Miracle-dz/UniFAR-FTUs huggingface dataset:
Metamorphic Testing of Image Captioning Systems via Reduction-based Transformations

—Recently, the Image Captioning (IC) technique has been widely applied to describe a given image in text form. However, IC systems including the commercial IC APIs like Microsoft Azure IC API can still produce incorrect captions and lead to misunderstandings. To tackle this problem, several methods have been proposed to test the IC systems. However, these approaches still rely on pre-annotated information and hence cannot really alleviate the oracle problem in the testing. Besides, they adopt AIGC techniques to create follow-up test images that may generate unrealistic images as test cases, which leads to meaningless testing results. Thirdly, existing methods have various restrictions on the eligibility of source test cases, and hence cannot fully utilize the given images to perform testing. To tackle these issues, we propose R E IC, which conducts metamorphic testing for IC systems with reduction-based transformations (e.g., cropping and stretching). Instead of relying on the pre-annotated information, we introduce a localization method to align the described objects in the caption with the corresponding objects in the test image and check whether each object in the caption retains or disappears after transformation. R E IC does not artificially manipulate any objects and hence can effectively avoid generating unreal follow-up images. Besides, it eliminates the restrictions in the metamorphic transformation process, as well as decreases the ambiguity, and boosts the diversity among the follow-up test cases, which consequently enables testing to be performed on any test image, and reveals more distinct valid violations. We employ R E IC to test four popular IC models and a commercial IC API. The results demonstrate that R E IC can sufficiently leverage provided test images to generate follow-up cases of good reality, and effectively detect a great number of distinct violations, without the need for any pre-annotated information.
Dependence of CCN activity of less volatile particles on the amount of coating observed in Tokyo

[1] A novel observation system has been developed to measure cloud condensation nuclei (CCN) activity simultaneously with volatility of aerosol particles at 400°C. CCN/CN ratios were measured using a CCN counter and a condensation particle counter, and volatility was measured with a volatility tandem differential mobility analyzer (VTDMA). This system was connected to a hygroscopicity tandem differential mobility analyzer in tandem to measure these properties of hygroscopicity-selected particles. These instruments were deployed in Tokyo in November 2004. In Tokyo, the dominant fraction of nonvolatile cores at 400°C has been found to be black carbon (BC). Therefore the number size distribution measured by VTDMA represents the mixing state of black carbon particles. The VTDMA measurement of hygroscopicity-selected particles revealed that most of the less hygroscopic particles were less volatile, while more hygroscopic particles were more volatile. CCN/CN ratios of less volatile particles [(CCN/CN)LV] are estimated from CCN activity and volatility measurements of less hygroscopic particles. (CCN/CN)LV at SS = 0.5% is correlated with the decrease of peak diameter due to volatilization (Δdm), and (CCN/CN)LV is nearly equal to unity at Δdm = 10 nm. This result suggests that the CCN activity of BC particles significantly depends on the amount of coating.
Approximate Linear Programming and Decentralized Policy Improvement in Cooperative Multi-agent Markov Decision Processes

In this work, we consider a `cooperative' multi-agent Markov decision process (MDP) involving m greater than 1 agents, where all agents are aware of the system model. At each decision epoch, all the m agents cooperatively select actions in order to maximize a common long-term objective. Since the number of actions grows exponentially in the number of agents, policy improvement is computationally expensive. Recent works have proposed using decentralized policy improvement in which each agent assumes that the decisions of the other agents are fixed and it improves its decisions unilaterally. Yet, in these works, exact values are computed. In our work, for cooperative multi-agent finite and infinite horizon discounted MDPs, we propose suitable approximate policy iteration algorithms, wherein we use approximate linear programming to compute the approximate value function and use decentralized policy improvement. Thus our algorithms can handle both large number of states as well as multiple agents. We provide theoretical guarantees for our algorithms and also demonstrate the performance of our algorithms on some numerical examples.
A Heterogeneous Spatial Model for Soil Carbon Mapping of the Contiguous United States Using VNIR Spectra

The Rapid Carbon Assessment, conducted by the U.S. Department of Agriculture, was implemented in order to obtain a representative sample of soil organic carbon across the contiguous United States. In conjunction with a statistical model, the dataset allows for mapping of soil carbon prediction across the U.S., however there are two primary challenges to such an effort. First, there exists a large degree of heterogeneity in the data, whereby both the first and second moments of the data generating process seem to vary both spatially and for different land-use categories. Second, the majority of the sampled locations do not actually have lab measured values for soil organic carbon. Rather, visible and near-infrared (VNIR) spectra were measured at most locations, which act as a proxy to help predict carbon content. Thus, we develop a heterogeneous model to analyze this data that allows both the mean and the variance to vary as a function of space as well as land-use category, while incorporating VNIR spectra as covariates. After a cross-validation study that establishes the effectiveness of the model, we construct a complete map of soil organic carbon for the contiguous U.S. along with uncertainty quantification.
Pre- to Post-Contrast Breast MRI Synthesis for Enhanced Tumour Segmentation

Despite its benefits for tumour detection and treatment, the administration of contrast agents in dynamic contrast-enhanced MRI (DCE-MRI) is associated with a range of issues, including their invasiveness, bioaccumulation, and a risk of nephrogenic systemic fibrosis. This study explores the feasibility of producing synthetic contrast enhancements by translating pre-contrast T1-weighted fat-saturated breast MRI to their corresponding first DCE-MRI sequence leveraging the capabilities of a generative adversarial network (GAN). Additionally, we introduce a Scaled Aggregate Measure (SAMe) designed for quantitatively evaluating the quality of synthetic data in a principled manner and serving as a basis for selecting the optimal generative model. We assess the generated DCE-MRI data using quantitative image quality metrics and apply them to the downstream task of 3D breast tumour segmentation. Our results highlight the potential of post-contrast DCE-MRI synthesis in enhancing the robustness of breast tumour segmentation models via data augmentation. Our code is available at https://github.com/RichardObi/pre_post_synthesis.
A simple consensus model for an increasing population of agents with i.i.d incoming opinions

In this short note we study what happens in a symmetric opinion model when we send the total interacting population $N(t)$ to infinity as $t \to \infty$. We assume that new population enters the system with opinions that are i.i. d random vectors with finite mean and variance. We give sharp conditions on the rate of population growth that is required for convergence to a global consensus in opinions. More particularly, we show that if the total population increases at a rate $N(t)\sim e^{t^\alpha}$, then $\alpha<1$ is necessary and sufficient condition for convergence to the mean of incoming opinions, and the convergence is achieved at an algebraic rate.
Generalized Category Discovery in Semantic Segmentation

This paper explores a novel setting called Generalized Category Discovery in Semantic Segmentation (GCDSS), aiming to segment unlabeled images given prior knowledge from a labeled set of base classes. The unlabeled images contain pixels of the base class or novel class. In contrast to Novel Category Discovery in Semantic Segmentation (NCDSS), there is no prerequisite for prior knowledge mandating the existence of at least one novel class in each unlabeled image. Besides, we broaden the segmentation scope beyond foreground objects to include the entire image. Existing NCDSS methods rely on the aforementioned priors, making them challenging to truly apply in real-world situations. We propose a straightforward yet effective framework that reinterprets the GCDSS challenge as a task of mask classification. Additionally, we construct a baseline method and introduce the Neighborhood Relations-Guided Mask Clustering Algorithm (NeRG-MaskCA) for mask categorization to address the fragmentation in semantic representation. A benchmark dataset, Cityscapes-GCD, derived from the Cityscapes dataset, is established to evaluate the GCDSS framework. Our method demonstrates the feasibility of the GCDSS problem and the potential for discovering and segmenting novel object classes in unlabeled images. We employ the generated pseudo-labels from our approach as ground truth to supervise the training of other models, thereby enabling them with the ability to segment novel classes. It paves the way for further research in generalized category discovery, broadening the horizons of semantic segmentation and its applications. For details, please visit https://github.com/JethroPeng/GCDSS
Multi-flavor quantum criticality

In a quantum critical metal, the electronic density of states, or quasiparticle mass on the Fermi surface, is strongly enhanced through electronic correlations. The density of states in the quantum critical unconventional superconductor CeCoIn$_5$, can be readily accessed in the normal state because all energy scales are small. However, the experimental challenges associated with large nuclear specific heat and long nuclear spin-lattice relaxation times have impeded unveiling a more detailed physical picture. Here we report an extensive thermal impedance spectroscopy study of CeCoIn$_5$ that assesses the density of states in two independent ways, via the nuclear spin-lattice relaxation rate and via the specific heat. We establish that the temperature- and magnetic field dependence of the nuclear spin-lattice relaxation rate is determined entirely by the energy-scale competition near the quantum critical point. In particular, mass enhancement is cut off at finite magnetic fields. However, the specific heat measurements reveal excess entropy in addition to that associated with the density of states on the Fermi surface. This excess entropy is direct thermodynamic evidence for a"second flavor"of fluctuating boson in CeCoIn$_5$. The electronic nature of this excess entropy is evidenced by its suppression in the superconducting state. We suggest such a multi-flavour character for a broader class of quantum critical metals.
Character groups of Metacyclic groups

Pontryagin duality provides a powerful tool for analyzing the structure and properties of locally compact abelian groups, in particular finite abelian groups. Now much can be done with non-abelian groups via the construction of character groups. In this paper we provide a set of conditions a group must satisfy to be realized as a balanced character group for a metacyclic group. Furthermore we provide a classification for the balanced character groups of a particular class of metacyclic groups, the dicyclic groups.
Normalized ground states for a coupled Schr\"odinger system: Mass super-critical case

We consider the existence of solutions $(\lambda_1,\lambda_2, u, v)\in \mathbb{R}^2\times (H^1(\mathbb{R}^N))^2$ to systems of coupled Schr\"odinger equations $$ \begin{cases} -\Delta u+\lambda_1 u=\mu_1 u^{p-1}+\beta r_1 u^{r_1-1}v^{r_2}\quad&\hbox{in}~\mathbb{R}^N,\\ -\Delta v+\lambda_2 v=\mu_2 v^{q-1}+\beta r_2 u^{r_1}v^{r_2-1}\quad&\hbox{in}~\mathbb{R}^N,\\ 0<u,v\in H^1(\mathbb{R}^N), \, 1\leq N\leq 4,&\end{cases} $$ satisfying the normalization $$ \int_{\mathbb{R}^N}u^2 \mathrm{d}x=a \quad \mbox{and} \quad \int_{\mathbb{R}^N}v^2 \mathrm{d}x=b. $$ Here $\mu_1,\mu_2,\beta>0$ and the prescribed masses $a,b>0$. We focus on the coupled purely mass super-critical case, i.e., $$2+\frac{4}{N}<p,q,r_1+r_2<2^*$$ with $2^*$ being the Sobolev critical exponent, defined by $2^*:=+\infty$ for $N=1,2$ and $2^*:=\frac{2N}{N-2}$ for $N=3,4$. We optimize the range of $(a,b,\beta,r_1,r_2)$ for the existence. In particular, for $N=3,4$ with $r_1,r_2\in (1,2)$, our result indicates the existence for all $a,b>0$ and $\beta>0$.
Stratified-NMF for Heterogeneous Data

Non-negative matrix factorization (NMF) is an important technique for obtaining low dimensional representations of datasets. However, classical NMF does not take into account data that is collected at different times or in different locations, which may exhibit heterogeneity. We resolve this problem by solving a modified NMF objective, Stratified-NMF, that simultaneously learns strata-dependent statistics and a shared topics matrix. We develop multiplicative update rules for this novel objective and prove convergence of the objective. Then, we experiment on synthetic data to demonstrate the efficiency and accuracy of the method. Lastly, we apply our method to three real world datasets and empirically investigate their learned features.
Peeking Inside the Schufa Blackbox: Explaining the German Housing Scoring System

Explainable Artificial Intelligence is a concept aimed at making complex algorithms transparent to users through a uniform solution. Researchers have highlighted the importance of integrating domain specific contexts to develop explanations tailored to end users. In this study, we focus on the Schufa housing scoring system in Germany and investigate how users information needs and expectations for explanations vary based on their roles. Using the speculative design approach, we asked business information students to imagine user interfaces that provide housing credit score explanations from the perspectives of both tenants and landlords. Our preliminary findings suggest that although there are general needs that apply to all users, there are also conflicting needs that depend on the practical realities of their roles and how credit scores affect them. We contribute to Human centered XAI research by proposing future research directions that examine users explanatory needs considering their roles and agencies.
Learning Deterministic Finite Automata from Confidence Oracles

We discuss the problem of learning a deterministic finite automaton (DFA) from a confidence oracle. That is, we are given access to an oracle $Q$ with incomplete knowledge of some target language $L$ over an alphabet $\Sigma$; the oracle maps a string $x\in\Sigma^*$ to a score in the interval $[-1,1]$ indicating its confidence that the string is in the language. The interpretation is that the sign of the score signifies whether $x\in L$, while the magnitude $|Q(x)|$ represents the oracle's confidence. Our goal is to learn a DFA representation of the oracle that preserves the information that it is confident in. The learned DFA should closely match the oracle wherever it is highly confident, but it need not do this when the oracle is less sure of itself.
Machine learning-based malware detection for IoT devices using control-flow data

Embedded devices are specialised devices designed for one or only a few purposes. They are often part of a larger system, through wired or wireless connection. Those embedded devices that are connected to other computers or embedded systems through the Internet are called Internet of Things (IoT for short) devices. With their widespread usage and their insufficient protection, these devices are increasingly becoming the target of malware attacks. Companies often cut corners to save manufacturing costs or misconfigure when producing these devices. This can be lack of software updates, ports left open or security defects by design. Although these devices may not be as powerful as a regular computer, their large number makes them suitable candidates for botnets. Other types of IoT devices can even cause health problems since there are even pacemakers connected to the Internet. This means, that without sufficient defence, even directed assaults are possible against people. The goal of this thesis project is to provide better security for these devices with the help of machine learning algorithms and reverse engineering tools. Specifically, I study the applicability of control-flow related data of executables for malware detection. I present a malware detection method with two phases. The first phase extracts control-flow related data using static binary analysis. The second phase classifies binary executables as either malicious or benign using a neural network model. I train the model using a dataset of malicious and benign ARM applications.
On the degree of polynomials computing square roots mod p

For an odd prime $p$, we say $f(X) \in {\mathbb F}_p[X]$ computes square roots in $\mathbb F_p$ if, for all nonzero perfect squares $a \in \mathbb F_p$, we have $f(a)^2 = a$. When $p \equiv 3 \mod 4$, it is well known that $f(X) = X^{(p+1)/4}$ computes square roots. This degree is surprisingly low (and in fact lowest possible), since we have specified $(p-1)/2$ evaluations (up to sign) of the polynomial $f(X)$. On the other hand, for $p \equiv 1 \mod 4$ there was previously no nontrivial bound known on the lowest degree of a polynomial computing square roots in $\mathbb F_p$; it could have been anywhere between $\frac{p}{4}$ and $\frac{p}{2}$. We show that for all $p \equiv 1 \mod 4$, the degree of a polynomial computing square roots has degree at least $p/3$. Our main new ingredient is a general lemma which may be of independent interest: powers of a low degree polynomial cannot have too many consecutive zero coefficients. The proof method also yields a robust version: any polynomial that computes square roots for 99\% of the squares also has degree almost $p/3$. In the other direction, a result of Agou, Deligl\'ese, and Nicolas (Designs, Codes, and Cryptography, 2003) shows that for infinitely many $p \equiv 1 \mod 4$, the degree of a polynomial computing square roots can be as small as $3p/8$.
Pudding: Private User Discovery in Anonymity Networks

Anonymity networks allow messaging with metadata privacy, providing better privacy than popular encrypted messaging applications. However, contacting a user on an anonymity network currently requires knowing their public key or similar high-entropy information, as these systems lack a privacy-preserving mechanism for contacting a user via a short, human-readable username. Previous research suggests that this is a barrier to widespread adoption. In this paper we propose Pudding, a novel private user discovery protocol that allows a user to be contacted on an anonymity network knowing only their email address. Our protocol hides contact relationships between users, prevents impersonation, and conceals which usernames are registered on the network. Pudding is Byzantine fault tolerant, remaining available and secure as long as less than one third of servers are crashed, unavailable, or malicious. It can be deployed on Loopix and Nym without changes to the underlying anonymity network protocol, and it supports mobile devices with intermittent network connectivity. We demonstrate the practicality of Pudding with a prototype using the Nym anonymity network. We also formally define the security and privacy goals of our protocol and conduct a thorough analysis to assess its compliance with these definitions.
Enabling Mobility-Oriented JCAS in 6G Networks: An Architecture Proposal

Sensing plays a crucial role in autonomous and assisted vehicular driving, as well as in the operation of autonomous drones. The traditional segregation of communication and onboard sensing systems in mobility applications is due to be merged using Joint Communication and Sensing (JCAS) in the development of the 6G mobile radio standard. The integration of JCAS functions into the future road traffic landscape introduces novel challenges for the design of the 6G system architecture. Special emphasis will be placed on facilitating direct communication between road users and aerial drones. In various mobility scenarios, diverse levels of integration will be explored, ranging from leveraging communication capabilities to coordinate different radars to achieving deep integration through a unified waveform. In this paper, we have identified use cases and derive five higher-level Tech Cases (TCs). Technical and functional requirements for the 6G system architecture for a device-oriented JCAS approach will be extracted from the TCs and used to conceptualize the architectural views.
On the Communication Complexity of Decentralized Bilevel Optimization

Decentralized bilevel optimization has been actively studied in the past few years since it has widespread applications in machine learning. However, existing algorithms suffer from large communication complexity caused by the estimation of stochastic hypergradient, limiting their application to real-world tasks. To address this issue, we develop a novel decentralized stochastic bilevel gradient descent algorithm under the heterogeneous setting, which enjoys a small communication cost in each round and a small number of communication rounds. As such, it can achieve a much better communication complexity than existing algorithms without any strong assumptions regarding heterogeneity. To the best of our knowledge, this is the first stochastic algorithm achieving these theoretical results under the heterogeneous setting. At last, the experimental results confirm the efficacy of our algorithm.
Operator Learning for Continuous Spatial-Temporal Model with A Hybrid Optimization Scheme

Partial differential equations are often used in the spatial-temporal modeling of complex dynamical systems in many engineering applications. In this work, we build on the recent progress of operator learning and present a data-driven modeling framework that is continuous in both space and time. A key feature of the proposed model is the resolution-invariance with respect to both spatial and temporal discretizations. To improve the long-term performance of the calibrated model, we further propose a hybrid optimization scheme that leverages both gradient-based and derivative-free optimization methods and efficiently trains on both short-term time series and long-term statistics. We investigate the performance of the spatial-temporal continuous learning framework with three numerical examples, including the viscous Burgers’ equation, the Navier–Stokes equations, and the Kuramoto–Sivashinsky equation. The results confirm the resolution-invariance of the proposed modeling framework and also demonstrate stable long-term simulations with only short-term time series data. In addition, we show that the proposed model can better predict long-term statistics via the hybrid optimization scheme with a combined use of short-term and long-term data.