nils-herrmann/clean_scirepeval huggingface dataset:
Extracting normative relationships from business contracts

The normative concepts offer a principled basis for engineering flexible multiagent systems for business and other crossorganizational settings. However, producing suitable specifications is nontrivial: the difficulty is an obstacle to the adoption of multiagent systems in industry. This paper considers normative relationships of six main types, namely, commitments (both practical and dialectical), authorizations, powers, prohibitions, and sanctions. It applies natural language processing and machine learning to extract these relationships from business contracts, establishing that they are realistic and their encoding can assist modelers, thereby lowering a barrier to adoption. A ten-fold cross-validation over more than 800 sentences randomly drawn from a corpus of real-life contracts (and manually labeled) yields promising results for the viability of this approach.
Ternary Neural Networks with Fine-Grained Quantization

We propose a novel fine-grained quantization method for ternarizing pre-trained full precision models, while also constraining activations to 8-bits. Using this method, we demonstrate minimal loss in classification accuracy on state-of-the-art topologies without additional training. This enables a full 8-bit inference pipeline, with best reported accuracy using ternary weights on ImageNet dataset. Further, we also provide an improved theoretical formulation that forms the basis for a higher quality solution with this approach. Our method involves ternarizing the original weight tensor in groups of N weights. Using N = 4, we achieve Top-1 accuracy within 3.7% and 5.8% of the baseline full precision result for Resnet-101 and Resnet-50 respectively, while eliminating 75% of all multiplications. We also study the impact of group size on both performance and accuracy. With a group size of N = 64, we eliminate ≈ 99% of the multiplications; however, this introduces a significant drop in accuracy, which necessitates fine tuning the parameters (retraining) at lower precision. To address this, we re-train Resnet-50 with 8-bit activations and ternary weights, improving the Top-1 accuracy to within 4% of the full precision result with < 30% additional overhead. Our final quantized model can run on a full 8-bit compute pipeline using 2-bit weights and has the potential of up to 16× improvement in performance compared to baseline full-precision models.
Code Mixing: A Challenge for Language Identification in the Language of Social Media

In social media communication, multilingual speakers often switch between languages, and, in such an environment, automatic language identification becomes both a necessary and challenging task. In this paper, we describe our work in progress on the problem of automatic language identification for the language of social media. We describe a new dataset that we are in the process of creating, which contains Facebook posts and comments that exhibit code mixing between Bengali, English and Hindi. We also present some preliminary word-level language identification experiments using this dataset. Different techniques are employed, including a simple unsupervised dictionary-based approach, supervised word-level classification with and without contextual clues, and sequence labelling using Conditional Random Fields. We find that the dictionary-based approach is surpassed by supervised classification and sequence labelling, and that it is important to take contextual clues into consideration.
Image Alignment and Stitching

Stitching multiple images together to create beautiful highresolution panoramas is one of the most popular consumer applications of image registration and blending. In this chapter, I review the motion models (geometric transformations) that underlie panoramic image stitching, discuss direct intensity-based and feature-based registration algorithms, and present global and local alignment techniques needed to establish highaccuracy correspondences between overlapping images. I then discuss various compositing options, including multi-band and gradient-domain blending, as well as techniques for removing blur and ghosted images. The resulting techniques can be used to create high-quality panoramas for static or interactive viewing.
Patch-Based Convolutional Neural Network for Whole Slide Tissue Image Classification

Convolutional Neural Networks (CNN) are state-of-theart models for many image classification tasks. However, to recognize cancer subtypes automatically, training a CNN on gigapixel resolution Whole Slide Tissue Images (WSI) is currently computationally impossible. The differentiation of cancer subtypes is based on cellular-level visual features observed on image patch scale. Therefore, we argue that in this situation, training a patch-level classifier on image patches will perform better than or similar to an image-level classifier. The challenge becomes how to intelligently combine patch-level classification results and model the fact that not all patches will be discriminative. We propose to train a decision fusion model to aggregate patch-level predictions given by patch-level CNNs, which to the best of our knowledge has not been shown before. Furthermore, we formulate a novel Expectation-Maximization (EM) based method that automatically locates discriminative patches robustly by utilizing the spatial relationships of patches. We apply our method to the classification of glioma and non-small-cell lung carcinoma cases into subtypes. The classification accuracy of our method is similar to the inter-observer agreement between pathologists. Although it is impossible to train CNNs on WSIs, we experimentally demonstrate using a comparable non-cancer dataset of smaller images that a patch-based CNN can outperform an image-based CNN.
A Capacitive-Type Novel Six-Axis Force/Torque Sensor for Robotic Applications

This paper presents an innovative design of a six-axis force/torque sensor, which enables ease of manufacturing, cost reduction, and ruggedness in operation. The advantages are realized by simplifying the structure of the sensor and reducing manual labor during the manufacturing process. In order to achieve a simple sensor structure, we propose a capacitive-type force sensing scheme in which the sensing elements are aligned in-plane. By using this method, all the sensing elements can be fabricated on single printed circuit board, and thus, manual tasks, such as bonding and coating the sensing elements, can be eliminated. In order to verify the feasibility of the idea, a prototype six-axis force/torque sensor was manufactured, and its performances were evaluated. The characteristics of the prototype are analyzed in terms of condition number, static response, time domain response, hysteresis, repeatability, and time drift.
Adapting to Artificial Intelligence: Radiologists and Pathologists as Information Specialists.

Artificial intelligence—the mimicking of human cognition by computers—was once a fable in science fiction but is becoming reality in medicine. The combination of big data and artificial intelligence, referred to by some as the fourth industrial revolution,1 will change radiology and pathology along with other medical specialties. Although reports of radiologists and pathologists being replaced by computers seem exaggerated,2 these specialties must plan strategically for a future in which artificial intelligence is part of the health care workforce. Radiologists have always revered machines and technology. In 1960, Lusted predicted “an electronic scannercomputer to examine chest photofluorograms, to separate the clearly normal chest films from the abnormal chest films.”3 Lusted further suggested that “the abnormal chest films would be marked for later study by the radiologists.”3 Lusted’s intuitions were prescient: interpreting radiographs is pattern recognition; computers can recognize patterns and may be helpful because some roentgenographic analyses can be automated. Nearly 60 years after Lusted’s prediction, Enlitic, a technology company in Silicon Valley, inputted images of normal radiographs and radiographs with fractures into a computerized database.4 Using deep learning, a refined version of artificial neural networks, the
Accelerated Gradient Descent Escapes Saddle Points Faster than Gradient Descent

Nesterov’s accelerated gradient descent (AGD), an instance of the general family of “momentum methods,” provably achieves faster convergence rate than gradient descent (GD) in the convex setting. While these methods are widely used in modern nonconvex applications, including training of deep neural networks, whether they are provably superior to GD in the nonconvex setting remains open. This paper studies a simple variant of Nesterov’s AGD, and shows that it escapes saddle points and finds a second-order stationary point in Õ(1/ ) iterations, matching the best known convergence rate, which is faster than the Õ(1/ ) iterations required by GD. To the best of our knowledge, this is the first direct acceleration (single-loop) algorithm that is provably faster than GD in general nonconvex setting—all previous nonconvex accelerated algorithms rely on more complex mechanisms such as nested loops and proximal terms. Our analysis is based on two key ideas: (1) the use of a simple Hamiltonian function, inspired by a continuous-time perspective, which AGD monotonically decreases on each step even for nonconvex functions, and (2) a novel framework called improve or localize, which is useful for tracking the long-term behavior of gradient-based optimization algorithms. We believe that these techniques may deepen our understanding of both acceleration algorithms and nonconvex optimization.
A systematic review of machine learning techniques for software fault prediction

Software fault prediction is the process of developing models that can be used by the software practitioners in the early phases of software development life cycle for detecting faulty constructs such as modules or classes. There are various machine learning techniques used in the past for predicting faults.  In this study we perform a systematic review studies from January 1991 to October 2013 in the literature that use the machine learning techniques for software fault prediction. We assess the performance capability of the machine learning techniques in existing research for software fault prediction. We also compare the performance of the machine learning techniques with the
Norms as a basis for governing sociotechnical systems

We understand a sociotechnical system as a multistakeholder cyber-physical system. We introduce governance as the administration of such a system by the stakeholders themselves. In this regard, governance is a peer-to-peer notion and contrasts with traditional management, which is a top-down hierarchical notion. Traditionally, there is no computational support for governance and it is achieved through out-of-band interactions among system administrators. Not surprisingly, traditional approaches simply do not scale up to large sociotechnical systems.
 We develop an approach for governance based on a computational representation of norms in organizations. Our approach is motivated by the Ocean Observatory Initiative, a thirty-year $400 million project, which supports a variety of resources dealing with monitoring and studying the world's oceans. These resources include autonomous underwater vehicles, ocean gliders, buoys, and other instrumentation as well as more traditional computational resources. Our approach has the benefit of directly reflecting stakeholder needs and assuring stakeholders of the correctness of the resulting governance decisions while yielding adaptive resource allocation in the face of changes in both stakeholder needs and physical circumstances.
A Bayesian method for reducing bias in neural representational similarity analysis

1 In neuroscience, the similarity matrix of neural activity patterns in response to different sensory stimuli or under different cognitive states reflects the structure of neural representational space. Existing methods derive point estimations of neural activity patterns from noisy neural imaging data, and the similarity is calculated from these point estimations. We show that this approach translates structured noise from estimated patterns into spurious bias structure in the resulting similarity matrix, which is especially severe when signal-to-noise ratio is low and experimental conditions cannot be fully randomized in a cognitive task. We propose an alternative Bayesian framework for computing representational similarity in which we treat the covariance structure of neural activity patterns as a hyperparameter in a generative model of the neural data, and directly estimate this covariance structure from imaging data while marginalizing over the unknown activity patterns. Converting the estimated covariance structure into a correlation matrix offers an unbiased estimate of neural representational similarity. Our method can also simultaneously estimate a signal-to-noise map that informs where the learned representational structure is supported more strongly, and the learned covariance matrix can be used as a structured prior to constrain Bayesian estimation of neural activity patterns. 1 Neural pattern similarity as a way to understand neural representations Understanding how patterns of neural activity relate to internal representations of the environment is one of the central themes of both system neuroscience and human neural imaging [20, 5, 7, 15]. One can record neural responses (e.g. by functional magnetic resonance imaging; fMRI) while participants observe sensory stimuli, and in parallel, build different computational models to mimic the brain’s encoding of these stimuli. Neural activity pattern corresponding to each feature of an encoding model can then be estimated from the imaging data. Such activity patterns can be used to decode the content of different brain areas to the encoding process, and to decode perceived stimuli This is not the final version to appear in the proceedings of the 29th NIPS conference (2016). Please cite the most up-to-date reference from NIPS proceedings if possible, or the corresponding journal paper, once published.  It is made available under a The copyright holder for this  (which was not .  first posted online Sep. 7, 2016;
Vision-Based Offline-Online Perception Paradigm for Autonomous Driving

Autonomous driving is a key factor for future mobility. Properly perceiving the environment of the vehicles is essential for a safe driving, which requires computing accurate geometric and semantic information in real-time. In this paper, we challenge state-of-the-art computer vision algorithms for building a perception system for autonomous driving. An inherent drawback in the computation of visual semantics is the trade-off between accuracy and computational cost. We propose to circumvent this problem by following an offline-online strategy. During the offline stage dense 3D semantic maps are created. In the online stage the current driving area is recognized in the maps via a re-localization process, which allows to retrieve the pre-computed accurate semantics and 3D geometry in real-time. Then, detecting the dynamic obstacles we obtain a rich understanding of the current scene. We evaluate quantitatively our proposal in the KITTI dataset and discuss the related open challenges for the computer vision community.
Neural Network-Based Face Detection

We present a neural network-based face detection system. A retinally connected neural network examines small windows of an image, and decides whether each window contains a face. The system arbitrates between multiple networks to improve performance over a single network. We use a bootstrap algorithm for training the networks, which adds false detections into the training set as training progresses. This eliminates the difficult task of manually selecting non-face training examples, which must be chosen to span the entire space of non-face images. Comparisons with other state-of-the-art face detection systems a re presented; our system has better performance in terms of detection and false-positive rates.
A systems architecture for ubiquitous video

Realityflythrough is a telepresence/tele-reality system that works in the dynamic, uncalibrated environments typically associated with ubiquitous computing. By harnessing networked mobile video cameras, it allows a user to remotely and immersively explore a physical space. RealityFlythrough creates the illusion of complete live camera coverage in a physical environment. This paper describes the architecture of RealityFlythrough, and evaluates it along three dimensions: (1) its support of the abstractions for infinite camera coverage, (2) its scalability, and (3) its robustness to changing user requirements.
Image Prediction for Limited-angle Tomography via Deep Learning with Convolutional Neural Network

Limited angle problem is a challenging issue in x-ray computed tomography (CT) field. Iterative reconstruction methods that utilize the additional prior can suppress artifacts and improve image quality, but unfortunately require increased computation time. An interesting way is to restrain the artifacts in the images reconstructed from the practical filtered back projection (FBP) method. Frikel and Quinto have proved that the streak artifacts in FBP results could be characterized. It indicates that the artifacts created by FBP method have specific and similar characteristics in a stationary limited-angle scanning configuration. Based on this understanding, this work aims at developing a method to extract and suppress specific artifacts of FBP reconstructions for limited-angle tomography. A data-driven learning-based method is proposed based on a deep convolutional neural network. An end-to-end mapping between the FBP and artifact-free images is learned and the implicit features involving artifacts will be extracted and suppressed via nonlinear mapping. The qualitative and quantitative evaluations of experimental results indicate that the proposed method show a † Corresponding authors: Liang Li, liliang@tsinghua.edu.cn Bin Yan, ybspace@hotmail.com Contacts for other authors: Hanmung Zhang: z.hanming@hotmail.com Kai Qiao: 15517181502@163.com Linyuan Wang: wanglinyuanwly@163.com Lei Li: leehotline@aliyun.com Guoen Hu: 13838265028@126.com stable and prospective performance on artifacts reduction and detail recovery for limited angle tomography. The presented strategy provides a simple and efficient approach for improving image quality of the reconstruction results from limited projection data.
A Handbook for Building an Approximate Query Engine

There has been much research on various aspects of Approximate Query Processing (AQP), such as different sampling strategies, error estimation mechanisms, and various types of data synopses. However, many subtle challenges arise when building an actual AQP engine that can be deployed and used by real world applications. These subtleties are often ignored (or at least not elaborated) by the theoretical literature and academic prototypes alike. For the first time to the best of our knowledge, in this article, we focus on these subtle challenges that one must address when designing an AQP system. Our intention for this article is to serve as a handbook listing critical design choices that database practitioners must be aware of when building or using an AQP system, not to prescribe a specific solution to each challenge.
Automatically Predicting Peer-Review Helpfulness

Identifying peer-review helpfulness is an important task for improving the quality of feedback that students receive from their peers. As a first step towards enhancing existing peerreview systems with new functionality based on helpfulness detection, we examine whether standard product review analysis techniques also apply to our new context of peer reviews. In addition, we investigate the utility of incorporating additional specialized features tailored to peer review. Our preliminary results show that the structural features, review unigrams and meta-data combined are useful in modeling the helpfulness of both peer reviews and product reviews, while peer-review specific auxiliary features can further improve helpfulness prediction.
Text to Image Synthesis Using Generative Adversarial Networks

Generating images from natural language is one of the primary applications of recent conditional generative models. Besides testing our ability to model conditional, highly dimensional distributions, text to image synthesis has many exciting and practical applications such as photo editing or computer-aided content creation. Recent progress has been made using Generative Adversarial Networks (GANs). This material starts with a gentle introduction to these topics and discusses the existent state of the art models. Moreover, I propose Wasserstein GAN-CLS, a new model for conditional image generation based on the Wasserstein distance which offers guarantees of stability. Then, I show how the novel loss function of Wasserstein GAN-CLS can be used in a Conditional Progressive Growing GAN. In combination with the proposed loss, the model boosts by 7.07% the best Inception Score (on the Caltech birds dataset) of the models which use only the sentence-level visual semantics. The only model which performs better than the Conditional Wasserstein Progressive growing GAN is the recently proposed AttnGAN which uses word-level visual semantics as well.
Cavs: An Efficient Runtime System for Dynamic Neural Networks

Recent deep learning (DL) models are moving more and more to dynamic neural network (NN) architectures, where the NN structure changes for every data sample. However, existing DL programming models are inefficient in handling dynamic network architectures because of: (1) substantial overhead caused by repeating dataflow graph construction and processing every example; (2) difficulties in batched execution of multiple samples; (3) inability to incorporate graph optimization techniques such as those used in static graphs. In this paper, we present “Cavs”, a runtime system that overcomes these bottlenecks and achieves efficient training and inference of dynamic NNs. Cavs represents a dynamic NN as a static vertex function F and a dynamic instance-specific graph G. It avoids the overhead of repeated graph construction by only declaring and constructing F once, and allows for the use of static graph optimization techniques on pre-defined operations in F . Cavs performs training and inference by scheduling the execution of F following the dependencies in G, hence naturally exposing batched execution opportunities over different samples. Experiments comparing Cavs to state-of-the-art frameworks for dynamic NNs (TensorFlow Fold, PyTorch and DyNet) demonstrate the efficacy of our approach: Cavs achieves a near one order of magnitude speedup on training of dynamic NN architectures, and ablations verify the effectiveness of our proposed design and optimizations.
A new planar marchand balun

In this paper, a novel planar Marchand balun using a patterned ground plane is presented. In the new design, with a slot under the coupled lines cut on the ground plane, the even-mode impedance can be increased substantially. Meanwhile, we propose that two additional separated rectangular conductors are placed under the coupled lines to act as two capacitors so that the odd-mode impedance is decreased. Design theory and procedure are presented to optimize the Marchand balun. As an example, one Marchand balun on a double-sided PCB is designed, simulated, fabricated and measured. The measured return loss is found to be better than – 10 dB over the frequency band from 1.2 GHz to 3.3 GHz, or around 100% bandwidth. The measured amplitude and phase imbalance between the two balanced output ports are within 1 dB and 4, respectively, over the operating frequency band. Index Terms — Baluns, coupled lines, wideband