nils-herrmann/clean_scirepeval huggingface dataset:
Generic Methods for Optimization-Based Modeling

“Energy” models for continuous domains can be applied to many problems, but often suffer from high computational expense in training, due to the need to repeatedly minimize the energy function to high accuracy. This paper considers a modified setting, where the model is trained in terms of results after optimization is truncated to a fixed number of iterations. We derive “backpropagating” versions of gradient descent, heavy-ball and LBFGS. These are simple to use, as they require as input only routines to compute the gradient of the energy with respect to the domain and parameters. Experimental results on denoising and image labeling problems show that learning with truncated optimization greatly reduces computational expense compared to “full” fitting.
Hand-Eye Calibration

Whenever a sensor is mounted on a robot hand it is important to know the relation ship between the sensor and the hand The problem of determining this relationship is referred to as the hand eye calibration problem Hand eye calibration is important in at least two types of tasks i map sensor centered measurements into the robot workspace frame and ii allow the robot to precisely move the sensor In the past some solutions were proposed in the particular case of the sensor being a TV camera With almost no exception all existing solutions attempt to solve a homogeneous matrix equation of the form AX XB This paper has the following main contributions First we show that there are two possible formulations of the hand eye calibration problem One formulation is the classical one that we just mentioned A second formulation takes the form of the following homogeneous matrix equation MY M Y B The advantage of the latter formulation is that the extrinsic and intrinsic parameters of the camera need not be made explicit Indeed this formulation directly uses the perspective matrices M and M associated with positions of the camera with respect to the calibration frame Moreover this formulation together with the classical one cover a wider range of camera based sensors to be calibrated with respect to the robot hand single scan line cameras stereo heads range nders etc Second we develop a common mathematical framework to solve for the hand eye calibration problem using either of the two formulations We represent rotation by a unit quaternion We present two methods i a closed form solution for solving for rotation using unit quaternions and then solving for translation and ii a non linear technique for simultaneously solving for rotation and translation Third we perform a stability analysis both for our two methods and for the classical linear method developed by Tsai Lenz TL This analysis allows the comparison of the three methods In the light of this comparison the non linear optimization method that solves for rotation and translation simultaneously seems to be the most robust one with respect to noise and to measurement errors This work has been supported by the Esprit programme through the SECOND project Esprit BRA No
Novel Isoflux Antenna Alternative for LEO Satellites Downlink

Antennas with a shaped pattern are attractive for Low Earth Orbiting (LEO) Satellites. The use of a strong shaping assists to maintain a nearly constant power density level on the ground during the passage of the satellite over the groundstation. A lightweight but very solid antenna has been developed by RYMSA (Spain) to provide such characteristics. The antenna exploits a balanced combination of a biconical horn antenna and a hybrid mode feed and its performances are a result of dedicated theoretical and experimental investigations. The antenna configuration as such is of interest for various applications. It has been selected as data downlink antenna for the polar orbiting satellites of the METOP program.
An Integrated Laser Radar Receiver Channel Utilizing a Time-Domain Walk Error Compensation Scheme

An integrated receiver channel for a pulsed time-of-flight (TOF) laser rangefinder has been designed and fabricated in a 0.35-μm SiGe BiCMOS process. The receiver channel generates a timing mark for the TDC by means of a leading-edge timing discriminator that detects the crossover of the received pulse with respect to a set reference level. The walk error generated by the amplitude variation is compensated in the time domain on the basis of the measured dependence of the walk on the length of the received pulse. The measurement accuracy is ±15 ps with compensation within a dynamic range of 1:100000, and the single-shot precision and power consumption are 120 ps for a minimum detectable signal of ~1 μA and 115 mW, respectively.
Systematic evaluation of convolution neural network advances on the Imagenet

The paper systematically studies the impact of a range of recent advances in CNN architectures and learning methods on the object categorization (ILSVRC) problem. The evalution tests the influence of the following choices of the architecture: non-linearity (ReLU, ELU, maxout, compatability with batch normalization), pooling variants (stochastic, max, average, mixed), network width, classifier design (convolutional, fully-connected, SPP), image pre-processing, and of learning parameters: learning rate, batch size, cleanliness of the data, etc. The performance gains of the proposed modifications are first tested individually and then in combination. The sum of individual gains is bigger than the observed improvement when all modifications are introduced, but the ”deficit” is small suggesting independence of their benefits. We show that the use of 128x128 pixel images is sufficient to make qualitative conclusions about optimal network structure that hold for the full size Caffe and VGG nets. The results are obtained an order of magnitude faster than with the standard 224 pixel images.
How effective is the IEEE 802.11 RTS/CTS handshake in ad hoc networks

IEEE 802.11 MAC mainly relies on two techniques to combat interference: physical carrier sensing and RTS/CTS handshake (also known as “virtual carrier sensing”). Ideally, the RTS/CTS handshake can eliminate most interference. However, the effectiveness of RTS/CTS handshake is based on the assumption that hidden nodes are within transmission range of receivers. In this paper, we prove using analytic models that in ad hoc networks, such an assumption cannot hold due to the fact that power needed for interrupting a packet reception is much lower than that of delivering a packet successfully. Thus, the “virtual carrier sensing” implemented by RTS/CTS handshake cannot prevent all interference as we expect in theory. Physical carrier sensing can complement this in some degree. However, since interference happens at receivers, while physical carrier sensing is detecting transmitters (the same problem causing the hidden terminal situation), physical carrier sensing cannot help much, unless a very large carrier sensing range is adopted, which is limited by the antenna sensitivity. In this paper, we investigate how effective is the RTS/CTS handshake in terms of reducing interference. We show that in some situations, the interference range is much larger than transmission range, where RTS/CTS cannot function well. Then, a simple MAC layer scheme is proposed to solve this problem. Simulation results verify that our scheme can help IEEE 802.11 resolve most interference caused by large interference range.
Deep Convolutional Neural Network for 6-DOF Image Localization

Wee present an accurate and robust method for six degree of freedom image localization. There are two key-points of our method, 1). automatic immense photo synthesis and labeling from point cloud model and, 2). pose estimation with deep convolutional neural networks (ConvNets) regression. Our model can directly regresses 6-DOF camera poses from images, accurately describing where and how it was captured. We achieved an accuracy within 1 meters and 1 degree on our outdoor dataset, which covers about 20, 000m on our school campus. Unlike previous point cloud registration solutions, our model supports low resolution images (i.e. 224×224 in our settings), and is tiny in size when finished training. Moreover, in pose estimation, our model uses O(1) time & space complexity as trainset grows. We will show the importance to localization using hundreds of thousands of generated and self-labeled ”photos” came from a short video. We will show our model’s robustness despite of illumination and seasonal variances, which usually fails methods that leverage image feature descriptors like SIFT. Furthermore, we will show the ability of transfer our model trained on one scene to another, and the gains in accuracy and efficiency.
Anti-jamming performance of cognitive radio networks under multiple uncoordinated jammers in fading environment

In this paper, we study both the jamming capability of the cognitive-radio-based jammers and the anti-jamming capability of the cognitive radio networks (CRN), by considering multiple uncooperative jammers and independent Rayleigh flat-fading propagations. A Markov model of CRN transmission is set up for the cross-layer analysis of the anti-jamming performance. The transitional probabilities are derived analytically by considering a smart jamming attack strategy. Average throughput expression is obtained and verified by simulations. The results indicate that CRN communications can be extremely susceptible to smart jamming attacks targeting the CRN spectrum sensing and channel switching procedures.
Universal Stanford dependencies: A cross-linguistic typology

Revisiting the now de facto standard Stanford dependency representation, we propose an improved taxonomy to capture grammatical relations across languages, including morphologically rich ones. We suggest a two-layered taxonomy: a set of broadly attested universal grammatical relations, to which language-specific relations can be added. We emphasize the lexicalist stance of the Stanford Dependencies, which leads to a particular, partially new treatment of compounding, prepositions, and morphology. We show how existing dependency schemes for several languages map onto the universal taxonomy proposed here and close with consideration of practical implications of dependency representation choices for NLP applications, in particular parsing.
REFLECTIONS ON FREQUENCY EFFECTS IN LANGUAGE PROCESSING

This response addresses the following points raised in the commentaries: (a) complementary learning mechanisms, the distinction between explicit and implicit memory, and the neuroscience of “noticing”; (b) what must and what need not be noticed for learning; (c) when frequency fails to drive learning, which addresses factors such as failing to notice cues, perseveration, transfer from L1, developmental readiness, thinking too hard, pedagogical input, and practicing; (d) attention and form-focused instruction; (e) conscious and unconscious knowledge of frequency; (f) sequences of acquisition—from formula, through low-scope pattern, to construction; (g) the Fundamental Difference hypothesis; (h) the blind faith of categorical grammar; (i) Labovian variationist perspectives; (j) parsimony and theory testing; (k) universals and predispositions; and (l) wanna-contractions. It concludes by emphasizing that language acquisition is a process of dynamic emergence and that learners’ language is a product of their history of usage in communicative interaction.
Scene Recognition with CNNs: Objects, Scales and Dataset Bias

Since scenes are composed in part of objects, accurate recognition of scenes requires knowledge about both scenes and objects. In this paper we address two related problems: 1) scale induced dataset bias in multi-scale convolutional neural network (CNN) architectures, and 2) how to combine effectively scene-centric and object-centric knowledge (i.e. Places and ImageNet) in CNNs. An earlier attempt, Hybrid-CNN[23], showed that incorporating ImageNet did not help much. Here we propose an alternative method taking the scale into account, resulting in significant recognition gains. By analyzing the response of ImageNet-CNNs and Places-CNNs at different scales we find that both operate in different scale ranges, so using the same network for all the scales induces dataset bias resulting in limited performance. Thus, adapting the feature extractor to each particular scale (i.e. scale-specific CNNs) is crucial to improve recognition, since the objects in the scenes have their specific range of scales. Experimental results show that the recognition accuracy highly depends on the scale, and that simple yet carefully chosen multi-scale combinations of ImageNet-CNNs and Places-CNNs, can push the stateof-the-art recognition accuracy in SUN397 up to 66.26% (and even 70.17% with deeper architectures, comparable to human performance).
Joint Optimization of Resource Provisioning in Cloud Computing

Cloud computing exploits virtualization to provision resources efficiently. Increasingly, Virtual Machines (VMs) have high bandwidth requirements; however, previous research does not fully address the challenge of both VM and bandwidth provisioning. To efficiently provision resources, a joint approach that combines VMs and bandwidth allocation is required. Furthermore, in practice, demand is uncertain. Service providers allow the reservation of resources. However, due to the dangers of over- and under-provisioning, we employ stochastic programming to account for this risk. To improve the efficiency of the stochastic optimization, we reduce the problem space with a scenario tree reduction algorithm, that significantly increases tractability, whilst remaining a good heuristic. Further we perform a sensitivity analysis that finds the tolerance of our solution to parameter changes. Based on historical demand data, we use a deterministic equivalent formulation to find that our solution is optimal and responds well to changes in parameter values. We also show that sensitivity analysis of prices can be useful for both users and providers in maximizing cost efficiency.
Calibrated recommendations

When a user has watched, say, 70 romance movies and 30 action movies, then it is reasonable to expect the personalized list of recommended movies to be comprised of about 70% romance and 30% action movies as well. This important property is known as calibration, and recently received renewed attention in the context of fairness in machine learning. In the recommended list of items, calibration ensures that the various (past) areas of interest of a user are reflected with their corresponding proportions. Calibration is especially important in light of the fact that recommender systems optimized toward accuracy (e.g., ranking metrics) in the usual offline-setting can easily lead to recommendations where the lesser interests of a user get crowded out by the user's main interests-which we show empirically as well as in thought-experiments. This can be prevented by calibrated recommendations. To this end, we outline metrics for quantifying the degree of calibration, as well as a simple yet effective re-ranking algorithm for post-processing the output of recommender systems.
A Leaf Vein Extraction Method Based On Snakes Technique

Leaf vein extraction is a key step of modeling plant organs and living plant recognition. An efficient leaf vein extraction method is proposed in this paper by combining snakes technique with cellular neural networks (CNN). The active contours technique based on CNN provide high flexibility and control for the contour dynamics of the snakes. This approach has the advantage of applying a priori knowledge, puts similar characteristics from both the implicit and parametric models, to improve the precise and robustness of the segmentation. The experimental results have also shown that the proposed method can obtain satisfactory results of leaf segmentation (to extract leaf veins and outlines), for the subsequent modeling leaf and leaf recognition
An integrated machine learning approach to stroke prediction

Stroke is the third leading cause of death and the principal cause of serious long-term disability in the United States. Accurate prediction of stroke is highly valuable for early intervention and treatment. In this study, we compare the Cox proportional hazards model with a machine learning approach for stroke prediction on the Cardiovascular Health Study (CHS) dataset. Specifically, we consider the common problems of data imputation, feature selection, and prediction in medical datasets. We propose a novel automatic feature selection algorithm that selects robust features based on our proposed heuristic: conservative mean. Combined with Support Vector Machines (SVMs), our proposed feature selection algorithm achieves a greater area under the ROC curve (AUC) as compared to the Cox proportional hazards model and L1 regularized Cox feature selection algorithm. Furthermore, we present a margin-based censored regression algorithm that combines the concept of margin-based classifiers with censored regression to achieve a better concordance index than the Cox model. Overall, our approach outperforms the current state-of-the-art in both metrics of AUC and concordance index. In addition, our work has also identified potential risk factors that have not been discovered by traditional approaches. Our method can be applied to clinical prediction of other diseases, where missing data are common and risk factors are not well understood.
Adaptive control of systems in cascade with saturation

Some systems may be approximated as block cascades structures where each block of the cascade can be approximately feedback linearized. In such systems, the states of the lower subsystem blocks affect the dynamics of the upper subsystems. In generating an inverse for a given block, a subset of its lower subsystem states can be treated as virtual actuators in addition to actual direct actuation that may be available. The desired virtual actuator signal arising from the upper subsystem's inverse now appears as a command to the lower subsystem. This paper introduces an adaptive element that is capable of canceling modeling errors arising due to feedback linearization. It also introduces reference models that include a pseudocontrol hedging signal which protects the adaptive element from lower subsystem's dynamics.
A Survey of Augmented Reality

This paper surveys the field of augmented reality (AR), in which 3D virtual objects are integrated into a 3D real environment in real time. It describes the medical, manufacturing, visualization, path planning, entertainment, and military applications that have been explored. This paper describes the characteristics of augmented reality systems, including a detailed discussion of the tradeoffs between optical and video blending approaches. Registration and sensing errors are two of the biggest problems in building effective augmented reality systems, so this paper summarizes current efforts to overcome these problems. Future directions and areas requiring further research are discussed. This survey provides a starting point for anyone interested in researching or using augmented reality.
Models of the self: self-construals and gender.

The authors first describe individual differences in the structure of the self. In the independent self-construal, representations of others are separate from the self. In the interdependent self-construal, others are considered part of the self (H. Markus & S. Kitayama, 1991). In general, men in the United States are thought to construct and maintain an independent self-construal, whereas women are thought to construct and maintain an interdependent self-construal. The authors review the psychological literature to demonstrate that many gender differences in cognition, motivation, emotion, and social behavior may be explained in terms of men's and women's different self-construals. Recognition of the interdependent self-construal as a possible alternative conception of the self may stimulate new investigations into the ways the self influences a person's thinking, feeling, and behaving.
Fast Influence Maximization in Dynamic Graphs: A Local Updating Approach

We propose a generalized framework for influence maximization in large-scale, time evolving networks. Many real-life influence graphs such as social networks, telephone networks, and IP traffic data exhibit dynamic characteristics, e.g., the underlying structure and communication patterns evolve with time. Correspondingly, we develop a dynamic framework for the influence maximization problem, where we perform effective local updates to quickly adjust the top-k influencers, as the structure and communication patterns in the network change. We design a novel NFamily method (N=1, 2, 3, . . .) based on the maximum influence arborescence (MIA) propagation model with approximation guarantee of (1− 1/e). We then develop heuristic algorithms by extending theN-Family approach to other information propagation models (e.g., independent cascade, linear threshold) and influence maximization algorithms (e.g., CELF, reverse reachable sketch). Based on a detailed empirical analysis over several real-world, dynamic, and large-scale networks, we find that our proposed solution, NFamily improves the updating time of the top-k influencers by 1 ∼ 2 orders ofmagnitude, compared to state-of-the-art algorithms, while ensuring similar memory usage and influence spreads.  Vijaya Krishna Yalavarthi and Arijit Khan.  A Local Updating Approach.
Research Paper: Automatic Detection of Acute Bacterial Pneumonia from Chest X-ray Reports

To evaluate the performance of a natural language processing system in extracting pneumonia-related concepts from chest x-ray reports.



Four physicians, three lay persons, a natural language processing system, and two keyword searches (designated AAKS and KS) detected the presence or absence of three pneumonia-related concepts and inferred the presence or absence of acute bacterial pneumonia from 292 chest x-ray reports. Gold standard: Majority vote of three independent physicians. Reliability of the gold standard was measured.



Recall, precision, specificity, and agreement (using Finn's R: statistic) with respect to the gold standard. Differences between the physicians and the other subjects were tested using the McNemar test for each pneumonia concept and for the disease inference of acute bacterial pneumonia.



Reliability of the reference standard ranged from 0.86 to 0.96. Recall, precision, specificity, and agreement (Finn R:) for the inference on acute bacterial pneumonia were, respectively, 0.94, 0.87, 0.91, and 0.84 for physicians; 0.95, 0.78, 0.85, and 0.75 for natural language processing system; 0.46, 0.89, 0.95, and 0.54 for lay persons; 0.79, 0.63, 0.71, and 0.49 for AAKS; and 0.87, 0.70, 0.77, and 0.62 for KS. The McNemar pairwise comparisons showed differences between one physician and the natural language processing system for the infiltrate concept and between another physician and the natural language processing system for the inference on acute bacterial pneumonia. The comparisons also showed that most physicians were significantly different from the other subjects in all pneumonia concepts and the disease inference.



In extracting pneumonia related concepts from chest x-ray reports, the performance of the natural language processing system was similar to that of physicians and better than that of lay persons and keyword searches. The encoded pneumonia information has the potential to support several pneumonia-related applications used in our institution. The applications include a decision support system called the antibiotic assistant, a computerized clinical protocol for pneumonia, and a quality assurance application in the radiology department.