Miracle-dz/UniFAR-FTUs huggingface dataset:
Designing and evaluating a misinformation warning mechanism based on expert voting

The aim of this thesis is to investigate a new method to counteract the negative effects of fake news. The WHO states an “infodemic” is currently taking place, with consequences that include but are not limited to: efforts by state-actors to destabilize democracy, an increase in deadly Covid misinformation and polarization in politics. To identify possible counter measures, we first look at how misinformation spreads, how humans conduct truth assessments, what counter methods research currently agrees on and how this links with fact checkers and trust scores. Based on the obtained insights, we propose a new warning mechanism in which experts vote on the credibility of tweets in order to decrease the impact of tweets containing misinformation. A mock-up of this warning mechanism has been designed for Twitter, that shows an aggregated trust score combining the votes of different experts in their related fields. We conduct an experiment that compares our idea to a control condition without any warning and a current method Twitter uses for alerting users to misinformation. Initial results indicate that only our method works to decrease perceived credibility of tweets and that users often do not notice a warning tag Twitter currently employs. Finally, we discuss what these findings mean and give directions for further work.
Uncertainty In Quantum Computation

We examine the effect of previous history on starting a computation on a quantum computer. Specifically, we assume that the quantum register has some unknown state on it, and it is required that this state be cleared and replaced by a specific superposition state without any phase uncertainty, as needed by quantum algorithms. We show that, in general, this task is computationally impossible.
A Note on Quality: The Views of Deming, Juran, and Crosby

Copyright © 1990 by the President and Fellows of Harvard College. To order copies or request permission to reproduce materials, call 1-800-545-7685, write Harvard Business School Publishing, Boston, MA 02163, or go to http://www.hbsp. harvard.edu. No part of this publication may be reproduced, stored in a retrieval system, used in a spreadsheet, or transmitted in any form or by any meanselectronic, mechanical, photocopying, recording, or otherwisewithout the permission of Harvard Business School.
On Convergence Lemma and Convergence Stability for Piecewise Analytic Functions

In this work, a convergence lemma for function $f$ being finite compositions of analytic mappings and the maximum operator is proved. The lemma shows that the set of $\delta$-stationary points near an isolated local minimum point $x^*$ is shrinking to $x^*$ as $\delta\to 0$. It is a natural extension of the version for strongly convex $C^1$ functions. However, the correctness of the lemma is subtle. Analytic mappings are necessary for the lemma in the sense that replacing it with differentiable or $C^\infty$ mappings makes the lemma false. The proof is based on stratification theorems of semi-analytic sets by {\L}ojasiewicz. An extension of this proof presents a geometric characterization of the set of stationary points of $f$. Finally, a notion of stability on stationary points, called convergence stability, is proposed. It asks, under small numerical errors, whether a reasonable convergent optimization method started near a stationary point should eventually converge to the same stationary point. The concept of convergence stability becomes nontrivial qualitatively only when the objective function is both nonsmooth and nonconvex. Via the convergence lemma, an intuitive equivalent condition for convergence stability of $f$ is proved. These results together provide a new geometric perspective to study the problem of"where-to-converge"in nonsmooth nonconvex optimization.
Optimal Correlated Rounding for Multi-item Orders in E-commerce Fulﬁllment

Problem deﬁnition. We study the dynamic fulﬁllment problem in e-commerce, in which incoming (multi-item) customer orders must be immediately dispatched to (a combination of) fulﬁllment centers that have the required inventory. The problem takes place over a ﬁnite time horizon, representing the duration until the next inventory replenishment. Methodology/results. A prevailing approach to this problem, pioneered by Jasin and Sinha (2015), is to write a “deterministic” LP that dictates how frequently inventory from each FC (fulﬁllment center) should be used to fulﬁll each item, in diﬀerent orders from diﬀerent regions. This allows the fulﬁllment decision for each order to be made independently. However, making this decision for a single multi-item order is still challenging—how can we avoid splitting the order across too many FC’s, while satisfying the inventory frequency constraints? Jasin and Sinha identify this as a correlated rounding problem and propose an intricate rounding scheme, which is suboptimal by a factor of ≈ q/ 4 on a q -item order. In this paper we provide to our knowledge the ﬁrst substantially improved correlated rounding scheme, which is suboptimal by a factor of only 1 + ln( q ). We provide another scheme for sparse networks, which is suboptimal by a factor of at most d if each item is stored in at most d FC’s. We show both of these guarantees to be optimal in terms of the dependence on q or d , by reducing to the classical Set Cover problem. Our schemes are also simple and fast, based on an intuitive new idea—items wait for FC’s to “open” at random times, but observe them on “dilated” time scales in order to satisfy the inventory frequency constraints. Managerial implications. We numerically test our new rounding schemes under the same realistics setups as Jasin and Sinha (2015) and ﬁnd that they improve runtimes, shorten code, and robustly improve performance. Our code is made publicly available.
A Principled Method for the Creation of Synthetic Multi-fidelity Data Sets

Multiﬁdelity and multioutput optimisation algorithms are of active interest in many areas of computational design as they allow cheaper computational proxies to be used intelligently to aid experimental searches for high performing species. Characterisation of these algorithms involves benchmarks that typically either use analytic functions or existing multiﬁdelity datasets. However, analytic functions are often not representative of relevant problems, while preexisting datasets do not allow systematic investigation of the inﬂuence of characteristics of the lower ﬁdelity proxies. To bridge this gap, we present a methodology for systematic generation of synthetic ﬁdelities derived from preexisting datasets. This allows for construction of benchmarks that are both representative of practical optimisation problems while also allowing systematic investigation of the inﬂuence of the lower ﬁdelity proxies.
Development of weighted model fit indexes for structural equation models using multiple imputation

ix CHAPTER 1. GENERAL INTRODUCTION . 1 1.
SSLEM: A Simplifier for MBA Expressions based on Semi-linear MBA Expressions and Program Synthesis

MBA (mixed boolean and arithmetic) expressions are hard to simplify, so used for malware obfuscation to hinder analysts’ diagno-sis. Some MBA simpliﬁcation methods with high performance have been developed, but they narrowed the target to ”linear” MBA expressions, which allows eﬃcient solutions based on logic/term-rewriting. However such restrictions are not appropriate for general forms of MBA expressions usually appearing in malware. To overcome this limitation, we introduce a ”semi-linear” MBA expression, a new class of MBA expression extended from a linear MBA expression, and propose a new MBA simpliﬁer called ”SSLEM”, based on a simpliﬁcation idea of semi-linear MBA expressions and program synthesis. a simplier.
Quantum-Enhanced Cluster Detection in Physical Images

Identifying clusters in data is an important task in many fields. In this paper, we consider situations in which data live in a physical world, so we have to first collect the images using sensors before clustering them. Using sensors enhanced by quantum entanglement, we can image surfaces more accurately than using purely classical strategies. However, it is not immediately obvious if the advantage we gain is robust enough to survive data processing steps such as clustering. It has previously been found that using quantum-enhanced sensors for imaging and pattern recognition can give an advantage for supervised learning tasks, and here we demonstrate that this advantage also holds for an unsupervised learning task, namely clustering.
Tightly confining lithium niobate photonic integrated circuits and lasers

Zihan Li,1, ∗ Rui Ning Wang,1, ∗ Grigorii Lihachev,1, ∗ Zelin Tan,1 Viacheslav Snigirev,1 Mikhail Churaev,1 Nikolai Kuznetsov,1 Anat Siddharth,1 Mohammad J. Bereyhi,1 Johann Riemensberger,1, † and Tobias J. Kippenberg1, 2, ‡ Institute of Physics, Swiss Federal Institute of Technology Lausanne (EPFL), CH-1015 Lausanne, Switzerland Center of Quantum Science and Engineering (EPFL), CH-1015 Lausanne, Switzerland
Cell Polarity and Movement with Reaction-Diffusion and Moving Boundary: Rigorous Modeling and Robust Simulations

Cell polarity and movement are fundamental to many biological functions. Experimental and theoretically studies have indicated that interactions of certain proteins lead to the cell polarization which plays a key role in controlling the cell movement. We study the cell polarity and movement based on a class of biophysical models that consist of reaction-diffusion equations for different proteins and the dynamics of moving cell boundary. Such a moving boundary is often simulated by a phase-filed model. We first apply the matched asymptotic analysis to give a rigorous derivation of the sharp-interface model of the cell boundary from a phase-field model. We then develop a robust numerical approach that combines the level-set method to track the sharp boundary of a moving cell and accurate discretization techniques for solving the reaction-diffusion equations on the moving cell region. Our extensive numerical simulations predict the cell polarization under various kinds of stimulus, and capture both the linear and circular trajectories of a moving cell for a long period of time. In particular, we have identified some key parameters controlling different cell trajectories that are less accurately predicted by reduced models. Our work has linked different models and also developed tools that can be adapted for the challenging three-dimensional simulations.
Spinning Nanoparticles Impacted by C-shock: Implications for Radio-millimeter Emission from Star-forming Regions

We investigate the impact of anomalous microwave emission (AME) on the radio-millimeter spectral energy distribution for three typical interstellar medium (ISM) conditions surrounding star-forming regions—cold neutral medium, warm neutral medium, and photodissociation regions—by comparing the emissivities of three major contributors: free–free, thermal dust emission, and AME. In particular, for spinning nanoparticles (i.e., potential carriers of AME), we consider a known grain destruction mechanism due to a centrifugal force from spin-up processes caused by collisions between dust grains and supersonic neutral streams in a magnetized shock (C-shock). We demonstrate that, if the ISM in a magnetic field is impacted by a C-shock developed by a supernova explosion in the early phase of massive star formation (≲10 Myr), AME can be significantly or almost entirely suppressed relative to free–free and thermal dust continuum emission if the grain tensile strength is small enough. This study may shed light on explaining the rare observations of AME from extragalactic star-forming regions preferentially observed from massive star clusters and suggest a scenario of “the rise and fall of AME” in accordance with the temporal evolution of star-forming regions.
Proposals for Resolving Consenting Issues with Signals and User-side Dialogues

Consent dialogues are a source of annoyance, malicious intent, dark patterns, illegal practices and a plethora of other issues. This work presents known problems based on GDPR requirements grouped into two categories: (i) UI/UX for consenting; and (ii) power imbalance in expressing consent. To resolve this, it presents two proposals: First, the use of automation through privacy signals to better govern consenting processes and to reduce ‘consent-fatigue’. Second, as generation of consent dialogues on the user side and its practicalities for both websites as well as users and agents (e.g. web browsers). Both proposals are discussed in terms of possibilities for implementation and suitability for stakeholders. The article concludes with a discussion on the diﬃculties in achieving such solutions owing to the conﬂicts of interest between ‘web-enablers’ and ‘web-consumers’, and the necessity for the EU to take a direct stance in addressing these in their future laws.
Visual Haptic Reasoning: Estimating Contact Forces by Observing Deformable Object Interactions

Robotic manipulation of highly deformable cloth presents a promising opportunity to assist people with several daily tasks, such as washing dishes; folding laundry; or dressing, bathing, and hygiene assistance for individuals with severe motor impairments. In this letter, we introduce a formulation that enables a collaborative robot to perform visual haptic reasoning with cloth—the act of inferring the location and magnitude of applied forces during physical interaction. We present two distinct model representations, trained in physics simulation, that enable haptic reasoning using only visual and robot kinematic observations. We conducted quantitative evaluations of these models in simulation for robot-assisted dressing, bathing, and dish washing tasks, and demonstrate that the trained models can generalize across different tasks with varying interactions, human body sizes, and object shapes. We also present results with a real-world mobile manipulator, which used our simulation-trained models to estimate applied contact forces while performing physically assistive tasks with cloth. Videos can be found at our project webpage.
Partition pooling for convolutional graph network applications in particle physics

Convolutional graph networks are used in particle physics for effective event reconstructions and classifications. However, their performances can be limited by the considerable amount of sensors used in modern particle detectors if applied to sensor-level data. We present a pooling scheme that uses partitioning to create pooling kernels on graphs, similar to pooling on images. Partition pooling can be used to adopt successful image recognition architectures for graph neural network applications in particle physics. The reduced computational resources allow for deeper networks and more extensive hyperparameter optimizations. To show its applicability, we construct a convolutional graph network with partition pooling that reconstructs simulated interaction vertices for an idealized neutrino detector. The pooling network yields improved performance and is less susceptible to overfitting than a similar network without pooling. The lower resource requirements allow the construction of a deeper network with further improved performance.
A Comprehensive Survey of Natural Language Generation Advances from the Perspective of Digital Deception

In recent years there has been substantial growth in the capabilities of systems designed to generate text that mimics the fluency and coherence of human language. From this, there has been considerable research aimed at examining the potential uses of these natural language generators (NLG) towards a wide number of tasks. The increasing capabilities of powerful text generators to mimic human writing convincingly raises the potential for deception and other forms of dangerous misuse. As these systems improve, and it becomes ever harder to distinguish between human-written and machine-generated text, malicious actors could leverage these powerful NLG systems to a wide variety of ends, including the creation of fake news and misinformation, the generation of fake online product reviews, or via chatbots as means of convincing users to divulge private information. In this paper, we provide an overview of the NLG field via the identification and examination of 119 survey-like papers focused on NLG research. From these identified papers, we outline a proposed high-level taxonomy of the central concepts that constitute NLG, including the methods used to develop generalised NLG systems, the means by which these systems are evaluated, and the popular NLG tasks and subtasks that exist. In turn, we provide an overview and discussion of each of these items with respect to current research and offer an examination of the potential roles of NLG in deception and detection systems to counteract these threats. Moreover, we discuss the broader challenges of NLG, including the risks of bias that are often exhibited by existing text generation systems. This work offers a broad overview of the field of NLG with respect to its potential for misuse, aiming to provide a high-level understanding of this rapidly developing area of research. Keywords—Natural Language Generation, NLG, Digital Deception, Survey, Taxonomy
Some refined criteria toward boundedness in an attraction-repulsion chemotaxis system with nonlinear productions

We study some zero-flux attraction-repulsion chemotaxis models, with nonlinear production rates for the chemorepellent and the chemoattractant. This paper partially improves some known results in the literature and moreover solves an open question. The research is also complemented with numerical simulations in bi-dimensional domains.
TIC 114936199: A Quadruple Star System with a 12 Day Outer-orbit Eclipse

We report the discovery with TESS of a remarkable quadruple star system with a 2+1+1 configuration. The two unique characteristics of this system are that (i) the inner eclipsing binary (stars Aa and Ab) eclipses the star in the outermost orbit (star C), and (ii) these outer fourth-body eclipses last for ∼12 days, the longest of any such system known. The three orbital periods are ∼3.3 days, ∼51 days, and ∼2100 days. The extremely long duration of the outer eclipses is due to the fact that star B slows binary A down on the sky relative to star C. We combine TESS photometric data, ground-based photometric observations, eclipse timing points, radial velocity measurements, the composite spectral energy distribution, and stellar isochrones in a spectrophotodynamical analysis to deduce all of the basic properties of the four stars (mass, radius, T eff, and age), as well as the orbital parameters for all three orbits. The four masses are M Aa = 0.382 M ⊙, M Ab = 0.300 M ⊙, M B = 0.540 M ⊙, and M C = 0.615 M ⊙, with a typical uncertainty of 0.015 M ⊙.
Twisted by the Pools: Detection of Selfish Anomalies in Proof-of-Work Mining

The core of many cryptocurrencies is the decentralised validation network operating on proof-of-work technology. In these systems, validation is done by so-called miners who can digitally sign blocks once they solve a computationally-hard problem. Conventional wisdom generally considers this protocol as secure and stable as miners are incentivised to follow the behaviour of the majority. However, whether some strategic mining behaviours occur in practice is still a major concern. In this paper we target this question by focusing on a security threat: a selfish mining attack in which malicious miners deviate from protocol by not immediately revealing their newly mined blocks. We propose a statistical test to analyse each miner's behaviour in five popular cryptocurrencies: Bitcoin, Litecoin, Monacoin, Ethereum and Bitcoin Cash. Our method is based on the realisation that selfish mining behaviour will cause identifiable anomalies in the statistics of miner's successive blocks discovery. Secondly, we apply heuristics-based address clustering to improve the detectability of this kind of behaviour. We find a marked presence of abnormal miners in Monacoin and Bitcoin Cash, and, to a lesser extent, in Ethereum. Finally, we extend our method to detect coordinated selfish mining attacks, finding mining cartels in Monacoin where miners might secretly share information about newly mined blocks in advance. Our analysis contributes to the research on security in cryptocurrency systems by providing the first empirical evidence that the aforementioned strategic mining behaviours do take place in practice.
Nonperturbative structure in coupled axion sectors and implications for direct detection

Pairs of misalignment-produced axions with nearby masses can experience a nonlinear resonance that leads to enhanced direct and astrophysical signatures of axion dark matter. In much of the relevant parameter space, self-interactions cause axion fluctuations to become nonperturbative and to collapse in the early Universe. We investigate the observational consequences of such nonperturbative structure in this"friendly axion"scenario with $3+1$ dimensional simulations. Critically, in a substantial fraction of parameter space we find that nonlinear dynamics work to equilibrate the abundance of the two axions, making it easier than previously expected to experimentally confirm the existence of a resonant pair. We also compute the gravitational wave emission from friendly axion dark matter; while the resulting stochastic background is likely undetectable for axion masses above $10^{-22} \, \text{eV}$, the polarization of the cosmic microwave background does constrain possible hyperlight, friendly subcomponents. Finally, we demonstrate that dense, self-interaction--bound oscillons formed during the period of strong nonlinearity are driven by the homogeneous axion background, enhancing their lifetime beyond the in-vacuum expectation.
Finite approximation properties of $C^{*}$-modules III

We introduce and study a notion of module nuclear dimension for a $C^{*}$-algebra $A$ which is $C^*$-module over another $C^*$-algebra $\mathfrak A$ with compatible actions. We show that the module nuclear dimension of $A$ is zero if $A$ is $\mathfrak A$-NF. The converse is shown to hold when $\mathfrak A$ is a $C(X)$-algebra with simple fibers, with $X$ compact and totally disconnected. We also introduce a notion of module decomposition rank, and show that when $\mathfrak A$ is unital and simple, if the module decomposition rank of $A$ is finite then $A$ is $\mathfrak A$-QD. We study the set $\mathcal T_\mathfrak A(A)$ of $\mathfrak A$-valued module traces on $A$ and relate the Cuntz semigroup of $A$ with lower semicontinuous affine functions on the set $\mathcal T_\mathfrak A(A)$. Along the way, we also prove a module Choi-Effros lifting theorem. We give estimates of the module nuclear dimension for a class of examples.
A Compositional Approach to Verifying Modular Robotic Systems

Robotic systems used in safety-critical industrial situations often rely on modular software architectures, and increasingly include autonomous components. Verifying that these modular robotic systems behave as expected requires approaches that can cope with, and preferably take advantage of, this inherent modularity. This paper describes a compositional approach to specifying the nodes in robotic systems built using the Robotic Operating System (ROS), where each node is specified using First-Order Logic (FOL) assume-guarantee contracts that link the specification to the ROS implementation. We introduce inference rules that facilitate the composition of these node-level contracts to derive system-level properties. We also present a novel Domain-Specific Language, the ROS Contract Language, which captures a node's FOL specification and links this contract to its implementation. RCL contracts can be automatically translated, by our tool Vanda, into executable monitors; which we use to verify the contracts at runtime. We illustrate our approach through the specification and verification of an autonomous rover engaged in the remote inspection of a nuclear site, and finish with smaller examples that illustrate other useful features of our framework.