nils-herrmann/clean_scirepeval huggingface dataset:
DeLS-3D: Deep Localization and Segmentation with a 3D Semantic Map

For applications such as augmented reality, autonomous driving, self-localization/camera pose estimation and scene parsing are crucial technologies. In this paper, we propose a unified framework to tackle these two problems simultaneously. The uniqueness of our design is a sensor fusion scheme which integrates camera videos, motion sensors (GPS/IMU), and a 3D semantic map in order to achieve robustness and efficiency of the system. Specifically, we first have an initial coarse camera pose obtained from consumer-grade GPS/IMU, based on which a label map can be rendered from the 3D semantic map. Then, the rendered label map and the RGB image are jointly fed into a pose CNN, yielding a corrected camera pose. In addition, to incorporate temporal information, a multi-layer recurrent neural network (RNN) is further deployed improve the pose accuracy. Finally, based on the pose from RNN, we render a new label map, which is fed together with the RGB image into a segment CNN which produces perpixel semantic label. In order to validate our approach, we build a dataset with registered 3D point clouds and video camera images. Both the point clouds and the images are semantically-labeled. Each video frame has ground truth pose from highly accurate motion sensors. We show that practically, pose estimation solely relying on images like PoseNet  may fail due to street view confusion, and it is important to fuse multiple sensors. Finally, various ablation studies are performed, which demonstrate the effectiveness of the proposed system. In particular, we show that scene parsing and pose estimation are mutually beneficial to achieve a more robust and accurate system.
Deep Domain Adaptation in Action Space

In the general settings of supervised learning, human action recognition has been a widely studied topic. The classifiers learned in this setting assume that the training and test data have been sampled from the same underlying probability distribution. However, in most of the practical scenarios, this assumption is not true, resulting in a suboptimal performance of the classifiers. This problem, referred to as Domain Shift, has been extensively studied, but mostly for image/object classification task. In this paper, we investigate the problem of Domain Shift in action videos, an area that has remained under-explored, and propose two new approaches named Action Modeling on Latent Subspace (AMLS) and Deep Adversarial Action Adaptation (DAAA). In the AMLS approach, the action videos in the target domain are modeled as a sequence of points on a latent subspace and adaptive kernels are successively learned between the source domain point and the sequence of target domain points on the manifold. In the DAAA approach, an end-to-end adversarial learning framework is proposed to align the two domains. The action adaptation experiments were conducted using various combinations of multi-domain action datasets, including six common classes of Olympic Sports and UCF50 datasets and all classes of KTH, MSR and our own SonyCam datasets. In this paper, we have achieved consistent improvements over chosen baselines and obtained some state-of-the-art results for the datasets.
Affordable and Energy-Efficient Cloud Computing Clusters: The Bolzano Raspberry Pi Cloud Cluster Experiment

We present our ongoing work building a Raspberry Pi cluster consisting of 300 nodes. The unique characteristics of this single board computer pose several challenges, but also offer a number of interesting opportunities. On the one hand, a single Raspberry Pi can be purchased cheaply and has a low power consumption, which makes it possible to create an affordable and energy-efficient cluster. On the other hand, it lacks in computing power, which makes it difficult to run computationally intensive software on it. Nevertheless, by combining a large number of Raspberries into a cluster, this drawback can be (partially) offset. Here we report on the first important steps of creating our cluster: how to set up and configure the hardware and the system software, and how to monitor and maintain the system. We also discuss potential use cases for our cluster, the two most important being an inexpensive and green test bed for cloud computing research and a robust and mobile data center for operating in adverse environments.
' s personal copy Comments and Controversies Ten ironic rules for non-statistical reviewers

In most cases authors are permitted to post their version of the article (e.g. in Word or Tex form) to their personal website or institutional repository. Authors requiring further information regarding Elsevier's archiving and manuscript policies are encouraged to visit: a b s t r a c t a r t i c l e i n f o Keywords: Statistical testing Sample-size Effect size Power Classical inference As an expert reviewer, it is sometimes necessary to ensure a paper is rejected. This can sometimes be achieved by highlighting improper statistical practice. This technical note provides guidance on how to critique the statistical analysis of neuroimaging studies to maximise the chance that the paper will be declined. We will review a series of critiques that can be applied universally to any neuroimaging paper and consider responses to potential rebuttals that reviewers might encounter from authors or editors.  This technical note is written for reviewers who may not have sufficient statistical expertise to provide an informed critique during the peer-reviewed process, but would like to recommend rejection on the basis of inappropriate or invalid statistical analysis. This guidance follows the 10 simple rules format and hopes to provide useful tips and criticisms for reviewers who find themselves in this difficult position. These rules are presented for reviewers in an ironic way 1 that makes it easier (and hopefully more entertaining) to discuss the issues from the point of view of both the reviewer and author — and to caricature both sides of the arguments. Some key issues are presented more formally in (non-ironic) appendices. There is a perceived need to reject peer-reviewed papers with the advent of open access publishing and the large number of journals available to authors. Clearly, there may be idiosyncratic reasons to block a paper – to ensure your precedence in the literature, personal rivalry etc. – however, we will assume that there is an imperative to reject papers for the good of the community: handling editors are often happy to receive recommendations to decline a paper. This is because they are placed under pressure to maintain a high rejection rate. This pressure is usually exerted by the editorial board (and publishers) and enforced by circulating quantitative information about their rejection rates (i.e., naming and shaming lenient editors). All journals want to maximise rejection rates, because this increases the quality of submissions, increases their impact factor …
Curvature-controlled curve editing using piecewise clothoid curves

Two-dimensional curves are conventionally designed using splines or Bézier curves. Although formally they are C or higher, the variation of the curvature of (piecewise) polynomial curves is difficult to control; in some cases it is practically impossible to obtain the desired curvature. As an alternative we propose piecewise clothoid curves (PCCs). We show that from the design point of view they have many advantages: control points are interpolated, curvature extrema lie in the control points, and adding control points does not change the curve. We present a fast localized clothoid interpolation algorithm that can also be used for curvature smoothing, for curve fitting, for curvature blending, and even for directly editing the curvature. We give a physical interpretation of variational curvature minimization, from which we derive our scheme. Finally, we demonstrate the achievable quality with a range of examples. & 2013 Elsevier Ltd. All rights reserved.
Adaptive edge-based stereo block matching algorithm for a mobile Graphics Processing Unit

Nowadays most of the applications of the stereo vision are related to the mobile devices, especially in the field of robotics. It is crucial to develop efficient and robust algorithms that would allow real time operation in a wide range of environments. This paper presents an efficient adaptive algorithm of stereo matching that was applied and optimized for the mobile Graphics Processing Unit. It is a well known problem that most of the stereo vision algorithms are based on the dense stereo matching methods that in most of the cases are the main factor for a demanding computation cost. The presented method introduces a novel approach to the stereo matching problem by adaptively combining the cost function that can be computed efficiently for small matching window and sparse accumulated windows similar to those applied in convolutional neural networks. Adaptability of this method is based on detecting edges in the processed images. Such a solution allows obtaining precise subpixel results on highly textured regions of the image as well as to obtain stable results on weakly matchable texture-less regions while sustaining high efficiency and not causing problems related to the memory bandwidth bottleneck.
Personalizing Information Retrieval Using Search Behaviors and Time Constraints

Studies have examined how time constraints influence search behaviors; however, no effort has been spent on how time constraints may help predict document usefulness for personalization purposes. This study aims to fill this gap by researching the relationships between time constraints, search behaviors, and usefulness judgments. A controlled lab experiment was conducted with 40 participants searching for four tasks of two types (fact finding and information understanding), under two time conditions (with or without time constraints). Results show that time constraints and usefulness had interaction effects on first dwell time; while usefulness had positive relationship with total dwell time. Results indicate that knowing time constraints helps predict document usefulness from dwell time. The findings provide implications on personalization in information search.
Anomalous video event detection using spatiotemporal context

Compared to other anomalous video event detection approaches that analyze object trajectories only, we propose a context-aware method to detect anomalies. By tracking all moving objects in the video, three different levels of spatiotemporal contexts are considered, i.e., point anomaly of a video object, sequential anomaly of an object trajectory, and co-occurrence anomaly of multiple video objects. A hierarchical data mining approach is proposed. At each level, frequency based analysis is performed to automatically discover regular rules of normal events. Events deviating from these rules are identified as anomalies. The proposed method is computationally efficient and can infer complex rules. Experiments on real traffic video validate that the detected video anomalies are hazardous or illegal according to traffic ∗Corresponding author (phone: 224-392-2622, fax: 847-491-4455) Email addresses: fanjiang2008@u.northwestern.edu (Fan Jiang), jsyuan@ntu.edu.sg (Junsong Yuan), s-tsaftaris@northwestern.edu (Sotirios A. Tsaftaris), aggk@eecs.northwestern.edu (Aggelos K. Katsaggelos)  2010 regulations.
A temporal pattern mining approach for classifying electronic health record data

We study the problem of learning classification models from complex multivariate temporal data encountered in electronic health record systems. The challenge is to define a good set of features that are able to represent well the temporal aspect of the data. Our method relies on temporal abstractions and temporal pattern mining to extract the classification features. Temporal pattern mining usually returns a large number of temporal patterns, most of which may be irrelevant to the classification task. To address this problem, we present the Minimal Predictive Temporal Patterns framework to generate a small set of predictive and nonspurious patterns. We apply our approach to the real-world clinical task of predicting patients who are at risk of developing heparin-induced thrombocytopenia. The results demonstrate the benefit of our approach in efficiently learning accurate classifiers, which is a key step for developing intelligent clinical monitoring systems.
Self-Enabled “Error-Free” Switching Circuit for Spin Transfer Torque MRAM and Logic

Spin transfer torque (STT) is one of the most promising switching approaches for magnetic tunnel junction (MTJ) nanopillars to build up innovative nonvolatile memory and logic circuits. It presents low critical current (e.g., <; 100 μA at 65 nm), simple switching scheme, and fast-speed; however, it suffers from a number of reliability issues like stochastic switching effects, process voltage temperature (PVT) variations, and erroneous reading etc. The mainstream solution is to enlarge the write pulse duration to reduce error rate, which sacrifices the speed and low power advantages. In this paper, we present a new switching circuit for STT memory and logic, allowing “error-free” as the switching operation becomes deterministic benefiting from the self-enabled mechanism. The switching power efficiency can be also improved thanks to a shorter switching duration. By using an accuracy spice model of STT-MTJ and CMOS 65 nm design-kit, mixed simulations have been performed to demonstrate its high-reliable write/read operations and evaluate its potential area, power, and speed performance.
AoA and ToA Accuracy for Antenna Arrays in Dense Multipath Channels

The accuracy that can be achieved in time of arrival (ToA) estimation strongly depends on the utilized signal bandwidth. In an indoor environment multipath propagation usually causes a degradation of the achievable accuracy due to the overlapping signals. A similar effect can be observed for the angle of arrival (AoA) estimation using arrays. This paper derives a closed-form equation for the Cramer-Rae lower bound (CRLB) of the achievable AoA and the ToA error variances, considering the presence of dense multipath. The Fisher information expressions for both parameters allows an evaluation of the influence of channel parameters and system parameters such as the array geometry. Our results demonstrate that the AoA estimation accuracy is strongly related to the signal bandwidth, due to the multipath influence. The theoretical results are evaluated with experimental data.
Performance comparison of TCP algorithms for D2D communication in LTE-A

Recent massive growth in the connected devices as well as the traffic on wireless cellular networks is posing serious issues in achieving higher data rates. Device-to-device (D2D) communication has received a great attention to meet such massive demands. D2D communications allow two devices to communicate directly without going through the base station. Therefore, D2D communications have potential contribution in offloading the traffic from the core network and improving the overall throughput. In this paper, we evaluate the performance of TCP flows in D2D communications in LTE-A networks using the system-level simulator called SimuLTE. This paper presents the results on the comparison of the throughputs obtained with various TCP implementations in D2D communications in LTE-A networks. It also provides explanation to the variations in the throughputs resulting from changing the receive window size and the way the mode switch is handled in different TCP implementations.
A Unified Theory of Underreaction , Momentum Trading , and Overreaction in Asset Markets

We model a market populated by two groups of boundedly rational agents: “newswatchers” and “momentum traders.” Each newswatcher observes some private information, but fails to extract other newswatchers’ information from prices. If information diffuses gradually across the population, prices underreact in the short run. The underreaction means that the momentum traders can profit by trendchasing. However, if they can only implement simple ~i.e., univariate! strategies, their attempts at arbitrage must inevitably lead to overreaction at long horizons. In addition to providing a unified account of underand overreactions, the model generates several other distinctive implications. OVER THE LAST SEVERAL YEARS, a large volume of empirical work has documented a variety of ways in which asset returns can be predicted based on publicly available information. Although different studies have used a host of different predictive variables, many of the results can be thought of as belonging to one of two broad categories of phenomena. On the one hand, returns appear to exhibit continuation, or momentum, in the short to medium run. On the other hand, there is also a tendency toward reversals, or fundamental reversion, in the long run.1 It is becoming increasingly clear that traditional asset-pricing models— such as the capital asset pricing model ~CAPM! of Sharpe ~1964! and Lintner ~1965!, Ross’s ~1976! arbitrage pricing theory ~APT!, or Merton’s ~1973! intertemporal capital asset pricing model ~ICAPM!—have a hard time explaining the growing set of stylized facts. In the context of these models, all of the predictable patterns in asset returns, at both short and long horizons, must ultimately be traced to differences in loadings on economically meaningful risk factors. And there is little affirmative evidence at this point to suggest that this can be done.  Stanford Business School and MIT Sloan School of Management and NBER. This research is supported by the National Science Foundation and the Finance Research Center at MIT. We are grateful to Denis Gromb, René Stulz, an anonymous referee, and seminar participants at MIT, Michigan, Wharton, Duke, UCLA, Berkeley, Stanford, and Illinois for helpful comments and suggestions. Thanks also to Melissa Cunniffe for help in preparing the manuscript. 1 We discuss this empirical work in detail and provide references in Section I below. THE JOURNAL OF FINANCE • VOL. LIV, NO. 6 • DECEMBER 1999
Feudal Multi-Agent Hierarchies for Cooperative Reinforcement Learning

We investigate how reinforcement learning agents can learn to cooperate. Drawing inspiration from human societies, in which successful coordination of many individuals is often facilitated by hierarchical organisation, we introduce Feudal Multiagent Hierarchies (FMH). In this framework, a ‘manager’ agent, which is tasked with maximising the environmentally-determined reward function, learns to communicate subgoals to multiple, simultaneously-operating, ‘worker’ agents. Workers, which are rewarded for achieving managerial subgoals, take concurrent actions in the world. We outline the structure of FMH and demonstrate its potential for decentralised learning and control. We find that, given an adequate set of subgoals from which to choose, FMH performs, and particularly scales, substantially better than cooperative approaches that use a shared reward function.
An automatic mitosis detection method for breast cancer histopathology slide images based on objective and pixel-wise textural features classification

Study of histopathological cancerous tissue is one of the most reliable ways to grade various types of cancers. The result of grading helps the physicians to diagnose and prescribe suitable prognosis. The focus of this paper is on a CAD for automatic analysis of breast cancer histopathological Images to count mitosis as an important criteria for the breast cancer grading. To achieve this aim, sets of specific digital histopathological data are used which are captured by particular microscopic scanners named as Aperio XT and Hamamatsu NanoZoomer scanners. In the proposed method, these acquired images are employed and processed based on digital image processing approaches like 2-D anisotropic diffusion as a pre-process and morphological process. For extraction of pixel-wise features from predetermined mitotic regions, an statistical approach based on color information such as maximum likelihood estimation is employed. To prevent misclassification of mitosis and non-mitosis objects, an object-wise completed local binary pattern (CLBP) is proposed to extract texture features robust against rotation and color-level changes, and finally support vector machine (SVM) is used to classify the extracted feature vectors. Having computed the evaluation criteria, our proposed method performs better f-measure (70.94% for Aperio XT scanner images and 70.11% for Hamamatsu images) among the methods proposed by other participants at ICPR2012 Mitosis detection in breast cancer histopathological images.
Experimental evaluation of user interfaces for visual indoor navigation

Mobile location recognition by capturing images of the environment (visual localization) is a promising technique for indoor navigation in arbitrary surroundings. However, it has barely been investigated so far how the user interface (UI) can cope with the challenges of the vision-based localization technique, such as varying quality of the query images. We implemented a novel UI for visual localization, consisting of Virtual Reality (VR) and Augmented Reality (AR) views that actively communicate and ensure localization accuracy. If necessary, the system encourages the user to point the smartphone at distinctive regions to improve localization quality. We evaluated the UI in a experimental navigation task with a prototype, informed by initial evaluation results using design mockups. We found that VR can contribute to efficient and effective indoor navigation even at unreliable location and orientation accuracy. We discuss identified challenges and share lessons learned as recommendations for future work.
Neural Map: Structured Memory for Deep Reinforcement Learning

A critical component to enabling intelligent reasoning in partially observable environments is memory. Despite this importance, Deep Reinforcement Learning (DRL) agents have so far used relatively simple memory architectures, with the main methods to overcome partial observability being either a temporal convolution over the past k frames or an LSTM layer. More recent work (Oh et al., 2016) has went beyond these architectures by using memory networks which can allow more sophisticated addressing schemes over the past k frames. But even these architectures are unsatisfactory due to the reason that they are limited to only remembering information from the last k frames. In this paper, we develop a memory system with an adaptable write operator that is customized to the sorts of 3D environments that DRL agents typically interact with. This architecture, called the Neural Map, uses a spatially structured 2D memory image to learn to store arbitrary information about the environment over long time lags. We demonstrate empirically that the Neural Map surpasses previous DRL memories on a set of challenging 2D and 3D maze environments and show that it is capable of generalizing to environments that were not seen during training.
Multiwave: Doppler Effect Based Gesture Recognition in Multiple Dimensions

We constructed an acoustic, gesture-based recognition system called Multiwave, which leverages the Doppler Effect to translate multidimensional movements into user interface commands. Our system only requires the use of two speakers and a microphone to be operational. Since these components are already built in to most end user systems, our design makes gesture-based input more accessible to a wider range of end users. By generating a known high frequency tone from multiple speakers and detecting movement using changes in the sound waves, we are able to calculate a Euclidean representation of hand velocity that is then used for more natural gesture recognition and thus, more meaningful interaction mappings. We present the results of a user study of Multiwave to evaluate recognition rates for different gestures and report accuracy rates comparable to or better than the current state of the art. We also report subjective user feedback and some lessons learned from our system that provide additional insight for future applications of multidimensional gesture recognition.