Miracle-dz/UniFAR-FTUs huggingface dataset:
Should Ensemble Members Be Calibrated?

Underlying the use of statistical approaches for a wide range of applications is the assumption that the probabilities obtained from a statistical model are representative of the"true"probability that event, or outcome, will occur. Unfortunately, for modern deep neural networks this is not the case, they are often observed to be poorly calibrated. Additionally, these deep learning approaches make use of large numbers of model parameters, motivating the use of Bayesian, or ensemble approximation, approaches to handle issues with parameter estimation. This paper explores the application of calibration schemes to deep ensembles from both a theoretical perspective and empirically on a standard image classification task, CIFAR-100. The underlying theoretical requirements for calibration, and associated calibration criteria, are first described. It is shown that well calibrated ensemble members will not necessarily yield a well calibrated ensemble prediction, and if the ensemble prediction is well calibrated its performance cannot exceed that of the average performance of the calibrated ensemble members. On CIFAR-100 the impact of calibration for ensemble prediction, and associated calibration is evaluated. Additionally the situation where multiple different topologies are combined together is discussed.
Data Engagement Reconsidered: A Study of Automatic Stress Tracking Technology in Use

In today’s fast-paced world, stress has become a growing health concern. While more automatic stress tracking technologies have recently become available on wearable or mobile devices, there is still a limited understanding of how they are actually used in everyday life. This paper presents an empirical study of automatic stress-tracking technologies in use in China, based on semi-structured interviews with 17 users. The study highlights three challenges of stress-tracking data engagement that prevent effective technology usage: the lack of immediate awareness, the lack of pre-required knowledge, and the lack of corresponding communal support. Drawing on the stress-tracking practices uncovered in the study, we bring these issues to the fore, and unpack assumptions embedded in related works on self-tracking and how data engagement is approached. We end by calling for a reconsideration of data engagement as part of self-tracking practices with technologies rather than simply looking at the user interface.
Scared into Action: How Partisanship and Fear are Associated with Reactions to Public Health Directives

Differences in political ideology are increasingly appearing as an impediment to successful bipartisan communication from local leadership. For example, recent empirical findings have shown that conservatives are less likely to adhere to COVID-19 health directives. This behavior is in direct contradiction to past research which indicates that conservatives are more rule abiding, prefer to avoid loss, and are more prevention-motivated than liberals. We reconcile this disconnect between recent empirical findings and past research by using insights gathered from press releases, millions of tweets, and mobility data capturing local movement in retail, grocery, workplace, parks, and transit domains during COVID-19 shelter-in-place orders. We find that conservatives adhere to health directives when they express more fear of the virus. In order to better understand this phenomenon, we analyze both official and citizen communications and find that press releases from local and federal government, along with the number of confirmed COVID-19 cases, lead to an increase in expressions of fear on Twitter.
Ab Initio Study of Ring Flipping of the Overcrowded Peri-Substituted Naphthalenes

Ab inintio molecular orbital and density functional theory method were used to investigate the structural and dynamic behavior of 1,8-di-tert-butyl naphthalene (1), 1,8-bis(trimethylsilyl)naphthalene (2), 1,8-bis(trimethylgermyl)naphthalene (3), and 1,8-bis(trimethylstannyl)naphthalene (4). HF/3-21G//HF/3-21G results revealed that the ring flipping barrier height of compound 1–4 is 92.59, 32.13, 26.76, and 15.46 kJ mol−1 respectively. The obtained results show that the transition state structure for ring flipping of the bulky-groups is in a planar form with naphthalene ring. Contrary to compound 1, the ring flipping of compounds 2–4 occurred easily at room temperature. Also, MP2/3-21G//HF/3-21G energy calculation, show that the enantiomerization energy of compounds 1–4 are 97.99, 33.24, 26.80, and 15.38 kJ·mol−1 respectively. The required energy for ring inversion of compounds 1–4 are 85.09, 27.26, 21.54, and 10.21 kJ mol−1 respectively, as calculated by B3LYP/3-21G//HF/3-21G method. It can be concluded that the lower energy barrier of the ring flipping of compounds 2–4 is related to the increasing of the bond lengths of Si—C, Ge—C, and Sn—C, in contrast to C—C bond.
Eating Garlic Prevents COVID-19 Infection: Detecting Misinformation on the Arabic Content of Twitter

The rapid growth of social media content during the current pandemic provides useful tools for disseminating information which has also become a root for misinformation. Therefore, there is an urgent need for fact-checking and effective techniques for detecting misinformation in social media. In this work, we study the misinformation in the Arabic content of Twitter. We construct a large Arabic dataset related to COVID-19 misinformation and gold-annotate the tweets into two categories: misinformation or not. Then, we apply eight different traditional and deep machine learning models, with different features including word embeddings and word frequency. The word embedding models (\textsc{FastText} and word2vec) exploit more than two million Arabic tweets related to COVID-19. Experiments show that optimizing the area under the curve (AUC) improves the models' performance and the Extreme Gradient Boosting (XGBoost) presents the highest accuracy in detecting COVID-19 misinformation online.
Uncertainty Quantification of Bifurcations in Random Ordinary Differential Equations

Many real-world phenomena can be described by nonlinear ordinary diﬀerential equations. Key characteristics of the model appear in terms of parameters. Oftentimes, these parameters need to be estimated using data assimilation techniques [1]. Parameter-related uncertainty can have a major impact on the system’s dynamics. It is well-known in the theory of dynamical systems that parameter variation can induce bifurcations [2]. This means that a qualitative change in the ﬂow of the vector ﬁeld corresponding to the ODE happens. In this talk, I will address the eﬀect of parameter uncertainty on the occurrence of diﬀerent bifurcation types. While some of them cause rather smooth qualitative changes, others can cause the system to suddenly jump to a new equilibrium far away from previous dynamics. Concerns have been raised that subsystems of the earth might undergo such critical transitions under sus-tained global warming with severe impacts on various ecosystems and habitat. This phenomenon is not restricted to science but appears also in ecology and epidemiology [4]. Therefore, we need estimates for the bifurcation type probabilities to contribute to a risk assessment of the exposure to critical transitions. In this talk, I will combine known statistical and probabilistic concepts with classical analysis and bifurcation theory. This will be done in a two-step procedure. The ﬁrst step consists in performing reduction and transformation steps leading to a normal form coeﬃcient. Its sign is known to determine the bifurcation type [2]. The probabilistic analysis of the normal form coeﬃcient is challenging as it generically comes as a nonlinear transformation of the uncertain input parameters. This problem is addressed in a second step by using probabilistic techniques from Uncertainty Quantiﬁcation and classical analysis. In a numerical case study, I will illustrate the performance of the estimation procedure.
Immune amnesia induced by measles and its effects on concurrent epidemics

It has been recently discovered that the measles virus can damage pre-existing immunological memory, destroying B lymphocytes and reducing the diversity of non-specific B cells of the infected host. In particular, this implies that previously acquired immunization from vaccination or direct exposition to other pathogens could be partially erased in a phenomenon named ‘immune amnesia’, whose effects can become particularly worrisome given the actual rise of anti-vaccination movements. Here, we present the first attempt to incorporate immune amnesia into standard models of epidemic spreading by proposing a simple model for the spreading of two concurrent pathogens causing measles and another generic disease. Different analyses confirm that immune amnesia can have important consequences for epidemic spreading, significantly altering the vaccination coverage required to reach herd immunity. We also uncover the existence of novel propagating and endemic phases induced by immune amnesia. Finally, we discuss the meaning and consequences of our results and their relation with, e.g. immunization strategies, together with the possibility that explosive types of transitions may emerge, making immune-amnesia effects particularly dramatic. This work opens the door to further developments and analyses of immune-amnesia effects, contributing also to the theory of interacting epidemics on complex networks.
Multi-Robot Symmetric Rendezvous Search on the Line

We study the Symmetric Rendezvous Search Problem for a multi-robot system. There are $n>2$ robots arbitrarily located on a line. Their goal is to meet somewhere on the line as quickly as possible. The robots do not know the initial location of any of the other robots or their own positions on the line. The symmetric version of the problem requires the robots to execute the same search strategy to achieve rendezvous. Therefore, we solve the problem in an online fashion with a randomized strategy. In this letter, we present a symmetric rendezvous algorithm which achieves a constant competitive ratio for the total distance traveled by the robots. We validate our theoretical results through simulations.
Anomaly Detection Support Using Process Classification

Anomaly detection systems need to consider a lot of information when scanning for anomalies. One example is the context of the process in which an anomaly might occur, because anomalies for one process might not be anomalies for a different one. Therefore data – such as system events – need to be assigned to the program they originate from. This paper investigates whether it is possible to infer from a list of system events the program whose behavior caused the occurrence of these system events. To that end, we model transition probabilities between non-equivalent events and apply the k-nearest neighbors algorithm. This system is evaluated on non-malicious, real-world data using four different evaluation scores. Our results suggest that the approach proposed in this paper is capable of correctly inferring program names from system events.
Entangled Kernels - Beyond Separability

We consider the problem of operator-valued kernel learning and investigate the possibility of going beyond the well-known separable kernels. Borrowing tools and concepts from the field of quantum computing, such as partial trace and entanglement, we propose a new view on operator-valued kernels and define a general family of kernels that encompasses previously known operator-valued kernels, including separable and transformable kernels. Within this framework, we introduce another novel class of operator-valued kernels called entangled kernels that are not separable. We propose an efficient two-step algorithm for this framework, where the entangled kernel is learned based on a novel extension of kernel alignment to operator-valued kernels. We illustrate our algorithm with an application to supervised dimensionality reduction, and demonstrate its effectiveness with both artificial and real data for multi-output regression.
Fast offline transformer‐based end‐to‐end automatic speech recognition for real‐world applications

With the recent advances in technology, automatic speech recognition (ASR) has been widely used in real‐world applications. The efficiency of converting large amounts of speech into text accurately with limited resources has become more vital than ever. In this study, we propose a method to rapidly recognize a large speech database via a transformer‐based end‐to‐end model. Transformers have improved the state‐of‐the‐art performance in many fields. However, they are not easy to use for long sequences. In this study, various techniques to accelerate the recognition of real‐world speeches are proposed and tested, including decoding via multiple‐utterance‐batched beam search, detecting end of speech based on a connectionist temporal classification (CTC), restricting the CTC‐prefix score, and splitting long speeches into short segments. Experiments are conducted with the Librispeech dataset and the real‐world Korean ASR tasks to verify the proposed methods. From the experiments, the proposed system can convert 8 h of speeches spoken at real‐world meetings into text in less than 3 min with a 10.73% character error rate, which is 27.1% relatively lower than that of conventional systems.
A Review of the Efforts and Hindrances of Modeling and Simulation of CAR T-cell Therapy

Chimeric Antigen Receptor (CAR) T-cell therapy is an immunotherapy that has recently become highly instrumental in the fight against life-threatening diseases. A variety of modeling and computational simulation efforts have addressed different aspects of CAR T therapy, including Tcell activation, Tand malignant cell population dynamics, therapeutic cost-effectiveness strategies, and patient survival analyses. In this article, we present a systematic review of those efforts, including mathematical, statistical, and stochastic models employing a wide range of algorithms, from differential equations to machine learning. To the best of our knowledge, this is the first review of all such models studying CAR T therapy. In this review, we provide a detailed summary of the strengths, limitations, methodology, data used, and data lacking in current published models. This information may help in designing and building better models for enhanced prediction and assessment of the benefit-risk balance associated with novel CAR T therapies, as well as with the data collection essential for building such models.
Picosecond energy transfer in a transition metal dichalcogenide–graphene heterostructure revealed by transient Raman spectroscopy

Significance Hot carrier–based energy harvesting is critically implied in the performances of optoelectronic devices based on van der Waals heterostructures composed by graphene (Gr) and monolayer transition metal dichalcogenides (TMD). The way electron–hole couples initially photogenerated in the TMD are converted into an electric current in Gr is a controversial issue. In this work we identify the interlayer interaction occurring during the first picoseconds following photoexcitation as an energy transfer process that is much faster than (other) photogating phenomena implied in optoelectronic applications.
First-order electroweak phase transition from weakly coupled sub-GeV physics and possible connection to fermion flavor

We propose that the dynamics of a scalar $\phi$ of mass $O(10)$ MeV that is weakly coupled to the Higgs can lead to a first order electroweak phase transition, fulfilling a key requirement for baryogenesis. Stability of the model near the weak scale requires a suppressed - possibly vanishing - top Yukawa coupling to the Higgs before the transition which rises to the Standard Model value afterwards. This can be accomplished through the dynamics of $\phi$ via a dimension-5 operator. We conjecture that the entire Standard Model flavor structure could turn on, mutatis mutandis, after the electroweak phase transition, via dimension-5 interactions of $\phi$ suppressed by scales ranging from $O(10^3)$ TeV to near Planck mass. Due to its suppressed couplings, $\phi$ is long-lived and can lead to missing energy signals in rare kaon decays, which can be probed by the KOTO experiment.
Thermodynamics of a Brownian particle in a nonconfining potential.

We consider the overdamped Brownian dynamics of a particle starting inside a square potential well which, upon exiting the well, experiences a flat potential where it is free to diffuse. We calculate the particle's probability distribution function (PDF) at coordinate x and time t, P(x,t), by solving the corresponding Smoluchowski equation. The solution is expressed by a multipole expansion, with each term decaying t^{1/2} faster than the previous one. At asymptotically large times, the PDF outside the well converges to the Gaussian PDF of a free Brownian particle. The average energy, which is proportional to the probability of finding the particle inside the well, diminishes as E∼1/t^{1/2}. Interestingly, we find that the free energy of the particle, F, approaches the free energy of a freely diffusing particle, F_{0}, as δF=F-F_{0}∼1/t, i.e., at a rate faster than E. We provide analytical and computational evidence that this scaling behavior of δF is a general feature of Brownian dynamics in nonconfining potential fields. Furthermore, we argue that δF represents a diminishing entropic component which is localized in the region of the potential, and which diffuses away with the spreading particle without being transferred to the heat bath.
Real or Virtual? Using Brain Activity Patterns to differentiate Attended Targets during Augmented Reality Scenarios

Augmented Reality is the fusion of virtual components and our real surroundings. The simultaneous visibility of generated and natural objects often requires users to direct their selective attention to a specific target that is either real or virtual. In this study, we investigated whether this target is real or virtual by using machine learning techniques to classify electroencephalographic (EEG) data collected in Augmented Reality scenarios. A shallow convolutional neural net classified 3 second data windows from 20 participants in a person-dependent manner with an average accuracy above 70\% if the testing data and training data came from different trials. Person-independent classification was possible above chance level for 6 out of 20 participants. Thus, the reliability of such a Brain-Computer Interface is high enough for it to be treated as a useful input mechanism for Augmented Reality applications.
Multi-Fidelity Digital Twins: a Means for Better Cyber-Physical Systems Testing?

Cyber-Physical Systems (CPSs) combine software and physical components. These systems are widely applied in society within many domains, including the automotive, aerospace, railway, etc. Testing these systems is extremely challenging, therefore, it has attracted significant attention from the research community. A driving CPS testing technique in industry is simulation-based testing. However, this poses significant challenges. In this new-idea paper we present a novel approach to enhance the testing processes of CPSs. This novel approach is motivated with examples and open questions.
Preferential Mixture-of-Experts: Interpretable Models that Rely on Human Expertise as much as Possible

We propose Preferential MoE, a novel human-ML mixture-of-experts model that augments human expertise in decision making with a data-based classifier only when necessary for predictive performance. Our model exhibits an interpretable gating function that provides information on when human rules should be followed or avoided. The gating function is maximized for using human-based rules, and classification errors are minimized. We propose solving a coupled multi-objective problem with convex subproblems. We develop approximate algorithms and study their performance and convergence. Finally, we demonstrate the utility of Preferential MoE on two clinical applications for the treatment of Human Immunodeficiency Virus (HIV) and management of Major Depressive Disorder (MDD).
DAIL: Dataset-Aware and Invariant Learning for Face Recognition*

To achieve good performance in face recognition, a large scale training dataset is usually required. A simple yet effective way to improve the recognition performance is to use a dataset as large as possible by combining multiple datasets in the training. However, it is problematic and troublesome to naively combine different datasets due to two major issues. First, the same person can possibly appear in different datasets, leading to an identity overlapping issue between different datasets. Naively treating the same person as different classes in different datasets during training will affect back-propagation and generate nonrepresentative embeddings. On the other hand, manually cleaning labels may take formidable human efforts, especially when there are millions of images and thousands of identities. Second, different datasets are collected in different situations and thus will lead to different domain distributions. Naively combining datasets will make it difficult to learn domain invariant embeddings across different datasets. In this paper, we propose DAIL: Dataset-Aware and Invariant Learning to resolve the above-mentioned issues. To solve the first issue of identity overlapping, we propose a dataset-aware loss for multi-dataset training by reducing the penalty when the same person appears in multiple datasets. This can be readily achieved with a modified softmax loss with a dataset-aware term. To solve the second issue, domain adaptation with gradient reversal layers is employed for dataset invariant learning. The proposed approach not only achieves the state-of-the-art results on several commonly used face recognition validation sets, including LFW, CFP-FP, and AgeDB-30, but also shows great benefit for practical use.
Privacy Analysis in Language Models via Training Data Leakage Report

Recent advances in neural network based language models lead to successful deployments of such models, improving user experience in various applications. It has been demonstrated that strong performance of language models may come along with the ability to memorize rare training samples, which poses serious privacy threats in case the model training is conducted on confidential user content. This necessitates privacy monitoring techniques to minimize the chance of possible privacy breaches for the models deployed in practice. In this work, we introduce a methodology that investigates identifying the user content in the training data that could be leaked under a strong and realistic threat model. We propose two metrics to quantify user-level data leakage by measuring a model's ability to produce unique sentence fragments within training data. Our metrics further enable comparing different models trained on the same data in terms of privacy. We demonstrate our approach through extensive numerical studies on real-world datasets such as email and forum conversations. We further illustrate how the proposed metrics can be utilized to investigate the efficacy of mitigations like differentially private training or API hardening.