nils-herrmann/clean_scirepeval huggingface dataset:
DeLight: Adding Energy Dimension To Deep Neural Networks

Physical viability, in particular energy efficiency, is a key challenge in realizing the true potential of Deep Neural Networks (DNNs). In this paper, we aim to incorporate the energy dimension as a design parameter in the higher-level hierarchy of DNN training and execution to optimize for the energy resources and constraints. We use energy characterization to bound the network size in accordance to the pertinent physical resources. An automated customization methodology is proposed to adaptively conform the DNN configurations to the underlying hardware characteristics while minimally affecting the inference accuracy. The key to our approach is a new context and resource aware projection of data to a lower-dimensional embedding by which learning the correlation between data samples requires significantly smaller number of neurons. We leverage the performance gain achieved as a result of the data projection to enable the training of different DNN architectures which can be aggregated together to further boost the inference accuracy. Accompanying APIs are provided to facilitate rapid prototyping of an arbitrary DNN application customized to the underlying platform. Proof-of-concept evaluations for deployment of different visual, audio, and smart-sensing benchmarks demonstrate up to 100-fold energy improvement compared to the prior-art DL solutions.
Learning to reinforcement learn

In recent years deep reinforcement learning (RL) systems have attained superhuman performance in a number of challenging task domains. However, a major limitation of such applications is their demand for massive amounts of training data. A critical present objective is thus to develop deep RL methods that can adapt rapidly to new tasks. In the present work we introduce a novel approach to this challenge, which we refer to as deep meta-reinforcement learning. Previous work has shown that recurrent networks can support meta-learning in a fully supervised context. We extend this approach to the RL setting. What emerges is a system that is trained using one RL algorithm, but whose recurrent dynamics implement a second, quite separate RL procedure. This second, learned RL algorithm can differ from the original one in arbitrary ways. Importantly, because it is learned, it is configured to exploit structure in the training domain. We unpack these points in a series of seven proof-of-concept experiments, each of which examines a key aspect of deep meta-RL. We consider prospects for extending and scaling up the approach, and also point out some potentially important implications for neuroscience.
Estimating the Class Prior in Positive and Unlabeled Data Through Decision Tree Induction

For tasks such as medical diagnosis and knowledge base completion, a classifier may only have access to positive and unlabeled examples, where the unlabeled data consists of both positive and negative examples. One way that enables learning from this type of data is knowing the true class prior. In this paper, we propose a simple yet effective method for estimating the class prior, by estimating the probability that a positive example is selected to be labeled. Our key insight is that subdomains of the data give a lower bound on this probability. This lower bound gets closer to the real probability as the ratio of labeled examples increases. Finding such subsets can naturally be done via top-down decision tree induction. Experiments show that our method makes estimates which are equivalently accurate as those of the state of the art methods, and is an order of magnitude faster.
Combining predictions for accurate recommender systems

We analyze the application of ensemble learning to recommender systems on the Netflix Prize dataset. For our analysis we use a set of diverse state-of-the-art collaborative filtering (CF) algorithms, which include: SVD, Neighborhood Based Approaches, Restricted Boltzmann Machine, Asymmetric Factor Model and Global Effects. We show that linearly combining (blending) a set of CF algorithms increases the accuracy and outperforms any single CF algorithm. Furthermore, we show how to use ensemble methods for blending predictors in order to outperform a single blending algorithm. The dataset and the source code for the ensemble blending are available online.
Ambient RF Energy-Harvesting Technologies for Self-Sustainable Standalone Wireless Sensor Platforms

In this paper, various ambient energy-harvesting technologies (solar, thermal, wireless, and piezoelectric) are reviewed in detail and their applicability in the development of self-sustaining wireless platforms is discussed. Specifically, far-field low-power-density energy-harvesting technology is thoroughly investigated and a benchmarking prototype of an embedded microcontroller-enabled sensor platform has been successfully powered by an ambient ultrahigh-frequency (UHF) digital TV signal (512-566 MHz) where a broadcasting antenna is 6.3 km away from the proposed wireless energy-harvesting device. A high-efficiency dual-band ambient energy harvester at 915 MHz and 2.45 GHz and an energy harvester for on-body application at 460 MHz are also presented to verify the capabilities of ambient UHF/RF energy harvesting as an enabling technology for Internet of Things and smart skins applications.
Path-Loss and Throughput Prediction of IEEE 802.11ad Systems

This paper presents a path-loss prediction analysis and a performance evaluation of the 60GHz IEEE 802.11ad standard. Received signal strength measurements were performed in indoor locations and the decay coefficients were estimated over a number of measured routes. Furthermore, real world maps were incorporated into a 3-D mm wave ray- tracing tool for the purpose of employing realistic channel matrices into an 802.11ad bit level simulator. Finally, performance metrics for various MCS modes are presented and analyzed for both Orthogonal Frequency Division Multiplexing (OFDM) and Single Carrier (SC) transmissions.
Towards a Universal Grammar for Natural Language Processing

Universal Dependencies is a recent initiative to develop cross-linguistically consistent treebank annotation for many languages, with the goal of facilitating multilingual parser development, cross-lingual learning, and parsing research from a language typology perspective. In this talk, I outline the motivation behind the initiative and explain how the basic design principles follow from these requirements. I then discuss the different components of the annotation standard, including principles for word segmentation, morphological annotation, and syntactic annotation. I conclude with some thoughts on the challenges that lie ahead.
Design and Evaluation of a Real-Time URL Spam Filtering Service

On the heels of the widespread adoption of web services such as social networks and URL shorteners, scams, phishing, and malware have become regular threats. Despite extensive research, email-based spam filtering techniques generally fall short for protecting other web services. To better address this need, we present Monarch, a real-time system that crawls URLs as they are submitted to web services and determines whether the URLs direct to spam. We evaluate the viability of Monarch and the fundamental challenges that arise due to the diversity of web service spam. We show that Monarch can provide accurate, real-time protection, but that the underlying characteristics of spam do not generalize across web services. In particular, we find that spam targeting email qualitatively differs in significant ways from spam campaigns targeting Twitter. We explore the distinctions between email and Twitter spam, including the abuse of public web hosting and redirector services. Finally, we demonstrate Monarch's scalability, showing our system could protect a service such as Twitter -- which needs to process 15 million URLs/day -- for a bit under $800/day.
Face recognition: A literature survey

As one of the most successful applications of image analysis and understanding, face recognition has recently received significant attention, especially during the past several years. At least two reasons account for this trend: the first is the wide range of commercial and law enforcement applications, and the second is the availability of feasible technologies after 30 years of research. Even though current machine recognition systems have reached a certain level of maturity, their success is limited by the conditions imposed by many real applications. For example, recognition of face images acquired in an outdoor environment with changes in illumination and/or pose remains a largely unsolved problem. In other words, current systems are still far away from the capability of the human perception system.This paper provides an up-to-date critical survey of still- and video-based face recognition research. There are two underlying motivations for us to write this survey paper: the first is to provide an up-to-date review of the existing literature, and the second is to offer some insights into the studies of machine recognition of faces. To provide a comprehensive survey, we not only categorize existing recognition techniques but also present detailed descriptions of representative methods within each category. In addition, relevant topics such as psychophysical studies, system evaluation, and issues of illumination and pose variation are covered.
From Hierarchical to Open Access Electric Power Systems

In this paper the modeling, monitoring, and control of electric power systems are presented from the point of view of large-scale dynamic systems. First a summary of current hierarchical operations is given, together with an assessment of the underlying assumptions. Next, the challenge of operating electric power systems over very broad ranges of system conditions is presented as an open sensing, estimation, and control problem. The latter part of this paper is motivated by fundamental technological and organizational changes. These are requiring a shift from hierarchical to multilayered open access modeling, monitoring, and control paradigms for large complex electric power systems. A vision of a novel information-based multilayered dynamic energy control protocols (DECPs) framework for facilitating evolution into open access just-in-time (JIT) and just-in-place (JIP) electricity services of the future is presented.
Consistent Generative Query Networks

Stochastic video prediction is usually framed as an extrapolation problem where the goal is to sample a sequence of consecutive future image frames conditioned on a sequence of observed past frames. For the most part, algorithms for this task generate future video frames sequentially in an autoregressive fashion, which is slow and requires the input and output to be consecutive. We introduce a model that overcomes these drawbacks – it learns to generate a global latent representation from an arbitrary set of frames within a video. This representation can then be used to simultaneously and efficiently sample any number of temporally consistent frames at arbitrary time-points in the video. We apply our model to synthetic video prediction tasks and achieve results that are comparable to state-of-the-art video prediction models. In addition, we demonstrate the flexibility of our model by applying it to 3D scene reconstruction where we condition on location instead of time. To the best of our knowledge, our model is the first to provide flexible and coherent prediction on stochastic video datasets, as well as consistent 3D scene samples. Please check the project website https://bit.ly/2jX7Vyu to view scene reconstructions and videos produced by our model.
Improving Cost, Performance, and Security of Memory Encryption and Authentication

Protection from hardware attacks such as snoopers and mod chips has been receiving increasing attention in computer architecture. This paper presents a new combined memory encryption/authentication scheme. Our new split counters for counter-mode encryption simultaneously eliminate counter overflow problems and reduce per-block counter size, and we also dramatically improve authentication performance and security by using the Galois/Counter Mode of operation (GCM), which leverages counter-mode encryption to reduce authentication latency and overlap it with memory accesses. Our results indicate that the split-counter scheme has a negligible overhead even with a small (32KB) counter cache and using only eight counter bits per data block. The combined encryption/authentication scheme has an IPC overhead of 5% on average across SPEC CPU 2000 benchmarks, which is a significant improvement over the 20% overhead of existing encryption/authentication schemes.
Design and perceptual validation of performance measures for salient object segmentation

Empirical evaluation of salient object segmentation methods requires i) a dataset of ground truth object segmentations and ii) a performance measure to compare the output of the algorithm with the ground truth. In this paper, we provide such a dataset, and evaluate 5 distinct performance measures that have been used in the literature practically and psychophysically. Our results suggest that a measure based upon minimal contour mappings is most sensitive to shape irregularities and most consistent with human judgements. In fact, the contour mapping measure is as predictive of human judgements as human subjects are of each other. Region-based methods, and contour methods such as Hausdorff distances that do not respect the ordering of points on shape boundaries are significantly less consistent with human judgements. We also show that minimal contour mappings can be used as the correspondence paradigm for Precision-Recall analysis. Our findings can provide guidance in evaluating the results of segmentation algorithms in the future.
A comprehensive review of the psychological effects of brainwave entrainment.

Brainwave entrainment (BWE), which uses rhythmic stimuli to alter brainwave frequency and thus brain states, has been investigated and used since the late 1800s, yet many clinicians and scientists are unaware of its existence. We aim to raise awareness and discuss its potential by presenting a systematic review of the literature from peer-reviewed journals on the psychological effects of BWE.



Terms used to describe BWE and psychological outcomes were used to search English language studies from OVID Medline (1950-2007), PsychInfo (1806-2007), and Scopus.



Twenty studies selected satisfied the following criterion: studies needed to use rhythmic stimuli with the aim ofaffecting psychological outcomes. Peer-reviewed experimental and quasi-experimental studies were accepted. Case studies and review articles were excluded. Psychological outcomes were measured using standard assessment methods or as deemed appropriate by peer review.



Other clinical measurements, including electroencephalogram response, galvanic skin response, and neurotransmitter levels were not included.



Psychological outcomes addressed cognition, stress and anxiety, pain relief, headaches or migraines, mood, behavior, and premenstrual syndrome (PMS). Protocols included the use of single, alternating, ascending, or descending frequencies or were determined by the subject, using auditory and/or photic stimulation. Studies examined single session effects and/or longer-term multiple session effects.



Findings to date suggest that BWE is an effective therapeutic tool. People suffering from cognitive functioning deficits, stress, pain, headache/migraines, PMS, and behavioral problems benefited from BWE. However, more controlled trials are needed to test additional protocols with outcomes.
The Gremlin graph traversal machine and language (invited talk)

Gremlin is a graph traversal machine and language designed, developed, and distributed by the Apache TinkerPop project. Gremlin, as a graph traversal machine, is composed of three interacting components: a graph, a traversal, and a set of traversers. The traversers move about the graph according to the instructions specified in the traversal, where the result of the computation is the ultimate locations of all halted traversers. A Gremlin machine can be executed over any supporting graph computing system such as an OLTP graph database and/or an OLAP graph processor. Gremlin, as a graph traversal language, is a functional language implemented in the user's native programming language and is used to define the traversal of a Gremlin machine. This article provides a mathematical description of Gremlin and details its automaton and functional properties. These properties enable Gremlin to naturally support imperative and declarative querying, host language agnosticism, user-defined domain specific languages, an extensible compiler/optimizer, single- and multi-machine execution models, hybrid depth- and breadth-first evaluation, as well as the existence of a Universal Gremlin Machine and its respective entailments.
Statistical Modeling : The Two Cultures

There are two cultures in the use of statistical modeling to reach conclusions from data. One assumes that the data are generated by a given stochastic data model. The other uses algorithmic models and treats the data mechanism as unknown. The statistical community has been committed to the almost exclusive use of data models. This commitment has led to irrelevant theory, questionable conclusions, and has kept statisticians from working on a large range of interesting current problems. Algorithmic modeling, both in theory and practice, has developed rapidly in fields outside statistics. It can be used both on large complex data sets and as a more accurate and informative alternative to data modeling on smaller data sets. If our goal as a field is to use data to solve problems, then we need to move away from exclusive dependence on data models and adopt a more diverse set of tools.
ActivityNet: A large-scale video benchmark for human activity understanding

In spite of many dataset efforts for human action recognition, current computer vision algorithms are still severely limited in terms of the variability and complexity of the actions that they can recognize. This is in part due to the simplicity of current benchmarks, which mostly focus on simple actions and movements occurring on manually trimmed videos. In this paper we introduce ActivityNet, a new large-scale video benchmark for human activity understanding. Our benchmark aims at covering a wide range of complex human activities that are of interest to people in their daily living. In its current version, ActivityNet provides samples from 203 activity classes with an average of 137 untrimmed videos per class and 1.41 activity instances per video, for a total of 849 video hours. We illustrate three scenarios in which ActivityNet can be used to compare algorithms for human activity understanding: untrimmed video classification, trimmed activity classification and activity detection.
Wallflower: Principles and Practice of Background Maintenance

Background maintenance is a frequent element of video surveillance systems. We develop Wallflower, a threecomponent system for background maintenance: the pixellevel component performs Wiener filtering to make probabilistic predictions of the expected background; the region-level component fills in homogeneous regions of foreground objects; and the frame-level component detects sudden, global changes in the image and swaps in better approximations of the background. We compare our system with 8 other background subtraction algorithms. Wallflower is shown to outperform previous algorithms by handling a greater set of the difficult situations that can occur. Finally, we analyze the experimental results and propose normative principles for background maintenance.
Detecting cohesive and 2-mode communities indirected and undirected networks

Networks are a general language for representing relational information among objects. An effective way to model, reason about, and summarize networks, is to discover sets of nodes with common connectivity patterns. Such sets are commonly referred to as network communities. Research on network community detection has predominantly focused on identifying communities of densely connected nodes in undirected networks.
 In this paper we develop a novel overlapping community detection method that scales to networks of millions of nodes and edges and advances research along two dimensions: the connectivity structure of communities, and the use of edge directedness for community detection. First, we extend traditional definitions of network communities by building on the observation that nodes can be densely interlinked in two different ways: In cohesive communities nodes link to each other, while in 2-mode communities nodes link in a bipartite fashion, where links predominate between the two partitions rather than inside them. Our method successfully detects both 2-mode as well as cohesive communities, that may also overlap or be hierarchically nested. Second, while most existing community detection methods treat directed edges as though they were undirected, our method accounts for edge directions and is able to identify novel and meaningful community structures in both directed and undirected networks, using data from social, biological, and ecological domains.
Instance Weighting for Domain Adaptation in NLP

Domain adaptation is an important problem in natural language processing (NLP) due to the lack of labeled data in novel domains. In this paper, we study the domain adaptation problem from the instance weighting perspective. We formally analyze and characterize the domain adaptation problem from a distributional view, and show that there are two distinct needs for adaptation, corresponding to the different distributions of instances and classification functions in the source and the target domains. We then propose a general instance weighting framework for domain adaptation. Our empirical results on three NLP tasks show that incorporating and exploiting more information from the target domain through instance weighting is effective.
Autonomous indoor object tracking with the Parrot AR.Drone

This article presents an image-based visual servoing system for indoor visual tracking of 3D moving objects by an Unmanned Aerial Vehicle. This system autonomously follows a 3D moving target object, maintaining it with a fixed distance and centered on its image plane. The initial setup is tested in a detailed simulation environment. The system is then validated on flights in indoor scenarios using the Parrot AR.Drone and the CMT tracker, demonstrating the robustness of the system to differences in object features, environmental clutter, and target trajectory. The obtained results indicate that the proposed system is suitable for complex controls task, such object surveillance and pursuit.
Physically Based Real-Time Translucency for Leaves

This paper presents a new shading model for real-time rendering of plant leaves that reproduces all important attributes of a leaf and allows for a large number of leaves to be shaded. In particular, we use a physically based model for accurate subsurface scattering on the translucent side of directly lit leaves. For real-time rendering of this model, we formulate it as an image convolution process and express the result in an efficient directional basis that is fast to evaluate. We also propose a data acquisition method for leaves that uses off-the-shelf devices.
eQTL Mapping Using RNA-seq Data.

As RNA-seq is replacing gene expression microarrays to assess genome-wide transcription abundance, gene expression Quantitative Trait Locus (eQTL) studies using RNA-seq have emerged. RNA-seq delivers two novel features that are important for eQTL studies. First, it provides information on allele-specific expression (ASE), which is not available from gene expression microarrays. Second, it generates unprecedentedly rich data to study RNA-isoform expression. In this paper, we review current methods for eQTL mapping using ASE and discuss some future directions. We also review existing works that use RNA-seq data to study RNA-isoform expression and we discuss the gaps between these works and isoform-specific eQTL mapping.