Miracle-dz/UniFAR-FTUs huggingface dataset:
Minimizing the Size of the Uncertainty Regions for Centers of Moving Entities

In this paper, we study the problems of computing the 1-center, centroid, and 1-median of objects moving with bounded speed in Euclidean space. We can acquire the exact location of only a constant number of objects (usually one) per unit time, but for every other object, its set of potential locations, called the object's uncertainty region, grows subject only to the speed limit. As a result, the center of the objects may be at several possible locations, called the center's uncertainty region. For each of these center problems, we design query strategies to minimize the size of the center's uncertainty region and compare its performance to an optimal query strategy that knows the trajectories of the objects, but must still query to reduce their uncertainty. For the static case of the 1-center problem in R^1, we show an algorithm that queries four objects per unit time and is 1-competitive against the optimal algorithm with one query per unit time. For the general case of the 1-center problem in R^1, the centroid problem in R^d, and the 1-median problem in R^1, we prove that the Round-robin scheduling algorithm is the best possible competitive algorithm. For the center of mass problem in R^d, we provide an O(log n)-competitive algorithm. In addition, for the general case of the 1-center problem in R^d (d>= 2), we argue that no algorithm can guarantee a bounded competitive ratio against the optimal algorithm.
Reducing Aggregate Electric Vehicle Battery Capacity Through Sharing

Meeting demand for automotive battery resources is predicted to be costly from both economic and environmental perspectives. To minimize costs, battery resources should be deployed as efficiently as possible. A potential source of inefficiency in battery deployment is that the batteries of personal vehicles are typically much larger than necessary to meet most daily mobility needs. In this paper, we consider whether battery resources can be used more efficiently in a setting where drivers, in addition to having personal vehicle batteries, have access to a shared battery resource. More precisely, we consider the problem of minimizing aggregate battery capacity in settings with and without a shared resource subject to the requirement that driver commuting needs are met with high reliability. To assess capacity reduction potential, we quantify the difference in deployed battery capacity in settings with and without a shared resource in a case study using real-world longitudinal mobility data from Puget Sound, Washington. We find that access to a shared battery resource can substantially reduce deployed battery capacity. Furthermore, relative reductions in battery capacity increase with number of drivers and the level of reliability desired.
Hotelling Deflation on Large Symmetric Spiked Tensors

This paper studies the deflation algorithm when applied to estimate a low-rank symmetric spike contained in a large tensor corrupted by additive Gaussian noise. Specifically, we provide a precise characterization of the large-dimensional performance of deflation in terms of the alignments of the vectors obtained by successive rank-1 approximation and of their estimated weights, assuming non-trivial (fixed) correlations among spike components. Our analysis allows an understanding of the deflation mechanism in the presence of noise and can be exploited for designing more efficient signal estimation methods.
Data as voters: instance selection using approval-based multi-winner voting

We present a novel approach to the instance selection problem in machine learning (or data mining). Our approach is based on recent results on (proportional) representation in approval-based multi-winner elections. In our model, instances play a double role as voters and candidates. Each instance in the training set (acting as a voter) approves of the instances (playing the role of candidates) belonging to its local set (except itself), a concept already existing in the literature. We then select the election winners using a representative voting rule, and such winners are the data instances kept in the reduced training set.
Secondary Controller Design for the Safety of Nonlinear Systems via Sum-of-Squares Programming

We consider the problem of ensuring the safety of nonlinear control systems under adversarial signals. Using Lyapunov-based reachability analysis, we first give sufficient conditions to assess safety, i.e., to guarantee that the states of the control system, when starting from a given initial set, always remain in a prescribed safe set. We consider polynomial systems with semi-algebraic safe sets. Using the S-procedure for polynomial functions, safety conditions can be formulated as a Sum-Of-Squares (SOS) programme, which can be solved efficiently. When safety cannot be guaranteed, we provide tools via SOS to synthesize polynomial controllers that enforce safety of the closed-loop system. The theoretical results are illustrated through numerical simulations.
BackCache: Mitigating Contention-Based Cache Timing Attacks by Hiding Cache Line Evictions

Caches are used to reduce the speed differential between the CPU and memory to improve the performance of modern processors. However, attackers can use contention-based cache timing attacks to steal sensitive information from victim processes through carefully designed cache eviction sets. And L1 data cache attacks are widely exploited and pose a significant privacy and confidentiality threat. Existing hardware-based countermeasures mainly focus on cache partitioning, randomization, and cache line flushing, which unfortunately either incur high overhead or can be circumvented by sophisticated attacks. In this paper, we propose a novel hardware-software co-design called BackCache with the idea of always achieving cache hits instead of cache misses to mitigate contention-based cache timing attacks on the L1 data cache. BackCache places the evicted cache lines from the L1 data cache into a fully-associative backup cache to hide the evictions. To improve the security of BackCache, we introduce a randomly used replacement policy (RURP) and a dynamic backup cache resizing mechanism. We also present a theoretical security analysis to demonstrate the effectiveness of BackCache. Our evaluation on the gem5 simulator shows that BackCache can degrade the performance by 1.33%, 7.34%, and 7.59% For OS kernel, single-thread, and multi-thread benchmarks.
Quadratic quantum speedup in evaluating bilinear risk functions

Computing nonlinear functions over multilinear forms is a general problem with applications in risk analysis. For instance in the domain of energy economics, accurate and timely risk management demands for efficient simulation of millions of scenarios, largely benefiting from computational speedups. We develop a novel hybrid quantum-classical algorithm based on polynomial approximation of nonlinear functions and compare different implementation variants. We prove a quadratic quantum speedup, up to polylogarithmic factors, when forms are bilinear and approximating polynomials have second degree, if efficient loading unitaries are available for the input data sets. We also enhance the bidirectional encoding, that allows tuning the balance between circuit depth and width, proposing an improved version that can be exploited for the calculation of inner products. Lastly, we exploit the dynamic circuit capabilities, recently introduced on IBM Quantum devices, to reduce the average depth of the Quantum Hadamard Product circuit. A proof of principle is implemented and validated on IBM Quantum systems.
The Face of Populism: Examining Differences in Facial Emotional Expressions of Political Leaders Using Machine Learning

Online media has revolutionized the way political information is disseminated and consumed on a global scale, and this shift has compelled political figures to adopt new strategies of capturing and retaining voter attention. These strategies often rely on emotional persuasion and appeal, and as visual content becomes increasingly prevalent in virtual space, much of political communication too has come to be marked by evocative video content and imagery. The present paper offers a novel approach to analyzing material of this kind. We apply a deep-learning-based computer-vision algorithm to a sample of 220 YouTube videos depicting political leaders from 15 different countries, which is based on an existing trained convolutional neural network architecture provided by the Python library fer. The algorithm returns emotion scores representing the relative presence of 6 emotional states (anger, disgust, fear, happiness, sadness, and surprise) and a neutral expression for each frame of the processed YouTube video. We observe statistically significant differences in the average score of expressed negative emotions between groups of leaders with varying degrees of populist rhetoric as defined by the Global Party Survey (GPS), indicating that populist leaders tend to express negative emotions to a greater extent during their public performance than their non-populist counterparts. Overall, our contribution provides insight into the characteristics of visual self-representation among political leaders, as well as an open-source workflow for further computational studies of their non-verbal communication.
A Meta-heuristic Approach to Estimate and Explain Classifier Uncertainty

Trust is a crucial factor affecting the adoption of machine learning (ML) models. Qualitative studies have revealed that end-users, particularly in the medical domain, need models that can express their uncertainty in decision-making allowing users to know when to ignore the model's recommendations. However, existing approaches for quantifying decision-making uncertainty are not model-agnostic, or they rely on complex statistical derivations that are not easily understood by laypersons or end-users, making them less useful for explaining the model's decision-making process. This work proposes a set of class-independent meta-heuristics that can characterize the complexity of an instance in terms of factors are mutually relevant to both human and ML decision-making. The measures are integrated into a meta-learning framework that estimates the risk of misclassification. The proposed framework outperformed predicted probabilities in identifying instances at risk of being misclassified. The proposed measures and framework hold promise for improving model development for more complex instances, as well as providing a new means of model abstention and explanation.
General mass formulas for charged Kerr-AdS black holes

It is well-known that the mass of a non-asymptotically flat spacetime cannot be uniquely defined. Some mass formulas for the Kerr-AdS black hole have been found and used in studying black hole thermodynamics. However, the derivations usually need a background subtraction to eliminate the divergence at infinity. It is also unknown whether the mass depends on the choice of coordinates. In this paper, we provide a more straightforward derivation for the mass formula, only demanding that the first law of black hole thermodynamics and Smarr formula are satisfied. We first make use of the Iyer-Wald formalism to derive a first law which avoids the divergence at infinity. Then we apply this formula to charged Kerr-AdS black hole expressed in the coordinates rotating at infinity. However, the first law associated with the timelike Killing vector field $\frac{\partial}{\partial t}$ is not integrable. Then, by making use of the gauge freedom of $t$, we find a favorite parameter $t'$ which just makes the mass integrable. Applying the scaling argument, we show that the mass satisfies the Smarr formula and takes the form $M/\Xi^{3/2}$. Moreover, applying the conformal method with the same time $t'$, we obtain the same mass. By applying the first law to the coordinates which is not rotating at infinity, we find a preferred time $T$ that makes the first law integrable and the mass is just the familiar mass $M/\Xi^2$ in the literature. This mass is also confirmed by the conformal method. We find that the two mass formulas correspond to different families of observers and the preferred Killing times. So our work clarifies the origins of masses in Kerr-AdS spacetimes.
Robust trajectory tracking for underactuated mechanical systems without velocity measurements

In this paper, the notion of contraction is used to solve the trajectory-tracking problem for a class of mechanical systems. Additionally, we propose a dynamic extension to remove velocity measurements from the controller while rejecting matched disturbances. In particular, we propose three control designs stemming from the Interconnection and Damping Assignment Passivity-Based Control approach. The first controller is a tracker that does not require velocity measurements. The second control design solves the trajectory-tracking problem while guaranteeing robustness with respect to matched disturbances. Then, the third approach is a combination of both mentioned controllers. It is shown that all proposed design methods guarantee exponential convergence of the mechanical system to the desired (feasible) trajectory due to the contraction property of the closed-loop system. The applicability of this method is illustrated via the design of a controller for an underactuated mechanical system.
"Can We Detect Substance Use Disorder?": Knowledge and Time Aware Classification on Social Media from Darkweb

Opioid and substance misuse is rampant in the United States today, with the phenomenon known as the"opioid crisis". The relationship between substance use and mental health has been extensively studied, with one possible relationship being: substance misuse causes poor mental health. However, the lack of evidence on the relationship has resulted in opioids being largely inaccessible through legal means. This study analyzes the substance use posts on social media with opioids being sold through crypto market listings. We use the Drug Abuse Ontology, state-of-the-art deep learning, and knowledge-aware BERT-based models to generate sentiment and emotion for the social media posts to understand users' perceptions on social media by investigating questions such as: which synthetic opioids people are optimistic, neutral, or negative about? or what kind of drugs induced fear and sorrow? or what kind of drugs people love or are thankful about? or which drugs people think negatively about? or which opioids cause little to no sentimental reaction. We discuss how we crawled crypto market data and its use in extracting posts for fentanyl, fentanyl analogs, and other novel synthetic opioids. We also perform topic analysis associated with the generated sentiments and emotions to understand which topics correlate with people's responses to various drugs. Additionally, we analyze time-aware neural models built on these features while considering historical sentiment and emotional activity of posts related to a drug. The most effective model performs well (statistically significant) with (macroF1=82.12, recall =83.58) to identify substance use disorder.
C~2 and C~3 Continuous Bézier Spline Curve with Given Tangent Polygon

This paper proposes an approach of constructing planar piecewise Bezier curve of 4th or 5th degree with all edges tangent to a given control polygon and the curve segments are joined together with C 2 and C 3 continuity. The segmented Bezier curves are all shape preserving to their tangent polygon. Finally, a few numerical examples illustrate that the method given in this paper is effective for CAGD.
It is hard to kill fake news

We study a model for the spread of fake news, where first a piece of fake news is spread from a location in a network, followed by a correction to the news. We assume that both the fake as well as correct news travel as first-passage percolations or SI epidemics with i.i. d. \ traversal times, possibly with different distributions and dependence. We make the (hopeful) assumption that once a vertex in the network hears the correct news, they are immediately convinced that this is indeed correct, and continue to spread the correct news. Even in this optimistic scenario, our main results show that it is very difficult to remove the fake news from the network, even when the correct news would spread much faster than the fake news. We show this on the configuration model, a model that has gained enormous popularity as a simple, yet flexible, model for real-world networks. The crux of the proof is the realization that this problem on a branching process tree (which is known to be the local limit of the configuration model) can be reformulated in terms of the maximum of a branching random walk, a topic that has attracted considerable attention in the past decade. We then extend the results to the graph setting using couplings to branching processes, local convergence and detailed estimates on first-passage percolation on random graphs as derived in \cite{BhaHofHoo17}. Remarkably, despite the fact that our proofs for the configuration model rely on its local branching process structure, the condition for strong survival on a finite number of generations of a branching process tree is {\em different} from that on the configuration model.
Optimal activation of halting multi‐armed bandit models

We study new types of dynamic allocation problems the Halting Bandit models. As an application, we obtain new proofs for the classic Gittins index decomposition result compare Gittins (Journal of the Royal Statistical Society, Series B, 1979, 41, 148–177), and recent results of the authors in Cowan and Katehakis (Probability in the Engineering and Informational Sciences, 2015, 29, 51–76).
A Survey on Global LiDAR Localization

Knowledge about the own pose is key for all mobile robot applications. Thus pose estimation is part of the core functionalities of mobile robots. Over the last two decades, LiDAR scanners have become the standard sensor for robot localization and mapping. This article aims to provide an overview of recent progress and advancements in LiDAR-based global localization. We begin by formulating the problem and exploring the application scope. We then present a review of the methodology, including recent advancements in several topics, such as maps, descriptor extraction, and cross-robot localization. The contents of the article are organized under three themes. The first theme concerns the combination of global place retrieval and local pose estimation. The second theme is upgrading single-shot measurements to sequential ones for sequential global localization. Finally, the third theme focuses on extending single-robot global localization to cross-robot localization in multi-robot systems. We conclude the survey with a discussion of open challenges and promising directions in global LiDAR localization. To our best knowledge, this is the first comprehensive survey on global LiDAR localization for mobile robots.
CKBP v2: An Expert-Annotated Evaluation Set for Commonsense Knowledge Base Population

Populating Commonsense Knowledge Bases (CSKB) is an important yet hard task in NLP, as it tackles knowledge from external sources with unseen events and entities. Fang et al. (2021a) proposed a CSKB Population benchmark with an evaluation set CKBP v1. However, CKBP v1 adopts crowdsourced annotations that suffer from a substantial fraction of incorrect answers, and the evaluation set is not well-aligned with the external knowledge source as a result of random sampling. In this paper, we introduce CKBP v2, a new high-quality CSKB Population benchmark, which addresses the two mentioned problems by using experts instead of crowd-sourced annotation and by adding diversified adversarial samples to make the evaluation set more representative. We conduct extensive experiments comparing state-of-the-art methods for CSKB Population on the new evaluation set for future research comparisons. Empirical results show that the population task is still challenging, even for large language models (LLM) such as ChatGPT. Codes and data are available at https://github.com/HKUST-KnowComp/CSKB-Population.
Constraining the Molecular Gas Content of Fast Radio Burst (FRB) Host Galaxies

We used Bands 6 and 7 of the Atacama Large Millimeter/submillimeter Array (ALMA) in Cycles 7 and 8 to search for $\mathrm{CO}\,(3-2)$ emission from a sample of five fast radio burst (FRB) host galaxies discovered by the Commensal Real-time ASKAP Fast Transients (CRAFT) survey and the Fast and Fortunate for FRB Follow-up (F$^4$) team. These galaxies have redshifts $z \approx 0.16-0.48$, masses log$(M_{\rm star}/M_{\odot})\approx 9.30-10.4$ characteristic of field galaxies, and emission lines indicative of ongoing star formation. We detected three of the five galaxies with luminosities $L'(3-2)\approx0.2-4\times10^8\,\rm K\,km \, s^{-1}\,pc^2$ and set upper limits for the other two. Adopting standard metallicity-dependent CO-to-H$_2$ conversion factors, we estimate molecular gas masses $M_{\rm gas}\approx 0.2-3\times 10^9 \, M_{\odot}$. As a population, FRB host galaxies track the main $M_{\rm star}-M_{\rm gas}$ locus of star-forming galaxies in the present-day universe, with gas fractions of $\mu_{\rm gas}\approx0.1$ and gas depletion times $t_{\rm dep} \gtrapprox 1\,$Gyr. We employ the Kaplan-Meier estimator to compare the redshift-corrected $\mu_{\rm gas}$ and $t_{\rm dep}$ for all known FRB hosts with measurements or upper limits with those from the xCOLD GASS survey and find statistically different gas fractions. The difference is not statistically significant when we consider only the five hosts studied here with consistently determined properties, suggesting more FRB hosts with measured molecular gas masses are needed to robustly study the population. Lastly, we present a multi-wavelength analysis of one host (HG20180924B) combining high-spatial resolution imaging and integral field spectroscopy to demonstrate that future high-resolution observations will allow us to study the host galaxy environments local to the FRBs.
Prospects and Applications of Incoherent Light in Non-contact Wireless Sensing Systems

The increasing demand for wireless sensing systems has led to the exploration of alternative technologies to overcome the spectrum scarcity of traditional approaches based on radio frequency (RF) waves or microwaves. Incoherent light sources such as light-emitting diodes (LED), paired with light sensors, have the potential to become an attractive option for wireless sensing due to their energy efficiency, longer lifespan, and lower cost. Although coherent light or laser may present safety risks to human eyes and skin, incoherent visible and infrared light has low intensity, and does not harm the human body. Incoherent light has the potential to supersede other wireless sensing technologies, namely RF, laser and camera, by providing many additional benefits including easy implementation, wide bandwidth, reusable frequency, minimum interference, enhanced privacy and simpler data processing. However, the application of incoherent light in the wireless sensing domain is still in its infancy and is an emerging research topic. This study explores the enormous potential and benefits of incoherent visible and infrared light in wireless sensing through various indoor and outdoor applications including speed estimation of vehicles, human vitals monitoring, blood glucose sensing, gesture recognition, occupancy estimation and structural health monitoring.