Miracle-dz/UniFAR-FTUs huggingface dataset:
Cross-Layer Optimization for Cooperative Content Distribution in Multihop Device-to-Device Networks

With the ubiquity of wireless network and the intelligentization of machines, Internet of Things (IoT) has come to people’s horizon. Device-to-device (D2D), as one advanced technique to achieve the vision of IoT, supports a high speed peer-to-peer transmission without fixed infrastructure forwarding which can enable fast content distribution in local area. In this paper, we address the content distribution problem by multihop D2D communication with decentralized content providers locating in the networks. We consider a cross-layer multidimension optimization involving frequency, space, and time, to minimize the network average delay. Considering the multicast feature, we first formulate the problem as a coalitional game based on the payoffs of content requesters, and then, propose a time-varying coalition formation-based algorithm to spread the popular content within the shortest possible time. Simulation results show that the proposed approach can achieve a fast content distribution across the whole area, and the performance on network average delay is much better than other heuristic approaches.
Representation of Time Averaged Vibrating Images in the Operator Format

The process of time averaging of greyscale images is expressed in the operator format. It is shown that the inverse problem of the reconstruction of the original image from its time averaged image is an ill-posed problem. Application of time averaging techniques is proposed for cryptographic applications especially when the density function of variable defining the dynamic deflection from the state of equilibrium is arcsine distribution.
Made in the U . S . A . ? A Study of Firm Responses to Domestic Production Incentives

∗Stanford Graduate School of Business, Stanford University. Accepted by Christian Leuz. This paper is based on my dissertation at the Massachusetts Institute of Technology, Sloan School of Management, and it received the MIT Sloan School of Management Thesis Prize, as well as the 2016 American Taxation Association/PricewaterhouseCoopers Outstanding Tax Dissertation Award. I appreciate comments from my dissertation committee—Michelle Hanlon (chair), S. P. Kothari, and Rodrigo Verdi— as well as from two anonymous referees, Joshua Anderson, Anna Costello, Lisa De Simone, John Gallemore, Joao Granja, Dominika Langenmayr, Patricia Naranjo, Michelle Nessa (discussant), Ralph Rector, Nemit Shroff, Eric So, Joe Weber, Bill Zeile, and seminar participants at the 2015 AAA Annual Meeting, Duke University, MIT, Harvard Business School, the U.S. Treasury Office of Tax Analysis, Stanford University, the University of Chicago, the University of Illinois, the University of Michigan, the University of Notre Dame, the University of Pennsylvania, and Washington University in St. Louis. I thank Jeff Hoopes for assistance with data collection and for sharing R&D credit tax data; Michelle Hanlon for sharing AJCA repatriation tax data; Scott Dyreng for providing Exhibit 21 data; and Wonhee Lee, Mason Jiang, Christina Becher, and Carl Rodriguez for research assistance. I appreciate institutional insight from Harry Moser of the Reshoring Initiative, Joanne Bonfiglio, Jeff Cote, David Hoffman, Anne-Marie Petersen, Larry Salus, Ryan Stecz, and Todd Videbeck. I gratefully acknowledge financial support from the MIT Sloan School of Management, the Stanford Graduate School of Business, and the Deloitte Foundation. The statistical analysis of firm-level data on U.S. multinational companies was conducted at the Bureau of Economic Analysis (BEA), Department of Commerce, under arrangements that maintain legal confidentiality requirements. The views expressed are those of the author and do not reflect official positions of the U.S. Department of Commerce. An online appendix to this paper can be downloaded at http://research.chicagobooth. edu/arc/journal-of-accounting-research/online-supplements.
The U.S. Electrical Power Grid as a Cyber-Physical System: Understanding Exposed Attack Surfaces

The U.S. power grid is one of the largest installations of critical infrastructure in the world, the failure of which can cause signiﬁcant socioeconomic damage. Due to the complexities involved in providing reliable power to customers over long distances, the grid’s engineers rely on industrial automation technology to make important operational decisions. This technology exposes the grid to the risk of electronic attack. In this paper, I describe the operation of the power grid and the control loops which govern it. I examine the industrial automation system that maintains the grid, and use it to present a model of the power grid as a cyber-physical system. Using this model, I enumerate the surfaces the grid exposes to electronic attack, and delve into several speciﬁc theoretical attacks. I conclude with an assessment of the grid’s overall susceptibility to electronic attack, and discuss attractive directions for future research.
Who's Your Daddy?: A Psychological Analysis of Father-Son Relationships in Rushmore and The Royal Tenenbaums

This paper offers an analysis of dysfunctional father-son relationships in Wes Anderson filmography. In particular, the films that will be analyzed are Rushmore and The Royal Tenenbaums. Anderson films have a unique style and present relationships that feel authentic to the audience. The realistic nature of these relationships makes them the perfect candidates for examination through psychological analysis. Four psychological principles applying to father-son relationships were chosen in order to analyze the relationships presented in the films. The principles, outlined in the body of the paper, pertain to the presence of the father in the home and specific attitudes fathers have towards their sons. Using the principles to conduct an analysis will display how important fathers are to their sons’ development, both behaviorally and emotionally. Despite the negative connotations of the principles, this paper seeks to show that there is the possibility for change in a relationship at any stage.
A robust scheme for the identification of centerlines of moiré fringes from optical experimental images

A robust scheme for the identification of centerlines of moiré fringes from optical experimental images is proposed in this paper. The proposed computational algorithm comprises three basic steps: the thresholding of the experimental image, thinning of the projected grating based on morphological and mid-point detection rules and, finally, the reconstruction of the map of continuous curves from the binary matrix of pixels representing fringe centerlines. The first steps can be considered as adaptations of standard image processing techniques, while the identification and the reconstruction of continuous curves is the original contribution specifically developed for optical projection moiré images. The functionality of such an approach is demonstrated for a demanding optical experimental image.
Automatic Debugging Approaches: A literature Review

Fixing failed computer programs involves completing two fundamental debugging tasks: first, the programmer has to reproduce the failure; second, s/he has to find the failure cause. Software debugging is the process of locating and correcting erroneous statements in a faulty program as a result of testing. It is extremely time consuming and very expensive. The term debugging collectively refers to fault localization, understanding and correction. Automated tools to locate and correct the erroneous statements in a program can significantly reduce the cost of software development and improve the overall quality of the software. This paper discusses fault localization, program slicing and delta debugging techniques. It identifies statistical fault localization tools such as Tarantula, GZoltar and others such as dbx and Microsoft Visual C++ debugger that provides a snapshot of the program state at various break points along an execution path. In conclusion we note that most software development companies spend a huge amount of resources in testing and debugging. A lot more research need to be conducted to fully automate the debugging process thereby reducing software production cost, time and improve quality.
The Empirical Canadian High Arctic Ionospheric Model ( E-CHAIM ) : NmF 2

It is well known that the International Reference Ionosphere (IRI) suffers reduced accuracy in its representation of monthly median ionospheric electron density at high latitudes. These inaccuracies are believed to stem from a historical lack of data from these regions. Now, roughly thirty and forty years after the development of the original URSI and CCIR foF2 maps, respectively, there exists a much larger dataset of high latitude observations of ionospheric electron density. These new measurements come in the form of new ionosonde deployments, such as those of the Canadian High Arctic Ionospheric Network, the CHAMP, GRACE, and COSMIC radio occultation missions, and the construction of the Poker Flat, Resolute, and EISCAT Incoherent Scatter Radar systems. These new datasets afford an opportunity to revise the IRI’s representation of the high latitude ionosphere. For this purpose, we here introduce the Empirical Canadian High Arctic Ionospheric Model (E-CHAIM), which incorporates all of the above datasets, as well as the older observation records, into a new climatological and storm-time representation of the high latitude ionosphere. In this presentation, we introduce the NmF2 portion of the model, focusing on both climatological and storm-time representations, and present a validation of the new model with respect to ionosonde observations from four high latitude stations. A comparison with respect to IRI performance is also presented, where we see improvements by up to 70% in the representation of peak electron density through using the new E-CHAIM model. In terms of RMS errors, the E-CHAIM model is shown to represent a near-universal improvement over the IRI, sometimes by more than 1 MHz in foF2.
Mixed Reality Based Interaction Techniques Using Smartphones in Bulletin Board Applications

Smartphones, i.e. full featured mobile phones with integrated functionality for personal computing, become not only increasingly popular but also increasingly powerful. Faster speed, higher screen resolution and improved camera and video capabilities facilitate the implementation of sophisticated imaging algorithms on recent devices. Based on methodologies for mobile mixed reality, we present a bulletin board application for smartphones. Our system allows users not only to retrieve location based information from the bulletin board but also to post their own multimedia content at designated points of information (POIs). To identify a POI we combine data from GSM with an optical tracking procedure. In our application, we devised novel interaction techniques in order to cope with the limited size of handheld devices and the peculiarities of their mobile usage. We use a magic lens metaphor and take advantage of the additional information that is available from using the camera of the smartphone as an input device. This enables the user to interact with multimedia content by simply moving or rotating the smartphone.
Crustal structure in the Changbaishan volcanic area , China , determined by modeling receiver functions

During 1998–1999, we installed a temporary broadband seismic network in the Changbaishan volcanic region, NE China. We estimated crustal structure using teleseismic seismograms collected at the network. We detected a near surface region of strong anisotropy directly under the main volcanic edifice of the volcanic area. We modeled 109 receiver functions from 19 broadband stations using three techniques. First we used a bslant-stackingQ method to model the principal crustal P reverberation phases to estimate crustal thickness and the average crustal P to S speed ratio (vp/vs), assuming an average P-wave velocity in the crust. We then estimated crustal S-wave velocity (vs) and vp/vs profiles by modeling stacked receiver functions using a direct search. Finally, we inverted several receiver functions recorded at stations closest to the main volcanic edifice using least squares to estimate vs velocity profiles, assuming a vp/vs value. The results from the three estimation techniques were consistent, and generally we found that the receiver functions constrained estimates of changes in wave speeds better than absolute values. We resolved that the crust is 30–39 km thick under the volcanic region and 28–32 km thick away from the volcanic region, with a midcrust velocity transition at about 10–15 km depth. We estimated that the average crust P-wave velocity is about 6.0–6.2 km/s surrounding the main volcanic region, while it is slightly lower in the vicinity of the main volcanic edifice. The estimates of vp/vs were more ambiguous, but we inferred that the bulk crustal Poisson’s ratio (which is related to vp/vs) ranges between 0.20 and 0.30, with a suggestion that the Poisson’s ratio is lower under the central volcanic region compared to the surrounding areas. We resolved low S-wave velocities (down to about 3 km/s) in the middle crust in the region of the main volcanic edifice. The low velocity anomaly extends from about 5–10 to 15–25 km below the surface, probably indicating a region of elevated temperatures. We were unable to determine if partial melt is present with the data we considered in this paper. D 2004 Elsevier B.V. All rights reserved.
(2019). Do selective schooling systems increase inequality? Oxford Economic Papers

We investigate the impact on earnings inequality of a selective education system in which school assignment is based on initial test scores. We use a large, representative household panel survey to compare adult earnings inequality of those growing up under a selective education system with those educated under a comprehensive system in England. Controlling for a range of background characteristics and the current location, the wage distribution for individuals who grew up in selective schooling areas is substantially and signiﬁcantly more unequal. The total effect sizes are large: 24% of the raw 90–10 earnings gap and 19% of the conditional 90–10 earn- ings gap can be explained by differences across schooling systems.
Practical Design of an Iterative Learning-Sliding Mode Controller for Electro-Pneumatic

The approach of combing the iterative learning control algorithm with the variable structure control is presented. The reasonable controller is designed and applied to electro-pneumatic servo system. It is able to accommodate smooth transition between different controllers without undue transient effects. Specifically in this work, the proposed controller can not only provide good position and pressure tracking abilities but also is able to obtain the good robustness. The convergence of the proposed algorithm is given. Simulation results are given to show the scheme is valid.
Copernican loss : Learning a Discriminative Cosine Embedding

Euclidean embeddings are ubiquitous in machine learning applications in classificaiton settings. However, typical testing schemes (such as in face recognition) utilize the cosine similarity for feature matching, which creates a disconnect between training and testing scenarios. In this paper, we present Copernican loss, which learns a discriminative cosine embedding that addresses this gap. Copernican loss discriminates between all samples from different classes within a batch of size m with just m gradient computations (as opposed to m −m in previous work). We demonstrate the efficacy of the proposed approach with extensive experiments on CIFAR 10 and 100. Further, while keeping away from performance boosting preprocessing steps such as face alignment in training and testing, we match high performance on large scale face recognition datasets namely LFW and IJB-A Janus.
PR ] 1 8 M ay 2 01 7 ON STATIONARY MARKOV PROCESSES WITH POLYNOMIAL CONDITIONAL MOMENTS

We study a class of stationary Markov processes with marginal distributions identifiable by moments such that every conditional moment of degree say m is a polynomial of degree at most m . We show that then under some additional, natural technical assumption there exists a family of orthogonal polynomial martingales. More precisely we show that such a family of processes is completely characterized by the sequence {(αn, pn)}n≥0 where
Locomotion Mode Selection Plus (LMS+) Algorithm for Resource Efficient Outdoor Navigation

: Mobile robots that can fly in air and drive on ground may offer fast and energy efficient navigation. In this paper, a unique method has been proposed for optimal outdoor navigation of robots that can fly and drive. An offline dual mode path planning algorithm is developed and it is named as Locomotion Mode Selection Plus (LMS+) algorithm. It uses a two dimensional ground route from a web based geographic map server and makes a three dimensional resource optimised path, by considering dual mode locomotion. The results showed that the LMS+ algorithm makes a path that is optimised for travel time and energy consumption. The output path can be directly given to dual mode robots for resource optimised autonomous outdoor navigation.
From statics of composites to acoustic metamaterials

The Hashin–Shtrikman formalism for the static response of a composite is first summarized, in a form applicable to anisotropic media and arbitrary two-point correlations. Its extension to dynamic response is then explained and in particular the extra coupling, for stress with velocity as well as strain, and momentum density with strain as well as velocity that inevitably follows, is highlighted. The more recent dynamical formulations which incorporate extra flexibility through the use of a weighted mean displacement also receive mention. The article is concluded with an explicit analysis of a simple model random composite and some lessons are drawn from the solution, which exposes at least one clear gap in existing knowledge and requires further research. This article is part of the theme issue ‘Modelling of dynamic phenomena and localization in structured media (part 1)’.
Online influence, offline violence: Linguistic responses to the 'Unite the Right' rally

The media frequently describes the 2017 Charlottesville ‘Unite the Right’ rally as a turning point for the alt-right and white supremacist movements. Related research into social movements also suggests that the media attention and public discourse concerning the rally may have influenced the alt-right. Empirical evidence for these claims is largely lacking. The current study investigates potential effects of the rally by examining a dataset of 7,142 YouTube video transcripts from alt-right and progressive channels. We examine sentiment surrounding the ten most frequent keywords (single words and word pairs) in transcripts from each group, eight weeks before to eight weeks after the rally. In the majority of cases, no significant differences in sentiment were found within and between the alt-right and progressive groups, both preand post-Charlottesville. However, we did observe more negative sentiment trends surrounding ‘Bernie Sanders’ and ‘black people’ in the alt-right and progressive groups, respectively. We also observed more negative sentiment after the rally regarding ‘Democratic Party’ in the alt-right videos compared to the progressive videos. We suggest that the observed results potentially reflect minor changes in political sentiment before and after the rally, as well as differences in political sentiment between the alt-right and progressive groups in general. Keywords—alt-right, charlottesville rally, sentiment analysis, youtube, quantitative text analysis
Encoders Help You Disambiguate Word Senses in Neural Machine Translation

Neural machine translation (NMT) has achieved new state-of-the-art performance in translating ambiguous words. However, it is still unclear which component dominates the process of disambiguation. In this paper, we explore the ability of NMT encoders and decoders to disambiguate word senses by evaluating hidden states and investigating the distributions of self-attention. We train a classifier to predict whether a translation is correct given the representation of an ambiguous noun. We find that encoder hidden states outperform word embeddings significantly which indicates that encoders adequately encode relevant information for disambiguation into hidden states. In contrast to encoders, the effect of decoder is different in models with different architectures. Moreover, the attention weights and attention entropy show that self-attention can detect ambiguous nouns and distribute more attention to the context.
An Ultra-Efficient Memristor-Based DNN Framework with Structured Weight Pruning and Quantization Using ADMM

The high computation and memory storage of large deep neural networks (DNNs) models pose intensive challenges to the conventional Von-Neumann architecture, incurring sub-stantial data movements in the memory hierarchy. The memristor crossbar array has emerged as a promising solution to mitigate the challenges and enable low-power acceleration of DNNs. Memristor-based weight pruning and weight quantization have been seperately investigated and proven effectiveness in reducing area and power consumption compared to the original DNN model. However, there has been no systematic investigation of memristor-based neuromorphic computing (NC) systems considering both weight pruning and weight quantization. In this paper, we propose an unified and systematic memristor-based framework considering both structured weight pruning and weight quantization by incorporating alternating direction method of multipliers (ADMM) into DNNs training. We consider hardware constraints such as crossbar blocks pruning, conductance range, and mismatch between weight value and real devices, to achieve high accuracy and low power and small area footprint. Our framework is mainly integrated by three steps, i.e., memristor-based ADMM regularized optimization, masked mapping and retraining. Experimental results show that our proposed framework achieves 29.81× (20.88×) weight compression ratio, with 98.38% (96.96%) and 98.29% (97.47%) power and area reduction on VGG-16 (ResNet-18) network where only have 0.5% (0.76%) accuracy loss, compared to the original DNN models. We share our models at anonymous link http://bit.ly/2Jp5LHJ.
BooVAE: A scalable framework for continual VAE learning under boosting approach

Variational Auto Encoders (VAE) are capable of generating realistic images, sounds and video sequences. From practitioners point of view, we are usually interested in solving problems where tasks are learned sequentially, in a way that avoids revisiting all previous data at each stage. We address this problem by introducing a conceptually simple and scalable end-to-end approach of incorporating past knowledge by learning prior directly from the data. We consider scalable boosting-like approximation for intractable theoretical optimal prior. We provide empirical studies on two commonly used benchmarks, namely MNIST and Fashion MNIST on disjoint sequential image generation tasks. For each dataset proposed method delivers the best results or comparable to SOTA, avoiding catastrophic forgetting in a fully automatic way.