Miracle-dz/UniFAR-FTUs huggingface dataset:
Auditable Homomorphic-based Decentralized Collaborative AI with Attribute-based Differential Privacy

In recent years, the notion of federated learning (FL) has led to the new paradigm of distributed artificial intelligence (AI) with privacy preservation. However, most current FL systems suffer from data privacy issues due to the requirement of a trusted third party. Although some previous works introduce differential privacy to protect the data, however, it may also significantly deteriorate the model performance. To address these issues, we propose a novel decentralized collaborative AI framework, named Auditable Homomorphic-based Decentralised Collaborative AI (AerisAI), to improve security with homomorphic encryption and fine-grained differential privacy. Our proposed AerisAI directly aggregates the encrypted parameters with a blockchain-based smart contract to get rid of the need of a trusted third party. We also propose a brand-new concept for eliminating the negative impacts of differential privacy for model performance. Moreover, the proposed AerisAI also provides the broadcast-aware group key management based on ciphertext-policy attribute-based encryption (CPABE) to achieve fine-grained access control based on different service-level agreements. We provide a formal theoretical analysis of the proposed AerisAI as well as the functionality comparison with the other baselines. We also conduct extensive experiments on real datasets to evaluate the proposed approach. The experimental results indicate that our proposed AerisAI significantly outperforms the other state-of-the-art baselines.
Weak non-linearities of amorphous polymer under creep

The creep behavior of an amorphous poly(etherimide) (PEI) polymer is investigated in the vicinity of its glass transition in a weakly non linear regime where the acceleration of the creep response is driven by local configurational rearrangements. From the time shifts of the creep compliance curves under increasing applied stresses in the range 1-15~\si{\mega\pascal}, we determine a macroscopic acceleration factor. At the start of creep, the stress is homogeneous and the macroscopic acceleration can be assimilated to that of the local rearrangements which is shown to vary as $f=e^{-(\sigma/Y)^n} $ with $n=2 \pm 0.2$, where $\sigma$ is the local stress and $Y$ is a decreasing function of compliance. This experimental result is in agreement with the recent theory of Long \textit{et al.} (\textit{Phys. Rev. Mat.} (2018) \textbf{2}, 105601 ) which predicts $n=2$. From a mean field approximation, we interpret the variation of $Y$ with compliance as the result of the development of stress heterogneities during creep.
Torsion pairs via the Ziegler spectrum

We establish a bijection between torsion pairs in the category of finite-dimensional modules over a finite-dimensional algebra A and pairs (Z, I) formed by a closed rigid set Z in the Ziegler spectrum of A and a set I of indecomposable injective A-modules. This can be regarded as an extension of a result from $\tau$-tilting theory which parametrises the functorially finite torsion pairs over A. We also obtain a one-one-correspondence between finite-dimensional bricks and certain (possibly infinite-dimensional) indecomposable modules satisfying a rigidity condition. Our results also hold when A is an artinian ring.
Improved Bounds for Point Selections and Halving Hyperplanes in Higher Dimensions

Let $(P,E)$ be a $(d+1)$-uniform geometric hypergraph, where $P$ is an $n$-point set in general position in $\mathbb{R}^d$ and $E\subseteq {P\choose d+1}$ is a collection of $\epsilon{n\choose d+1}$ $d$-dimensional simplices with vertices in $P$, for $0<\epsilon\leq 1$. We show that there is a point $x\in {\mathbb R}^d$ that pierces $\displaystyle \Omega\left(\epsilon^{(d^4+d)(d+1)+\delta}{n\choose d+1}\right)$ simplices in $E$, for any fixed $\delta>0$. This is a dramatic improvement in all dimensions $d\geq 3$, over the previous lower bounds of the general form $\displaystyle \epsilon^{(cd)^{d+1}}n^{d+1}$, which date back to the seminal 1991 work of Alon, B\'{a}r\'{a}ny, F\"{u}redi and Kleitman. As a result, any $n$-point set in general position in $\mathbb{R}^d$ admits only $\displaystyle O\left(n^{d-\frac{1}{d(d-1)^4+d(d-1)}+\delta}\right)$ halving hyperplanes, for any $\delta>0$, which is a significant improvement over the previously best known bound $\displaystyle O\left(n^{d-\frac{1}{(2d)^{d}}}\right)$ in all dimensions $d\geq 5$. An essential ingredient of our proof is the following semi-algebraic Tur\'an-type result of independent interest: Let $(V_1,\ldots,V_k,E)$ be a hypergraph of bounded semi-algebraic description complexity in ${\mathbb R}^d$ that satisfies $|E|\geq \varepsilon |V_1|\cdot\ldots \cdot |V_k|$ for some $\varepsilon>0$. Then there exist subsets $W_i\subseteq V_i$ that satisfy $W_1\times W_2\times\ldots\times W_k\subseteq E$, and $|W_1|\cdot\ldots\cdots|W_k|=\Omega\left(\varepsilon^{d(k-1)+1}|V_1|\cdot |V_2|\cdot\ldots\cdot|V_k|\right)$.
Safe Hybrid-Action Reinforcement Learning-Based Decision and Control for Discretionary Lane Change

Autonomous lane-change, a key feature of advanced driver-assistance systems, can enhance traffic efficiency and reduce the incidence of accidents. However, safe driving of autonomous vehicles remains challenging in complex environments. How to perform safe and appropriate lane change is a popular topic of research in the field of autonomous driving. Currently, few papers consider the safety of reinforcement learning in autonomous lane-change scenarios. We introduce safe hybrid-action reinforcement learning into discretionary lane change for the first time and propose Parameterized Soft Actor-Critic with PID Lagrangian (PASAC-PIDLag) algorithm. Furthermore, we conduct a comparative analysis of the Parameterized Soft Actor-Critic (PASAC), which is an unsafe version of PASAC-PIDLag. Both algorithms are employed to train the lane-change strategy of autonomous vehicles to output discrete lane-change decision and longitudinal vehicle acceleration. Our simulation results indicate that at a traffic density of 15 vehicles per kilometer (15 veh/km), the PASAC-PIDLag algorithm exhibits superior safety with a collision rate of 0%, outperforming the PASAC algorithm, which has a collision rate of 1%. The outcomes of the generalization assessments reveal that at low traffic density levels, both the PASAC-PIDLag and PASAC algorithms are proficient in attaining a 0% collision rate. Under conditions of high traffic flow density, the PASAC-PIDLag algorithm surpasses PASAC in terms of both safety and optimality.
Attacking Delay-based PUFs with Minimal Adversary Model

Physically Unclonable Functions (PUFs) provide a streamlined solution for lightweight device authentication. Delay-based Arbiter PUFs, with their ease of implementation and vast challenge space, have received significant attention; however, they are not immune to modelling attacks that exploit correlations between their inputs and outputs. Research is therefore polarized between developing modelling-resistant PUFs and devising machine learning attacks against them. This dichotomy often results in exaggerated concerns and overconfidence in PUF security, primarily because there lacks a universal tool to gauge a PUF's security. In many scenarios, attacks require additional information, such as PUF type or configuration parameters. Alarmingly, new PUFs are often branded `secure' if they lack a specific attack model upon introduction. To impartially assess the security of delay-based PUFs, we present a generic framework featuring a Mixture-of-PUF-Experts (MoPE) structure for mounting attacks on various PUFs with minimal adversarial knowledge, which provides a way to compare their performance fairly and impartially. We demonstrate the capability of our model to attack different PUF types, including the first successful attack on Heterogeneous Feed-Forward PUFs using only a reasonable amount of challenges and responses. We propose an extension version of our model, a Multi-gate Mixture-of-PUF-Experts (MMoPE) structure, facilitating multi-task learning across diverse PUFs to recognise commonalities across PUF designs. This allows a streamlining of training periods for attacking multiple PUFs simultaneously. We conclude by showcasing the potent performance of MoPE and MMoPE across a spectrum of PUF types, employing simulated, real-world unbiased, and biased data sets for analysis.
The wide-angle imaging lens

PROBLEM TO BE SOLVED: To inexpensively provide an imaging lens which is capable of excellently correcting various aberrations, small in size, light in weight, and comparatively bright, while achieving a wide angle of view, that is, a total angle of view of about 90°. SOLUTION: An imaging lens for a solid-state imaging element comprises, in order from an object side to an image surface side, a first lens which has a convex surface facing the object side in the vicinity of an optical axis and has positive or negative refractive power and whose both surfaces are aspherical, an aperture stop, a second lens which has a convex surface facing the image surface side and has positive refractive power, a third lens which has a concave surface facing the image surface side and has negative refractive power, a fourth lens which has a convex surface facing the image surface side and has positive refractive power, and a fifth lens which has a meniscus shape in which a concave surface faces the image surface side in the vicinity of the optical axis and has negative refractive power. The imaging lens satisfies the following conditional expression (1): 0.9
Inertia onset in disordered porous media flow

We investigate the very onset of the inertial regime in pore-scale fluid flow in a three-dimensional, disordered porous media. We analyze how the flow structure changes with increasing Reynolds number. In particular, we compute tortuosity, spatial kinetic energy localization, and the pore-space volume fraction containing negative streamwise velocity. Our analysis shows that the onset of inertia observed in a standard way by computing the friction factor appears at a Reynolds number two orders of magnitude higher than indicated by analyzing tortuosity and spatial distribution of kinetic energy.
Towards Explaining Deep Neural Network Compression Through a Probabilistic Latent Space

Despite the impressive performance of deep neural networks (DNNs), their computational complexity and storage space consumption have led to the concept of network compression. While DNN compression techniques such as pruning and low-rank decomposition have been extensively studied, there has been insufficient attention paid to their theoretical explanation. In this paper, we propose a novel theoretical framework that leverages a probabilistic latent space of DNN weights and explains the optimal network sparsity by using the information-theoretic divergence measures. We introduce new analogous projected patterns (AP2) and analogous-in-probability projected patterns (AP3) notions for DNNs and prove that there exists a relationship between AP3/AP2 property of layers in the network and its performance. Further, we provide a theoretical analysis that explains the training process of the compressed network. The theoretical results are empirically validated through experiments conducted on standard pre-trained benchmarks, including AlexNet, ResNet50, and VGG16, using CIFAR10 and CIFAR100 datasets. Through our experiments, we highlight the relationship of AP3 and AP2 properties with fine-tuning pruned DNNs and sparsity levels.
Tracking Interannual‐ to Multidecadal‐Scale Climate Variability in the Atlantic Warm Pool Using Central Caribbean Coral Data

Atlantic Warm Pool (AWP) climate variability is subject to multiple influences of remote and local forcing. However, shortage of observational data before the midtwentieth century and of long‐term sea surface temperature (SST) and climate records has hampered the detection and investigation of decadal‐ and longer‐scale variability. We present new seasonally resolved 125‐year records of coral δ18O and Sr/Ca variations in the Central Caribbean Sea (Little Cayman, Cayman Islands; Diploria strigosa). Both geochemical proxies show decreasing long‐term trends, indicating long‐term warming. Sr/Ca indicates much stronger regional warming than large‐scale grid‐SST data, while δ18O tracks large‐scale SST changes in the Atlantic Warm Pool. Seawater δ18O variations are reconstructed, indicating a drying trend over the past century. High spatial correlation between coral δ18O and SST in the region of the Loop Current and Gulf Stream system suggests that Little Cayman is a sensitive location for detecting past large‐scale temperature variability beyond the Central Caribbean region. More specifically, our δ18O data track changes in North Atlantic Oscillation variability on decadal and multidecadal time scales providing insights into the temporal and spatial nonstationarity of the North Atlantic Oscillation. A combination of our δ18O record with two coral records from different Caribbean sites reveals high spatial correspondence between coral δ18O and SST variability in the North Atlantic subtropical gyre, where few instrumental measurements and proxies are available prior to the twentieth century. Our results clearly demonstrate the potential of combining proxy data to provide information from sparsely sampled areas, helping to reduce uncertainty in model‐based projections.
Towards localized accuracy assessment of remote-sensing derived built-up land layers across the rural-urban continuum

The accuracy assessment of remote-sensing derived built-up land data represents a specific case of binary map comparison, where class imbalance varies considerably across rural-urban trajectories. Thus, local accuracy characterization of such datasets requires specific strategies that are robust to low sample sizes and different levels of class imbalance. Herein, we examine the suitability of commonly used spatial agreement measures for their localized accuracy characterization of built-up land layers across the rural-urban continuum, using the Global Human Settlement Layer and a reference database of built-up land derived from cadastral and building footprint data.
Identification of Craving Maps among Marijuana Users via the Analysis of Functional Brain Networks with High-Order Attention Graph Neural Networks

The excessive consumption of marijuana can induce substantial psychological and social consequences. In this investigation, we propose an elucidative framework termed high-order graph attention neural networks (HOGANN) for the classification of Marijuana addiction, coupled with an analysis of localized brain network communities exhibiting abnormal activities among chronic marijuana users. HOGANN integrates dynamic intrinsic functional brain networks, estimated from resting-state functional magnetic resonance imaging (rs-fMRI), using long short-term memory (LSTM) to capture temporal network dynamics. We employ a high-order attention module for information fusion and message passing among neighboring nodes, enhancing the network community analysis. Our model is validated across two distinct data cohorts, yielding substantially higher classification accuracy than benchmark algorithms. Furthermore, we discern the most pertinent subnetworks and cognitive regions affected by persistent marijuana consumption, indicating adverse effects on functional brain networks, particularly within the dorsal attention and frontoparietal networks. Intriguingly, our model demonstrates superior performance in cohorts exhibiting prolonged dependence, implying that prolonged marijuana usage induces more pronounced alterations in brain networks. The model proficiently identifies craving brain maps, thereby delineating critical brain regions for analysis.
Ensemble-Based Unsupervised Discontinuous Constituency Parsing by Tree Averaging

We address unsupervised discontinuous constituency parsing, where we observe a high variance in the performance of the only previous model. We propose to build an ensemble of different runs of the existing discontinuous parser by averaging the predicted trees, to stabilize and boost performance. To begin with, we provide comprehensive computational complexity analysis (in terms of P and NP-complete) for tree averaging under different setups of binarity and continuity. We then develop an efficient exact algorithm to tackle the task, which runs in a reasonable time for all samples in our experiments. Results on three datasets show our method outperforms all baselines in all metrics; we also provide in-depth analyses of our approach.
An improved calendar ring hole-count for the Antikythera mechanism

We present a new analysis of the positions of holes beneath the calendar ring of the Antikythera mechanism, as measured by Budiselic et al. (2020). We significantly refine their estimate for the number of holes that were present in the full ring. Our $68\%$-credible estimate for this number, taking account of all the data, is $355.24^{ +1.39 }_{ -1.36 }$. If holes adjacent to fractures are removed from the analysis, our estimate becomes $354.08^{ +1.47}_{-1.41}$. A ring of 360 holes is strongly disfavoured, and one of 365 holes is not plausible, given our model assumptions.
Developing a Taxonomy of Elements Adversarial to Autonomous Vehicles

As highly automated vehicles reach higher deployment rates, they find themselves in increasingly dangerous situations. Knowing that the consequence of a crash is significant for the health of occupants, bystanders, and properties, as well as to the viability of autonomy and adjacent businesses, we must search for more efficacious ways to comprehensively and reliably train autonomous vehicles to better navigate the complex scenarios with which they struggle. We therefore introduce a taxonomy of potentially adversarial elements that may contribute to poor performance or system failures as a means of identifying and elucidating lesser-seen risks. This taxonomy may be used to characterize failures of automation, as well as to support simulation and real-world training efforts by providing a more comprehensive classification system for events resulting in disengagement, collision, or other negative consequences. This taxonomy is created from and tested against real collision events to ensure comprehensive coverage with minimal class overlap and few omissions. It is intended to be used both for the identification of harm-contributing adversarial events and in the generation thereof (to create extreme edge- and corner-case scenarios) in training procedures.
Learning Quadrupedal Locomotion with Impaired Joints Using Random Joint Masking

Quadrupedal robots have played a crucial role in various environments, from structured environments to complex harsh terrains, thanks to their agile locomotion ability. However, these robots can easily lose their locomotion functionality if damaged by external accidents or internal malfunctions. In this paper, we propose a novel deep reinforcement learning framework to enable a quadrupedal robot to walk with impaired joints. The proposed framework consists of three components: 1) a random joint masking strategy for simulating impaired joint scenarios, 2) a joint state estimator to predict an implicit status of current joint condition based on past observation history, and 3) progressive curriculum learning to allow a single network to conduct both normal gait and various joint-impaired gaits. We verify that our framework enables the Unitree's Go1 robot to walk under various impaired joint conditions in real-world indoor and outdoor environments.
TELEClass: Taxonomy Enrichment and LLM-Enhanced Hierarchical Text Classification with Minimal Supervision

Hierarchical text classification aims to categorize each document into a set of classes in a label taxonomy. Most earlier works focus on fully or semi-supervised methods that require a large amount of human annotated data which is costly and time-consuming to acquire. To alleviate human efforts, in this paper, we work on hierarchical text classification with the minimal amount of supervision: using the sole class name of each node as the only supervision. Recently, large language models (LLM) show competitive performance on various tasks through zero-shot prompting, but this method performs poorly in the hierarchical setting, because it is ineffective to include the large and structured label space in a prompt. On the other hand, previous weakly-supervised hierarchical text classification methods only utilize the raw taxonomy skeleton and ignore the rich information hidden in the text corpus that can serve as additional class-indicative features. To tackle the above challenges, we propose TELEClass, Taxonomy Enrichment and LLM-Enhanced weakly-supervised hierarchical text classification, which (1) automatically enriches the label taxonomy with class-indicative topical terms mined from the corpus to facilitate classifier training and (2) utilizes LLMs for both data annotation and creation tailored for the hierarchical label space. Experiments show that TELEClass can outperform previous weakly-supervised hierarchical text classification methods and LLM-based zero-shot prompting methods on two public datasets.
Homomorphism Preservation Theorems for Many-Valued Structures

A canonical result in model theory is the homomorphism preservation theorem which states that a first-order formula is preserved under homomorphisms on all structures if and only if it is equivalent to an existential-positive formula, standardly proved compactness. Rossman (2008) established that the h.p.t. remains valid when restricted to finite structures. This is a significant result in the field of finite model theory, standing in contrast to other preservation theorems and as an theorem which remains true in the finite but whose proof uses entirely different methods. It also has importance to the field of constraint satisfaction due to the equivalence of existential-positive formulas and unions of conjunctive queries. Adjacently, Dellunde and Vidal (2019) established that a version of the h.p.t. holds for a collection of first-order many-valued logics, those whose (possibly infinite) structures are defined over a fixed finite MTL-chain. In this paper we unite these two strands, showing how one can extend Rossman's proof of a finite h.p.t. to a very wide collection of many-valued predicate logics and simultaneously establishing a finite variant to Dellunde and Vidal's result, one which not only applies to structures defined over algebras more general than MTL-chains but also where we allow for those algebra to vary between models. This investigation provides a starting point in a wider development of finite model theory for many-valued logics and, just as the classical finite h.p.t. has implications for constraint satisfaction, the many-valued finite h.p.t. has implications for valued constraint satisfaction problems.