nils-herrmann/clean_scirepeval huggingface dataset:
Shifting the MIMO Paradigm

Multi-user MIMO (MU-MIMO) networks reveal the unique opportunities arising from a joint optimization of antenna combining techniques with resource allocation protocols. Furthermore, it brings robustness with respect to multipath richness, allowing for compact antenna spacing at the BS and, crucially, yielding the diversity and multiplexing gains without the need for multiple antenna user terminals. To realize these gains, however, the BS should be informed with the user's channel coefficients, which may limit practical application to TDD or low-mobility settings. To circumvent this problem and reduce feedback load, combining MU-MIMO with opportunistic scheduling seems a promising direction. The success for this type of scheduler is strongly traffic and QoS-dependent, however.
Adam: A Method for Stochastic Optimization

We introduce Adam, an algorithm for first-order gradient-based optimization of stochastic objective functions, based on adaptive estimates of lower-order moments. The method is straightforward to implement, is computationally efficient, has little memory requirements, is invariant to diagonal rescaling of the gradients, and is well suited for problems that are large in terms of data and/or parameters. The method is also appropriate for non-stationary objectives and problems with very noisy and/or sparse gradients. The hyper-parameters have intuitive interpretations and typically require little tuning. Some connections to related algorithms, on which Adam was inspired, are discussed. We also analyze the theoretical convergence properties of the algorithm and provide a regret bound on the convergence rate that is comparable to the best known results under the online convex optimization framework. Empirical results demonstrate that Adam works well in practice and compares favorably to other stochastic optimization methods. Finally, we discuss AdaMax, a variant of Adam based on the infinity norm.
Predictive State Temporal Difference Learning

We propose a new approach to value function approximation which combines linear temporal difference reinforcement learning with subspace identification. In practical applications, reinforcement learning (RL) is complicated by the fact that state is either high-dimensional or partially observable. Therefore, RL methods are designed to work with features of state rather than state itself, and the success or failure of learning is often determined by the suitability of the selected features. By comparison, subspace identification (SSID) methods are designed to select a feature set which preserves as much information as possible about state. In this paper we connect the two approaches, looking at the problem of reinforcement learning with a large set of features, each of which may only be marginally useful for value function approximation. We introduce a new algorithm for this situation, called Predictive State Temporal Difference (PSTD) learning. As in SSID for predictive state representations, PSTD finds a linear compression operator that projects a large set of features down to a small set that preserves the maximum amount of predictive information. As in RL, PSTD then uses a Bellman recursion to estimate a value function. We discuss the connection between PSTD and prior approaches in RL and SSID. We prove that PSTD is statistically consistent, perform several experiments that illustrate its properties, and demonstrate its potential on a difficult optimal stopping problem.
Validation of Decentralised Smart Contracts Through Game Theory and Formal Methods

Decentralised smart contracts represent the next step in the development of protocols that support the interaction of independent players without the presence of a coercing authority. Based on protocols à la BitCoin for digital currencies, smart contracts are believed to be a potentially enabling technology for a wealth of future applications. The validation of such an early developing technology is as necessary as it is complex. In this paper we combine game theory and formal models to tackle the new challenges posed by the validation of such systems.
Design of an artificial muscle continuum robot

This paper introduces a novel continuum manipulator consisting of two flexible sections connected by rigid base plates. Each section uses 6-8 opposing contracting and extending McKibben actuators to provide two-axis bending. The contracting/extending actuator pairs are sized and positioned to provide matched rotation and torque. The experimental manipulator generates large rotation (30 - 35 degrees per section and 50 degrees for the entire manipulator) and load capacity (25 lb vertical lift) at 60 psi. With a large strength to weight ratio, this manipulator also is highly flexible, simple to manufacture, and is cost-effective.
Working memory deficits in adults with attention-deficit/hyperactivity disorder (ADHD): an examination of central executive and storage/rehearsal processes.

The current study was the first to use a regression approach to examine the unique contributions of central executive (CE) and storage/rehearsal processes to working memory (WM) deficits in adults with ADHD. Thirty-seven adults (ADHD = 21, HC = 16) completed phonological (PH) and visuospatial (VS) working memory tasks. While both groups performed significantly better during the PH task relative to the VS task, adults with ADHD exhibited significant deficits across both working memory modalities. Further, the ADHD group recalled disproportionately fewer PH and VS stimuli as set-size demands increased. Overall, the CE and PH storage/rehearsal processes of adults with ADHD were both significantly impaired relative to those of the healthy control adults; however, the magnitude of the CE effect size was much smaller compared to previous studies of children with the disorder. Collectively, results provide support for a lifelong trajectory of WM deficits in ADHD.
Discovering facts with boolean tensor tucker decomposition

Open Information Extraction (Open IE) has gained increasing research interest in recent years. The first step in Open IE is to extract raw subject--predicate--object triples from the data. These raw triples are rarely usable per se, and need additional post-processing. To that end, we proposed the use of Boolean Tucker tensor decomposition to simultaneously find the entity and relation synonyms and the facts connecting them from the raw triples. Our method represents the synonym sets and facts using (sparse) binary matrices and tensor that can be efficiently stored and manipulated. We consider the presentation of the problem as a Boolean tensor decomposition as one of this paper's main contributions. To study the validity of this approach, we use a recent algorithm for scalable Boolean Tucker decomposition. We validate the results with empirical evaluation on a new semi-synthetic data set, generated to faithfully reproduce real-world data features, as well as with real-world data from existing Open IE extractor. We show that our method obtains high precision while the low recall can easily be remedied by considering the original data together with the decomposition.
Clustering of Gaze During Dynamic Scene Viewing is Predicted by Motion

Where does one attend when viewing dynamic scenes? Research into the factors influencing gaze location during static scene viewing have reported that low-level visual features contribute very little to gaze location especially when opposed by high-level factors such as viewing task. However, the inclusion of transient features such as motion in dynamic scenes may result in a greater influence of visual features on gaze allocation and coordination of gaze across viewers. In the present study, we investigated the contribution of low- to mid-level visual features to gaze location during free-viewing of a large dataset of videos ranging in content and length. Signal detection analysis on visual features and Gaussian Mixture Models for clustering gaze was used to identify the contribution of visual features to gaze location. The results show that mid-level visual features including corners and orientations can distinguish between actual gaze locations and a randomly sampled baseline. However, temporal features such as flicker, motion, and their respective contrasts were the most predictive of gaze location. Additionally, moments in which all viewers’ gaze tightly clustered in the same location could be predicted by motion. Motion and mid-level visual features may influence gaze allocation in dynamic scenes, but it is currently unclear whether this influence is involuntary or due to correlations with higher order factors such as scene semantics.
For Whom Is a Picture Worth a Thousand Words ? Extensions of a Dual-Coding Theory of Multimedia Learning

In 2 experiments, highand low-spatial ability students viewed a computer-generated animation and listened simultaneously (concurrent group) or successively (successive group) to a narration that explained the workings either of a bicycle tire pump (Experiment 1) or of the human respiratory system (Experiment 2). The concurrent group generated more creative solutions to subsequent transfer problems than did the successive group; this contiguity effect was strong for highbut not for low-spatial ability students. Consistent with a dual-coding theory, spatial ability allows high-spatial learners to devote more cognitive resources to building referential connections between visual and verbal representations of the presented material, whereas low-spatial ability learners must devote more cognitive resources to building representation connections between visually presented material and its visual representation.
NFV: Security Threats and Best Practices

Network function virtualization (NFV) yields numerous benefits, particularly the possibility of a cost-efficient transition of telco hardware functionalities on the software platform to break the vendor lock-in problem. These benefits come at the price of some security flaws. Indeed, with NFV, virtual mobile networks become vulnerable to a number of security threats. These threats can be leveraged using some available mitigation techniques and also through other emerging solutions. This article presents critical security threats that exist in the NFV infrastructure, proposes best security practices to protect against them.
Gaussian KD-trees for fast high-dimensional filtering

We propose a method for accelerating a broad class of non-linear filters that includes the bilateral, non-local means, and other related filters. These filters can all be expressed in a similar way: First, assign each value to be filtered a position in some vector space. Then, replace every value with a weighted linear combination of all values, with weights determined by a Gaussian function of distance between the positions. If the values are pixel colors and the positions are (x, y) coordinates, this describes a Gaussian blur. If the positions are instead (x, y, r, g, b) coordinates in a five-dimensional space-color volume, this describes a bilateral filter. If we instead set the positions to local patches of color around the associated pixel, this describes non-local means. We describe a Monte-Carlo kd-tree sampling algorithm that efficiently computes any filter that can be expressed in this way, along with a GPU implementation of this technique. We use this algorithm to implement an accelerated bilateral filter that respects full 3D color distance; accelerated non-local means on single images, volumes, and unaligned bursts of images for denoising; and a fast adaptation of non-local means to geometry. If we have n values to filter, and each is assigned a position in a d-dimensional space, then our space complexity is O(dn) and our time complexity is O(dn log n), whereas existing methods are typically either exponential in d or quadratic in n.
CryptoNets: Applying Neural Networks to Encrypted Data with High Throughput and Accuracy

Applying machine learning to a problem which involves medical, financial, or other types of sensitive data, not only requires accurate predictions but also careful attention to maintaining data privacy and security. Legal and ethical requirements may prevent the use of cloud-based machine learning solutions for such tasks. In this work, we will present a method to convert learned neural networks to CryptoNets, neural networks that can be applied to encrypted data. This allows a data owner to send their data in an encrypted form to a cloud service that hosts the network. The encryption ensures that the data remains confidential since the cloud does not have access to the keys needed to decrypt it. Nevertheless, we will show that the cloud service is capable of applying the neural network to the encrypted data to make encrypted predictions, and also return them in encrypted form. These encrypted predictions can be sent back to the owner of the secret key who can decrypt them. Therefore, the cloud service does not gain any information about the raw data nor about the prediction it made. We demonstrate CryptoNets on the MNIST optical character recognition tasks. CryptoNets achieve 99% accuracy and can make around 59000 predictions per hour on a single PC. Therefore, they allow high throughput, accurate, and private predictions. This is a joint work with Nathan Dowlin, Kim Laine, Kristin Lauter, Michael Naehrig, John Wernsing.
Development of a Compact Centrifugal Pump With a Two-Axis Actively Positioned Consequent-Pole Bearingless Motor

A novel centrifugal pump has been developed that employs a bearingless motor with two-axis radial active positioning control. The structure of this motor is an extension of a hybrid of consequent-pole and homopolar-type bearingless motors. Based on 3-D finite-element-method analysis, the influence of the permanent-magnet dimension and motor current on magnetic suspension characteristics has been investigated. The passive stiffness of this motor has been enhanced. The test pump was fabricated and found to provide a maximum pump output of 1 W with a flow rate of 4 L/min against a head pressure of water of 1.5 m. An impeller with a shroud contributes to a significant reduction in axial impeller movement caused by axial thrust. The rotor vibration is significantly smaller than the fluid gaps in the pump casing. The experimental results demonstrate that the proposed consequent-pole bearingless motor is feasible and effective for use with a centrifugal pump.
Performance-Effective and Low-Complexity Task Scheduling for Heterogeneous Computing

ÐEfficient application scheduling is critical for achieving high performance in heterogeneous computing environments. The application scheduling problem has been shown to be NP-complete in general cases as well as in several restricted cases. Because of its key importance, this problem has been extensively studied and various algorithms have been proposed in the literature which are mainly for systems with homogeneous processors. Although there are a few algorithms in the literature for heterogeneous processors, they usually require significantly high scheduling costs and they may not deliver good quality schedules with lower costs. In this paper, we present two novel scheduling algorithms for a bounded number of heterogeneous processors with an objective to simultaneously meet high performance and fast scheduling time, which are called the Heterogeneous Earliest-Finish-Time (HEFT) algorithm and the Critical-Path-on-a-Processor (CPOP) algorithm. The HEFT algorithm selects the task with the highest upward rank value at each step and assigns the selected task to the processor, which minimizes its earliest finish time with an insertion-based approach. On the other hand, the CPOP algorithm uses the summation of upward and downward rank values for prioritizing tasks. Another difference is in the processor selection phase, which schedules the critical tasks onto the processor that minimizes the total execution time of the critical tasks. In order to provide a robust and unbiased comparison with the related work, a parametric graph generator was designed to generate weighted directed acyclic graphs with various characteristics. The comparison study, based on both randomly generated graphs and the graphs of some real applications, shows that our scheduling algorithms significantly surpass previous approaches in terms of both quality and cost of schedules, which are mainly presented with schedule length ratio, speedup, frequency of best results, and average scheduling time metrics.
Memory engram storage and retrieval

A great deal of experimental investment is directed towards questions regarding the mechanisms of memory storage. Such studies have traditionally been restricted to investigation of the anatomical structures, physiological processes, and molecular pathways necessary for the capacity of memory storage, and have avoided the question of how individual memories are stored in the brain. Memory engram technology allows the labeling and subsequent manipulation of components of specific memory engrams in particular brain regions, and it has been established that cell ensembles labeled by this method are both sufficient and necessary for memory recall. Recent research has employed this technology to probe fundamental questions of memory consolidation, differentiating between mechanisms of memory retrieval from the true neurobiology of memory storage.
DACC: Distributed Access Control in Clouds

We propose a new model for data storage and access in clouds. Our scheme avoids storing multiple encrypted copies of same data. In our framework for secure data storage, cloud stores encrypted data (without being able to decrypt them). The main novelty of our model is addition of key distribution centers (KDCs). We propose DACC ( algorithm, where one or more KDCs distribute keys to data owners and users. KDC may provide access to particular fields in all records. Thus, a single key replaces separate keys from owners. Owners and users are assigned certain set of attributes. Owner encrypts the data with the attributes it has and stores them in the cloud. The users with matching set of attributes can retrieve the data from the cloud. We apply attribute-based encryption based on bilinear pairings on elliptic curves. The scheme is collusion secure, two users cannot together decode any data that none of them has individual right to access. DACC also supports revocation of users, without redistributing keys to all the users of cloud services. We show that our approach results in lower communication, computation and storage overheads, compared to existing models and schemes.
Selecting image pairs for SfM by introducing Jaccard similarity

We present a new approach for selecting image pairs that are more likely to match in Structure from Motion (SfM). We propose to use Jaccard Similarity (JacS) which shows how many different visual words is shared by an image pair. In our method, the similarity between images is evaluated using JacS of bag-of-visual-words in addition to tf-idf, which is popular for this purpose. To evaluate the efficiency of our method, we carry out experiments on our original datasets as well as on “Pantheon” dataset, which is derived from Flickr. The result of our method using both JacS and tf-idf is better than the results of a standard method using tf-idf only.
Survey on mining subjective data on the web

In the past years we have witnessed Sentiment Analysis and Opinion Mining becoming increasingly popular topics in Information Retrieval and Web data analysis. With the rapid growth of the user-generated content represented in blogs, wikis and Web forums, such an analysis became a useful tool for mining the Web, since it allowed us to capture sentiments and opinions at a large scale. Opinion retrieval has established itself as an important part of search engines. Ratings, opinion trends and representative opinions enrich the search experience of users when combined with traditional document retrieval, by revealing more insights about a subject. Opinion aggregation over product reviews can be very useful for product marketing and positioning, exposing the customers’ attitude towards a product and its features along different dimensions, such as time, geographical location, and experience. Tracking how opinions or discussions evolve over time can help us identify interesting trends and patterns and better understand the ways that information is propagated in the Internet. In this study, we review the development of Sentiment Analysis and Opinion Mining during the last years, and also discuss the evolution of a relatively new research direction, namely, Contradiction Analysis. We give an overview of the proposed methods and recent advances in these areas, and we try to layout the future research directions in the field.
Beyond the rainbow: self-adaptive failure avoidance in configurable systems

Self-adaptive software systems monitor their state and then adapt when certain conditions are met, guided by a global utility function. In prior work we developed algorithms and conducted a post-hoc analysis demonstrating the possibility of adapting to software failures by judiciously changing configurations. In this paper we present the REFRACT framework that realizes this idea in practice by building on the self-adaptive Rainbow architecture. REFRACT extends Rainbow with new components and algorithms targeting failure avoidance. We use REFRACT in a case study running four independently executing Firefox clients with 36 passing test cases and 7 seeded faults. The study show that workarounds for all but one of the seeded faults are found and the one that is not found never fails -- it is guarded from failing by a related workaround. Moreover, REFRACT finds workarounds for eight configuration-related unseeded failures from tests that were expected to pass (and did under the default configuration). Finally, the data show that when a failure and its workaround are found, configuration guards prevent the failure from appearing again. In a simulation lasting 24 hours we see over 150 guard activations and no failures with workarounds remaining beyond 16 hours.
Nonparametric Canonical Correlation Analysis

Canonical correlation analysis (CCA) is a classical representation learning technique for finding correlated variables in multi-view data. Several nonlinear extensions of the original linear CCA have been proposed, including kernel and deep neural network methods. These approaches seek maximally correlated projections among families of functions, which the user specifies (by choosing a kernel or neural network structure), and are computationally demanding. Interestingly, the theory of nonlinear CCA, without functional restrictions, had been studied in the population setting by Lancaster already in the 1950s, but these results have not inspired practical algorithms. We revisit Lancaster’s theory to devise a practical algorithm for nonparametric CCA (NCCA). Specifically, we show that the solution can be expressed in terms of the singular value decomposition of a certain operator associated with the joint density of the views. Thus, by estimating the population density from data, NCCA reduces to solving an eigenvalue system, superficially like kernel CCA but, importantly, without requiring the inversion of any kernel matrix. We also derive a partially linear CCA (PLCCA) variant in which one of the views undergoes a linear projection while the other is nonparametric. Using a kernel density estimate based on a small number of nearest neighbors, our NCCA and PLCCA algorithms are memory-efficient, often run much faster, and perform better than kernel CCA and comparable to deep CCA. Proceedings of the 33 rd International Conference on Machine Learning, New York, NY, USA, 2016.  W&CP volume 48.