Miracle-dz/UniFAR-FTUs huggingface dataset:
Distribution-on-Distribution Regression via Optimal Transport Maps

We present a framework for performing regression when both covariate and response are probability distributions on a compact interval. Our regression model is based on the theory of optimal transportation and links the conditional Fréchet mean of the response to the covariate via an optimal transport map. We define a Fréchet-least-squares estimator of this regression map, and establish its consistency and rate of convergence to the true map, under both full and partial observation of the regression pairs. Computation of the estimator is shown to reduce to a standard convex optimization problem, and thus our regression model can be implemented with ease. We illustrate our methodology using real and simulated data.
Blockchain-enabled End-to-End Encryption for Instant Messaging Applications

In this era of ubiquitous social media and messaging applications, users are becoming increasingly aware of the data privacy issues associated with such apps. Major messaging applications are moving towards end-to-end encryption (E2EE) to give their users the privacy they are demanding. However the current security mechanisms employed by different service providers are not unfeigned E2EE implementations, and are blended with many vulnerabilities. At present, the major part of the E2EE mechanism is controlled by the service provider’s servers, and the decryption keys are also stored by them in case of backup restoration. These shortcomings diminish user confidence in the privacy of their data when using these apps. A public key infrastructure (PKI) can be used to circumvent some of these issues, but it comes with high monetary costs, which makes it impossible to roll out on a global scale. This paper proposes a blockchain-based E2EE framework that can mitigate many of the contemporary vulnerabilities in today’s messaging applications. A user’s device generates the public/private key pair during application installation, and asks its mobile network operator (MNO) to issue a digital certificate and store it on a public blockchain. Any user can fetch a certificate for another user from the application server, and communicate securely with them using a ratchet forward encryption mechanism.
A novel background field approach to the confinement-deconfinement transition

We propose a novel approach to the confinement-deconfinement transition in Yang-Mills theories in the context of gauge-fixed calculations. The method is based on a background-field generalisation of the Landau gauge (to which it reduces at vanishing temperature) with a given, center-symmetric background. This is to be contrasted with most implementations of background field methods in gauge theories, where one uses a variable, self-consistent background. Our proposal is a bona fide gauge fixing that can easily be implemented on the lattice and in continuum approaches. The resulting gauge-fixed action explicitly exhibits the center symmetry of the nonzero temperature theory that controls the confinement-deconfinement transition. We show that, in that gauge, the electric susceptibility diverges at a second order transition [e.g., in the SU(2) theory], so that the gluon propagator is a clear probe of the transition. We implement our proposal in the perturbative Curci-Ferrari model, known for its successful description of various infrared aspects of Yang-Mills theories, including the confinement-deconfinement transition. Our one-loop calculation confirms our general expectation for the susceptibility while providing transition temperatures in excellent agreement with the SU(2) and SU(3) lattice values. Finally, the Polyakov loops above the transition show a more moderate rise, in contrast to previous implementations of the Curci-Ferrari model using a self-consistent background, and our SU(3) result agrees quite well with the lattice data in the range [0,2T_c][0,2Tc].
CEAR: Cross-Entity Aware Reranker for Knowledge Base Completion

Pre-trained language models (LMs) like BERT have shown to store factual knowledge about the world. This knowledge can be used to augment the information present in Knowledge Bases, which tend to be incomplete. However, prior attempts at using BERT for task of Knowledge Base Completion (KBC) resulted in performance worse than embedding based techniques that rely only on the graph structure. In this work we develop a novel model, Cross-Entity Aware Reranker (CEAR), that uses BERT to re-rank the output of existing KBC models with cross-entity attention. Unlike prior work that scores each entity independently, CEAR uses BERT to score the entities together, which is effective for exploiting its factual knowledge. CEAR achieves a new state of art for the OLPBench dataset.
Improving Zero-Shot Cross-Lingual Transfer Learning via Robust Training

Pre-trained multilingual language encoders, such as multilingual BERT and XLM-R, show great potential for zero-shot cross-lingual transfer. However, these multilingual encoders do not precisely align words and phrases across languages. Especially, learning alignments in the multilingual embedding space usually requires sentence-level or word-level parallel corpora, which are expensive to be obtained for low-resource languages. An alternative is to make the multilingual encoders more robust; when fine-tuning the encoder using downstream task, we train the encoder to tolerate noise in the contextual embedding spaces such that even if the representations of different languages are not aligned well, the model can still achieve good performance on zero-shot cross-lingual transfer. In this work, we propose a learning strategy for training robust models by drawing connections between adversarial examples and the failure cases of zero-shot cross-lingual transfer. We adopt two widely used robust training methods, adversarial training and randomized smoothing, to train the desired robust model. The experimental results demonstrate that robust training improves zero-shot cross-lingual transfer on text classification tasks. The improvement is more significant in the generalized cross-lingual transfer setting, where the pair of input sentences belong to two different languages.
Keyphrase Generation with Fine-Grained Evaluation-Guided Reinforcement Learning

Aiming to generate a set of keyphrases, Keyphrase Generation (KG) is a classical task for capturing the central idea from a given document. Based on Seq2Seq models, the previous reinforcement learning framework on KG tasks utilizes the evaluation metrics to further improve the well-trained neural models. However, these KG evaluation metrics such as $F_1@5$ and $F_1@M$ are only aware of the exact correctness of predictions on phrase-level and ignore the semantic similarities between similar predictions and targets, which inhibits the model from learning deep linguistic patterns. In response to this problem, we propose a new fine-grained evaluation metric to improve the RL framework, which considers different granularities: token-level $F_1$ score, edit distance, duplication, and prediction quantities. On the whole, the new framework includes two reward functions: the fine-grained evaluation score and the vanilla $F_1$ score. This framework helps the model identifying some partial match phrases which can be further optimized as the exact match ones. Experiments on KG benchmarks show that our proposed training framework outperforms the previous RL training frameworks among all evaluation scores. In addition, our method can effectively ease the synonym problem and generate a higher quality prediction. The source code is available at \url{https://github.com/xuyige/FGRL4KG}.
Perspectives on Machine Learning from Psychology's Reproducibility Crisis

In the early 2010s, a crisis of reproducibility rocked the field of psychology. Following a period of reflection, the field has responded with radical reform of its scientific practices. More recently, similar questions about the reproducibility of machine learning research have also come to the fore. In this short paper, we present select ideas from psychology's reformation, translating them into relevance for a machine learning audience.
Comparing Correspondences: Video Prediction with Correspondence-wise Losses

Image prediction methods often struggle on tasks that require changing the positions of objects, such as video prediction, producing blurry images that average over the many positions that objects might occupy. In this paper, we propose a simple change to existing image similarity metrics that makes them more robust to positional errors: we match the images using optical flow, then measure the visual similarity of corresponding pixels. This change leads to crisper and more perceptually accurate predictions, and does not require modifications to the image prediction network. We apply our method to a variety of video prediction tasks, where it obtains strong performance with simple network architectures, and to the closely related task of video interpolation. Code and results are available at our webpage: https://dangeng.github. io/CorrWiseLosses
TFROM: A Two-sided Fairness-Aware Recommendation Model for Both Customers and Providers

At present, most research on the fairness of recommender systems is conducted either from the perspective of customers or from the perspective of product (or service) providers. However, such a practice ignores the fact that when fairness is guaranteed to one side, the fairness and rights of the other side are likely to reduce. In this paper, we consider recommendation scenarios from the perspective of two sides (customers and providers). From the perspective of providers, we consider the fairness of the providers' exposure in recommender system. For customers, we consider the fairness of the reduced quality of recommendation results due to the introduction of fairness measures. We theoretically analyzed the relationship between recommendation quality, customers fairness, and provider fairness, and design a two-sided fairness-aware recommendation model (TFROM) for both customers and providers. Specifically, we design two versions of TFROM for offline and online recommendation. The effectiveness of the model is verified on three real-world data sets. The experimental results show that TFROM provides better two-sided fairness while still maintaining a higher level of personalization than the baseline algorithms.
Photonic Microwave and RF Channelizers using Kerr Micro-combs

We review recent work on broadband RF channelizers based on integrated optical frequency Kerr micro-combs combined with passive micro-ring resonator filters, with microcombs having channel spacings of 200GHz and 49GHz. This approach to realizing RF channelizers offers reduced complexity, size, and potential cost for a wide range of applications to microwave signal detection. Keywords—Microwave photonic, signal channelization, integrated optical frequency comb.
Fourier and Zak transforms of multiplicative characters

In this paper we derive formulas for the N -point discrete Fourier transform and the R1 × R2 finite Zak transform of multiplicative characters on Z/N , where N is an odd integer, and R1 and R2 are co-prime factors of N . In one special case this permits computation of the discrete Fourier transform and the finite Zak transform of the Jacobi symbol, the modified Jacobi sequence, and the Golomb sequence. In other cases, not addressed here, this permits computation of the discrete Fourier transform and the finite Zak transform of certain complex-valued sequences. These results constitute, to our knowledge, the first unified treatment of key Fourier and Zak space properties of multiplicative characters. These results also provide a convenient framework for the design of new character-based sequences.
Optimal Transmit Strategy for MIMO WPT Systems With Non-linear Energy Harvesting

In this paper, we study multiple-input multiple-output (MIMO) wireless power transfer (WPT) systems, where the energy harvesting (EH) node is equipped with multiple nonlinear rectennas. We characterize the optimal transmit strategy by the optimal distribution of the transmit symbol vector that maximizes the average harvested power at the EH node subject to a constraint on the power budget of the transmitter. We show that the optimal transmit strategy employs scalar unit-norm input symbols with arbitrary phase and two beamforming vectors, which are determined as solutions of a non-convex optimization problem. To solve this problem, we propose an iterative algorithm based on a two-dimensional grid search, semi-definite relaxation, and successive convex approximation. Our simulation results reveal that the proposed MIMO WPT design significantly outperforms two baseline schemes based on a linear EH model and a single beamforming vector, respectively. Finally, we show that the average harvested power grows linearly with the number of rectennas at the EH node and saturates for a large number of TX antennas.
Introductory Models of COVID-19 in the United States

Students develop and test simple kinetic models of the spread of coronavirus disease 2019 (COVID-19) caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) virus. Microsoft Excel is used as the modeling platform because it is nonthreatening to students and it is widely available. Students develop finite difference models and implement them in the cells of preformatted spreadsheets following a guided inquiry pedagogy that introduces new model parameters in a scaffolded step-by-step manner. That approach allows students to investigate the implications of new model parameters in a systematic way. Students fit the resulting models to reported cases per day data for the United States using least squares techniques with Excel's Solver. Using their own spreadsheets, students discover for themselves that the initial exponential growth of COVID-19 can be explained by a simplified unlimited growth model and by the susceptible-infected-recovered (SIR) model. They also discover that the effects of social distancing can be modeled using a Gaussian transition function for the infection rate coefficient and that the summer surge was caused by prematurely relaxing social distancing and then reimposing stricter social distancing. Students then model the effect of vaccinations and validate the resulting susceptible-infected-recovered-vaccinated (SIRV) model by showing that it successfully predicts the reported cases per day data from Thanksgiving through the holiday period up to 14 February 2021. The same SIRV model is then extended and successfully fits the fourth peak up to 1 June 2021, caused by further relaxation of social distancing measures. Finally, students extend the model up to the present day (27 August 2021) and successfully account for the appearance of the delta variant of the SARS-CoV-2 virus. The fitted model also predicts that the delta variant peak will be comparatively short, and the cases per day data should begin to fall off in early September 2021, counter to current expectations. This case study makes an excellent capstone experience for students interested in scientific modeling.
Electromagnetic Signatures from Thermonuclear Explosions and Accretion-Induced Collapses of White Dwarfs Embedded in AGN disks

White dwarfs (WDs) embedded in gaseous disks of active galactic nucleus (AGNs) can rapidly accrete materials from the disks and grow in mass to reach or even exceed the Chandrasekhar limit. Binary WD (BWD) mergers are also believed to occur in AGN accretion disks. We study observational signatures from these events. We suggest that mass-accreting WDs and BWD mergers in AGN disks can lead to thermonuclear explosions that drive an ejecta shock breakout from the disk surface and power a slow-rising, relatively dim Type Ia supernova (SN). Such SNe Ia may be always outshone by the emission of the AGN disk around the supermassive black hole (BH) with a mass of MSMBH & 10M . Besides, accretion-induced collapses (AICs) of WDs in AGN disks may occur sometimes, which may form highly-magnetized millisecond neutron stars (NSs). The subsequent spin-down process of this nascent magnetar can deposit its rotational energy into the disk materials, resulting in a magnetardriven shock breakout and a luminous magnetar-powered transient. We show that such an AIC event could power a rapidly evolving and luminous transient for a magnetic field of B ∼ 10 G. The rising time and peak luminosity of the transient, powered by a magnetar with B ∼ 10 G, are predicted to have similar properties with those of superluminous supernovae. AIC events taking place in the inner parts of the disk around a relatively less massive supermassive BHs (MSMBH . 10M ) are more likely to power the transients that are much brighter than the AGN disk emission and hence easily to be identified.
RANSIC: Fast and Highly Robust Estimation for Rotation Search and Point Cloud Registration Using Invariant Compatibility

Correspondence-based rotation search and point cloud registration are two fundamental problems in robotics and computer vision. However, the presence of outliers, sometimes even occupying the great majority of the putative correspondences, can make many existing algorithms either fail or have very high computational cost. In this letter, we present RANSIC (RANdom Sampling with Invariant Compatibility), a fast and highly robust method applicable to both problems based on a new paradigm combining random sampling with invariance and compatibility. RANSIC starts with randomly selecting small subsets from the correspondence set, then seeks potential inliers as the graph vertices from random subsets through the compatibility tests based on invariants established in each problem, and eventually returns the eligible inliers when there exists a $K$-degree vertex (where $K$ is initially set and updated during the algorithm) and the residual errors satisfy a certain termination condition at the same time. In synthetic and real experiments, we show that RANSIC is fast for use, robust against over 95% outliers, and also able to recall approximately 100% inliers, outperforming other state-of-the-art solvers for both the rotation search and point cloud registration problems.
The alternative fact of probable vaccine damage A typology of vaccination beliefs in 28 European countries

Background: Despite lacking scientific support, vaccine hesitancy is widespread. While “vaccine damage” as a scientific fact is statistically highly uncommon, emerging social and technological forces have converted “probable vaccine damage” into an alternative fact, thus making it a widely shared intersubjective reality. Methods: Using the Eurobarometer 91.2 survey on a statistically representative EU27-UK sample interviewed in March 2019, we documented perceptions of vaccine risks and identified three belief configurations regarding vaccine effectiveness, safety, and usefulness, through exploratory cluster analysis. Results: The public beliefs in vaccine risks are frequent. Approximatively one-tenth of the EU27-UK population consider that vaccines are not rigorously tested before authorization, one-third believe that vaccines can overload or weaken the immune system and that they can cause the disease against which they protect, and almost one-half believe that vaccines can cause serious side effects. We identified three belief configurations: the skeptical, the confident, and the trade-off clusters. The skeptical type (approx. 11% of EU27-UK respondents) is defined by the belief that vaccines are rather ineffective, affected by risks of probable vaccine damage, not well-tested, and useless; the confident type (approx. 59%) is defined by beliefs that vaccines are effective, safe, well-tested, and useful; and the trade-off type (approx. 29%) combines beliefs that vaccines are effective, well-tested and useful, with beliefs of probable vaccine damage. The vaccine-confident and the trade-off types profess having similar vaccination histories, indicating the significant role of other factors besides beliefs in inducing behavior. Conclusions: The high proportion of varying public beliefs in vaccine risks and the presence of a trade-off type of vaccination assessment indicate the social normality of beliefs in probable vaccine damage. Probable
Wireless Information and Power Transfer for IoT: Pulse Position Modulation, Integrated Receiver, and Experimental Validation

Simultaneous wireless information and power transfer (SWIPT) has emerged as a viable technique to energize and connect low-power autonomous devices and enable future Internet of Things (IoT). A major challenge of SWIPT is the energy consumption of the receiver of such low-power devices. An attractive low-power solution consists of an integrated information decoder (ID) and energy harvester (EH) architecture for SWIPT receiver (IntRx) where the received radiofrequency (RF) signal is first rectified before being used for information decoding. Such architecture eliminates the need for energy-consuming RF components, such as local oscillators and mixers. This article proposes a novel modulation and demodulation method for the IntRx SWIPT architecture based on pulse position modulation (PPM) where information is encoded in the position of the pulse. The new method transmits high amplitude pulses to increase the peak-to-average power ratio (PAPR) of the transmit signal and exploits the EH’s nonlinearity so as to boost the harvested dc power. Simultaneously, the information can be decoded from the rectifier signal by simply finding the position of the pulse in a certain symbol duration. We have analyzed both the information and the power transfer performance of the newly proposed PPM for IntRx SWIPT theoretically, numerically, and experimentally. To that end, we have established a SWIPT system testbed in an indoor environment by prototyping a base station to transfer information-power signal and the IntRx SWIPT receiver, including ID and EH blocks. The performance evaluation of the PPM was carried out in various conditions, and the results have been compared and contrasted to conventional signals. Theoretical, numerical, and experimental results highlight the significant benefits of the proposed PPM scheme to enhance the power transfer performance and operate information decoding with low-power consumption.
A Choreographed Outline Instrumentation Algorithm for Asynchronous Components

The runtime analysis of decentralised software requires instrumentation methods that are scalable, but also minimally invasive. This paper presents a new algorithm that instruments choreographed outline monitors. Our instrumentation algorithm scales and reorganises monitors dynamically as the system executes. We demonstrate the implementability of choreographed outline instrumentation and compare it to inline instrumentation, subject to rigorous and comprehensive benchmarking. Our results debunk the general notion that outline monitoring is necessarily infeasible, and show that our implementation induces runtime overhead comparable to that of its inline counterpart for many practical cases.