Miracle-dz/UniFAR-FTUs huggingface dataset:
Displays: Fundamentals and Applications

In the last decade, new displays have been developed at an ever-increasing pace: bulky cathode ray tubes have been replaced by flat panels and mobile phones, tablets, and navigation systems have proliferated. Seeing this explosion raises tantalizing questions about the future evolution of visual displays: Will printed displays be sold by the square yard and glued to the wall? Will disposable displays, powered by printed batteries and with built-in storage chips, talk to us from cereal boxes? Will we begin wearing display glasses that simulate any kind or number of virtual displays we would ever need? Will chip implants directly interface to our brains, eliminating the need for any displays at all? These and other questions are explored in Displays: Fundamentals &Applications, which describes existing and emerging display technology. The bookbegins by presenting the basics of wave optics, geometric optics, light modulation, visual perception, and display measures, along with the principles of holography. It then describes the technology and techniques behind projection displays, projector-camera systems, stereoscopic and autostereoscopic displays, computer-generated holography, and near-eye displays. In addition, the authors discuss how real-time computer graphics and computer vision enable the visualization of graphical 2D and 3D content. The text is complemented by more than 400 rich illustrations, which give readers a clear understanding of existing and emerging display technology.
Submodularity-based False Data Injection Attack Scheme in Multi-agent Dynamical Systems

Consensus in multi-agent dynamical systems is prone to be sabotaged by the adversary, which has attracted much attention due to its key role in broad applications. In this paper, we study a new false data injection (FDI) attack design problem, where the adversary with limited capability aims to maximize the consensus convergence error by compromising a subset of agents and manipulating their local high-dimensional states. We first formulate the FDI attack design problem as a combinatorial optimization problem, which is NP-hard. Then, based on the submodularity optimization theory, we provide the necessary and sufficient conditions to guarantee the submodularity of the objective function, which satisfies the property of diminishing marginal returns. In other words, the profit of adding an extra agent to the compromised set decreases as that set becomes larger. With this property, we exploit the greedy scheme which aims to find the optimal compromised agent set to produce the maximum convergence error when adding one extra agent to that set each time. Thus, an FDI attack selection algorithm is formed to obtain the near-optimal subset of the compromised agents. Furthermore, we derive the analytical suboptimality bounds and the worst-case running time under the proposed algorithm. Extensive simulation results are conducted to show the effectiveness of the proposed algorithm.
Baryogenesis from combined Higgs–scalar field inflation

We study a modification of the Higgs inflation scenario where we introduce an extra scalar φ, with mass m, coupled to the Ricci scalar as gφR, and mixed with the Higgs field h via the Lagrangian term μφh. Both fields participate in the inflation process in a unitary theory that predicts values of the cosmological observables in agreement with the results from the Planck/BICEP/Keck collaborations. In addition, by means of a CP-odd effective operator that couples φ to the ChernSimons term of the hypercharge gauge group as f−1 φ φYμν Ỹ μν , maximally helical magnetic fields are produced during the last e-folds of inflation. We found a window in the coupling fφ where these fields survive all constraints until the electroweak phase transition, and source the baryon asymmetry of the Universe through the Standard Model chiral anomaly. From a phenomenological perspective, the model can solve the Standard Model instability problem at the scale QI ' 10 GeV, provided that μ . m . QI , and for m . O(few) TeV, the φ-h mixing becomes sizable while the theory turns natural. The latter thus predicts modifications of the trilinear and quartic couplings that could be explored at the HE-LHC, as well as at future colliders, and allows for direct φ production at the LHC followed by decay into hh. Present results from ATLAS and CMS already put (mild) bounds on the mass of the heavy scalar as m & 0.55 TeV at 95% C.L. ar X iv :2 20 1. 06 42 2v 2 [ he pph ] 1 6 Se p 20 22
When Leaving the Solar System: Dark Matter Makes a Difference

A resultant gravitational force due to the current estimates of the virial mass of the Milky Way galaxy, dominated by dark matter, is estimated near the Sun and is described in two different analytical models yielding consistent results. One is a two step Hernquist model, the other is a Navarro-Frenk-White model. The effect of this force is estimated on trajectories for spacecraft sufficiently far from the Sun. The difficulty of detecting this force is studied. It is concluded that its effect should be considered for certain spacecraft missions. Its effect on the Pioneer and New Horizons spacecrafts is discussed. A future mission is discussed that may be able to detect this force. Implications of this force are discussed with its impact for problems in planetary astronomy and astrophysics.
Planning Not to Talk: Multiagent Systems that are Robust to Communication Loss

In a cooperative multiagent system, a collection of agents executes a joint policy in order to achieve some common objective. The successful deployment of such systems hinges on the availability of reliable inter-agent communication. However, many sources of potential disruption to communication exist in practice, such as radio interference, hardware failure, and adversarial attacks. In this work, we develop joint policies for cooperative multiagent systems that are robust to potential losses in communication. More specifically, we develop joint policies for cooperative Markov games with reach-avoid objectives. First, we propose an algorithm for the decentralized execution of joint policies during periods of communication loss. Next, we use the total correlation of the state-action process induced by a joint policy as a measure of the intrinsic dependencies between the agents. We then use this measure to lower-bound the performance of a joint policy when communication is lost. Finally, we present an algorithm that maximizes a proxy to this lower bound in order to synthesize minimum-dependency joint policies that are robust to communication loss. Numerical experiments show that the proposed minimum-dependency policies require minimal coordination between the agents while incurring little to no loss in performance; the total correlation value of the synthesized policy is one fifth of the total correlation value of the baseline policy which does not take potential communication losses into account. As a result, the performance of the minimum-dependency policies remains consistently high regardless of whether or not communication is available. By contrast, the performance of the baseline policy decreases by twenty percent when communication is lost.
GCS: Graph-based Coordination Strategy for Multi-Agent Reinforcement Learning

Many real-world scenarios involve a team of agents that have to coordinate their policies to achieve a shared goal. Previous studies mainly focus on decentralized control to maximize a common reward and barely consider the coordination among control policies, which is critical in dynamic and complicated environments. In this work, we propose factorizing the joint team policy into a graph generator and graph-based coordinated policy to enable coordinated behaviours among agents. The graph generator adopts an encoder-decoder framework that outputs directed acyclic graphs (DAGs) to capture the underlying dynamic decision structure. We also apply the DAGness-constrained and DAG depth-constrained optimization in the graph generator to balance efficiency and performance. The graph-based coordinated policy exploits the generated decision structure. The graph generator and coordinated policy are trained simultaneously to maximize the discounted return. Empirical evaluations on Collaborative Gaussian Squeeze, Cooperative Navigation, and Google Research Football demonstrate the superiority of the proposed method.
Proactive Query Expansion for Streaming Data Using External Sources

Queries used to draw data from high-volume, high-velocity social media data streams, such as Twitter, typically require a set of keywords to filter the data. When topics and conversations change rapidly, initial keywords may become outdated and irrelevant, which may result in incomplete data. We propose a novel technique that improves data collection from social media streams in two ways. First, we develop a query expansion method that identifies and adds emergent keywords to the initial query, which makes the data collection a dynamic process that adapts to changes in social conversations. Second, we develop a "predictive query expansion" method that combines keywords from the streams with external data sources, which enables the construction of new queries that effectively capture emergent events that a user may not have anticipated when initiating the data collection stream. We demonstrate the effectiveness of our approach with an analysis of more than 20.5 million Twitter messages related to the 2015 Baltimore protests. We use newspaper archives as an external data source from which we collect keywords to expand the queries built from the primary stream.Reproducibility: https://github.com/FarahAlshanik/QE
Theoretical analysis and computation of the sample Frechet mean for sets of large graphs based on spectral information

To characterize the location (mean, median) of a set of graphs, one needs a notion of centrality that is adapted to metric spaces, since graph sets are not Euclidean spaces. A standard approach is to consider the Frechet mean. In this work, we equip a set of graphs with the pseudometric defined by the norm between the eigenvalues of their respective adjacency matrix. Unlike the edit distance, this pseudometric reveals structural changes at multiple scales, and is well adapted to studying various statistical problems for graph-valued data. We describe an algorithm to compute an approximation to the sample Frechet mean of a set of undirected unweighted graphs with a fixed size using this pseudometric.
OrchestRAN: Network Automation through Orchestrated Intelligence in the Open RAN

The next generation of cellular networks will be characterized by softwarized, open, and disaggregated architectures exposing analytics and control knobs to enable network intelligence via innovative data-driven algorithms. How to practically realize this vision, however, is largely an open problem. For a given network optimization/automation objective, it is currently unknown how to select which data-driven models should be deployed and where, which parameters to control, and how to feed them appropriate inputs. In this paper, we take a decisive step forward by presenting and prototyping OrchestRAN, a novel orchestration framework for next generation systems that embraces and builds upon the Open Radio Access Network (RAN) paradigm to provide a practical solution to these challenges. OrchestRAN has been designed to execute in the non-Real-time (RT) RAN Intelligent Controller (RIC) and allows Network Operators (NOs) to specify high-level control/inference objectives (i.e., adapt scheduling, and forecast capacity in near-RT, e.g., for a set of base stations in Downtown New York). OrchestRAN automatically computes the optimal set of data-driven algorithms and their execution location (e.g., in the cloud, or at the edge) to achieve intents specified by the NOs while meeting the desired timing requirements and avoiding conflicts between different data-driven algorithms controlling the same parameters set. We show that the intelligence orchestration problem in Open RAN is NP-hard, and design low-complexity solutions to support real-world applications. We prototype OrchestRAN and test it at scale on Colosseum, the world’s largest wireless network emulator with hardware in the loop. Our experimental results on a network with 7 base stations and 42 users demonstrate that OrchestRAN is able to instantiate data-driven services on demand with minimal control overhead and latency.
Using machine learning to parametrize postmerger signals from binary neutron stars

Tim Whittaker, 2 William E. East, Stephen R. Green, Luis Lehner, and Huan Yang 2 Department of Physics & Astronomy, University of Waterloo, Waterloo, ON N2L 3G1, Canada Perimeter Institute for Theoretical Physics, Waterloo, Ontario N2L 2Y5, Canada Max Planck Institute for Gravitational Physics (Albert Einstein Institute) Am Mühlenberg 1, 14476 Potsdam, Germany University of Guelph, Guelph, Ontario N1G 2W1, Canada (Dated: January 19, 2022)
Model Stability with Continuous Data Updates

In this paper, we study the"stability"of machine learning (ML) models within the context of larger, complex NLP systems with continuous training data updates. For this study, we propose a methodology for the assessment of model stability (which we refer to as jitter under various experimental conditions. We find that model design choices, including network architecture and input representation, have a critical impact on stability through experiments on four text classification tasks and two sequence labeling tasks. In classification tasks, non-RNN-based models are observed to be more stable than RNN-based ones, while the encoder-decoder model is less stable in sequence labeling tasks. Moreover, input representations based on pre-trained fastText embeddings contribute to more stability than other choices. We also show that two learning strategies -- ensemble models and incremental training -- have a significant influence on stability. We recommend ML model designers account for trade-offs in accuracy and jitter when making modeling choices.
Quantized circular photoinduced orbital magnetization in multifold Weyl semimetal

Jin Cao,1, 2, ∗ Chuanchang Zeng,1, 2, ∗ Xiao-Ping Li,1, 2 Maoyuan Wang,1, 2 Zhi-Ming Yu,1, 2, † and Yugui Yao1, 2, ‡ Centre for Quantum Physics, Key Laboratory of Advanced Optoelectronic Quantum Architecture and Measurement (MOE), School of Physics, Beijing Institute of Technology, Beijing, 100081, China Beijing Key Lab of Nanophotonics & Ultrafine Optoelectronic Systems, School of Physics, Beijing Institute of Technology, Beijing, 100081, China
Minimax risk classifiers with 0-1 loss

Supervised classification techniques use training samples to learn a classification rule with small expected 0-1 loss (error probability). Conventional methods enable tractable learning and provide out-of-sample generalization by using surrogate losses instead of the 0-1 loss and considering specific families of rules (hypothesis classes). This paper presents minimax risk classifiers (MRCs) that minize the worst-case 0-1 loss with respect to uncertainty sets of distributions that can include the underlying distribution, with a tunable confidence. We show that MRCs can provide tight performance guarantees at learning and are strongly universally consistent using feature mappings given by characteristic kernels. The paper also proposes efficient optimization techniques for MRC learning and shows that the methods presented can provide accurate classification together with tight performance guarantees in practice.
Deep Unified Representation for Heterogeneous Recommendation

Recommendation system has been a widely studied task both in academia and industry. Previous works mainly focus on homogeneous recommendation and little progress has been made for heterogeneous recommender systems. However, heterogeneous recommendations, e.g., recommending different types of items including products, videos, celebrity shopping notes, among many others, are dominant nowadays. State-of-the-art methods are incapable of leveraging attributes from different types of items and thus suffer from data sparsity problems. And it is indeed quite challenging to represent items with different feature spaces jointly. To tackle this problem, we propose a kernel-based neural network, namely deep unified representation (or DURation) for heterogeneous recommendation, to jointly model unified representations of heterogeneous items while preserving their original feature space topology structures. Theoretically, we prove the representation ability of the proposed model. Besides, we conduct extensive experiments on the real-world datasets. Experimental results demonstrate that with the unified representation, our model achieves remarkable improvement (e.g., 4.1% ~34.9% lift by AUC score and 3.7% lift by online CTR) over existing state-of-the-art models.
TWikiL - The Twitter Wikipedia Link Dataset

Recent research has shown how strongly Wikipedia and other web services or platforms are connected. For example, search engines rely heavily on surfacing Wikipedia links to satisfy their users' information needs and volunteer-created Wikipedia content frequently gets re-used on other social media platforms like Reddit. However, publicly accessible datasets that enable researchers to study the interrelationship between Wikipedia and other platforms are sparse. In addition to that, most studies only focus on certain points in time and don't consider the historical perspective. To begin solving these problems we developed TWikiL, the Twitter Wikipedia Link Dataset, which contains all Wikipedia links posted on Twitter in the period 2006 to January 2021. We extract Wikipedia links from Tweets and enrich the referenced articles with their respective Wikidata identifiers and Wikipedia topic categories, which will make this dataset immediately useful for a large range of scholarly use cases. In this paper, we describe the data collection process, perform an initial exploratory analysis and present a comprehensive overview of how this dataset can be useful for the research community.
Neighboring Backdoor Attacks on Graph Convolutional Network

Backdoor attacks have been widely studied to hide the misclassification rules in the normal models, which are only activated when the model is aware of the specific inputs (i.e., the trigger). However, despite their success in the conventional Euclidean space, there are few studies of backdoor attacks on graph structured data. In this paper, we propose a new type of backdoor which is specific to graph data, called neighboring backdoor. Considering the discreteness of graph data, how to effectively design the triggers while retaining the model accuracy on the original task is the major challenge. To address such a challenge, we set the trigger as a single node, and the backdoor is activated when the trigger node is connected to the target node. To preserve the model accuracy, the model parameters are not allowed to be modified. Thus, when the trigger node is not connected, the model performs normally. Under these settings, in this work, we focus on generating the features of the trigger node. Two types of backdoors are proposed: (1) Linear Graph Convolution Backdoor which finds an approximation solution for the feature generation (can be viewed as an integer programming problem) by looking at the linear part of GCNs. (2) Variants of existing graph attacks. We extend current gradient-based attack methods to our backdoor attack scenario. Extensive experiments on two social networks and two citation networks datasets demonstrate that all proposed backdoors can achieve an almost 100\% attack success rate while having no impact on predictive accuracy.
Improving Clinical Diagnosis Performance with Automated X-ray Scan Quality Enhancement Algorithms

In clinical diagnosis, diagnostic images that are obtained from the scanning devices serve as preliminary evidence for further investigation in the process of delivering quality healthcare. However, often the medical image may contain fault artifacts, introduced due to noise, blur and faulty equipment. The reason for this may be the low-quality or older scanning devices, the test environment or technician’s lack of training etc; however, the net result is that the process of fast and reliable diagnosis is hampered. Resolving these issues automatically can have a significant positive impact in a hospital clinical workflow, where often, there is no other way but to work with faulty/older equipment or inadequately qualified radiology technicians. In this paper, automated image quality improvement approaches for adapted and benchmarked for the task of medical image super-resolution. During experimental evaluation on standard open datasets, the observations showed that certain algorithms perform better and show significant improvement in the diagnostic quality of medical scans, thereby enabling better visualization for human diagnostic purposes.
Nonequilibrium phase transition in a single-electron micromaser

Phase transitions occur in a wide range of physical systems and are characterized by the abrupt change of a physical observable in response to the variation of an external control parameter. Phase transitions are not restricted to equilibrium situations but can also be found in nonequilibrium settings, both for classical and quantum mechanical systems. Here, we investigate a nonequilibrium phase transition in a single-electron micromaser consisting of a microwave cavity that is driven by the electron transport in a double quantum dot. For weak electron-photon couplings, only a tiny fraction of the transferred electrons lead to the emission of photons into the cavity, which essentially remains empty. However, as the coupling is increased, many photons are suddenly emitted into the cavity. Employing ideas and concepts from full counting statistics and Lee-Yang theory, we analyze this nonequilibrium phase transition based on the dynamical zeros of the factorial moment generating function of the electronic charge transport, and we find that the phase transition can be predicted from short-time measurements of the higher-order factorial cumulants. These results pave the way for further investigations of critical behavior in open quantum systems.
Continual Transformers: Redundancy-Free Attention for Online Inference

Transformers in their common form are inherently limited to operate on whole token sequences rather than on one token at a time. Consequently, their use during online inference on time-series data entails considerable redundancy due to the overlap in successive token sequences. In this work, we propose novel formulations of the Scaled Dot-Product Attention, which enable Transformers to perform efficient online token-by-token inference on a continual input stream. Importantly, our modifications are purely to the order of computations, while the outputs and learned weights are identical to those of the original Transformer Encoder. We validate our Continual Transformer Encoder with experiments on the THUMOS14, TVSeries and GTZAN datasets with remarkable results: Our Continual one- and two-block architectures reduce the floating point operations per prediction by up to 63x and 2.6x, respectively, while retaining predictive performance.
Extracting Space Situational Awareness Events from News Text

Space situational awareness typically makes use of physical measurements from radar, telescopes, and other assets to monitor satellites and other spacecraft for operational, navigational, and defense purposes. In this work we explore using textual input for the space situational awareness task. We construct a corpus of 48.5k news articles spanning all known active satellites between 2009 and 2020. Using a dependency-rule-based extraction system designed to target three high-impact events – spacecraft launches, failures, and decommissionings, we identify 1,787 space-event sentences that are then annotated by humans with 15.9k labels for event slots. We empirically demonstrate a state-of-the-art neural extraction system achieves an overall F1 between 53 and 91 per slot for event extraction in this low-resource, high-impact domain.