Miracle-dz/UniFAR-FTUs huggingface dataset:
FontCLIP: A Semantic Typography Visual-Language Model for Multilingual Font Applications

Acquiring the desired font for various design tasks can be challenging and requires professional typographic knowledge. While previous font retrieval or generation works have alleviated some of these difficulties, they often lack support for multiple languages and semantic attributes beyond the training data domains. To solve this problem, we present FontCLIP: a model that connects the semantic understanding of a large vision-language model with typographical knowledge. We integrate typography-specific knowledge into the comprehensive vision-language knowledge of a pretrained CLIP model through a novel finetuning approach. We propose to use a compound descriptive prompt that encapsulates adaptively sampled attributes from a font attribute dataset focusing on Roman alphabet characters. FontCLIP's semantic typographic latent space demonstrates two unprecedented generalization abilities. First, FontCLIP generalizes to different languages including Chinese, Japanese, and Korean (CJK), capturing the typographical features of fonts across different languages, even though it was only finetuned using fonts of Roman characters. Second, FontCLIP can recognize the semantic attributes that are not presented in the training data. FontCLIP's dual-modality and generalization abilities enable multilingual and cross-lingual font retrieval and letter shape optimization, reducing the burden of obtaining desired fonts.
Moduli space theory for complete, constant Q-curvature metrics on finitely punctured spheres

We study constant Q-curvature metrics conformal to the round metric on the sphere with finitely many point singularities. We show that the moduli space of solutions with finitely many punctures in fixed positions, equipped with the Gromov-Hausdorff topology, has the local structure of a real analytic variety with formal dimension equal to the number of the punctures. If a nondegeneracy hypothesis holds, we show that a neighborhood in the moduli space is actually a real-analytic manifold of the expected dimension. We also construct a geometrically natural set of parameters, construct a symplectic structure on this parameter space and show that in the smooth case a small neighborhood of the moduli space embeds as a Lagrangian submanifold in the parameter space.
The AL$\ell_0$CORE Tensor Decomposition for Sparse Count Data

This paper introduces AL$\ell_0$CORE, a new form of probabilistic non-negative tensor decomposition. AL$\ell_0$CORE is a Tucker decomposition where the number of non-zero elements (i.e., the $\ell_0$-norm) of the core tensor is constrained to a preset value $Q$ much smaller than the size of the core. While the user dictates the total budget $Q$, the locations and values of the non-zero elements are latent variables and allocated across the core tensor during inference. AL$\ell_0$CORE -- i.e., $allo$cated $\ell_0$-$co$nstrained $core$-- thus enjoys both the computational tractability of CP decomposition and the qualitatively appealing latent structure of Tucker. In a suite of real-data experiments, we demonstrate that AL$\ell_0$CORE typically requires only tiny fractions (e.g.,~1%) of the full core to achieve the same results as full Tucker decomposition at only a correspondingly tiny fraction of the cost.
From Algorithm Worship to the Art of Human Learning: Insights from 50-year journey of AI in Education

Current discourse surrounding Artificial Intelligence (AI) oscillates between hope and apprehension, painting a future where AI reshapes every facet of human life, including Education. This paper delves into the complexities of AI's role in Education, addressing the mixed messages that have both enthused and alarmed educators, policymakers, and the public. It explores the promises that AI holds for enhancing learning through personalisation at scale, against the backdrop of concerns about ethical implications, the devaluation of non-STEM subjects, and the potential transformative impact on our neurocognitive and socio-emotional functioning. Drawing on recent research and global discourse, the paper seeks to unpack the reasons behind the vagueness of current discussions on AI in Education (AIED) and the implications of this ambiguity for future educational practices and policies. By highlighting insights from educational research and synthesising evidence-based best practices in AIED, the aim is to provide a clearer understanding of how AI technologies can be aligned with the fundamental principles of learning and teaching, and explore what concrete actions may need to be prioritised now to truly enhance learning experiences and outcomes for all in the future.
Data-Driven Ergonomic Risk Assessment of Complex Hand-intensive Manufacturing Processes

Hand-intensive manufacturing processes, such as composite layup and textile draping, require significant human dexterity to accommodate task complexity. These strenuous hand motions often lead to musculoskeletal disorders and rehabilitation surgeries. We develop a data-driven ergonomic risk assessment system with a special focus on hand and finger activity to better identify and address ergonomic issues related to hand-intensive manufacturing processes. The system comprises a multi-modal sensor testbed to collect and synchronize operator upper body pose, hand pose and applied forces; a Biometric Assessment of Complete Hand (BACH) formulation to measure high-fidelity hand and finger risks; and industry-standard risk scores associated with upper body posture, RULA, and hand activity, HAL. Our findings demonstrate that BACH captures injurious activity with a higher granularity in comparison to the existing metrics. Machine learning models are also used to automate RULA and HAL scoring, and generalize well to unseen participants. Our assessment system, therefore, provides ergonomic interpretability of the manufacturing processes studied, and could be used to mitigate risks through minor workplace optimization and posture corrections.
Are Classification Robustness and Explanation Robustness Really Strongly Correlated? An Analysis Through Input Loss Landscape

This paper delves into the critical area of deep learning robustness, challenging the conventional belief that classification robustness and explanation robustness in image classification systems are inherently correlated. Through a novel evaluation approach leveraging clustering for efficient assessment of explanation robustness, we demonstrate that enhancing explanation robustness does not necessarily flatten the input loss landscape with respect to explanation loss - contrary to flattened loss landscapes indicating better classification robustness. To deeply investigate this contradiction, a groundbreaking training method designed to adjust the loss landscape with respect to explanation loss is proposed. Through the new training method, we uncover that although such adjustments can impact the robustness of explanations, they do not have an influence on the robustness of classification. These findings not only challenge the prevailing assumption of a strong correlation between the two forms of robustness but also pave new pathways for understanding relationship between loss landscape and explanation loss.
Design and Performance Comparison of FuzzyPID and Non-linear Model Predictive Controller for 4-Wheel Omni-drive Robot

Trajectory tracking for an Omni-drive robot presents a challenging task that demands an efficient controller design. To address the limitations of manual tuning, we introduce a self-optimizing controller named fuzzyPID, leveraging the analysis of responses from various dynamic and static systems. The rule-based controller design is implemented using Matlab/Simulink, and trajectory tracking simulations are conducted within the CoppeliaSim environment. Similarly, a non-linear model predictive controller(NMPC) is proposed to compare tracking performance with fuzzyPID. We also assess the impact of tunable parameters of NMPC on its tracking accuracy. Simulation results validate the precision and effectiveness of NMPC over fuzzyPID controller while trading computational complexity.
Wide-Field, High-Resolution Reconstruction in Computational Multi-Aperture Miniscope Using a Fourier Neural Network

Traditional fluorescence microscopy is constrained by inherent trade-offs among resolution, field-of-view, and system complexity. To navigate these challenges, we introduce a simple and low-cost computational multi-aperture miniature microscope, utilizing a microlens array for single-shot wide-field, high-resolution imaging. Addressing the challenges posed by extensive view multiplexing and non-local, shift-variant aberrations in this device, we present SV-FourierNet, a novel multi-channel Fourier neural network. SV-FourierNet facilitates high-resolution image reconstruction across the entire imaging field through its learned global receptive field. We establish a close relationship between the physical spatially-varying point-spread functions and the network's learned effective receptive field. This ensures that SV-FourierNet has effectively encapsulated the spatially-varying aberrations in our system, and learned a physically meaningful function for image reconstruction. Training of SV-FourierNet is conducted entirely on a physics-based simulator. We showcase wide-field, high-resolution video reconstructions on colonies of freely moving C. elegans and imaging of a mouse brain section. Our computational multi-aperture miniature microscope, augmented with SV-FourierNet, represents a major advancement in computational microscopy and may find broad applications in biomedical research and other fields requiring compact microscopy solutions.
Local Computing By Partial Quantifier Elimination

Localization of computations plays a crucial role in solving hard problems efficiently. We will refer to the techniques implementing such localization as ${\mathit local}$ ${\mathit computing}$. We relate local computing with $\mathit{partial}$ $\mathit{quantifier}$ $\mathit{elimination}$ (PQE). The latter is a generalization of regular quantifier elimination where one can take a $\mathit{part}$ of the formula out of the scope of quantifiers. The objective of this paper is to show that PQE can be viewed as a language of local computing and hence building efficient PQE solvers is of great importance. We describe application of local computing by PQE to three different problems of hardware verification: property generation, equivalence checking and model checking. Besides, we discuss using local computing by PQE for SAT solving. Finally, we relate PQE and interpolation, a form of local computing.
Bounding Stochastic Safety: Leveraging Freedman's Inequality with Discrete-Time Control Barrier Functions

When deployed in the real world, safe control methods must be robust to unstructured uncertainties such as modeling error and external disturbances. Typical robust safety methods achieve their guarantees by always assuming that the worst-case disturbance will occur. In contrast, this paper utilizes Freedman's inequality in the context of discrete-time control barrier functions (DTCBFs) and c-martingales to provide stronger (less conservative) safety guarantees for stochastic systems. Our approach accounts for the underlying disturbance distribution instead of relying exclusively on its worst-case bound and does not require the barrier function to be upper-bounded, which makes the resulting safety probability bounds more directly useful for intuitive safety constraints such as signed distance. We compare our results with existing safety guarantees, such as input-to-state safety (ISSf) and martingale results that rely on Ville's inequality. When the assumptions for all methods hold, we provide a range of parameters for which our guarantee is stronger. Finally, we present simulation examples, including a bipedal walking robot, that demonstrate the utility and tightness of our safety guarantee.
Towards Generalizable and Interpretable Motion Prediction: A Deep Variational Bayes Approach

Estimating the potential behavior of the surrounding human-driven vehicles is crucial for the safety of autonomous vehicles in a mixed traffic flow. Recent state-of-the-art achieved accurate prediction using deep neural networks. However, these end-to-end models are usually black boxes with weak interpretability and generalizability. This paper proposes the Goal-based Neural Variational Agent (GNeVA), an interpretable generative model for motion prediction with robust generalizability to out-of-distribution cases. For interpretability, the model achieves target-driven motion prediction by estimating the spatial distribution of long-term destinations with a variational mixture of Gaussians. We identify a causal structure among maps and agents' histories and derive a variational posterior to enhance generalizability. Experiments on motion prediction datasets validate that the fitted model can be interpretable and generalizable and can achieve comparable performance to state-of-the-art results.
Extreme Point Pursuit -- Part II: Further Error Bound Analysis and Applications

In the first part of this study, a convex-constrained penalized formulation was studied for a class of constant modulus (CM) problems. In particular, the error bound techniques were shown to play a vital role in providing exact penalization results. In this second part of the study, we continue our error bound analysis for the cases of partial permutation matrices, size-constrained assignment matrices and non-negative semi-orthogonal matrices. We develop new error bounds and penalized formulations for these three cases, and the new formulations possess good structures for building computationally efficient algorithms. Moreover, we provide numerical results to demonstrate our framework in a variety of applications such as the densest k-subgraph problem, graph matching, size-constrained clustering, non-negative orthogonal matrix factorization and sparse fair principal component analysis.
Knowledge-aware Alert Aggregation in Large-scale Cloud Systems: a Hybrid Approach

Due to the scale and complexity of cloud systems, a system failure would trigger an"alert storm", i.e., massive correlated alerts. Although these alerts can be traced back to a few root causes, the overwhelming number makes it infeasible for manual handling. Alert aggregation is thus critical to help engineers concentrate on the root cause and facilitate failure resolution. Existing methods typically utilize semantic similarity-based methods or statistical methods to aggregate alerts. However, semantic similarity-based methods overlook the causal rationale of alerts, while statistical methods can hardly handle infrequent alerts. To tackle these limitations, we introduce leveraging external knowledge, i.e., Standard Operation Procedure (SOP) of alerts as a supplement. We propose COLA, a novel hybrid approach based on correlation mining and LLM (Large Language Model) reasoning for online alert aggregation. The correlation mining module effectively captures the temporal and spatial relations between alerts, measuring their correlations in an efficient manner. Subsequently, only uncertain pairs with low confidence are forwarded to the LLM reasoning module for detailed analysis. This hybrid design harnesses both statistical evidence for frequent alerts and the reasoning capabilities of computationally intensive LLMs, ensuring the overall efficiency of COLA in handling large volumes of alerts in practical scenarios. We evaluate COLA on three datasets collected from the production environment of a large-scale cloud platform. The experimental results show COLA achieves F1-scores from 0.901 to 0.930, outperforming state-of-the-art methods and achieving comparable efficiency. We also share our experience in deploying COLA in our real-world cloud system, Cloud X.
Unveiling Ancient Maya Settlements Using Aerial LiDAR Image Segmentation

Manual identification of archaeological features in LiDAR imagery is labor-intensive, costly, and requires archaeological expertise. This paper shows how recent advancements in deep learning (DL) present efficient solutions for accurately segmenting archaeological structures in aerial LiDAR images using the YOLOv8 neural network. The proposed approach uses novel pre-processing of the raw LiDAR data and dataset augmentation methods to produce trained YOLOv8 networks to improve accuracy, precision, and recall for the segmentation of two important Maya structure types: annular structures and platforms. The results show an IoU performance of 0.842 for platforms and 0.809 for annular structures which outperform existing approaches. Further, analysis via domain experts considers the topological consistency of segmented regions and performance vs. area providing important insights. The approach automates time-consuming LiDAR image labeling which significantly accelerates accurate analysis of historical landscapes.
PSS-BA: LiDAR Bundle Adjustment with Progressive Spatial Smoothing

Accurate and consistent construction of point clouds from LiDAR scanning data is fundamental for 3D modeling applications. Current solutions, such as multiview point cloud registration and LiDAR bundle adjustment, predominantly depend on the local plane assumption, which may be inadequate in complex environments lacking of planar geometries or substantial initial pose errors. To mitigate this problem, this paper presents a LiDAR bundle adjustment with progressive spatial smoothing, which is suitable for complex environments and exhibits improved convergence capabilities. The proposed method consists of a spatial smoothing module and a pose adjustment module, which combines the benefits of local consistency and global accuracy. With the spatial smoothing module, we can obtain robust and rich surface constraints employing smoothing kernels across various scales. Then the pose adjustment module corrects all poses utilizing the novel surface constraints. Ultimately, the proposed method simultaneously achieves fine poses and parametric surfaces that can be directly employed for high-quality point cloud reconstruction. The effectiveness and robustness of our proposed approach have been validated on both simulation and real-world datasets. The experimental results demonstrate that the proposed method outperforms the existing methods and achieves better accuracy in complex environments with low planar structures.
Evidence, Definitions and Algorithms regarding the Existence of Cohesive-Convergence Groups in Neural Network Optimization

Understanding the convergence process of neural networks is one of the most complex and crucial issues in the field of machine learning. Despite the close association of notable successes in this domain with the convergence of artificial neural networks, this concept remains predominantly theoretical. In reality, due to the non-convex nature of the optimization problems that artificial neural networks tackle, very few trained networks actually achieve convergence. To expand recent research efforts on artificial-neural-network convergence, this paper will discuss a different approach based on observations of cohesive-convergence groups emerging during the optimization process of an artificial neural network.
Multipolar magnetism in $5d^2$ vacancy-ordered halide double perovskites

Vacancy-ordered halide double perovskites hosting 4d/5d transition metals have emerged as a distinct platform for investigating unconventional magnetism arising out of the interplay of strong atomic spin-orbit coupling (SOC) and Coulomb interactions. Focusing on the $d^2$ system Cs$_2$WCl$_6$, our ab initio electronic structure calculation reveals very narrow electronic bands, fulfilling the necessary condition to realize exotic orders. Using this input, we solve the many-body spin-orbit coupled single-site problem by exact diagonalization and show that the multiplet structure of Cs$_2$WCl$_6$ hosts ground non-Kramers doublets on W, with vanishing dipole moment and a small gap to an excited magnetic triplet. Our work provides the rationale for the observed strong deviation from the classic Kotani behaviour in Cs$_2$WCl$_6$ for the measured temperature dependence of the magnetic moment. The non-Kramers doublets on W exhibit non-zero quadrupolar and octupolar moments, and our calculated two-site exchange supports the dominance of inter-site octupolar exchange over quadrupolar interactions. We predict ferro-octupolar order with a transition temperature $T_c \sim 5$K which may get somewhat suppressed by quantum fluctuations and disorder; this could be tested in future low-temperature experiments.
Hot Carrier Nanowire Transistors at the Ballistic Limit

We demonstrate experimentally non-equilibrium transport in unipolar quasi-1D hot electron devices reaching ballistic limit. The devices are realized with heterostructure engineering in nanowires to obtain dopant- and dislocation-free 1D-epitaxy and flexible bandgap engineering. We show experimentally the control of hot electron injection with a graded conduction band profile and subsequent filtering of hot and relaxed electrons with rectangular energy barriers. The number of electron passing the barrier depends exponentially on the transport length with a mean free path of 200 - 260 nm and reaches ballistic transport regime for the shortest devices with 70 % of the electrons flying freely through the base electrode and the barrier reflections limiting the transport to the collector.
Higher-Order Reverse Isoperimetric Inequalities for Log-concave Functions

The Rogers-Shephard and Zhang's projection inequalities are two reverse, affine isoperimetric inequalities relating the volumes of a convex body and its difference body and polar projection body, respectively. Following a classical work by Schneider, both inequalities have been extended to the so-called higher-order setting. In this work, we establish the higher-order analogues for these inequalities in the setting of log-concave functions.
First detection of white spot syndrome virus (WSSV) and infectious hypodermal and hematopoietic necrosis virus (IHHNV) from wild-caught giant tiger prawn, Penaeus monodon Fabricius, 1798 (Penaeoidea: Penaeidae) from the Gulf of Mexico and Northwestern Atlantic Ocean

We screened the gill and somatic muscle of 152 wild-caught invasive giant tiger prawns (GTPs), Penaeus monodon Fabricius, 1798 (Penaeoidea: Penaeidae) for infection by white spot syndrome virus (WSSV), infectious hypodermal and hematopoietic necrosis virus (IHHNV), and Taura syndrome virus (TSV) using molecular methods (PCR and qPCR) and transmission electron microscopy (TEM). The sampled GTPs comprised 1 freshly-collected specimen from Mississippi Sound (Northern Gulf of Mexico) during 2020; 54 frozen specimens originally captured from the Northern Gulf of Mexico off Mississippi, Alabama, and Florida during 2014–2016; 76 frozen specimens originally captured from the Northwestern Atlantic Ocean off North Carolina, South Carolina, Georgia, and Florida during 2014–2020; and 21 museum-accessioned specimens (19 initially ethanol-preserved; 2 initially formalin-fixed) captured from the Gulf of Mexico and Northwestern Atlantic Ocean off Florida during 1988, 2011–2013, and 2016. Molecular viral detection relied upon qPCR with TaqMan chemistry for WSSV, conventional PCR for IHHNV, and rt-PCR for Taura virus. TEM was performed on WSSV qPCR+ positive GTP gill to confirm viral infection. A total of 18 GTPs were positive for WSSV by qPCR, 1 was positive for IHHNV by conventional PCR, and none were positive for Taura virus. This is the first report of a WSSV or IHHNV infection in a wild-caught GTP from the Gulf of Mexico or Northwestern Atlantic Ocean and first detection of an IHHNV infection in a wild-caught host in the Northwestern Atlantic Ocean. The phylogenetic analyses indicated that, broadly, sympatric WSSV isolates (unless identical) do not share a recent common ancestor (they are paraphyletic), suggesting that the virus has been repeatedly translocated and introduced into the Gulf of Mexico and Northwestern Atlantic Ocean and that it originated from different localities.