Miracle-dz/UniFAR-FTUs huggingface dataset:
Asymptotic behavior of massless fields and the memory effect

We investigate the behavior of massless scalar, electromagnetic, and linearized gravitational perturbations near null infinity in d \geq 4 dimensional Minkowski spacetime (of both even and odd dimension) under the assumption that these fields admit a suitable expansion in 1/r. We also investigate the behavior of asymptotically flat, nonlinear gravitational perturbations near null infinity in all dimensions d\geq 4. We then consider the memory effect in fully nonlinear general relativity. We show that in even dimensions, the memory effect first arises at Coulombic order--i.e., order 1/r^{d-3}--and can naturally be decomposed into `null memory' and `ordinary memory. ' Null memory is associated with an energy flux to null infinity. We show that ordinary memory is associated with the metric failing to be stationary at one order faster fall-off than Coulombic in the past and/or future. In odd dimensions, we show that the total memory effect at Coulombic order and slower fall-off always vanishes. Null memory is always of `scalar type', but the ordinary memory can be of any (i.e., scalar, vector, or tensor) type. In 4-spacetime dimensions, we give an explicit example in linearized gravity which gives rise to a nontrivial vector (i.e., magnetic parity) ordinary memory effect at order 1/r. We show that scalar memory is described by a diffeomorphism; vector and tensor memory cannot be. In d=4 dimensions, we show that there is a close relationship between memory and the charge and flux expressions associated with supertranslations. We analyze the behavior of solutions that are stationary at Coulombic order and show how these suggest `antipodal matching' between future and past null infinity, which gives rise to conservation laws. The relationship between memory and infrared divergences of the `out' state in quantum gravity is analyzed, and the nature of the `soft theorems' is explained.
The Impact of Twitter Features on Credibility Ratings - An Explorative Examination Combining Psychological Measurements and Feature Based Selection Methods

In a post-truth age determined by Social Media channels providing large amounts of information of questionable credibility while at the same time people increasingly tend to rely on online information, the ability to detect whether content is believable is developing into an important challenge. Most of the work in that field suggested automated approaches to perform binary classification to determine information veracity. Recipients´ perspectives and multidimensional psychological credibility measurements have rarely been considered. To fill this gap and gain more insights into the impact of a tweet´s features on perceived credibility, we conducted a survey asking participants (N=2626) to rate the credibility of crisis-related tweets. The resulting 24.823 ratings were used for an explorative feature selection analysis revealing that mostly meta-related features like the number of followers of the author, the count of tweets produced and the ratio of tweet number and days since account creation affect credibility judgments.
Self-Supervised Spatio-Temporal Representation Learning for Videos by Predicting Motion and Appearance Statistics

We address the problem of video representation learning without human-annotated labels. While previous efforts address the problem by designing novel self-supervised tasks using video data, the learned features are merely on a frame-by-frame basis, which are not applicable to many video analytic tasks where spatio-temporal features are prevailing. In this paper we propose a novel self-supervised approach to learn spatio-temporal features for video representation. Inspired by the success of two-stream approaches in video classification, we propose to learn visual features by regressing both motion and appearance statistics along spatial and temporal dimensions, given only the input video data. Specifically, we extract statistical concepts (fast-motion region and the corresponding dominant direction, spatio-temporal color diversity, dominant color, etc.) from simple patterns in both spatial and temporal domains. Unlike prior puzzles that are even hard for humans to solve, the proposed approach is consistent with human inherent visual habits and therefore easy to answer. We conduct extensive experiments with C3D to validate the effectiveness of our proposed approach. The experiments show that our approach can significantly improve the performance of C3D when applied to video classification tasks. Code is available at https://github.com/laura-wang/video_repres_mas.
Dense-Haze: A Benchmark for Image Dehazing with Dense-Haze and Haze-Free Images

Single image dehazing is an ill-posed problem that has recently drawn important attention. Despite the significant increase in interest shown for dehazing over the past few years, the validation of the dehazing methods remains largely unsatisfactory, due to the lack of pairs of real hazy and corresponding haze-free reference images. To address this limitation, we introduce Dense-Haze – a novel dehazing dataset. Characterized by dense and homogeneous hazy scenes, Dense-Haze contains 33 pairs of real hazy and corresponding haze-free images of various outdoor scenes. The hazy scenes have been recorded by introducing real haze, generated by professional haze machines. The hazy and haze-free corresponding scenes contain the same visual content captured under the same illumination parameters. Dense-Haze dataset aims to push significantly the state-of-the-art in single-image dehazing by promoting robust methods for real and various hazy scenes. We also provide a comprehensive qualitative and quantitative evaluation of state-of-the-art single image dehazing techniques based on the Dense-Haze dataset. Not surprisingly, our study reveals that the existing dehazing techniques perform poorly for dense homogeneous hazy scenes and that there is still much room for improvement.
Behavioral Mimicry in Chinese and Canadian Negotiations: Frequency, Duration and Impact

Negotiation literature stresses the importance of mimicry in improving relational and economic outcomes. Yet, there is a dearth of work examining how culture influences the display and impact of mimicry in negotiations. In this research, we systematically coded behavioral mimicry among Chinese and Canadian dyadic, intracultural, video-taped negotiations. Using cultural theories of high/low context communication, and individualism/collectivism, we predicted and found that low-context, individualistic Canadian negotiators were more direct in their behavioral mimicry by exhibiting higher frequency of postural mimicry, than Chinese negotiators. In contrast, Chinese negotiators were more indirect in their displays of mimicry via longer durations of mirrored postures. Interestingly, gender moderated the effects of culture on the frequency and duration of mimicry. Mimicry led to higher joint gains, only when dyads did not attend to the indirect meanings of the mimicked behaviors. We discuss implications of behavioral mimicry in cross-cultural negotiations.
Single-Carrier Index Modulation for IoT Uplink

For the Internet of Things (IoT), there might be a large number of devices to be connected to the Internet through wireless technologies. In general, IoT devices would have various constraints due to limited processing capability, memory, energy source, and so on, and it is desirable to employ efficient wireless transmission schemes, especially for uplink transmissions. For example, orthogonal frequency division multiplexing (OFDM) with index modulation (IM) or OFDM-IM can be considered for IoT devices due to its energy efficiency. In this paper, we study a different IM scheme for a single-carrier (SC) system, which is referred to as SCIM. While SCIM is similar to OFDM-IM in terms of energy efficiency, SCIM may be better suited for IoT uplink because it has a low peak-to-average power ratio and does not require inverse fast Fourier transform at devices compared to OFDM-IM. We also consider precoding for SCIM and generalize it to multiple access channel so that multiple IoT devices can share the same radio resource block. To detect precoded SCIM signals, low-complexity detectors are derived. For a better performance, based on variational inference that is widely used in machine learning, we consider a detector that provides an approximate solution to an optimal detection.
How do users respond to technostress? An empirical analysis of proactive and reactive coping

As technostress costs organizations financial resources and threatens the well-being, it is essential for users as well as companies to manage technostress. To do so, users cope proactive by removing or reducing techno-stressors or reactive by restoring users’ emotional state. However, literature is limited by explaining what factors lead to proactive and reactive coping in a short-term technostress situation. The present paper addresses these shortcomings by investigating in how techno-stressors and emotional exhaustion influences proactive and reactive coping. Results based on 110 users show that users respond to techno-stressors in a proactive way, whereas users reactively respond to emotional exhaustion. In addition, proactive coping is stronger affected by technostressors, and reactive coping is stronger affected by emotional exhaustion. Thereby, we contribute to technostress and coping literature by demonstrating how users respond in short-term technostress situation and highlight the importance of time in the present context.
Consistent Quality Oriented Rate Control in HEVC Via Balancing Intra and Inter Frame Coding

Consistent quality oriented rate control (RC) in video coding has attracted much more attention. However, the existing efforts only focus on decreasing variations between every two adjacent frames, but neglect coding tradeoff problem between intraframes and interframes. In this article, we deal with it from a new perspective, where intraframe quantization parameter (IQP) and RC are optimized for balanced coding. First, due to the importance of intraframes, a new framework is proposed for consistent quality oriented IQP prediction, and then we remove unqualified IQP candidates using the proposed penalty term. Second, we extensively evaluate possible features, and select target bits per pixel for all remaining frames, average and standard variance of frame QPs, where equivalent acquisition methods for QP features are given. Third, predicted IQPs are clipped effectively according to bandwidth and previous information for better bit rate accuracy. Compared with high efficiency video coding reference baseline, experiments demonstrate that our method reduces quality fluctuation greatly by 37.2% on frame-level standard variance of peak-signal-noise-ratio and 45.1% on that of structural similarity. Moreover, it also can have satisfactory results on rate-distortion performance, bit accuracy and buffer control.
Polarized electron-beam acceleration driven by vortex laser pulses

We propose a new approach based on an all-optical set-up for generating relativistic polarized electron beams via vortex Laguerre-Gaussian (LG) laser-driven wakefield acceleration. Using a pre-polarized gas target, we find that the topology of the vortex wakefield resolves the depolarization issue of the injected electrons. In full three-dimensional particle-in-cell simulations, incorporating the spin dynamics via the Thomas-Bargmann Michel Telegdi equation, the LG laser preserves the electron spin polarization by more than 80% while assuring efficient electron injection. The method releases the limit on beam flux for polarized electron acceleration and promises more than an order of magnitude boost in peak flux, as compared to Gaussian beams. These results suggest a promising table-top method to produce energetic polarized electron beams.
O(3) nonlinear sigma model in 
1+1
 dimensions with matrix product states

We numerically study the spectral properties, the entanglement and the zero-temperature phase structure at nonvanishing chemical potential of the O(3) nonlinear sigma model. Using matrix product states, a particular kind of one-dimensional tensor network state, we show that we are able to reach the asymptotic scaling regime and to reproduce the analytical predictions for the mass gap at vanishing chemical potential. In addition, we study the scaling of the entanglement entropy towards the continuum limit obtaining a central charge consistent with 2. Moreover, our approach does not suffer from the sign problem and we also explore the phase structure of the model for nonzero chemical potential and map out the location of the transitions between different charge sectors with high precision.
Meta Filter Pruning to Accelerate Deep Convolutional Neural Networks

Existing methods usually utilize pre-defined criterions, such as p-norm, to prune unimportant filters. There are two major limitations in these methods. First, the relations of the filters are largely ignored. The filters usually work jointly to make an accurate prediction in a collaborative way. Similar filters will have equivalent effects on the network prediction, and the redundant filters can be further pruned. Second, the pruning criterion remains unchanged during training. As the network updated at each iteration, the filter distribution also changes continuously. The pruning criterions should also be adaptively switched. In this paper, we propose Meta Filter Pruning (MFP) to solve the above problems. First, as a complement to the existing p-norm criterion, we introduce a new pruning criterion considering the filter relation via filter distance. Additionally, we build a meta pruning framework for filter pruning, so that our method could adaptively select the most appropriate pruning criterion as the filter distribution changes. Experiments validate our approach on two image classification benchmarks. Notably, on ILSVRC-2012, our MFP reduces more than 50% FLOPs on ResNet-50 with only 0.44% top-5 accuracy loss.
Step-by-Step: Separating Planning from Realization in Neural Data-to-Text Generation

Data-to-text generation can be conceptually divided into two parts: ordering and structuring the information (planning), and generating fluent language describing the information (realization). Modern neural generation systems conflate these two steps into a single end-to-end differentiable system. We propose to split the generation process into a symbolic text-planning stage that is faithful to the input, followed by a neural generation stage that focuses only on realization. For training a plan-to-text generator, we present a method for matching reference texts to their corresponding text plans. For inference time, we describe a method for selecting high-quality text plans for new inputs. We implement and evaluate our approach on the WebNLG benchmark. Our results demonstrate that decoupling text planning from neural realization indeed improves the system’s reliability and adequacy while maintaining fluent output. We observe improvements both in BLEU scores and in manual evaluations. Another benefit of our approach is the ability to output diverse realizations of the same input, paving the way to explicit control over the generated text structure.
Knowledge Adoption: A New Perspective and the Influence of Knowledge Characteristics

Knowledge may be regarded as one type of innovations that are potentially beneficial to knowledge recipients. However, benefits of knowledge sharing or transfer can only be realized if knowledge recipients are aware of the existence of knowledge, adopt it and deploy it. This paper first provides a review and comparison of innovation characteristics and knowledge characteristics examined in the existing literatures. Then, integrating ideas from the innovation adoption, cognitive psychology and financial real options literatures, this paper provides a new perspective of knowledge adoption that includes two processes (i.e., awareness and evaluation) and three possible adoption decision outcomes (i.e., strong adoption whereby the knowledge is adopted for immediate deployments, weak adoption whereby the knowledge is consciously archived for possible future applications, and faint adoption whereby the knowledge is not adopted for immediate application nor archived for future application). Using the new perspective of knowledge adoption, a set of propositions is offered regarding the influence of knowledge characteristics on adoption decisions. This paper contributes to both knowledge management and innovation adoption literatures.
Modeling Recurrence for Transformer

Recently, the Transformer model that is based solely on attention mechanisms, has advanced the state-of-the-art on various machine translation tasks. However, recent studies reveal that the lack of recurrence modeling hinders its further improvement of translation capacity. In response to this problem, we propose to directly model recurrence for Transformer with an additional recurrence encoder. In addition to the standard recurrent neural network, we introduce a novel attentive recurrent network to leverage the strengths of both attention models and recurrent networks. Experimental results on the widely-used WMT14 English⇒German and WMT17 Chinese⇒English translation tasks demonstrate the effectiveness of the proposed approach. Our studies also reveal that the proposed model benefits from a short-cut that bridges the source and target sequences with a single recurrent layer, which outperforms its deep counterpart.
The Changing Face of Winter: Lessons and Questions From the Laurentian Great Lakes

Among its many impacts, climate warming is leading to increasing winter air temperatures, decreasing ice cover extent, and changing winter precipitation patterns over the Laurentian Great Lakes and their watershed. Understanding and predicting the consequences of these changes is impeded by a shortage of winter‐period studies on most aspects of Great Lake limnology. In this review, we summarize what is known about the Great Lakes during their 3–6 months of winter and identify key open questions about the physics, chemistry, and biology of the Laurentian Great Lakes and other large, seasonally frozen lakes. Existing studies show that winter conditions have important effects on physical, biogeochemical, and biological processes, not only during winter but in subsequent seasons as well. Ice cover, the extent of which fluctuates dramatically among years and the five lakes, emerges as a key variable that controls many aspects of the functioning of the Great Lakes ecosystem. Studies on the properties and formation of Great Lakes ice, its effect on vertical and horizontal mixing, light conditions, and biota, along with winter measurements of fundamental state and rate parameters in the lakes and their watersheds are needed to close the winter knowledge gap. Overcoming the formidable logistical challenges of winter research on these large and dynamic ecosystems may require investment in new, specialized research infrastructure. Perhaps more importantly, it will demand broader recognition of the value of such work and collaboration between physicists, geochemists, and biologists working on the world's seasonally freezing lakes and seas.
DPOD: 6D Pose Object Detector and Refiner

In this paper we present a novel deep learning method for 3D object detection and 6D pose estimation from RGB images. Our method, named DPOD (Dense Pose Object Detector), estimates dense multi-class 2D-3D correspondence maps between an input image and available 3D models. Given the correspondences, a 6DoF pose is computed via PnP and RANSAC. An additional RGB pose refinement of the initial pose estimates is performed using a custom deep learning-based refinement scheme. Our results and comparison to a vast number of related works demonstrate that a large number of correspondences is beneficial for obtaining high-quality 6D poses both before and after refinement. Unlike other methods that mainly use real data for training and do not train on synthetic renderings, we perform evaluation on both synthetic and real training data demonstrating superior results before and after refinement when compared to all recent detectors. While being precise, the presented approach is still real-time capable.
Simple Question Answering with Subgraph Ranking and Joint-Scoring

Knowledge graph based simple question answering (KBSQA) is a major area of research within question answering. Although only dealing with simple questions, i.e., questions that can be answered through a single knowledge base (KB) fact, this task is neither simple nor close to being solved. Targeting on the two main steps, subgraph selection and fact selection, the literature has developed sophisticated approaches. However, the importance of subgraph ranking and leveraging the subject–relation dependency of a KB fact have not been sufficiently explored. Motivated by this, we present a unified framework to describe and analyze existing approaches. Using this framework as a starting point we focus on two aspects: improving subgraph selection through a novel ranking method, and leveraging the subject–relation dependency by proposing a joint scoring CNN model with a novel loss function that enforces the well-order of scores. Our methods achieve a new state of the art (85.44% in accuracy) on the SimpleQuestions dataset.
Interpolation in generative models

We show how to construct smooth and realistic interpolations for generative models, with arbitrary, not necessarily Gaussian, prior. The crucial idea is based on the construction on the realisticity index of a curve, which maximisation, as we show, leads to a search of a geodesic with respect to the corresponding Riemann structure.
Detecting dark photon dark matter with Gaia-like astrometry observations

A class of dark photon dark matter models with ultralight masses would lead to oscillation of a test body through a coupling with baryons or B−L charge. This periodical oscillation of an observer results in swing of a star's apparent position due to the effect of aberration of light, which could be probed with high-precision astrometry observations of stars in the Milky Way. We propose to use the observations of stellar positions of a number of stars by Gaia to search for this kind of dark photon dark matter. We show that this astrometry method is able to give promising sensitivities to search for the dark photon dark matter in the mass range of 10−23∼10−21 eV.
"The Bright and the Dark Side of Smart Lights" The Protective Effect of Smart City Infrastructures

In this paper, we investigate the protective effect of smart street lighting on public safety. Smart lights have a variety of features, such as video surveillance or gun-shot detection. Some of these features can have a deterrent effect on crime. Other features, however, such as adaptive brightness control, may also encourage crime. Using a comprehensive dataset on the crimes committed in downtown San Diego (CA) during 1st May 2017 and 30th April 2018, we investigate the crime rates a priori and posterior to the installation of smart lights in this area. The results of the empirical analysis suggest that smart lights have a statistically significant negative impact on crime and that their installation increases the safety of citizens.