nils-herrmann/clean_scirepeval huggingface dataset:
Sarcastic or Not: Word Embeddings to Predict the Literal or Sarcastic Meaning of Words

Sarcasm is generally characterized as a figure of speech that involves the substitution of a literal by a figurative meaning, which is usually the opposite of the original literal meaning. We re-frame the sarcasm detection task as a type of word sense disambiguation problem, where the sense of a word is either literal or sarcastic. We call this the Literal/Sarcastic Sense Disambiguation (LSSD) task. We address two issues: 1) how to collect a set of target words that can have either literal or sarcastic meanings depending on context; and 2) given an utterance and a target word, how to automatically detect whether the target word is used in the literal or the sarcastic sense. For the latter, we investigate several distributional semantics methods and show that a Support Vector Machines (SVM) classifier with a modified kernel using word embeddings achieves a 7-10% F1 improvement over a strong lexical baseline.
Performance Isolation and Fairness for Multi-Tenant Cloud Storage

Shared storage services enjoy wide adoption in commercial clouds. But most systems today provide weak performance isolation and fairness between tenants, if at all. Misbehaving or high-demand tenants can overload the shared service and disrupt other well-behaved tenants, leading to unpredictable performance and violating SLAs. This paper presents Pisces, a system for achieving datacenter-wide per-tenant performance isolation and fairness in shared key-value storage. Today’s approaches for multi-tenant resource allocation are based either on perVM allocations or hard rate limits that assume uniform workloads to achieve high utilization. Pisces achieves per-tenant weighted fair shares (or minimal rates) of the aggregate resources of the shared service, even when different tenants’ partitions are co-located and when demand for different partitions is skewed, time-varying, or bottlenecked by different server resources. Pisces does so by decomposing the fair sharing problem into a combination of four complementary mechanisms—partition placement, weight allocation, replica selection, and weighted fair queuing—that operate on different time-scales and combine to provide system-wide max-min fairness. An evaluation of our Pisces storage prototype achieves nearly ideal (0.99 Min-Max Ratio) weighted fair sharing, strong performance isolation, and robustness to skew and shifts in tenant demand. These properties are achieved with minimal overhead (<3%), even when running at high utilization (more than 400,000 requests/second/server for 10B requests).
Active Mobile Robot Localization

Localization is the problem of determining the position of a mobile robot from sensor data. Most existing localization approaches are passive, i.e., they do not exploit the opportunity to control the robot's effectors during localization. This paper proposes an active localization approach. The approach provides rational criteria for (1) setting the robot's motion direction (exploration), and (2) determining the pointing direction of the sensors so as to most efficiently localize the robot. Furthermore, it is able to deal with noisy sensors and approximative world models. The appropriateness of our approach is demonstrated empirically using a mobile robot in a structured office environment.
Mining User Opinions in Mobile App Reviews: A Keyword-Based Approach (T)

User reviews of mobile apps often contain complaints or suggestions which are valuable for app developers to improve user experience and satisfaction. However, due to the large volume and noisy-nature of those reviews, manually analyzing them for useful opinions is inherently challenging. To address this problem, we propose MARK, a keyword-based framework for semi-automated review analysis. MARK allows an analyst describing his interests in one or some mobile apps by a set of keywords. It then finds and lists the reviews most relevant to those keywords for further analysis. It can also draw the trends over time of those keywords and detect their sudden changes, which might indicate the occurrences of serious issues. To help analysts describe their interests more effectively, MARK can automatically extract keywords from raw reviews and rank them by their associations with negative reviews. In addition, based on a vector-based semantic representation of keywords, MARK can divide a large set of keywords into more cohesive subsets, or suggest keywords similar to the selected ones.
Variable Stiffness Actuators: Review on Design and Components

Variable stiffness actuators (VSAs) are complex mechatronic devices that are developed to build passively compliant, robust, and dexterous robots. Numerous different hardware designs have been developed in the past two decades to address various demands on their functionality. This review paper gives a guide to the design process from the analysis of the desired tasks identifying the relevant attributes and their influence on the selection of different components such as motors, sensors, and springs. The influence on the performance of different principles to generate the passive compliance and the variation of the stiffness are investigated. Furthermore, the design contradictions during the engineering process are explained in order to find the best suiting solution for the given purpose. With this in mind, the topics of output power, potential energy capacity, stiffness range, efficiency, and accuracy are discussed. Finally, the dependencies of control, models, sensor setup, and sensor quality are addressed.
Fine-Grained Image Classification by Exploring Bipartite-Graph Labels

Given a food image, can a fine-grained object recognition engine tell "which restaurant which dish" the food belongs to? Such ultra-fine grained image recognition is the key for many applications like search by images, but it is very challenging because it needs to discern subtle difference between classes while dealing with the scarcity of training data. Fortunately, the ultra-fine granularity naturally brings rich relationships among object classes. This paper proposes a novel approach to exploit the rich relationships through bipartite-graph labels (BGL). We show how to model BGL in an overall convolutional neural networks and the resulting system can be optimized through back-propagation. We also show that it is computationally efficient in inference thanks to the bipartite structure. To facilitate the study, we construct a new food benchmark dataset, which consists of 37,885 food images collected from 6 restaurants and totally 975 menus. Experimental results on this new food and three other datasets demonstrate BGL advances previous works in fine-grained object recognition. An online demo is available at http: //www.f-zhou.com/fg_demo/.
SuperPoint: Self-Supervised Interest Point Detection and Description

This paper presents a self-supervised framework for training interest point detectors and descriptors suitable for a large number of multiple-view geometry problems in computer vision. As opposed to patch-based neural networks, our fully-convolutional model operates on full-sized images and jointly computes pixel-level interest point locations and associated descriptors in one forward pass. We introduce Homographic Adaptation, a multi-scale, multi-homography approach for boosting interest point detection repeatability and performing cross-domain adaptation (e.g., synthetic-to-real). Our model, when trained on the MS-COCO generic image dataset using Homographic Adaptation, is able to repeatedly detect a much richer set of interest points than the initial pre-adapted deep model and any other traditional corner detector. The final system gives rise to state-of-the-art homography estimation results on HPatches when compared to LIFT, SIFT and ORB.
Hierarchical Solution of Markov Decision Processes using Macro-actions

We investigate the use of temporally abstract actions, or macro-actions, in the solution of Markov decision processes. Unlike current models that combine both primitive actions and macro-actions and leave the state space unchanged, we propose a hierarchical model (using anabstract MDP) that works with macro-actions only, and that significantly reduces the size of the state space. This is achieved by treating macroactions as local policies that act in certain regions of state space, and by restricting states in the abstract MDP to those at the boundaries of regions. The abstract MDP approximates the original and can be solved more efficiently. We discuss several ways in which macro-actions can be generated to ensure good solution quality. Finally, we consider ways in which macro-actions can be reused to solve multiple, related MDPs; and we show that this can justify the computational overhead of macro-action generation.
An Analysis of Private Browsing Modes in Modern Browsers

We study the security and privacy of private browsing modes recently added to all major browsers. We first propose a clean definition of the goals of private browsing and survey its implementation in different browsers. We conduct a measurement study to determine how often it is used and on what categories of sites. Our results suggest that private browsing is used differently from how it is marketed. We then describe an automated technique for testing the security of private browsing modes and report on a few weaknesses found in the Firefox browser. Finally, we show that many popular browser extensions and plugins undermine the security of private browsing. We propose and experiment with a workable policy that lets users safely run extensions in private browsing mode.
High-Accuracy Stereo Depth Maps Using Structured Light

Recent progress in stereo algorithm performance is quickly outpacing the ability of existing stereo data sets to discriminate among the best-performing algorithms, motivating the need for more challenging scenes with accurate ground truth information. This paper describes a method for acquiring high-complexity stereo image pairs with pixel-accurate correspondence information using structured light. Unlike traditional range-sensing approaches, our method does not require the calibration of the light sources and yields registered disparity maps between all pairs of cameras and illumination projectors. We present new stereo data sets acquired with our method and demonstrate their suitability for stereo algorithm evaluation. Our results are available at http://www.middlebury.edu/stereo/.
New insights and perspectives on the natural gradient method

Natural gradient descent is an optimization method traditionally motivated from the perspective of information geometry, and works well for many applications as an alternative to stochastic gradient descent. In this paper we critically analyze this method and its properties, and show how it can be viewed as a type of approximate 2nd-order optimization method, where the Fisher information matrix used to compute the natural gradient direction can be viewed as an approximation of the Hessian. This perspective turns out to have significant implications for how to design a practical and robust version of the method. Among our various other contributions is a thorough analysis of the convergence speed of natural gradient descent and more general stochastic methods, a critical examination of the oft-used “empirical” approximation of the Fisher matrix, and an analysis of the (approximate) parameterization invariance property possessed by the method, which we show still holds for certain other choices of the curvature matrix, but notably not the Hessian. ∗jmartens@cs.toronto.edu 1 ar X iv :1 41 2. 11 93 v5 [ cs .L G ] 1 O ct 2 01 5
Deep Residual Networks with Exponential Linear Unit

The depth of convolutional neural networks is a crucial ingredient for reduction in test errors on benchmarks like ImageNet and COCO. However, training a neural network becomes difficult with increasing depth. Problems like vanishing gradient and diminishing feature reuse are quite trivial in very deep convolutional neural networks.
 The notable recent contributions towards solving these problems and simplifying the training of very deep models are Residual and Highway Networks. These networks allow earlier representations (from the input or those learned in earlier layers) to flow unimpededly to later layers through skip connections. Such very deep models with hundreds or more layers have lead to a considerable decrease in test errors, on benchmarks like ImageNet and COCO.
 In this paper, we propose to replace the combination of ReLU and Batch Normalization with Exponential Linear Unit (ELU) in Residual Networks. Our experiments show that this not only speeds up the learning behavior in Residual Networks, but also improves the classification performance as the depth increases. Our model increases the accuracy on datasets like CIFAR-10 and CIFAR-100 by a significant margin.
Hierarchical Memory Networks

Memory networks are neural networks with an explicit memory component that can be both read and written to by the network. The memory is often addressed in a soft way using a softmax function, making end-to-end training with backpropagation possible. However, this is not computationally scalable for applications which require the network to read from extremely large memories. On the other hand, it is well known that hard attention mechanisms based on reinforcement learning are challenging to train successfully. In this paper, we explore a form of hierarchical memory network, which can be considered as a hybrid between hard and soft attention memory networks. The memory is organized in a hierarchical structure such that reading from it is done with less computation than soft attention over a flat memory, while also being easier to train than hard attention over a flat memory. Specifically, we propose to incorporate Maximum Inner Product Search (MIPS) in the training and inference procedures for our hierarchical memory network. We explore the use of various state-of-the art approximate MIPS techniques and report results on SimpleQuestions, a challenging large scale factoid question answering task.
Appearance-from-motion: recovering spatially varying surface reflectance under unknown lighting

We present "appearance-from-motion", a novel method for recovering the spatially varying isotropic surface reflectance from a video of a rotating subject, with known geometry, under unknown natural illumination. We formulate the appearance recovery as an iterative process that alternates between estimating surface reflectance and estimating incident lighting. We characterize the surface reflectance by a data-driven microfacet model, and recover the microfacet normal distribution for each surface point separately from temporal changes in the observed radiance. To regularize the recovery of the incident lighting, we rely on the observation that natural lighting is sparse in the gradient domain. Furthermore, we exploit the sparsity of strong edges in the incident lighting to improve the robustness of the surface reflectance estimation. We demonstrate robust recovery of spatially varying isotropic reflectance from captured video as well as an internet video sequence for a wide variety of materials and natural lighting conditions.
Learning and Game AI

The incorporation of learning into commercial games can enrich the player experience, but may concern developers in terms of issues such as losing control of their game world. We explore a number of applied research and some fielded applications that point to the tremendous possibilities of machine learning research including game genres such as real-time strategy games, flight simulation games, car and motorcycle racing games, board games such as Go, an even traditional game-theoretic problems such as the prisoners dilemma. A common trait of these works is the potential of machine learning to reduce the burden of game developers. However a number of challenges exists that hinder the use of machine learning more broadly. We discuss some of these challenges while at the same time exploring opportunities for a wide use of machine learning in games.
Asymmetry in media spaces

In any collaborative system, there are both symmetries and asymmetries present in the design of the technology and in the ways that technology is appropriated. Yet media space research tends to focus more on supporting and fostering the symmetries than the asymmetries. Throughout more than 20 years of media space research, the pursuit of increased symmetry, whether achieved through technical or social means, has been a recurrent theme. The research literature on the use of contemporary awareness systems, in contrast, displays little if any of this emphasis on symmetrical use; indeed, this body of research occasionally highlights the perceived value of asymmetry. In this paper, we unpack the different forms of asymmetry present in both media spaces and contemporary awareness systems. We argue that just as asymmetry has been demonstrated to have value in contemporary awareness systems, so might asymmetry have value in media spaces and in other CSCW systems, more generally. To illustrate, we present a media space that emphasizes and embodies multiple forms of asymmetry and does so in response to the needs of a particular work context.
Context sensitive article ranking with citation context analysis

It is hard to detect important articles in a specific context. Information retrieval techniques based on full text search can be inaccurate to identify main topics and they are not able to provide an indication about the importance of the article. Generating a citation network is a good way to find most popular articles but this approach is not context aware. The text around a citation mark is generally a good summary of the referred article. So citation context analysis presents an opportunity to use the wisdom of crowd for detecting important articles in a context sensitive way. In this work, we analyze citation contexts to rank articles properly for a given topic. The model proposed uses citation contexts in order to create a directed and edge-labeled citation network based on the target topic. Then we apply common ranking algorithms in order to find important articles in this newly created network. We showed that this method successfully detects a good subset of most prominent articles in a given topic. The biggest contribution of this approach is that we are able to identify important articles for a given search term even though these articles do not contain this search term. This technique can be used in other linked documents including web pages, legal documents, and patents as well as scientific papers.
Simple memory: a theory for archicortex.

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phyloseq: An R Package for Reproducible Interactive Analysis and Graphics of Microbiome Census Data

the analysis of microbial communities through dna sequencing brings many challenges: the integration of different types of data with methods from ecology, genetics, phylogenetics, multivariate statistics, visualization and testing. With the increased breadth of experimental designs now being pursued, project-specific statistical analyses are often needed, and these analyses are often difficult (or impossible) for peer researchers to independently reproduce. The vast majority of the requisite tools for performing these analyses reproducibly are already implemented in R and its extensions (packages), but with limited support for high throughput microbiome census data.



Here we describe a software project, phyloseq, dedicated to the object-oriented representation and analysis of microbiome census data in R. It supports importing data from a variety of common formats, as well as many analysis techniques. These include calibration, filtering, subsetting, agglomeration, multi-table comparisons, diversity analysis, parallelized Fast UniFrac, ordination methods, and production of publication-quality graphics; all in a manner that is easy to document, share, and modify. We show how to apply functions from other R packages to phyloseq-represented data, illustrating the availability of a large number of open source analysis techniques. We discuss the use of phyloseq with tools for reproducible research, a practice common in other fields but still rare in the analysis of highly parallel microbiome census data. We have made available all of the materials necessary to completely reproduce the analysis and figures included in this article, an example of best practices for reproducible research.



The phyloseq project for R is a new open-source software package, freely available on the web from both GitHub and Bioconductor.
Automatic Chemical Design Using a Data-Driven Continuous
Representation of Molecules

We report a method to convert discrete representations of molecules to and from a multidimensional continuous representation. This model allows us to generate new molecules for efficient exploration and optimization through open-ended spaces of chemical compounds. A deep neural network was trained on hundreds of thousands of existing chemical structures to construct three coupled functions: an encoder, a decoder, and a predictor. The encoder converts the discrete representation of a molecule into a real-valued continuous vector, and the decoder converts these continuous vectors back to discrete molecular representations. The predictor estimates chemical properties from the latent continuous vector representation of the molecule. Continuous representations of molecules allow us to automatically generate novel chemical structures by performing simple operations in the latent space, such as decoding random vectors, perturbing known chemical structures, or interpolating between molecules. Continuous representations also allow the use of powerful gradient-based optimization to efficiently guide the search for optimized functional compounds. We demonstrate our method in the domain of drug-like molecules and also in a set of molecules with fewer that nine heavy atoms.
Design and Reliability in Wafer Level Packaging

Wafer Level Packaging (WLP) has the highest potential for future single chip packages because the WLP is intrinsically a chip size package. The package is completed directly on the wafer then singulated by dicing for the assembly. All packaging and testing operations of the dice are replaced by whole wafer fabrication and wafer level testing. Therefore, it becomes more cost-effective with decreasing die size or increasing wafer size. However, due to the intrinsic mismatch of the coefficient of thermal expansion (CTE) between silicon chip and plastic PCB material, solder ball reliability subject to temperature cycling becomes the weakest point of the technology. In this paper some fundamental principles in designing WLP structure to achieve the robust reliability are demonstrated through a comprehensive study of a variety of WLP technologies. The first principle is the 'structural flexibility' principle. The more flexible a WLP structure is, the less the stresses that are applied on the solder balls will be. Ball on polymer WLP, Cu post WLP, polymer core solder balls are such examples to achieve better flexibility of overall WLP structure. The second principle is the 'local enhancement' at the interface region of solder balls where fatigue failures occur. Polymer collar WLP, and increasing solder opening size are examples to reduce the local stress level. In this paper, the reliability improvements are discussed through various existing and tested WLP technologies at silicon level and ball level, respectively. The fan-out wafer level packaging is introduced, which is expected to extend the standard WLP to the next stage with unlimited potential applications in future.