Miracle-dz/UniFAR-FTUs huggingface dataset:
De Casteljau's Algorithm in Geometric Data Analysis: Theory and Application

For decades, de Casteljau's algorithm has been used as a fundamental building block in curve and surface design and has found a wide range of applications in fields such as scientific computing, and discrete geometry to name but a few. With increasing interest in nonlinear data science, its constructive approach has been shown to provide a principled way to generalize parametric smooth curves to manifolds. These curves have found remarkable new applications in the analysis of parameter-dependent, geometric data. This article provides a survey of the recent theoretical developments in this exciting area as well as its applications in fields such as geometric morphometrics and longitudinal data analysis in medicine, archaeology, and meteorology.
Context-aware Multi-Model Object Detection for Diversely Heterogeneous Compute Systems

In recent years, deep neural networks (DNNs) have gained widespread adoption for continuous mobile object detection (OD) tasks, particularly in autonomous systems. However, a prevalent issue in their deployment is the one-size-fits-all approach, where a single DNN is used, resulting in inefficient utilization of computational resources. This inefficiency is particularly detrimental in energy-constrained systems, as it degrades overall system efficiency. We identify that, the contextual information embedded in the input data stream (e.g. the frames in the camera feed that the OD models are run on) could be exploited to allow a more efficient multi-model-based OD process. In this paper, we propose SHIFT which continuously selects from a variety of DNN-based OD models depending on the dynamically changing contextual information and computational constraints. During this selection, SHIFT uniquely considers multi-accelerator execution to better optimize the energy-efficiency while satisfying the latency constraints. Our proposed methodology results in improvements of up to 7.5x in energy usage and 2.8x in latency compared to state-of-the-art GPU-based single model OD approaches.
Rate-Quality or Energy-Quality Pareto Fronts for Adaptive Video Streaming?

Adaptive video streaming is a key enabler for optimising the delivery of offline encoded video content. The research focus to date has been on optimisation, based solely on rate-quality curves. This paper adds an additional dimension, the energy expenditure, and explores construction of bitrate ladders based on decoding energy-quality curves rather than the conventional rate-quality curves. Pareto fronts are extracted from the rate-quality and energy-quality spaces to select optimal points. Bitrate ladders are constructed from these points using conventional rate-based rules together with a novel quality-based approach. Evaluation on a subset of YouTube-UGC videos encoded with x.265 shows that the energy-quality ladders reduce energy requirements by 28-31% on average at the cost of slightly higher bitrates. The results indicate that optimising based on energy-quality curves rather than rate-quality curves and using quality levels to create the rungs could potentially improve energy efficiency for a comparable quality of experience.
Noncommutative Poisson structure and invariants of matrices

We introduce a novel approach that employs techniques from noncommutative Poisson geometry to comprehend the algebra of invariants of two $n\times n$ matrices. We entirely solve the open problem of computing the algebra of invariants of two $4 \times 4$ matrices. As an application, we derive the complete description of the invariant commuting variety of $4 \times 4$ matrices and the fourth Calogero-Moser space.
Nucleon electric and magnetic polarizabilities in Holographic QCD

Novel experimental results for the proton generalized electric polarizability, suggest an unexpected deviation from current theoretical predictions at low momentum transfer squared $Q^2$. Motivated by this puzzle, we analyze the resonance contributions to the sum of the generalized electric and magnetic nucleon polarizabilities $\alpha_E(Q^2)$ and $\beta_M(Q^2)$, within the Holographic QCD model by Witten, Sakai, and Sugimoto (WSS). In particular, we account for the contributions from the first low-lying nucleon resonances with spin 1/2 and 3/2 and both parities. After having extrapolated the WSS model parameters to fit experimental data on baryonic observables, our findings suggest that the resonance contributions alone do not solve the above-mentioned puzzle. Moreover, at least for the proton case, where data are available, our results are in qualitative agreement with resonance contributions extracted from experimental nucleon-resonance helicity amplitudes.
Robust and accurate simulations of flows over orography using non-conforming meshes

We systematically validate the static local mesh refinement capabilities of a recently proposed IMEX-DG scheme implemented in the framework of the deal.II library. Non-conforming meshes are employed in atmospheric flow simulations to increase the resolution around complex orography. A number of numerical experiments based on classical benchmarks with idealized as well as real orography profiles demonstrate that simulations with the refined mesh are stable for long lead times and no spurious effects arise at the interfaces of mesh regions with different resolutions. Moreover, correct values of the momentum flux are retrieved and the correct large-scale orographic response is established. Hence, large-scale orography-driven flow features can be simulated without loss of accuracy using a much lower total amount of degrees of freedom. In a context of spatial resolutions approaching the hectometric scale in numerical weather prediction models, these results support the use of locally refined, non-conforming meshes as a reliable and effective tool to greatly reduce the dependence of atmospheric models on orographic wave drag parametrizations.
SC-FDMA as a Delay-Doppler Domain Modulation Technique

This paper compares orthogonal time frequency space (OTFS) modulation and single-carrier frequency division multiple access (SC-FDMA). It shows that these are equivalent except for a set of linear phase shifts, applied to the transmit/receive data symbols, which can be absorbed into the channel. Through mathematical and numerical analysis, it is confirmed that SC-FDMA is in fact a delay-Doppler domain multiplexing technique that can achieve the same performance gains as those of OTFS in time-varying wireless environments. This is a promising result as SC-FDMA is already a part of the current wireless standards. The derivations in this paper also shed light on the time-frequency resources used by the delay-Doppler domain data symbols with the fine granularity of delay and Doppler spacings. While comparing the detection performance of the two waveforms, a timing offset (TO) estimation technique with orders of magnitude higher accuracy than the existing solutions in the literature is proposed. From multiple access viewpoint, the underlying tile structures in the time-frequency domain for OTFS and SC-FDMA are discussed. Finally, multiuser input-output relationships for both waveforms in the uplink are derived.
Simulations of Linear Polarization of Precessing AGN Jets at Parsec Scales

The latest results of the most detailed analysis of multi-epoch polarization-sensitive observations of active galactic nuclei (AGN) jets at parsecs scales by very long baseline interferometry (VLBI) reveal several characteristic patterns of linear polarization distribution and its variability (Pushkarev et al., 2023; Zobnina et al., 2023). Some of the observed profiles can be reproduced by a simple model of a jet threaded by a helical magnetic field. However, none of the models presented to date can explain the observed polarization profiles with an increase in its degree towards the edges of the jet, and accompanied by a 'fountain' type electric vector pattern and its high temporal variability in the center. Based on simulations of the VLBI observations of relativistic jets, we show here that the observed transverse linear polarization profiles, atypical for the simple magnetic field models can be naturally produced assuming the finite resolution of VLBI arrays and precession of a jet on ten-years scales, observational indications of which are found in an increasing number of AGN. In our simulations, we qualitatively reproduce the distribution of the electric vector and its variability, though the polarization images are characterized by a bright spine due to weak smearing, which is poorly consistent with observations. More effective depolarization can be obtained in models with the suppressed emission of the jet spine.
Only the Curve Shape Matters: Training Foundation Models for Zero-Shot Multivariate Time Series Forecasting through Next Curve Shape Prediction

We present General Time Transformer (GTT), an encoder-only style foundation model for zero-shot multivariate time series forecasting. GTT is pretrained on a large dataset of 200M high-quality time series samples spanning diverse domains. In our proposed framework, the task of multivariate time series forecasting is formulated as a channel-wise next curve shape prediction problem, where each time series sample is represented as a sequence of non-overlapping curve shapes with a unified numerical magnitude. GTT is trained to predict the next curve shape based on a window of past curve shapes in a channel-wise manner. Experimental results demonstrate that GTT exhibits superior zero-shot multivariate forecasting capabilities on unseen time series datasets, even surpassing state-of-the-art supervised baselines. Additionally, we investigate the impact of varying GTT model parameters and training dataset scales, observing that the scaling law also holds in the context of zero-shot multivariate time series forecasting.
Linear combinations of cluster mean-field states applied to spin systems

We present an innovative cluster-based method employing linear combinations of diverse cluster mean-field (cMF) states, and apply it to describe the ground state of strongly-correlated spin systems. In cluster mean-field theory, the ground state wavefunction is expressed as a factorized tensor product of optimized cluster states. While our prior work concentrated on a single cMF tiling, this study removes that constraint by combining different tilings of cMF states. Selection criteria, including translational symmetry and spatial proximity, guide this process. We present benchmark calculations for the one- and two-dimensional $J_1-J_2$ and $XXZ$ Heisenberg models. Our findings highlight two key aspects. First, the method offers a semi-quantitative description of the $0.4 \lessapprox J_2/J_1 \lessapprox 0.6$ regime of the $J_1-J_2$ model - a particularly challenging regime for existing methods. Second, our results demonstrate the capability of our method to provide qualitative descriptions for all the models and regimes considered, establishing it as a valuable reference. However, the inclusion of additional (weak) correlations is necessary for quantitative agreement, and we explore methods to incorporate these extra correlations.
Overconfident and Unconfident AI Hinder Human-AI Collaboration

As artificial intelligence (AI) advances, human-AI collaboration has become increasingly prevalent across both professional and everyday settings. In such collaboration, AI can express its confidence level about its performance, serving as a crucial indicator for humans to evaluate AI's suggestions. However, AI may exhibit overconfidence or underconfidence--its expressed confidence is higher or lower than its actual performance--which may lead humans to mistakenly evaluate AI advice. Our study investigates the influences of AI's overconfidence and underconfidence on human trust, their acceptance of AI suggestions, and collaboration outcomes. Our study reveal that disclosing AI confidence levels and performance feedback facilitates better recognition of AI confidence misalignments. However, participants tend to withhold their trust as perceiving such misalignments, leading to a rejection of AI suggestions and subsequently poorer performance in collaborative tasks. Conversely, without such information, participants struggle to identify misalignments, resulting in either the neglect of correct AI advice or the following of incorrect AI suggestions, adversely affecting collaboration. This study offers valuable insights for enhancing human-AI collaboration by underscoring the importance of aligning AI's expressed confidence with its actual performance and the necessity of calibrating human trust towards AI confidence.
Towards an Understanding of Stepwise Inference in Transformers: A Synthetic Graph Navigation Model

Stepwise inference protocols, such as scratchpads and chain-of-thought, help language models solve complex problems by decomposing them into a sequence of simpler subproblems. Despite the significant gain in performance achieved via these protocols, the underlying mechanisms of stepwise inference have remained elusive. To address this, we propose to study autoregressive Transformer models on a synthetic task that embodies the multi-step nature of problems where stepwise inference is generally most useful. Specifically, we define a graph navigation problem wherein a model is tasked with traversing a path from a start to a goal node on the graph. Despite is simplicity, we find we can empirically reproduce and analyze several phenomena observed at scale: (i) the stepwise inference reasoning gap, the cause of which we find in the structure of the training data; (ii) a diversity-accuracy tradeoff in model generations as sampling temperature varies; (iii) a simplicity bias in the model's output; and (iv) compositional generalization and a primacy bias with in-context exemplars. Overall, our work introduces a grounded, synthetic framework for studying stepwise inference and offers mechanistic hypotheses that can lay the foundation for a deeper understanding of this phenomenon.
A Closer Look at the Robustness of Contrastive Language-Image Pre-Training (CLIP)

Contrastive Language-Image Pre-training (CLIP) models have demonstrated remarkable generalization capabilities across multiple challenging distribution shifts. However, there is still much to be explored in terms of their robustness to the variations of specific visual factors. In real-world applications, reliable and safe systems must consider other safety objectives beyond classification accuracy, such as predictive uncertainty. Yet, the effectiveness of CLIP models on such safety-related features is less-explored. Driven by the above, this work comprehensively investigates the safety objectives of CLIP models, specifically focusing on three key properties: resilience to visual factor variations, calibrated uncertainty estimations, and the ability to detect anomalous inputs. To this end, we study 83 CLIP models and 127 ImageNet classifiers. They are diverse in architecture, (pre)training distribution and training strategies. We consider 10 visual factors (e.g., shape and pattern), 5 types of out-of-distribution data, and 8 natural and challenging test conditions with different shift types, such as texture, style, and perturbation shifts. Our study has unveiled several previously unknown insights into CLIP models. For instance, they are not consistently more calibrated than other ImageNet models, which contradicts existing findings. Additionally, our analysis underscores the significance of training source design by showcasing its profound influence on the three safety-related properties. We believe our comprehensive study can shed light on and help guide the development of more robust and reliable CLIP models.
FAST: Factorizable Attention for Speeding up Transformers

Motivated by the factorization inherent in the original fast multipole method and the improved fast Gauss transform we introduce a factorable form of attention that operates efficiently in high dimensions. This approach reduces the computational and memory complexity of the attention mechanism in transformers from $O(N^2)$ to $O(N)$. In comparison to previous attempts, our work presents a linearly scaled attention mechanism that maintains the full representation of the attention matrix without compromising on sparsification and incorporates the all-to-all relationship between tokens. We explore the properties of our new attention metric and conduct tests in various standard settings. Results indicate that our attention mechanism has a robust performance and holds significant promise for diverse applications where self-attention is used.
Shadowcast: Stealthy Data Poisoning Attacks Against Vision-Language Models

Vision-Language Models (VLMs) excel in generating textual responses from visual inputs, yet their versatility raises significant security concerns. This study takes the first step in exposing VLMs' susceptibility to data poisoning attacks that can manipulate responses to innocuous, everyday prompts. We introduce Shadowcast, a stealthy data poisoning attack method where poison samples are visually indistinguishable from benign images with matching texts. Shadowcast demonstrates effectiveness in two attack types. The first is Label Attack, tricking VLMs into misidentifying class labels, such as confusing Donald Trump for Joe Biden. The second is Persuasion Attack, which leverages VLMs' text generation capabilities to craft narratives, such as portraying junk food as health food, through persuasive and seemingly rational descriptions. We show that Shadowcast are highly effective in achieving attacker's intentions using as few as 50 poison samples. Moreover, these poison samples remain effective across various prompts and are transferable across different VLM architectures in the black-box setting. This work reveals how poisoned VLMs can generate convincing yet deceptive misinformation and underscores the importance of data quality for responsible deployments of VLMs. Our code is available at: https://github.com/umd-huang-lab/VLM-Poisoning.
Seasonal Variations of Trace Metal Concentrations in the Soft Tissue of Lithophaga Lithophaga Collected from the Bizerte Bay (Northern Tunisia, Mediterranean Sea)

The concentration of four trace metals (Zn, Cu, Pb and Cd) was determined in the soft tissue of the date mussel Lithophaga lithophaga that is specifically and strictly protected by international law. Samples were collected from the Bizerte bay in the northern coast of Tunisia. Comparisons were made on the basis of season and sex. Trace metal concentration decreased in the order Zn > Cu > Pb > Cd. The average concentration was 54.15 ± 23.037, 3.429 ± 1.453 and 1.809 ± 2.252 μg g-1 dry weight, for Zn, Cu and Pb respectively. Cd was not detected in all samples. Significant temporal variation was observed for Zn, Cu and Pb (P < 0.05). There is no significant difference between females and males except for Zn in spring and Pb in winter. Analyses of the condition index (CI) revealed the presence of a single reproductive cycle per year affecting metal bioaccumulation of L. lithophaga. The main objective of metal analysis was to provide useful information about trace metal pollution in the Tunisian date mussel L. lithophaga which will enable to evaluate the potential effect of the Bizerte Marina Project on this species.
Certified homotopy tracking using the Krawczyk method

We revisit the problem of certifying the correctness of approximate solution paths computed by numerical homotopy continuation methods. We propose a conceptually simple approach based on a parametric variant of the Krawczyk method from interval arithmetic. Unlike most previous methods for certified path-tracking, our approach is applicable in the general setting of parameter homotopies commonly used to solve polynomial systems of equations. We also describe a novel preconditioning strategy and give theoretical correctness and termination results. Experiments using a preliminary implementation of the method indicate that our approach is competitive with specialized methods appearing previously in the literature, in spite of our more general setting.
Trilayer multi-orbital models of $\mathrm{La_{4}Ni_{3}O_{10}}$

Recently, the discovery of superconductivity in Ruddlesden-Popper (RP) $\mathrm{La_4Ni_3O_{10}}$ under pressure has further expanded the realm of nickelate-based superconductor family. In this paper, we performed a first-principle study of $\mathrm{La_4Ni_3O_{10}}$ for both $P2_1/a$ phase at ambient pressure and $I4/mmm$ phase at high pressure, with $U$=0, 3.5 eV. Our results confirmed the characteristic upward shift of Ni-$d_{z^2}$ bonding band under pressure. Moreover, our analysis of electronic spectrum and orbital occupancy unveil the dynamical mechanism of electronic reconstructions under pressure, embedded in a critical dual effect. Based on our DFT results, we further proposed a trilayer two-orbital model by performing Wannier downfolding on Ni-$e_g$ orbitals. Our model reveals four Fermi surface sheets with $\alpha,\beta,\beta^\prime,\gamma$ pockets, bearing resemblance to that of bilayer $\mathrm{La_3Ni_2O_7}$. According to the model, our calculated spin susceptibility under random phase approximation predicts an analogous magnetic signal at ${\rm q}=(\frac{\pi}{2},\frac{\pi}{2})$, which is more associated with nesting within $\beta,\beta^\prime$ pockets. Finally, a high energy sixteen-orbital model with direct $dp,pp$ hoppings is proposed, which implies that $\mathrm{La_4Ni_3O_{10}}$ also lies in charge-transfer picture within Zaanen-Sawatzky-Allen scheme. Our exposition of electronic reconstructions and multi-orbital models shed light on theoretical electronic correlation study and experimental exploration for lower pressure in RP series.
Irrigação com águas salinas e aplicação de prolina foliar em cultivo de pimentão ‘All Big’

It is proposed in this study the evaluation of the growth and yield of ‘All Big’ sweet pepper under foliar application of proline and irrigation with saline water. The research was conducted in pots adapted as drainage lisimeters under greenhouse conditions, using an Eutrophic Entisol with sandy-loam texture in the municipality of Campina Grande, PB, Brazil. A randomized block design was used testing two levels of electrical conductivity of irrigation water - ECw (0.6 and 3.0 dS m-1) associated to four proline levels (0, 10, 20 and 30 mmol L-1). Irrigation with water with ECw= 3.0 dS m-1 negatively affected the ‘All Big’pepper growth and the stem dry mass, being the most sensitive variable; the highest values for fresh mass, number and average weight of pepper fruits were obtained when water with ECw=0.6 dS m-1 was used, with proline doses of 12.17 and 0 mmol L-1, respectively; increasing proline doses did not mitigate the deleterious effects caused by irrigation water salinity of 3.0 dS m-1 on growth and yield of ‘All Big ‘ sweet pepper.