nils-herrmann/clean_scirepeval huggingface dataset:
Alternating Directions Dual Decomposition

We propose AD, a new algorithm for approximate maximum a posteriori (MAP) inference on factor graphs based on the alternating directions method of multipliers. Like dual decomposition algorithms, AD uses worker nodes to iteratively solve local subproblems and a controller node to combine these local solutions into a global update. The key characteristic of AD is that each local subproblem has a quadratic regularizer, leading to a faster consensus than subgradient-based dual decomposition, both theoretically and in practice. We provide closed-form solutions for these AD subproblems for binary pairwise factors and factors imposing first-order logic constraints. For arbitrary factors (large or combinatorial), we introduce an active set method which requires only an oracle for computing a local MAP configuration, making AD applicable to a wide range of problems. Experiments on synthetic and realworld problems show that AD compares favorably with the state-of-the-art.
The neural correlates of maternal and romantic love

Romantic and maternal love are highly rewarding experiences. Both are linked to the perpetuation of the species and therefore have a closely linked biological function of crucial evolutionary importance. Yet almost nothing is known about their neural correlates in the human. We therefore used fMRI to measure brain activity in mothers while they viewed pictures of their own and of acquainted children, and of their best friend and of acquainted adults as additional controls. The activity specific to maternal attachment was compared to that associated to romantic love described in our earlier study and to the distribution of attachment-mediating neurohormones established by other studies. Both types of attachment activated regions specific to each, as well as overlapping regions in the brain's reward system that coincide with areas rich in oxytocin and vasopressin receptors. Both deactivated a common set of regions associated with negative emotions, social judgment and 'mentalizing', that is, the assessment of other people's intentions and emotions. We conclude that human attachment employs a push-pull mechanism that overcomes social distance by deactivating networks used for critical social assessment and negative emotions, while it bonds individuals through the involvement of the reward circuitry, explaining the power of love to motivate and exhilarate.
Focused Trajectory Planning for autonomous on-road driving

On-road motion planning for autonomous vehicles is in general a challenging problem. Past efforts have proposed solutions for urban and highway environments individually. We identify the key advantages/shortcomings of prior solutions, and propose a novel two-step motion planning system that addresses both urban and highway driving in a single framework. Reference Trajectory Planning (I) makes use of dense lattice sampling and optimization techniques to generate an easy-to-tune and human-like reference trajectory accounting for road geometry, obstacles and high-level directives. By focused sampling around the reference trajectory, Tracking Trajectory Planning (II) generates, evaluates and selects parametric trajectories that further satisfy kinodynamic constraints for execution. The described method retains most of the performance advantages of an exhaustive spatiotemporal planner while significantly reducing computation.
Iterative Methods for Total Variation Denoising

Total Variation (TV) methods are very eeective for recovering \blocky", possibly discontinuous, images from noisy data. A xed point algorithm for minimizing a TV-penalized least squares functional is presented and compared to existing minimization schemes. A multigrid method for solving (large, sparse) linear subproblems is investigated. Numerical results are presented for one-and two-dimensional examples; in particular, the algorithm is applied to actual data obtained from confocal microscopy.
Antisocial behavior from a developmental psychopathology perspective.

This paper reviews research on chronic patterns of antisocial behavior and places this research into a developmental psychopathology framework. Specifically, research suggests that there are at least three important pathways through which children and adolescents can develop severe antisocial behaviors. One group of youth shows antisocial behavior that begins in adolescence, and two groups show antisocial behavior that begins in childhood but differ on the presence or absence of callous-unemotional traits. In outlining these distinct pathways to antisocial behavior, we have tried to illustrate some key concepts from developmental psychopathology such as equifinality and multifinality, the importance of understanding the interface between normal and abnormal development, and the importance of using multiple levels of analyses to advance causal theories. Finally, we discuss how this development model can be used to enhance existing interventions for antisocial individuals.
DFT-based Transformation Invariant Pooling Layer for Visual Classification

We propose a novel discrete Fourier transform-based pooling layer for convolutional neural networks. The DFT magnitude pooling replaces the traditional max/average pooling layer between the convolution and fully-connected layers to retain translation invariance and shape preserving (aware of shape difference) properties based on the shift theorem of the Fourier transform. Thanks to the ability to handle image misalignment while keeping important structural information in the pooling stage, the DFT magnitude pooling improves the classification accuracy significantly. In addition, we propose the DFT method for ensemble networks using the middle convolution layer outputs. The proposed methods are extensively evaluated on various classification tasks using the ImageNet, CUB 2010-2011, MIT Indoors, Caltech 101, FMD and DTD datasets. The AlexNet, VGG-VD 16, Inception-v3, and ResNet are used as the base networks, upon which DFT and DFT methods are implemented. Experimental results show that the proposed methods improve the classification performance in all networks and datasets.
Dynamic Adaptive Streaming over HTTP – Design Principles and Standards

In this paper, we provide some insight and background into the Dynamic Adaptive Streaming over HTTP (DASH) specifications as available from 3GPP and in draft version also from MPEG. Specifically, the 3GPP version provides a normative description of a Media Presentation, the formats of a Segment, and the delivery protocol. In addition, it adds an informative description on how a DASH Client may use the provided information to establish a streaming service for the user. The solution supports different service types (e.g., On-Demand, Live, Time-Shift Viewing), different features (e.g., adaptive bitrate switching, multiple language support, ad insertion, trick modes, DRM) and different deployment options. Design principles and some forward-looking considerations are provided.
Active Learning with Oracle Epiphany

We present a theoretical analysis of active learning with more realistic interactions with human oracles. Previous empirical studies have shown oracles abstaining on difficult queries until accumulating enough information to make label decisions. We formalize this phenomenon with an “oracle epiphany model” and analyze active learning query complexity under such oracles for both the realizable and the agnostic cases. Our analysis shows that active learning is possible with oracle epiphany, but incurs an additional cost depending on when the epiphany happens. Our results suggest new, principled active learning approaches with realistic oracles.
Can homomorphic encryption be practical?

The prospect of outsourcing an increasing amount of data storage and management to cloud services raises many new privacy concerns for individuals and businesses alike. The privacy concerns can be satisfactorily addressed if users encrypt the data they send to the cloud. If the encryption scheme is homomorphic, the cloud can still perform meaningful computations on the data, even though it is encrypted.
 In fact, we now know a number of constructions of fully homomorphic encryption schemes that allow arbitrary computation on encrypted data. In the last two years, solutions for fully homomorphic encryption have been proposed and improved upon, but it is hard to ignore the elephant in the room, namely efficiency -- can homomorphic encryption ever be efficient enough to be practical? Certainly, it seems that all known fully homomorphic encryption schemes have a long way to go before they can be used in practice. Given this state of affairs, our contribution is two-fold.
 First, we exhibit a number of real-world applications, in the medical, financial, and the advertising domains, which require only that the encryption scheme is "somewhat" homomorphic. Somewhat homomorphic encryption schemes, which support a limited number of homomorphic operations, can be much faster, and more compact than fully homomorphic encryption schemes.
 Secondly, we show a proof-of-concept implementation of the recent somewhat homomorphic encryption scheme of Brakerski and Vaikuntanathan, whose security relies on the "ring learning with errors" (Ring LWE) problem. The scheme is very efficient, and has reasonably short ciphertexts. Our unoptimized implementation in magma enjoys comparable efficiency to even optimized pairing-based schemes with the same level of security and homomorphic capacity. We also show a number of application-specific optimizations to the encryption scheme, most notably the ability to convert between different message encodings in a ciphertext.
Face Recognition Using Pyramid Histogram of Oriented Gradients and SVM

Face recognition has become an important issue in many applications such as security systems, credit card verification and criminal identification. The recently proposed shape descriptor, Pyramid Histogram of Oriented Gradients (PHOG) features, counts occurrences of gradient orientation in localized portions of an image and has been proved as an efficient tool for providing spatial distribution of edges. In this paper, we proposed to use pyramid histogram of oriented gradients (PHOG) as the features extracted for face recognition. The obtained feature vectors are fed up into Support Vector Machine (SVM) classifier for classification. Experiments carried out on UMIST Face Database databases that both frontal head images and rotated head images are investigated. The experimental results show this approach can be used to automatically identify the faces more effective.
Blue-Fi: enhancing Wi-Fi performance using bluetooth signals

Mobile devices are increasingly equipped with multiple network interfaces with complementary characteristics. In particular, the Wi-Fi interface has high throughput and transfer power efficiency, but its idle power consumption is prohibitive. In this paper we present,  Blue-Fi, a sytem that predicts the availability of the Wi-Fi connectivity by using a combination of bluetooth contact-patterns and cell-tower information. This allows the device to intelligently switch the Wi-Fi interface on only when there is Wi-Fi connectivity available, thus avoiding the long periods in idle state and significantly reducing the the number of scans for discovery.
 Our prediction results on traces collected from real users show an average coverage of 94% and an average accuracy of 84%, a 47% accuracy improvement over pure cell-tower based prediction, and a 57% coverage improvement over the pure bluetooth based prediction. For our workload, Blue-Fi is up to 62% more energy efficient, which results in increasing our mobile device's lifetime by more than a day.
A Mixed Reality Virtual Clothes Try-On System

Virtual try-on of clothes has received much attention recently due to its commercial potential. It can be used for online shopping or intelligent recommendation to narrow down the selections to a few designs and sizes. In this paper, we present a mixed reality system for 3D virtual clothes try-on that enables a user to see herself wearing virtual clothes while looking at a mirror display, without taking off her actual clothes. The user can select various virtual clothes for trying-on. The system physically simulates the selected virtual clothes on the user's body in real-time and the user can see virtual clothes fitting on the her mirror image from various angles as she moves. The major contribution of this paper is that we automatically customize an invisible (or partially visible) avatar based on the user's body size and the skin color and use it for proper clothes fitting, alignment and clothes simulation in our virtual try-on system. We present three scenarios: i) virtual clothes on the avatar, ii) virtual clothes on the user's image and iii) virtual clothes on the avatar blended with the user's face image. We have conducted a user study to evaluate the effectiveness of these three solutions from the end user's perception of quality attributes, cognitive attributes and attitude towards using. The user study shows that among these three scenarios, the second one is most preferred by the users and for 50% of them the experience they had with our system was sufficient to make the purchase decision for the outfits they virtually tried-on.
Spike-Based Synaptic Plasticity in Silicon: Design, Implementation, Application, and Challenges

The ability to carry out signal processing, classification, recognition, and computation in artificial spiking neural networks (SNNs) is mediated by their synapses. In particular, through activity-dependent alteration of their efficacies, synapses play a fundamental role in learning. The mathematical prescriptions under which synapses modify their weights are termed synaptic plasticity rules. These learning rules can be based on abstract computational neuroscience models or on detailed biophysical ones. As these rules are being proposed and developed by experimental and computational neuroscientists, engineers strive to design and implement them in silicon and en masse in order to employ them in complex real-world applications. In this paper, we describe analog very large-scale integration (VLSI) circuit implementations of multiple synaptic plasticity rules, ranging from phenomenological ones (e.g., based on spike timing, mean firing rates, or both) to biophysically realistic ones (e.g., calcium-dependent models). We discuss the application domains, weaknesses, and strengths of various representative approaches proposed in the literature, and provide insight into the challenges that engineers face when designing and implementing synaptic plasticity rules in VLSI technology for utilizing them in real-world applications.
Proofs of Useful Work

We give Proofs of Work (PoWs) whose hardness is based on a wide array of computational problems, including Orthogonal Vectors, 3SUM, All-Pairs Shortest Path, and any problem that reduces to them (this includes deciding any graph property that is statable in first-order logic). This results in PoWs whose completion does not waste energy but instead is useful for the solution of computational problems of practical interest. The PoWs that we propose are based on delegating the evaluation of low-degree polynomials originating from the study of average-case fine-grained complexity. We prove that, beyond being hard on the average (based on worst-case hardness assumptions), the task of evaluating our polynomials cannot be amortized across multiple instances. For applications such as Bitcoin, which use PoWs on a massive scale, energy is typically wasted in huge proportions. We give a framework that can utilize such otherwise wasteful work.
Analysis of human faces using a measurement-based skin reflectance model

We have measured 3D face geometry, skin reflectance, and subsurface scattering using custom-built devices for 149 subjects of varying age, gender, and race. We developed a novel skin reflectance model whose parameters can be estimated from measurements. The model decomposes the large amount of measured skin data into a spatially-varying analytic BRDF, a diffuse albedo map, and diffuse subsurface scattering. Our model is intuitive, physically plausible, and -- since we do not use the original measured data -- easy to edit as well. High-quality renderings come close to reproducing real photographs. The analysis of the model parameters for our sample population reveals variations according to subject age, gender, skin type, and external factors (e.g., sweat, cold, or makeup). Using our statistics, a user can edit the overall appearance of a face (e.g., changing skin type and age) or change small-scale features using texture synthesis (e.g., adding moles and freckles). We are making the collected statistics publicly available to the research community for applications in face synthesis and analysis.
1-Bit Matrix Completion

In this paper we develop a theory of matrix completion for the extreme case of noisy 1-bit observations. Instead of observing a subset of the real-valued entries of a matrix M , we obtain a small number of binary (1-bit) measurements generated according to a probability distribution determined by the realvalued entries of M . The central question we ask is whether or not it is possible to obtain an accurate estimate of M from this data. In general this would seem impossible, but we show that the maximum likelihood estimate under a suitable constraint returns an accurate estimate of M when ‖M‖∞ ≤ α and rank(M) ≤ r. If the log-likelihood is a concave function (e.g., the logistic or probit observation models), then we can obtain this maximum likelihood estimate by optimizing a convex program. In addition, we also show that if instead of recovering M we simply wish to obtain an estimate of the distribution generating the 1-bit measurements, then we can eliminate the requirement that ‖M‖∞ ≤ α. For both cases, we provide lower bounds showing that these estimates are near-optimal. We conclude with a suite of experiments that both verify the implications of our theorems as well as illustrate some of the practical applications of 1-bit matrix completion. In particular, we compare our program to standard matrix completion methods on movie rating data in which users submit ratings from 1 to 5. In order to use our program, we quantize this data to a single bit, but we allow the standard matrix completion program to have access to the original ratings (from 1 to 5). Surprisingly, the approach based on binary data performs significantly better.
A model to estimate intrinsic document relevance from the clickthrough logs of a web search engine

We propose a new model to interpret the clickthrough logs of a web search engine. This model is based on explicit assumptions on the user behavior. In particular, we draw conclusions on a document relevance by observing the user behavior after he examined the document and not based on whether a user clicks or not a document url. This results in a model based on intrinsic relevance, as opposed to perceived relevance. We use the model to predict document relevance and then use this as feature for a "Learning to Rank" machine learning algorithm. Comparing the ranking functions obtained by training the algorithm with and without the new feature we observe surprisingly good results. This is particularly notable given that the baseline we use is the heavily optimized ranking function of a leading commercial search engine. A deeper analysis shows that the new feature is particularly helpful for non navigational queries and queries with a large abandonment rate or a large average number of queries per session. This is important because these types of query is considered to be the most difficult to solve.
Explicitly representing expected cost: an alternative to ROC representation

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Anonymizing sequential releases

An organization makes a new release as new information become available, releases a tailored view for each data request, releases sensitive information and identifying information separately. The availability of related releases sharpens the identification of individuals by a global quasi-identifier consisting of attributes from related releases. Since it is not an option to anonymize previously released data, the current release must be anonymized to ensure that a global quasi-identifier is not effective for identification. In this paper, we study the sequential anonymization problem under this assumption. A key question is how to anonymize the current release so that it cannot be linked to previous releases yet remains useful for its own release purpose. We introduce the lossy join, a negative property in relational database design, as a way to hide the join relationship among releases, and propose a scalable and practical solution.
LSUN: Construction of a Large-scale Image Dataset using Deep Learning with Humans in the Loop

While there has been remarkable progress in the performance of visual recognition algorithms, the state-of-the-art models tend to be exceptionally data-hungry. Large labeled training datasets, expensive and tedious to produce, are required to optimize millions of parameters in deep network models. Lagging behind the growth in model capacity, the available datasets are quickly becoming outdated in terms of size and density. To circumvent this bottleneck, we propose to amplify human effort through a partially automated labeling scheme, leveraging deep learning with humans in the loop. Starting from a large set of candidate images for each category, we iteratively sample a subset, ask people to label them, classify the others with a trained model, split the set into positives, negatives, and unlabeled based on the classification confidence, and then iterate with the unlabeled set. To assess the effectiveness of this cascading procedure and enable further progress in visual recognition research, we construct a new image dataset, LSUN. It contains around one million labeled images for each of 10 scene categories and 20 object categories. We experiment with training popular convolutional networks and find that they achieve substantial performance gains when trained on this dataset.
Approximating dynamic global illumination in image space

Physically plausible illumination at real-time framerates is often achieved using approximations. One popular example is ambient occlusion (AO), for which very simple and efficient implementations are used extensively in production. Recent methods approximate AO between nearby geometry in screen space (SSAO). The key observation described in this paper is, that screen-space occlusion methods can be used to compute many more types of effects than just occlusion, such as directional shadows and indirect color bleeding. The proposed generalization has only a small overhead compared to classic SSAO, approximates direct and one-bounce light transport in screen space, can be combined with other methods that simulate transport for macro structures and is visually equivalent to SSAO in the worst case without introducing new artifacts. Since our method works in screen space, it does not depend on the geometric complexity. Plausible directional occlusion and indirect lighting effects can be displayed for large and fully dynamic scenes at real-time frame rates.