retrieval systems, digital libraries and citation indexes.
Action recognition with trajectory-pooled deep-convolutional descriptors

Visual features are of vital importance for human action understanding in videos. This paper presents a new video representation, called trajectory-pooled deep-convolutional descriptor (TDD), which shares the merits of both hand-crafted features  and deep-learned features [24]. Specifically, we utilize deep architectures to learn discriminative convolutional feature maps, and conduct trajectory-constrained pooling to aggregate these convolutional features into effective descriptors. To enhance the robustness of TDDs, we design two normalization methods to transform convolutional feature maps, namely spatiotemporal normalization and channel normalization. The advantages of our features come from (i) TDDs are automatically learned and contain high discriminative capacity compared with those hand-crafted features; (ii) TDDs take account of the intrinsic characteristics of temporal dimension and introduce the strategies of trajectory-constrained sampling and pooling for aggregating deep-learned features. We conduct experiments on two challenging datasets: HMD-B51 and UCF101. Experimental results show that TDDs outperform previous hand-crafted features  and deep-learned features [24]. Our method also achieves superior performance to the state of the art on these datasets.
Capturing the Temporal Domain in Echonest Features for Improved Classification Effectiveness

This paper proposes Temporal Echonest Features to harness the information available from the beat-aligned vector sequences of the features provided by The Echo Nest. Rather than aggregating them via simple averaging approaches, the statistics of temporal variations are analyzed and used to represent the audio content. We evaluate the performance on four traditional music genre classification test collections and compare them to state of the art audio descriptors. Experiments reveal, that the exploitation of temporal variability from beat-aligned vector sequences and combinations of different descriptors leads to an improvement of classification accuracy. Comparing the results of Temporal Echonest Features to those of approved conventional audio descriptors used as benchmarks, these approaches perform well, often significantly outperforming their predecessors, and can be effectively used for large scale music genre classification.
Opinion and Perspectivesy Why We Need Quantum Physics for Cognitive Neuroscience

For the past 20 years and more, arguments about the role of quantum mechanics in consciousness and mind theory have been mounting. On the one side are traditional neuroscientists who believe that the way to understanding the brain is through looking at the nerve cells. On the other side are various physicists who suggest that the laws of quantum mechanics may have an influence on the dynamics of consciousness and the mind. At the same time however, consciousness and the mind cannot be separated from matter. They originate in the microscopic world of the human brain. There can be no definite separation between mind and matter; there is no 'mind' without 'matter', and no 'matter' without 'mind'. In terms of cognitive neuroscience, we know a great deal about the working of nerve cells. For example, we understand quite well about the formation of action potential, ion exchange, energy use, axonal transport, the vesicle cycle, and formation, oscillation and breakdown in nerve transmission. However, we still do not understand how experience is formed in our material brain (color, sound, smell, taste, pain, imagination, decision, dreams, love, or orgasm) and how consciousness arises in an unconscious material organ. The insufficiency of these answers no doubt arises from the insufficiency of the methods used by cognitive science.
Analysis Enhanced Particle-based Flow Visualization

Particle-based fluid simulation (PFS), such as Smoothed Particle Hydrodynamics (SPH) and Position-based Fluid (PBF), is a mesh-free method that has been widely used in various fields, including astrophysics, mechanical engineering, and biomedical engineering for the study of liquid behaviors under different circumstances. Due to its meshless nature, most analysis techniques that are developed for mesh-based data need to be adapted for the analysis of PFS data. In this work, we study a number of flow analysis techniques and their extension for PFS data analysis, including the FTLE approach, Jacobian analysis, and an attribute accumlation framework. In particular, we apply these analysis techniques to free surface fluids. We demonstrate that these analyses can reveal some interesting underlying flow patterns that would be hard to see otherwise via a number of PFS simulated flows with different parameters and boundary settings. In addition, we point out that an in-situ analysis framework that performs these analyses can potentially be used to guide the adaptive PFS to allocate the computation and storage power to the regions of interest during the simulation.
Computational models of trust and reputation: agents, evolutionary games, and social networks

Many recent studies of trust and reputation are made in the context of commercial reputation or rating systems for online communities. Most of these systems have been constructed without a formal rating model or much regard for our sociological understanding of these concepts. We first provide a critical overview of the state of research on trust and reputation. We then propose a formal quantitative model for the rating process. Based on this model, we formulate two personalized rating schemes and demonstrate their effectiveness at inferring trust experimentally using a simulated dataset and a real world movie-rating dataset. Our experiments show that the popular global rating scheme widely used in commercial electronic communities is inferior to our personalized rating schemes when sufficient ratings among members are available. The level of sufficiency is then discussed. In comparison with other models of reputation, we quantitatively show that our framework provides significantly better estimations of reputation. "Better" is discussed with respect to a rating process and specific games as defined in this work. Secondly, we propose a mathematical framework for modeling trust and reputation that is rooted in findings from the social sciences. In particular, our framework makes explicit the importance of social information (i.e., indirect channels of inference) in aiding members of a social network choose whom they want to partner with or to avoid. Rating systems that make use of such indirect channels of inference are necessarily personalized in nature, catering to the individual context of the rater. Finally, we have extended our trust and reputation framework toward addressing a fundamental problem for social science and biology: evolution of cooperation. We show that by providing an indirect inference mechanism for the propagation of trust and reputation, cooperation among selfish agents can be explained for a set of game theoretic simulations. For these simulations in particular, our proposal is shown to have provided more cooperative agent communities than existing schemes are able to. Thesis Supervisor: Peter Szolovits Title: Professor of Electrical Engineering and Computer Science
Signal compensation and extraction of high resolution position for sinusoidal magnetic encoders

MEs (magnetic encoders) are widely used in industrial control systems because of its significant advantages, such as high resolution, high speed, operation in harsh environments, and low cost. This paper describes a method to extract the position information with high resolution for sinusoidal MEs. A code compensator based on optimization theories is applied to correct non-ideal signals of the encoder outputs. This algorithm can effectively eliminate the DC offset, phase-shift, amplitude difference and sinusoidal deformation from these outputs. Then, a size-reduced look-up table is built-up off-line to generate the high resolution quadrature pulses suitable for standard incremental encoder signals. These LUTs (Look-up tables) rely on the approximate linear property of the amplitude of sinusoids in section [-sin(pi/4) ; sin(pi/4)] and is decreased by converting the values of LUTs into binary values (0 and 1). The firmware is implemented on a 16-bit DSP (digital signal processor) TMS320F2812 hardware platform with minimum computation. The experimental results are also presented in this paper.
SSR-Net: A Compact Soft Stagewise Regression Network for Age Estimation

This paper presents a novel CNN model called Soft Stagewise Regression Network (SSR-Net) for age estimation from a single image with a compact model size. Inspired by DEX, we address age estimation by performing multi-class classification and then turning classification results into regression by calculating the expected values. SSR-Net takes a coarse-to-fine strategy and performs multi-class classification with multiple stages. Each stage is only responsible for refining the decision of its previous stage for more accurate age estimation. Thus, each stage performs a task with few classes and requires few neurons, greatly reducing the model size. For addressing the quantization issue introduced by grouping ages into classes, SSR-Net assigns a dynamic range to each age class by allowing it to be shifted and scaled according to the input face image. Both the multi-stage strategy and the dynamic range are incorporated into the formulation of soft stagewise regression. A novel network architecture is proposed for carrying out soft stagewise regression. The resultant SSR-Net model is very compact and takes only 0.32 MB. Despite its compact size, SSR-Net’s performance approaches those of the state-of-the-art methods whose model sizes are often more than 1500× larger.
A Unified Approach for Conventional Zero-Shot, Generalized Zero-Shot, and Few-Shot Learning

Prevalent techniques in zero-shot learning do not generalize well to other related problem scenarios. Here, we present a unified approach for conventional zero-shot, generalized zero-shot, and few-shot learning problems. Our approach is based on a novel class adapting principal directions’ (CAPDs) concept that allows multiple embeddings of image features into a semantic space. Given an image, our method produces one principal direction for each seen class. Then, it learns how to combine these directions to obtain the principal direction for each unseen class such that the CAPD of the test image is aligned with the semantic embedding of the true class and opposite to the other classes. This allows efficient and class-adaptive information transfer from seen to unseen classes. In addition, we propose an automatic process for the selection of the most useful seen classes for each unseen class to achieve robustness in zero-shot learning. Our method can update the unseen CAPD taking the advantages of few unseen images to work in a few-shot learning scenario. Furthermore, our method can generalize the seen CAPDs by estimating seen–unseen diversity that significantly improves the performance of generalized zero-shot learning. Our extensive evaluations demonstrate that the proposed approach consistently achieves superior performance in zero-shot, generalized zero-shot, and few/one-shot learning problems.
Real-Time CPU-Based Large-Scale Three-Dimensional Mesh Reconstruction

In robotics, especially in this era of autonomous driving, mapping is one key ability of a robot to be able to navigate through an environment, localize on it, and analyze its traversability. To allow for real-time execution on constrained hardware, the map usually estimated by feature-based or semidense SLAM algorithms is a sparse point cloud; a richer and more complete representation of the environment is desirable. Existing dense mapping algorithms require extensive use of graphics processing unit (GPU) computing and they hardly scale to large environments; incremental algorithms from sparse points still represent an effective solution when light computational effort is needed and big sequences have to be processed in real time. In this letter, we improved and extended the state-of-the-art incremental manifold mesh algorithm proposed by Litvinov and Lhuillier and extended by Romanoni and Matteucci. While these algorithms do not reconstruct the map in real time and they embed points from SLAM or structure from motion only when their position is fixed, in this letter, we propose the first incremental algorithm able to reconstruct a manifold mesh in real time through single core CPU processing, which is also able to modify the mesh according to three-dimensional points updates from the underlying SLAM algorithm. We tested our algorithm against two state-of-the-art incremental mesh mapping systems on the KITTI dataset, and we showed that, while accuracy is comparable, our approach is able to reach real-time performances thanks to an order of magnitude speed-up.
Investigating Social Entrepreneurship in Developing Countries

Social entrepreneurship has drawn interest from global policy makers and social entrepreneurs to target developing countries. Generally, not-forprofit organizations, funded by government and donor grants have played a significant role in poverty alleviation. We argue that, by applying entrepreneurial concepts, organizations can create social value, hence mitigate poverty. This is a theoretical paper that builds upon a multidimensional model in analysing how three social enterprises from India and Kenya create social value to address social problems. The findings suggest that whilst the social mission is central to all these organizations, they also create social value through innovation and pro-activeness. Additionally, the cultural and political environmental contexts hinder their attempt to create social value. Building networks and partnerships to achieve social value creation is vital for these organizations. Policy makers should devise policies that would assist social enterprises to achieve development goals.
Marx generator using power mosfets

A compact Marx generator has been developed by using power MOSFETs as the switches. The objective is to develop repetitive, compact, efficient, short-pulse, high-voltage generator for industrial applications. The initial tests were carried out by using 16 switches, achieving output of ∼ 10 kV and ∼ 250 ns in pulse width at repetition rate of ∼ 400 Hz.
High-power-transfer-density capacitive wireless power transfer system for electric vehicle charging

This paper introduces a large air-gap capacitive wireless power transfer (WPT) system for electric vehicle charging that achieves a power transfer density exceeding the state-of-the-art by more than a factor of four. This high power transfer density is achieved by operating at a high switching frequency (6.78 MHz), combined with an innovative approach to designing matching networks that enable effective power transfer at this high frequency. In this approach, the matching networks are designed such that the parasitic capacitances present in a vehicle charging environment are absorbed and utilized as part of the wireless power transfer mechanism. A new modeling approach is developed to simplify the complex network of parasitic capacitances into equivalent capacitances that are directly utilized as the matching network capacitors. A systematic procedure to accurately measure these equivalent capacitances is also presented. A prototype capacitive WPT system with 150 cm2 coupling plates, operating at 6.78 MHz and incorporating matching networks designed using the proposed approach, is built and tested. The prototype system transfers 589 W of power across a 12-cm air gap, achieving a power transfer density of 19.6 kW/m2.
3-D Path Planning and Target Trajectory Prediction for the Oxford Aerial Tracking System

For the Oxford Aerial Tracking System (OATS) we are developing a robot helicopter that can track moving ground objects. Here we describe algorithms for the device to perform path planning and trajectory prediction. The path planner uses superquadratic potential fields and incorporates a height change mechanism that is triggered where necessary and in order to avoid local minima traps. The goal of the trajectory prediction system is to reliably predict the target trajectory during occlusion caused by ground obstacles. For this we use two artificial neural networks in parallel whose retraining is automatically triggered if major changes in the target behaviour pattern are detected. Simulation results for both systems are presented.
Neural Kinematic Networks for Unsupervised Motion Retargetting

We propose a recurrent neural network architecture with a Forward Kinematics layer and cycle consistency based adversarial training objective for unsupervised motion retargetting. Our network captures the high-level properties of an input motion by the forward kinematics layer, and adapts them to a target character with different skeleton bone lengths (e.g., shorter, longer arms etc.). Collecting paired motion training sequences from different characters is expensive. Instead, our network utilizes cycle consistency to learn to solve the Inverse Kinematics problem in an unsupervised manner. Our method works online, i.e., it adapts the motion sequence on-the-fly as new frames are received. In our experiments, we use the Mixamo animation data1 to test our method for a variety of motions and characters and achieve state-of-the-art results. We also demonstrate motion retargetting from monocular human videos to 3D characters using an off-the-shelf 3D pose estimator.
Tanzer group IIB constricted ear repair with helical advancement and superior auricular artery chondrocutaneous flap.

Constricted ear deformity was first described by Tanzer and classified it into 3 groups according to the degree of constriction. The group IIB deformity involves the helix, scapha, and antihelical fold. The height of the ear is sharply reduced, and the soft tissue envelope is not sufficient to close the cartilage framework after expansion and reshaping.This study describes expanding the cartilage and increasing the height by advancing the helical root superiorly and repairing the skin-cartilage defect with a superior auricular artery chondrocutaneous flap in Tanzer group IIB constricted ear deformity.Six ears of 6 patients were treated with this technique during the past 3 years. All patients were satisfied with the appearance of their corrected ears, and the increase in height was maintained through the follow-up period.The described technique does not have the disadvantages and possible complications of harvesting a costal cartilage graft. Moving and fixing the root of helix to a more superior position provide the auricle with additional length. The superior auricular artery chondrocutaneous flap not only provides adequate soft tissue for primary closure of the anterior portion of the auricle but also aids in repairing the cartilage defect resulting from the superior advancement of the helix as well.
Apply blockchain technology to electric vehicle battery refueling

Battery swapping is a solution of electric vehicle (EV) battery refueling. For EV owners, the battery information and transaction’s correctness, openness, traceability and immutability is difficult to get guarantee in traditional centralized system. The trust lacking between EV owners and swapping station is caused, and becomes a big challenge to EV’s rapid development. An objective mechanism based on decentralized blockchain system is proposed to manage battery swapping and solve the trust lacking issue. With this solution, both battery’s life-cycle information and all operations histories are permanently saved in blockchain network. All key logics are driven by smart contracts, the battery price calculation and the digital currency exchange between EV owners and station are realized by smart contracts automatically and accurately. A primary prototype based on Ethereum is analyzed and implemented to illustrate the feasibility of managing battery swapping and refueling based on blockchain system to solve the trust lacking issue.