Miracle-dz/UniFAR-FTUs huggingface dataset:
Interactive Set Discovery

We study the problem of set discovery where given a few example tuples of a desired set, we want to find the set in a collection of sets. A challenge is that the example tuples may not uniquely identify a set, and a large number of candidate sets may be returned. Our focus is on interactive exploration to set discovery where additional example tuples from the candidate sets are shown and the user either accepts or rejects them as members of the target set. The goal is to find the target set with the least number of user interactions. The problem can be cast as an optimization problem where we want to find a decision tree that can guide the search to the target set with the least number of questions to be answered by the user. We propose a general algorithm that is capable of reaching an optimal solution and two variations of it that strike a balance between the quality of a solution and the running time. We also propose a novel pruning strategy that safely reduces the search space without introducing false negatives. We evaluate the efficiency and the effectiveness of our algorithms through an extensive experimental study using both real and synthetic datasets and comparing them to previous approaches in the literature. We show that our pruning strategy reduces the running time of the search algorithms by 2-5 orders of magnitude.
Loss Functions for Discrete Contextual Pricing with Observational Data

We study a pricing setting where each customer is offered a contextualized price based on customer and/or product features. Often only historical sales data are available, so we observe whether a customer purchased a product at the price prescribed rather than the customer's true valuation. Such observational data are influenced by historical pricing policies, which introduce difficulties in evaluating the effectiveness of future policies. The goal of this paper is to formulate loss functions that can be used for evaluating pricing policies directly from observational data, rather than going through an intermediate demand estimation stage, which may suffer from bias. To achieve this, we adapt ideas from machine learning with corrupted labels, where we consider each observed purchase decision as a known probabilistic transformation of the customer's valuation. From this transformation, we derive a class of unbiased loss functions. Within this class, we identify minimum variance estimators and estimators robust to poor demand estimation. Furthermore, we show that for contextual pricing, estimators popular in the off-policy evaluation literature fall within this class of loss functions. We offer managerial insights into scenarios under which these estimators are effective.
Meta Adversarial Perturbations

A plethora of attack methods have been proposed to generate adversarial examples, among which the iterative methods have been demonstrated the ability to find a strong attack. However, the computation of an adversarial perturbation for a new data point requires solving a time-consuming optimization problem from scratch. To generate a stronger attack, it normally requires updating a data point with more iterations. In this paper, we show the existence of a meta adversarial perturbation (MAP), a better initialization that causes natural images to be misclassified with high probability after being updated through only a one-step gradient ascent update, and propose an algorithm for computing such perturbations. We conduct extensive experiments, and the empirical results demonstrate that state-of-the-art deep neural networks are vulnerable to meta perturbations. We further show that these perturbations are not only image-agnostic, but also model-agnostic, as a single perturbation generalizes well across unseen data points and different neural network architectures.
Global and Local Alignment Networks for Unpaired Image-to-Image Translation

The goal of unpaired image-to-image translation is to produce an output image reflecting the target domain's style while keeping unrelated contents of the input source image unchanged. However, due to the lack of attention to the content change in existing methods, the semantic information from source images suffers from degradation during translation. In the paper, to address this issue, we introduce a novel approach, Global and Local Alignment Networks (GLA-Net). The global alignment network aims to transfer the input image from the source domain to the target domain. To effectively do so, we learn the parameters (mean and standard deviation) of multivariate Gaussian distributions as style features by using an MLP-Mixer based style encoder. To transfer the style more accurately, we employ an adaptive instance normalization layer in the encoder, with the parameters of the target multivariate Gaussian distribution as input. We also adopt regularization and likelihood losses to further reduce the domain gap and produce high-quality outputs. Additionally, we introduce a local alignment network, which employs a pretrained self-supervised model to produce an attention map via a novel local alignment loss, ensuring that the translation network focuses on relevant pixels. Extensive experiments conducted on five public datasets demonstrate that our method effectively generates sharper and more realistic images than existing approaches. Our code is available at https://github.com/ygjwd12345/GLANet.
Generalized Decision Transformer for Offline Hindsight Information Matching

How to extract as much learning signal from each trajectory data has been a key problem in reinforcement learning (RL), where sample inefficiency has posed serious challenges for practical applications. Recent works have shown that using expressive policy function approximators and conditioning on future trajectory information -- such as future states in hindsight experience replay or returns-to-go in Decision Transformer (DT) -- enables efficient learning of multi-task policies, where at times online RL is fully replaced by offline behavioral cloning, e.g. sequence modeling. We demonstrate that all these approaches are doing hindsight information matching (HIM) -- training policies that can output the rest of trajectory that matches some statistics of future state information. We present Generalized Decision Transformer (GDT) for solving any HIM problem, and show how different choices for the feature function and the anti-causal aggregator not only recover DT as a special case, but also lead to novel Categorical DT (CDT) and Bi-directional DT (BDT) for matching different statistics of the future. For evaluating CDT and BDT, we define offline multi-task state-marginal matching (SMM) and imitation learning (IL) as two generic HIM problems, propose a Wasserstein distance loss as a metric for both, and empirically study them on MuJoCo continuous control benchmarks. CDT, which simply replaces anti-causal summation with anti-causal binning in DT, enables the first effective offline multi-task SMM algorithm that generalizes well to unseen and even synthetic multi-modal state-feature distributions. BDT, which uses an anti-causal second transformer as the aggregator, can learn to model any statistics of the future and outperforms DT variants in offline multi-task IL. Our generalized formulations from HIM and GDT greatly expand the role of powerful sequence modeling architectures in modern RL.
Rethinking Query, Key, and Value Embedding in Vision Transformer under Tiny Model Constraints

There are several attempts in vision transformers to reduce quadratic time complexity to linear time complexity according to increases in the number of tokens. Cross-covariance image transformers (XCiT) are also one of the techniques utilized to address the issue. However, despite these efforts, the increase in token dimensions still results in quadratic growth in time complexity, and the dimension is a key parameter for achieving superior generalization performance. In this paper, a novel method is proposed to improve the generalization performances of XCiT models without increasing token dimensions. We redesigned the embedding layers of queries, keys, and values, such as separate non-linear embedding (SNE), partially-shared non-linear embedding (P-SNE), and fully-shared non-linear embedding (F-SNE). Finally, a proposed structure with different model size settings achieved 71.4%,77.8%, and 82.1% on ImageNet-1k compared with 69.9%,77.1%, and 82.0% acquired by the original XCiT models, namely XCiT-N12, XCiT-T12, and XCiT-S12, respectively. Additionally, the proposed model achieved 94.8% in transfer learning experiments, on average, for CIFAR-10, CIFAR-100, Stanford Cars, and STL-10, which is superior to the baseline model of XCiT-S12 (94.5%). In particular, the proposed models demonstrated considerable improvements on the out-of-distribution detection task compared to the original XCiT models.
More on emergent dark energy from unparticles

In a recent paper [1] we suggested the possibility that the present acceleration of the Universe is due to thermodynamical behavior of unparticles. The model is free of scalar ﬁelds, modiﬁed gravity,a Cosmological Constant (CC), the coincidence problem, initial conditions problem and possesses interesting distinct predictions regarding the equation of state of Dark Energy, the growth rate and the number of relativistic degrees of freedom at BBN and CMB decoupling. In this work we relate to a recent paper [2], which discusses a similar setup of unparticles with and without a CC as an external source of late-time acceleration. The authors have shown how such a model is inconsistent with the data. We show that these claims are viable only in a particular part of the parameter space and that model [1] stands tall. We further suggest a consistency condition in terms of observables. We then ﬁt publicly available supernovae data to derive the expected Hubble parameter and constrain the parameters of the model.
Efficiently Transforming Tables for Joinability

Data from different sources rarely conform to a single formatting even if they describe the same set of entities, and this raises concerns when data from multiple sources must be joined or cross-referenced. Such a formatting mismatch is unavoidable when data is gathered from various public and third-party sources. Commercial database systems are not able to perform the join when there exist differences in data representation or formatting, and manual reformatting is both time consuming and error-prone. We study the problem of efficiently joining textual data under the condition that the join columns are not formatted the same and cannot be equi-joined, but they become joinable under some transformations. The problem is challenging simply because the number of possible transformations explodes with both the length of the input and the number of rows, even if each transformation is formed using very few basic units. We show that an efficient algorithm can be developed based on the common characteristics of the joined columns and over a rich set of basic operations that can be composed to form transformations. Compared to a state-of-the-art approach, our algorithm covers every transformation that is covered in the state-of-the-art approach but is a few orders of magnitude faster, as evaluated on various real and synthetic data.
On the error term in the explicit formula of Riemann--von Mangoldt

We provide an explicit $O(x\log x/T)$ error term for the Riemann--von Mangoldt formula by making results of Wolke (1983) and Ramar\'e (2016) explicit. We also include applications to primes between consecutive powers, the error term in the prime number theorem and an inequality of Ramanujan.
DO YOUTHS WITH NEUROMOTOR DISORDER AND THEIR THERAPISTS PREFER A MIXED OR VIRTUAL REALITY HEAD-MOUNTED DISPLAY?

Objective To evaluate the usability of 2 head-mounted displays in youths undergoing neurorehabilitation; a mixed reality head-mounted display and a virtual reality head-mounted display. Design Observational cross-sectional study. Patients Thirteen youths (age range 7.8–16.5 years) with neuromotor disorder. Methods Youths wore a mixed reality or a virtual reality head-mounted display while being verbally guided through a scene with virtual objects. Differences between the 2 systems, regarding usability, user experience, and acceptability, were evaluated using standardized questions for the youths and their therapists. System preferences and symptoms of cybersickness were noted. Results Both head-mounted displays were easy to mount and adjust to the children’s heads, but the mixed reality system was unstable in 40% of the youths. Participants stated that they could move naturally with both devices. Object appearance scored higher with the virtual reality system, while therapists rated youths’ movement execution and needed additional support in favour of the mixed reality system. Most youths preferred the virtual reality device, mainly due to the more distinct appearance of objects and the objects’ richer colours. Therapists’ preferences were balanced. Two children reported minimal signs of cybersickness. Conclusion Youths and therapists accepted both systems well, with advantages regarding usability, user experience, and preference for the virtual reality, and acceptability for the mixed reality head-mounted display. LAY ABSTRACT Many essential walking activities in daily life, such as crossing a street or stepping over an obstacle, can be challenging or tedious to practice in conventional rehabilitation settings. Extended reality applications can create a safe and attractive training environment; therefore we evaluated the usability of 2 head-mounted displays in children and adolescents with a neuromotor impairment. Thirteen youths used a mixed and a virtual reality head-mounted display and were verbally guided through a scene with virtual objects. Both headsets were easy to mount and adjust to the children’s heads, but the mixed reality device was unstable in 40% of the youths. Most youths preferred the virtual reality device, mainly due to the more distinct appearance of objects. Therapists’ preferences were balanced. Two children reported minimal signs of cybersickness. This study demonstrates the usability of head-mounted displays for youths undergoing rehabilitation, offering exciting possibilities for therapy and training in this field.
Stakeholders, issues and the shaping of large engineering projects

A critical element in the shaping of large engineering projects (LEPs) is associated with the multi-type and networked relationships between LEPs and their stakeholders in both market and nonmarket environments. In this paper, we advance a multidisciplinary network approach, namely Stakeholder Value Network (SVN), as a lens to examine, understand, model, and manage the stakeholder relationships in LEPs. The SVN approach brings together knowledge from three domains: engineering systems, organizational sociology, and strategic management. Specifically, we put the focus of this paper on exploring the underlying connections between stakeholders and issues; extending the methodological framework of “Stakeholder-based SVN” to “Issue-based SVN”; as well as testing three propositions about the strategic implications of Issue-based SVN and demonstrating the benefits of the integration of stakeholders and issues, through a retrospective case study of a large real-world engineering project, Project Phoenix.
Structural Numbers for Reclaimed Asphalt Pavement Base and Subbase Course Mixes

The 1993 AASHTO pavement design guide equation for flexible pavement structures requires the determination of a structural number, which is a function of the layer coefficient, thickness, and drainage coefficient of each layer of pavement. Empirical correlations for layer coefficients have been developed based on resilient moduli. The principal factor affecting the drainage coefficient is the hydraulic conductivity. The objective was to determine how the layer and drainage coefficients of Massachusetts Highway Department (MHD) base and subbase aggregates would be affected by the introduction of reclaimed asphalt pavement (RAP). MHD gray, dense, graded, crushed-stone base and gravel-borrow subbase were combined with 0 to 50 percent RAP (by weight); 9 mixtures were tested, and 27 resilient modulus and 12 hydraulic conductivity tests were conducted. Results from the resilient modulus tests indicate that the resilient moduli of the RAP/base and RAP/subbase mixtures generally increased with an increase in the percentage of RAP. Therefore, the layer coefficient and the structural number increased. The hydraulic conductivity of the RAP/crushed-stone base was not affected by the percentage of RAP in the mixture, but the hydraulic conductivity of the RAP/gravel-borrow subbase increased with an increase in the percentage of RAP. Nonlinear elastic analyses of typical MHD pavement structures indicated a small increase in the structural number with an increase in the percentage of RAP in the base and subbase. Therefore, the addition of RAP to the base and subbase materials tested is considered to be beneficial.
Prediction of performance characteristics of a universal motor using parametric finite element analysis

A procedure is derived that uses finite element analysis to predict the performance characteristics of a typical universal (AC series) motor. A number of different finite element analyses are made with parametric variations of current and rotor position. Then the AC voltage waveform is applied and the resulting performance is computed using a time-stepping algorithm. The computed torque-speed, current-speed, and power-speed curves are shown to agree closely with the measured curves.
Fast transient capacitor-less LDO regulator using low-power output voltage detector

An output-capacitor-less low-dropout regulator (LDO) with a low-power output voltage detector (OVD) is proposed. The OVD is based on an R/C highpass filter which is able to detect the fast-changing voltage at the LDO output and activate an additional path to control the power transistor. The OVD consumes only additional quiescent current to monitor the variation in output voltage in the steady state. In the simulated load-transient response, a maximum undershoot of 1.2 V and a settling time (Δt 2 ) of 67 ns are observed with the proposed OVD for a load current variation from 1 to 50 mA at a rise time of 1 ns. These results are 0.2 V and 473 ns better than the results obtained without the proposed OVD.
A decision-making framework to determine the value of on-orbit servicing compared to replacement of space telescopes

The Hubble Space Telescope has demonstrated that on-orbit servicing can provide significant benefits for scientific space programs. Specifically, servicing missions can replace failed components to keep spacecraft operational, and can upgrade onboard components to improve spacecraft performance. Hubble was able to capture these benefits because it was designed to be serviceable; however, many other programs have excluded serviceability from the design due to cost considerations. Often, the value of serviceability cannot be quantitatively justified. This thesis develops a framework to determine the value of including serviceability in a space telescope. Various principles to evaluate serviceability are proposed throughout the literature, and this thesis incorporates three main principles to construct the framework. First, the costs and benefits of servicing are separated so that the “cost” of servicing is expressed as the maximum price the customer is willing to pay. Second, the value of serviceability will be determined by comparing a telescope servicing program to a telescope replacement program. Third, the value of flexibility provided by servicing is analyzed by a Monte-Carlo simulation and decision rule analysis. A case study was performed to demonstrate how the framework is used, using representative data from Hubble. For a simple space telescope, the case study calculated the increase in science return gained by servicing and the maximum price for servicing missions. The case study illustrated an important trade between science return and risk of telescope downtime. Finally, the principles and techniques used in this framework are more generally applicable to non-revenue generating spacecraft. Thesis Supervisor: David W. Miller Title: Professor of Aeronautics and Astronautics
Understanding and Measuring eGovernment: International Benchmarking Studies

This paper is aimed at those involved – in planning, in undertaking, in using or in evaluating – the benchmarking or measurement of e-government. It draws on models of e-government and experience of benchmarking to answer four questions: why benchmark e-government? what to benchmark? how to benchmark? how to report? It provides a series of recommendations based on good practice or innovative practice, backed up by a set of conceptual frameworks and statistical findings. There is a particular sensitivity to benchmarking e-government in the "majority world": the developing countries that are home to 80% of the world's population but, as yet, just 20% of e-government users.
Percolation of temporal hierarchical mobility networks during COVID-19

Percolation theory is essential for understanding disease transmission patterns on the temporal mobility networks. However, the traditional approach of the percolation process can be inefficient when analysing a large-scale, dynamic network for an extended period. Not only is it time-consuming but it is also hard to identify the connected components. Recent studies demonstrate that spatial containers restrict mobility behaviour, described by a hierarchical topology of mobility networks. Here, we leverage crowd-sourced, large-scale human mobility data to construct temporal hierarchical networks composed of over 175 000 block groups in the USA. Each daily network contains mobility between block groups within a Metropolitan Statistical Area (MSA), and long-distance travels across the MSAs. We examine percolation on both levels and demonstrate the changes of network metrics and the connected components under the influence of COVID-19. The research reveals the presence of functional subunits even with high thresholds of mobility. Finally, we locate a set of recurrent critical links that divide components resulting in the separation of core MSAs. Our findings provide novel insights into understanding the dynamical community structure of mobility networks during disruptions and could contribute to more effective infectious disease control at multiple scales. This article is part of the theme issue ‘Data science approaches to infectious disease surveillance’.