nils-herrmann/clean_scirepeval huggingface dataset:
Perpetual environmentally powered sensor networks

Environmental energy is an attractive power source for low power wireless sensor networks. We present Prometheus, a system that intelligently manages energy transfer for perpetual operation without human intervention or servicing. Combining positive attributes of different energy storage elements and leveraging the intelligence of the microprocessor, we introduce an efficient multi-stage energy transfer system that reduces the common limitations of single energy storage systems to achieve near perpetual operation. We present our design choices, tradeoffs, circuit evaluations, performance analysis, and models. We discuss the relationships between system components and identify optimal hardware choices to meet an application's needs. Finally we present our implementation of a real system that uses solar energy to power Berkeley's Telos Mote. Our analysis predicts the system will operate for 43 years under 1% load, 4 years under 10% load, and 1 year under 100% load. Our implementation uses a two stage storage system consisting of supercapacitors (primary buffer) and a lithium rechargeable battery (secondary buffer). The mote has full knowledge of power levels and intelligently manages energy transfer to maximize lifetime.
Deep Neural Network Approximation Theory

Deep neural networks have become state-of-the-art technology for a wide range of practical machine learning tasks such as image classification, handwritten digit recognition, speech recognition, or game intelligence. This paper develops the fundamental limits of learning in deep neural networks by characterizing what is possible if no constraints on the learning algorithm and the amount of training data are imposed. Concretely, we consider informationtheoretically optimal approximation through deep neural networks with the guiding theme being a relation between the complexity of the function (class) to be approximated and the complexity of the approximating network in terms of connectivity and memory requirements for storing the network topology and the associated quantized weights. The theory we develop educes remarkable universality properties of deep networks. Specifically, deep networks are optimal approximants for vastly different function classes such as affine systems and Gabor systems. Affine systems are generated by the affine group (scalings and translations) whereas Gabor systems are generated by the Weyl-Heisenberg group (time-shifts and modulations). This universality is afforded by a concurrent invariance property of deep networks to time-shifts, scalings, and frequency-shifts. In addition, deep networks provide exponential approximation accuracy— i.e., the approximation error decays exponentially in the number of non-zero weights in the network—of vastly different functions such as the squaring operation, multiplication, polynomials, sinusoidal functions, general smooth functions, and even one-dimensional oscillatory textures and fractal functions such as the Weierstrass function, both of which do not have any known methods achieving exponential approximation accuracy. In summary, deep neural networks provide information-theoretically optimal approximation of a very wide range of functions and function classes used in mathematical signal processing. We also show that in the approximation of sufficiently smooth functions finite-width deep networks require strictly smaller connectivity than finite-depth wide networks.
Achieving Privacy in the Adversarial Multi-Armed Bandit

In this paper, we improve the previously best known regret bound to achieve -differential privacy in oblivious adversarial bandits from O(T / ) to O( √ T lnT/ ). This is achieved by combining a Laplace Mechanism with EXP3. We show that though EXP3 is already differentially private, it leaks a linear amount of information in T . However, we can improve this privacy by relying on its intrinsic exponential mechanism for selecting actions. This allows us to reach O( √ lnT )-DP, with a regret of O(T ) that holds against an adaptive adversary, an improvement from the best known of O(T ). This is done by using an algorithm that run EXP3 in a mini-batch loop. Finally, we run experiments that clearly demonstrate the validity of our theoretical analysis.
Millimeter-Wave Microstrip Comb-Line Antenna Using Reflection-Canceling Slit Structure

A microstrip comb-line antenna is developed in the millimeter-wave band. When the element spacing is one guide wavelength for the broadside beam in the traveling-wave excitation, reflections from all the radiating elements are synthesized in phase. Therefore, the return loss increases significantly. Furthermore, re-radiation from elements due to the reflection wave degrades the design accuracy for the required radiation pattern. We propose the way to improve the reflection characteristic of the antenna with arbitrary beam directions including strictly a broadside direction. To suppress the reflection, we propose a reflection-canceling slit structure installed on the feeding line around each radiating element. A 27-element linear array antenna with a broadside beam is developed at 76.5 GHz. To confirm the feasibility of the simple design procedure, the performance is evaluated through the measurement in the millimeter-wave band.
Does Barbie make girls want to be thin? The effect of experimental exposure to images of dolls on the body image of 5- to 8-year-old girls.

The ubiquitous Barbie doll was examined in the present study as a possible cause for young girls' body dissatisfaction. A total of 162 girls, from age 5 to age 8, were exposed to images of either Barbie dolls, Emme dolls (U.S. size 16), or no dolls (baseline control) and then completed assessments of body image. Girls exposed to Barbie reported lower body esteem and greater desire for a thinner body shape than girls in the other exposure conditions. However, this immediate negative impact of Barbie doll was no longer evident in the oldest girls. These findings imply that, even if dolls cease to function as aspirational role models for older girls, early exposure to dolls epitomizing an unrealistically thin body ideal may damage girls' body image, which would contribute to an increased risk of disordered eating and weight cycling.
CONVOLUTIONAL SEQUENCE LEARNING

We present Deep Voice 3, a fully-convolutional attention-based neural textto-speech (TTS) system. Deep Voice 3 matches state-of-the-art neural speech synthesis systems in naturalness while training an order of magnitude faster. We scale Deep Voice 3 to dataset sizes unprecedented for TTS, training on more than eight hundred hours of audio from over two thousand speakers. In addition, we identify common error modes of attention-based speech synthesis networks, demonstrate how to mitigate them, and compare several different waveform synthesis methods. We also describe how to scale inference to ten million queries per day on a single GPU server.
WILDSENSING: Design and deployment of a sustainable sensor network for wildlife monitoring

The increasing adoption of wireless sensor network technology in a variety of applications, from agricultural to volcanic monitoring, has demonstrated their ability to gather data with unprecedented sensing capabilities and deliver it to a remote user. However, a key issue remains how to maintain these sensor network deployments over increasingly prolonged deployments. In this article, we present the challenges that were faced in maintaining continual operation of an automated wildlife monitoring system over a one-year period. This system analyzed the social colocation patterns of European badgers (Meles meles) residing in a dense woodland environment using a hybrid RFID-WSN approach. We describe the stages of the evolutionary development, from implementation, deployment, and testing, to various iterations of software optimization, followed by hardware enhancements, which in turn triggered the need for further software optimization. We highlight the main lessons learned: the need to factor in the maintenance costs while designing the system; to consider carefully software and hardware interactions; the importance of rapid prototyping for initial deployment (this was key to our success); and the need for continuous interaction with domain scientists which allows for unexpected optimizations.
Mobile ECG measurement and analysis system using mobile phone as the base station

In this paper, we introduce an ECG measurement, analysis and transmission system which uses a mobile phone as a base station. The system is based on a small-sized mobile ECG recording device which sends measurement data wirelessly to the mobile phone. In the mobile phone the received data is analyzed and if abnormalities are found part of the measurement data is sent to a server for the use of medical personnel. The prototype of the system was made with a portable ECG monitor and Nokia 6681 mobile phone. The results show that modern smart phones are indeed capable for this kind of tasks. Thus, with their good networking and data processing capabilities, they are a potential part of the future wireless health care systems.
Smart irrigation using low-cost moisture sensors and XBee-based communication

Deficiency in fresh water resources globally has raised serious alarms in the last decade. Efficient management of water resources play an important role in the agriculture sector. Unfortunately, this is not given prime importance in the third world countries because of adhering to traditional practices. This paper presents a smart system that uses a bespoke, low cost soil moisture sensor to control water supply in water deficient areas. The sensor, which works on the principle of moisture dependent resistance change between two points in the soil, is fabricated using affordable materials and methods. Moisture data acquired from a sensor node is sent through XBEE wireless communication modules to a centralized server that controls water supply. A user-friendly interface is developed to visualize the daily moisture data. The low-cost and wireless nature of the sensing hardware presents the possibility to monitor the moisture levels of large agricultural fields. Moreover, the proposed moisture sensing method has the ability to be incorporated into an automated drip-irrigation scheme and perform automated, precision agriculture in conjunction with de-centralized water control.
Thickness Reduction and Performance Enhancement of Steerable Square Loop Antenna Using Hybrid High Impedance Surface

In order to reduce the thickness of a steerable beam square loop antenna, the effects of combining it with various periodic high impedance surfaces (HISs) are investigated. When a via-less HIS is used, the radiation pattern has high side lobes, which are shown to be due to surface waves propagating in the HIS lattice. Using a HIS with vias between the plates and ground removes the surface waves, but the beam is distorted due to strong coupling between the HIS's vias and the antenna element. Consequently a hybrid HIS is designed. This uses a via-less lattice beneath the loop, with vias at the edge of the HIS to suppress surface wave propagation. Consequently, a square loop antenna with four feeds on a hybrid HIS substrate is proposed for beam steering applications. This antenna has a low-profile with a total thickness of 4.69 mm for a test frequency band of 4.3 GHz to 5.0 GHz. It exhibits a gain of 8.65 dB at the test frequency (4.7 GHz). Compared to the earlier reported steerable square loop antenna, the new antenna achieves a 61% reduction in substrate thickness, a bandwidth enhancement by 150 MHz and an increase in gain by 1.95 dB.
A learning algorithm for adaptive canonical correlation analysis of several data sets

Canonical correlation analysis (CCA) is a classical tool in statistical analysis to find the projections that maximize the correlation between two data sets. In this work we propose a generalization of CCA to several data sets, which is shown to be equivalent to the classical maximum variance (MAXVAR) generalization proposed by Kettenring. The reformulation of this generalization as a set of coupled least squares regression problems is exploited to develop a neural structure for CCA. In particular, the proposed CCA model is a two layer feedforward neural network with lateral connections in the output layer to achieve the simultaneous extraction of all the CCA eigenvectors through deflation. The CCA neural model is trained using a recursive least squares (RLS) algorithm. Finally, the convergence of the proposed learning rule is proved by means of stochastic approximation techniques and their performance is analyzed through simulations.
Unsupervised Domain Adaptation for Personalized Facial Emotion Recognition

The way in which human beings express emotions depends on their specific personality and cultural background. As a consequence, person independent facial expression classifiers usually fail to accurately recognize emotions which vary between different individuals. On the other hand, training a person-specific classifier for each new user is a time consuming activity which involves collecting hundreds of labeled samples. In this paper we present a personalization approach in which only unlabeled target-specific data are required. The method is based on our previous paper [20] in which a regression framework is proposed to learn the relation between the user's specific sample distribution and the parameters of her/his classifier. Once this relation is learned, a target classifier can be constructed using only the new user's sample distribution to transfer the personalized parameters. The novelty of this paper with respect to [20] is the introduction of a new method to represent the source sample distribution based on using only the Support Vectors of the source classifiers. Moreover, we present here a simplified regression framework which achieves the same or even slightly superior experimental results with respect to [20] but it is much easier to reproduce.
MED: The LMU System for the SIGMORPHON 2016 Shared Task on Morphological Reinflection

This paper presents MED, the main system of the LMU team for the SIGMORPHON 2016 Shared Task on Morphological Reinflection as well as an extended analysis of how different design choices contribute to the final performance. We model the task of morphological reinflection using neural encoder-decoder models together with an encoding of the input as a single sequence of the morphological tags of the source and target form as well as the sequence of letters of the source form. The Shared Task consists of three subtasks, three different tracks and covers 10 different languages to encourage the use of language-independent approaches. MED was the system with the overall best performance, demonstrating our method generalizes well for the low-resource setting of the SIGMORPHON 2016
Automatic Filling of Hidden Web Forms: A Survey

A significant part of the information available on the Web is stored in online databases which compose what is known as Hidden Web or Deep Web. In order to access information from the Hidden Web, one must fill an HTML form that is submitted as a query to the underlying database. In recent years, many works have focused on how to automate the process of form filling by creating methods for choosing values to fill the fields in the forms. This is a challenging task since forms may contain fields for which there are no predefined values to choose from. This article presents a survey of methods for Web Form Filling, analyzing the existing solutions with respect to the type of forms that they handle and the filling strategy adopted. We provide a comparative analysis of 15 key works in this area and discuss directions for future research.
An Energy-Efficient Middleware for Computation Offloading in Real-Time Embedded Systems

Embedded systems have limited resources, such as computation capabilities and battery life. The Dynamic Voltage and Frequency Scaling (DVFS) technique is used to save energy by running the processor of the embedded system at low voltage and frequency levels. However, this prolongs the execution time, which may cause potential deadline misses for real-time tasks. In this paper, we propose a general-purpose middleware to reduce the energy consumption in embedded systems without violating the real-time constraints. The algorithms in the middleware adopt the computation offloading concept to reduce the workload on the processor of the embedded system by sending the computation-intensive tasks to a powerful server. The algorithms are further combined with the DVFS technique to find the running frequency (or speed) such that the energy consumption is minimized and the real-time constraints are satisfied. The evaluation shows that our approach reduces the average energy consumption down to nearly 60%, compared to executing all the tasks locally at the maximum processor speed.
A Cooperative Coevolutionary Approach to Function Optimization

A general model for the coevolution of cooperating species is presented. This model is instantiated and tested in the domain of function optimization, and compared with a traditional GA-based function optimizer. The results are encouraging in two respects. They suggest ways in which the performance of GA and other EA-based optimizers can be improved, and they suggest a new approach to evolving complex structures such as neural networks and rule sets.
Robustness and generalization

We derive generalization bounds for learning algorithms based on their robustness: the property that if a testing sample is “similar” to a training sample, then the testing error is close to the training error. This provides a novel approach, different from complexity or stability arguments, to study generalization of learning algorithms. One advantage of the robustness approach, compared to previous methods, is the geometric intuition it conveys. Consequently, robustness-based analysis is easy to extend to learning in non-standard setups such as Markovian samples or quantile loss. We further show that a weak notion of robustness is both sufficient and necessary for generalizability, which implies that robustness is a fundamental property that is required for learning algorithms to work.
Mention and Entity Description Co-Attention for Entity Disambiguation

For the task of entity disambiguation, mention contexts and entity descriptions both contain various kinds of information content while only a subset of them are helpful for disambiguation. In this paper, we propose a type-aware co-attention model for entity disambiguation, which tries to identify the most discriminative words from mention contexts and most relevant sentences from corresponding entity descriptions simultaneously. To bridge the semantic gap between mention contexts and entity descriptions, we further incorporate entity type information to enhance the co-attention mechanism. Our evaluation shows that the proposed model outperforms the state-of-the-arts on three public datasets. Further analysis also confirms that both the co-attention mechanism and the type-aware mechanism are effective.
Towards Instrument Segmentation for Music Content Description: a Critical Review of Instrument Classification Techniques

A system capable of describing the musical content of any kind of soundfile or soundstream, as it is supposed to be done in MPEG7-compliant applications, should provide an account of the different moments where a certain instrument can be listened to. This segmentation according to instrument taxonomies must be solved with different strategies than segmentation according to perceptual features. In this paper we concentrate on reviewing the different techniques that have been so far proposed for automatic classification of musical instruments. Although the ultimate goal should be the segmentation of complex sonic mixtures, it is still far from being solved. Therefore, the practical approach is to reduce the scope of the classification systems to only deal with isolated, and out-of-context, sounds. There is an obvious tradeoff in endorsing this strategy: we gain simplicity and tractability, but we lose contextual and time-dependent cues that can be exploited as relevant features for classifying the sounds.
Towards a Better Understanding of Context and Context-Awareness

The use of context is important in interactive applications. It is particularly important for applications where the user’s context is changing rapidly, such as in both handheld and ubiquitous computing. In order to better understand how we can use context and facilitate the building of context-aware applications, we need to more fully understand what constitutes a contextaware application and what context is. Towards this goal, we have surveyed existing work in context-aware computing. In this paper, we provide an overview of the results of this survey and, in particular, definitions and categories of context and context-aware. We conclude with recommendations for how this better understanding of context inform a framework for the development of context-aware applications.
Diagnosis, prevention, and treatment of catheter-associated urinary tract infection in adults: 2009 International Clinical Practice Guidelines from the Infectious Diseases Society of America.

Guidelines for the diagnosis, prevention, and management of persons with catheter-associated urinary tract infection (CA-UTI), both symptomatic and asymptomatic, were prepared by an Expert Panel of the Infectious Diseases Society of America. The evidence-based guidelines encompass diagnostic criteria, strategies to reduce the risk of CA-UTIs, strategies that have not been found to reduce the incidence of urinary infections, and management strategies for patients with catheter-associated asymptomatic bacteriuria or symptomatic urinary tract infection. These guidelines are intended for use by physicians in all medical specialties who perform direct patient care, with an emphasis on the care of patients in hospitals and long-term care facilities.
Constrained delaunay triangulations

Given a set ofn vertices in the plane together with a set of noncrossing, straight-line edges, theconstrained Delaunay triangulation (CDT) is the triangulation of the vertices with the following properties: (1) the prespecified edges are included in the triangulation, and (2) it is as close as possible to the Delaunay triangulation. We show that the CDT can be built in optimalO(n logn) time using a divide-and-conquer technique. This matches the time required to build an arbitrary (unconstrained) Delaunay triangulation and the time required to build an arbitrary constrained (non-Delaunay) triagulation. CDTs, because of their relationship with Delaunay triangulations, have a number of properties that make them useful for the finite-element method. Applications also include motion planning in the presence of polygonal obstacles and constrained Euclidean minimum spanning trees, spanning trees subject to the restriction that some edges are prespecified.
Multiview Absolute Pose Using 3D – 2D Perspective Line Correspondences and Vertical Direction

In this paper, we address the problem of estimating the absolute pose of a multiview calibrated perspective camera system from 3D - 2D line correspondences. We assume, that the vertical direction is known, which is often the case when the camera system is coupled with an IMU sensor, but it can also be obtained from vanishing points constructed in the images. Herein, we propose two solutions, both can be used as a minimal solver as well as a least squares solver without reformulation. The first solution consists of a single linear system of equations, while the second solution yields a polynomial equation of degree three in one variable and one systems of linear equations which can be efficiently solved in closed-form. The proposed algorithms have been evaluated on various synthetic datasets as well as on real data. Experimental results confirm state of the art performance both in terms of quality and computing time.