nils-herrmann/clean_scirepeval huggingface dataset:
Transfer printing techniques for materials assembly and micro/nanodevice fabrication.

Transfer printing represents a set of techniques for deterministic assembly of micro-and nanomaterials into spatially organized, functional arrangements with two and three-dimensional layouts. Such processes provide versatile routes not only to test structures and vehicles for scientific studies but also to high-performance, heterogeneously integrated functional systems, including those in flexible electronics, three-dimensional and/or curvilinear optoelectronics, and bio-integrated sensing and therapeutic devices. This article summarizes recent advances in a variety of transfer printing techniques, ranging from the mechanics and materials aspects that govern their operation to engineering features of their use in systems with varying levels of complexity. A concluding section presents perspectives on opportunities for basic and applied research, and on emerging use of these methods in high throughput, industrial-scale manufacturing.
The State of Automated Bridge Play

The game of Bridge provides a number of research areas to AI researchers due to the many components that constitute the game. Bidding provides the subtle challenge of potential outcome maximization while learning through information gathering, but constrained to a limited rule set. Declarer play can be accomplished through planning and inference. Both the bidding and the play can also be accomplished through Monte Carlo analysis using a perfect information solver. Double-dummy play is a perfect information search, but over an enormous state-space, and thus requires α-β pruning, transposition tables and other tree-minimization techniques. As such, researchers have made much progress in each of these sub-fields over the years, particularly double-dummy play, but are yet to produce a consistent expert level player.
Your AP knows how you move: fine-grained device motion recognition through WiFi

Recent WiFi standards use Channel State Information (CSI) feedback for better MIMO and rate adaptation. CSI provides detailed information about current channel conditions for different subcarriers and spatial streams. In this paper, we show that CSI feedback from a client to the AP can be used to recognize different fine-grained motions of the client. We find that CSI can not only identify if the client is in motion or not, but also classify different types of motions. To this end, we propose APsense, a framework that uses CSI to estimate the sensor patterns of the client. It is observed that client's sensor (e.g. accelerometer) values are correlated to CSI values available at the AP. We show that using simple machine learning classifiers, APsense can classify different motions with accuracy as high as 90%.
From image parsing to painterly rendering

We present a semantics-driven approach for stroke-based painterly rendering, based on recent image parsing techniques [Tu et al. 2005; Tu and Zhu 2006] in computer vision. Image parsing integrates segmentation for regions, sketching for curves, and recognition for object categories. In an interactive manner, we decompose an input image into a hierarchy of its constituent components in a parse tree representation with occlusion relations among the nodes in the tree. To paint the image, we build a brush dictionary containing a large set (760) of brush examples of four shape/appearance categories, which are collected from professional artists, then we select appropriate brushes from the dictionary and place them on the canvas guided by the image semantics included in the parse tree, with each image component and layer painted in various styles. During this process, the scene and object categories also determine the color blending and shading strategies for inhomogeneous synthesis of image details. Compared with previous methods, this approach benefits from richer meaningful image semantic information, which leads to better simulation of painting techniques of artists using the high-quality brush dictionary. We have tested our approach on a large number (hundreds) of images and it produced satisfactory painterly effects.
A probabilistic image jigsaw puzzle solver

We explore the problem of reconstructing an image from a bag of square, non-overlapping image patches, the jigsaw puzzle problem. Completing jigsaw puzzles is challenging and requires expertise even for humans, and is known to be NP-complete. We depart from previous methods that treat the problem as a constraint satisfaction problem and develop a graphical model to solve it. Each patch location is a node and each patch is a label at nodes in the graph. A graphical model requires a pairwise compatibility term, which measures an affinity between two neighboring patches, and a local evidence term, which we lack. This paper discusses ways to obtain these terms for the jigsaw puzzle problem. We evaluate several patch compatibility metrics, including the natural image statistics measure, and experimentally show that the dissimilarity-based compatibility – measuring the sum-of-squared color difference along the abutting boundary – gives the best results. We compare two forms of local evidence for the graphical model: a sparse-and-accurate evidence and a dense-and-noisy evidence. We show that the sparse-and-accurate evidence, fixing as few as 4 – 6 patches at their correct locations, is enough to reconstruct images consisting of over 400 patches. To the best of our knowledge, this is the largest puzzle solved in the literature. We also show that one can coarsely estimate the low resolution image from a bag of patches, suggesting that a bag of image patches encodes some geometric information about the original image.
The Starr County Diabetes Education Study: development of the Spanish-language diabetes knowledge questionnaire.

This study reports the psychometric properties of the 24-item version of the Diabetes Knowledge Questionnaire (DKQ).



The original 60-item DKQ was administered to 502 adult Mexican-Americans with type 2 diabetes who are part of the Starr County Diabetes Education Study. The sample was composed of 252 participants and 250 support partners. The subjects were randomly assigned to the educational and social support intervention (n = 250) or to the wait-listed control group (n = 252). A shortened 24-item version of the DKQ was derived from the original instrument after data collection was completed. Reliability was assessed by means of Cronbach's coefficient alpha. To determine validity, differentiation between the experimental and control groups was conducted at baseline and after the educational portion of the intervention.



The 24-item version of the DKQ (DKQ-24) attained a reliability coefficient of 0.78, indicating internal consistency, and showed sensitivity to the intervention, suggesting construct validation.



The DKQ-24 is a reliable and valid measure of diabetes-related knowledge that is relatively easy to administer to either English or Spanish speakers.
Yelp Food Recommendation System

We apply principles and techniques of recommendation systems to develop a predictive model of customers’ restaurant ratings. Using Yelp’s dataset, we extract collaborative and content based features to identify customer and restaurant profiles. In particular, we implement singular value decomposition, hybrid cascade of K-nearest neighbor clustering, weighted bi-partite graph projection, and several other learning algorithms. Using Root metrics Mean Squared Error and Mean Absolute Error, we then evaluate and compare the algorithms’ performances.
Viral Marketing – More than a

The goal of the study is to formalize the concept of viral marketing (VM) as a close derivative of contagion models from epidemiology. The study examines in detail the two common mathematical models of epidemic spread and their marketing implications. The SIR and SEIAR models of infectious disease spread are examined in detail. From this analysis of the epidemiological foundations along with a review of relevant marketing literature, a marketing model of VM is developed. This study demonstrates the key elements that define viral marketing as a formal marketing concept and the distinctive mechanical features that differ from conventional marketing.
Defining and translating a "safe" subset of simulink/stateflow into lustre

The Simulink/Stateflow toolset is an integrated suite enabling model-based design and has become popular in the automotive and aeronautics industries. We have previously developed a translator called Simtolus from Simulink to the synchronous language Lustre and we build upon that work by encompassing Stateflow as well. Stateflow is problematical for synchronous languages because of its unbounded behaviour so we propose analysis techniques to define a subset of Stateflow for which we can define a synchronous semantics. We go further and define a "safe" subset of Stateflow which elides features which are potential sources of errors in Stateflow designs. We give an informal presentation of the Stateflow to Lustre translation process and show how our model-checking tool Lesar can be used to verify some of the semantical checks we have proposed. Finally, we present a small case-study.
AttnGAN: Fine-Grained Text to Image Generation with Attentional Generative Adversarial Networks

In this paper, we propose an Attentional Generative Adversarial Network (AttnGAN) that allows attention-driven, multi-stage refinement for fine-grained text-to-image generation. With a novel attentional generative network, the AttnGAN can synthesize fine-grained details at different sub-regions of the image by paying attentions to the relevant words in the natural language description. In addition, a deep attentional multimodal similarity model is proposed to compute a fine-grained image-text matching loss for training the generator. The proposed AttnGAN significantly outperforms the previous state of the art, boosting the best reported inception score by 14.14% on the CUB dataset and 170.25% on the more challenging COCO dataset. A detailed analysis is also performed by visualizing the attention layers of the AttnGAN. It for the first time shows that the layered attentional GAN is able to automatically select the condition at the word level for generating different parts of the image.
The performance measurement revolution 205 The performance measurement revolution : why now and what next ?

Asks why business performance measurement has become so topical, so recently. Argues that there are seven main reasons: the changing nature of work; increasing competition; specific improvement initiatives; national and international quality awards; changing organisational roles; changing external demands; and the power of information technology. Evidence to support this assertion is drawn from the academic and practitioner literatures, interviews and discussions with people specialising in the field and a broad review of the current state-of-the-art in business performance measurement. Presents a framework onto which current research in business performance measurement can be mapped and identifies areas which require further work.  Business performance measurement is on the management agenda. Professional conference organisers such as Business Intelligence and IIR recognise this. Hence the plethora of conferences on “Business Performance Measurement”. The RSA (Royal Society of Arts, Manufacturers and Commerce) recognise this, as demonstrated by the findings of their inquiry into the role of tomorrow’s company: To achieve sustainable business success in the demanding world marketplace, a company must ... use relevant performance measures (RSA, 1994). Politicians recognise this, as can be seen by the current and previous UK Government’s obsession with league tables, and the fact that the above quote appeared in one of the UK’s White Papers on competitiveness. What is not immediately obvious is why business performance measurement is on the agenda. What is it that makes the topic so relevant to management today? After all, it has long been recognised that performance measures are an integral part of the planning and control cycle (Barnard, 1962) and managers must have been planning and controlling the deployment of resources since the first organisation was established. Indeed, Chandler (1977) argues that most of the basic methods used to manage big businesses today were in place by 1910: In 1903, three Du Pont cousins consolidated their small enterprises with many other small single-unit family firms. They then completely reorganised the American explosives industry and installed an organisational structure that incorporated the “best practice” of the day. The highly rational managers at Du Pont continued to perfect these techniques, so that by 1910 that company was employing nearly all the basic methods that are currently used in managing big business (Chandler, 1977, p. 417). International Journal of Operations & Production Management, Vol. 19 No. 2, 1999, pp. 205-228,
Characterization of noise and vibration sources in interior permanent-magnet brushless DC motors

This work characterizes electromagnetic excitation forces in interior permanent-magnet (IPM) brushless direct current (BLDC) motors and investigates their effects on noise and vibration. First, the electromagnetic excitations are classified into three sources: 1) so-called cogging torque, for which we propose an efficient technique of computation that takes into account saturation effects as a function of rotor position; 2) ripples of mutual and reluctance torque, for which we develop an equation to characterize the combination of space harmonics of inductances and flux linkages related to permanent magnets and time harmonics of current; and 3) fluctuation of attractive forces in the radial direction between the stator and rotor, for which we analyze contributions of electric currents as well as permanent magnets by the finite-element method. Then, the paper reports on an experimental investigation of influences of structural dynamic characteristics such as natural frequencies and mode shapes, as well as electromagnetic excitation forces, on noise and vibration in an IPM motor used in washing machines.
Multiscale Combinatorial Grouping for Image Segmentation and Object Proposal Generation.

We propose a unified approach for bottom-up hierarchical image segmentation and object proposal generation for recognition, called Multiscale Combinatorial Grouping (MCG). For this purpose, we first develop a fast normalized cuts algorithm. We then propose a high-performance hierarchical segmenter that makes effective use of multiscale information. Finally, we propose a grouping strategy that combines our multiscale regions into highly-accurate object proposals by exploring efficiently their combinatorial space. We also present Single-scale Combinatorial Grouping (SCG), a faster version of MCG that produces competitive proposals in under five seconds per image. We conduct an extensive and comprehensive empirical validation on the BSDS500, SegVOC12, SBD, and COCO datasets, showing that MCG produces state-of-the-art contours, hierarchical regions, and object proposals.
Experimental Case Studies for Investigating E-Banking Phishing Techniques and Attack Strategies

Phishing is a form of electronic identity theft in which a combination of social engineering and Web site spoofing techniques is used to trick a user into revealing confidential information with economic value. The problem of social engineering attack is that there is no single solution to eliminate it completely, since it deals largely with the human factor. This is why implementing empirical experiments is very crucial in order to study and to analyze all malicious and deceiving phishing Web site attack techniques and strategies. In this paper, three different kinds of phishing experiment case studies have been conducted to shed some light into social engineering attacks, such as phone phishing and phishing Web site attacks for designing effective countermeasures and analyzing the efficiency of performing security awareness about phishing threats. Results and reactions to our experiments show the importance of conducting phishing training awareness for all users and doubling our efforts in developing phishing prevention techniques. Results also suggest that traditional standard security phishing factor indicators are not always effective for detecting phishing websites, and alternative intelligent phishing detection approaches are needed.
Radio Frequency Identification Sensors

Radio Frequency Identification (RFID) Systems are used when non-line-of-sight operation is required to identify an object and store its information. Some of the applications of this system are toll collection and inventory control. The following paper presents the ongoing research for the Auburn University Detection and Food Safety (AUDFS) project to use RFID technology combined with sensors for detection of pathogens in food. AUDFS aims to integrate the breakthroughs in the detection of food borne illnesses with advances in wireless and biosensor technologies. It explains the basic building blocks and operation of an RFID system. It also illustrates the data transfer process taking place in the system.
Hybrid Task Cascade for Instance Segmentation

Cascade is a classic yet powerful architecture that has boosted performance on various tasks. However, how to introduce cascade to instance segmentation remains an open question. A simple combination of Cascade R-CNN and Mask R-CNN only brings limited gain. In exploring a more effective approach, we find that the key to a successful instance segmentation cascade is to fully leverage the reciprocal relationship between detection and segmentation. In this work, we propose a new framework, Hybrid Task Cascade (HTC), which differs in two important aspects: (1) instead of performing cascaded refinement on these two tasks separately, it interweaves them for a joint multi-stage processing; (2) it adopts a fully convolutional branch to provide spatial context, which can help distinguishing hard foreground from cluttered background. Overall, this framework can learn more discriminative features progressively while integrating complementary features together in each stage. Without bells and whistles, a single HTC obtains 38.4% and 1.5% improvement over a strong Cascade Mask RCNN baseline on MSCOCO dataset. More importantly, our overall system achieves 48.6 mask AP on the test-challenge dataset and 49.0 mask AP on test-dev, which are the stateof-the-art performance.
Why People Fail to Recognize Their Own Incompetence

Successful negotiation of everyday life would seem to require people to possess insight about deficiencies in their intellectual and social skills. However, people tend to be blissfully unaware of their incompetence. This lack of awareness arises because poor performers are doubly cursed: Their lack of skill deprives them not only of the ability to produce correct responses, but also of the expertise necessary to surmise that they are not producing them. People base their perceptions of performance, in part, on their preconceived notions about their skills. Because these notions often do not correlate with objective performance, they can lead people to make judgments about their performance that have little to do with actual accomplishment.
Beacon Vector Routing: Scalable Point-to-Point Routing in Wireless Sensornets

We propose a practical and scalable technique for point-to-point routing in wireless sensornets. This method, called Beacon Vector Routing (BVR), assigns coordinates to nodes based on the vector of hop count distances to a small set of beacons, and then defines a distance metric on these coordinates. BVR routes packets greedily, forwarding to the next hop that is the closest (according to this beacon vector distance metric) to the destination. We evaluate this approach through a combination of high-level simulation to investigate scaling and design tradeoffs, and a prototype implementation over real testbeds as a necessary reality check.
Alternating Direction Method for Image Inpainting in Wavelet Domains

Image inpainting in wavelet domain refers to the recovery of an image from incomplete and/or inaccurate wavelet coefficients. To reconstruct the image, total variation (TV) models have been widely used in the literature and they produce high-quality reconstructed images. In this paper, we consider an unconstrained TV-regularized, l2-data-fitting model to recover the image. The model is solved by the alternating direction method (ADM). At each iteration, ADM needs to solve three subproblems, all of which have closed-form solutions. The per-iteration computational cost of ADM is dominated by two Fourier transforms and two wavelet transforms, all of which admit fast computation. Convergence of the ADM iterative scheme is readily obtained. We also discuss extensions of this ADM scheme to solving two closely related constrained models. We present numerical results to show the efficiency and stability of ADM for solving wavelet domain image inpainting problems. Numerical comparison results of ADM with some recent algorithms are also reported.
Algorithmic Regularization in Over-parameterized Matrix Sensing and Neural Networks with Quadratic Activations

We show that the gradient descent algorithm provides an implicit regularization effect in the learning of over-parameterized matrix factorization models and one-hidden-layer neural networks with quadratic activations. Concretely, we show that given Õ(dr) random linear measurements of a rank r positive semidefinite matrix X, we can recover X by parameterizing it by UU> with U ∈ Rd×d and minimizing the squared loss, even if r d. We prove that starting from a small initialization, gradient descent recovers X in Õ( √ r) iterations approximately. The results solve the conjecture of Gunasekar et al. Gunasekar et al. (2017) under the restricted isometry property. The technique can be applied to analyzing neural networks with one-hidden-layer quadratic activations with some technical modifications.
Massive MIMO with Multi-cell MMSE Processing: Exploiting All Pilots for Interference Suppression

In this paper, a new state-of-the-art multi-cell MMSE schem e is proposed for massive MIMO networks, which includes an uplink MMSE detector and a downl ink MMSE precoder. The main novelty is that it exploits all available pilots for interference su ppression. Specifically, let K andB denote the number of users per cell and the number of orthogonal pilot se quences in the network, respectively, whereβ = B/K is the pilot reuse factor. Then our multi-cell MMSE scheme ut ilizes all B channel directions, that can be estimated locally at each base stati on, to actively suppress both intra-cell and inter-cell interference. The proposed scheme is particula rly practical and general, since power control for the pilot and payload, imperfect channel estimation and arb itr y pilot allocation are all accounted for. Simulations show that significant spectral efficiency (SE) g ains are obtained over the single-cell MMSE scheme and the multi-cell ZF, particularly for large β and/orK. Furthermore, large-scale approximations of the uplink and downlink SINRs are derived, which are asymp totically tight in the large-system limit. The approximations are easy to compute and very accurate eve n for small system dimensions. Using these SINR approximations, a low-complexity power control algorithm is also proposed to maximize the sum SE.