Miracle-dz/UniFAR-FTUs huggingface dataset:
GPS Attack Detection and Mitigation for Safe Autonomous Driving using Image and Map based Lateral Direction Localization

The accuracy and robustness of vehicle localization are critical for achieving safe and reliable high-level autonomy. Recent results show that GPS is vulnerable to spoofing attacks, which is one major threat to autonomous driving. In this paper, a novel anomaly detection and mitigation method against GPS attacks that utilizes onboard camera and high-precision maps is proposed to ensure accurate vehicle localization. First, lateral direction localization in driving lanes is calculated by camera-based lane detection and map matching respectively. Then, a real-time detector for GPS spoofing attack is developed to evaluate the localization data. When the attack is detected, a multi-source fusion-based localization method using Unscented Kalman filter is derived to mitigate GPS attack and improve the localization accuracy. The proposed method is validated in various scenarios in Carla simulator and open-source public dataset to demonstrate its effectiveness in timely GPS attack detection and data recovery.
Host-feeding preferences and temperature shape the dynamics of West Nile virus: a mathematical model of assessing the abatement planning

West Nile virus (WNV) is prevalent in the United States but it shows considerable divergence in transmission patterns and spatio-temporal intensity.It is to be noted that the mechanism that drives the transmission potential of WNV is described by the abilities of host species to maintain and disseminate the pathogens pertinent with different eco-epidemiological factors that have an influence on the contact rates amongst the interacting species.There is growing evidence that several vectors exhibit strong feeding preferences towards different host communities.We construct a process based weather driven ordinary differential equation (ODE) model to understand the impact of one vector species Culex pipiens, preferred avian and non-preferred human hosts and compared it surveillance data for the Culex pipiens complex collected in Cook County, Illinois, USA.In our mechanistic model, we also demonstrate that adulticide treatments produced significant reductions in the Culex pipiens population.We take into account the feeding index that can be described as the ratio between observed frequency of mosquitoes feeding on one host compared to another host, divided by the expected frequency of mosquitoes feeding on these two hosts based on the presence of the particular hosts to develop this transmission model for WNV. Our findings demonstrate that the interplay between the feeding index and mosquito abatement strategy is rather a complex phenomenon and it induces a heterogeneous contact rates that should be included while modelling multi-host, multi-vector transmission model.
Topological data analysis of human vowels: Persistent homologies across representation spaces

Topological Data Analysis (TDA) has been successfully used for various tasks in signal/image processing, from visualization to supervised/unsupervised classification. Often, topological characteristics are obtained from persistent homology theory. The standard TDA pipeline starts from the raw signal data or a representation of it. Then, it consists in building a multiscale topological structure on the top of the data using a pre-specified filtration, and finally to compute the topological signature to be further exploited. The commonly used topological signature is a persistent diagram (or transformations of it). Current research discusses the consequences of the many ways to exploit topological signatures, much less often the choice of the filtration, but to the best of our knowledge, the choice of the representation of a signal has not been the subject of any study yet. This paper attempts to provide some answers on the latter problem. To this end, we collected real audio data and built a comparative study to assess the quality of the discriminant information of the topological signatures extracted from three different representation spaces. Each audio signal is represented as i) an embedding of observed data in a higher dimensional space using Taken's representation, ii) a spectrogram viewed as a surface in a 3D ambient space, iii) the set of spectrogram's zeroes. From vowel audio recordings, we use topological signature for three prediction problems: speaker gender, vowel type, and individual. We show that topologically-augmented random forest improves the Out-of-Bag Error (OOB) over solely based Mel-Frequency Cepstral Coefficients (MFCC) for the last two problems. Our results also suggest that the topological information extracted from different signal representations is complementary, and that spectrogram's zeros offers the best improvement for gender prediction.
Black hole interior quantization: a minimal uncertainty approach

In a previous work we studied the interior of the Schwarzschild black hole implementing an effective minimal length, by applying a modification to the Poisson brackets of the theory. In this work we perform a proper quantization of such a system. Specifically, we quantize the interior of the Schwarzschild black hole in two ways: once by using the standard quantum theory, and once by following a minimal uncertainty approach. Then, we compare the obtained results from the two approaches. We show that, as expected, the wave function in the standard approach diverges in the region where classical singularity is located and the expectation value of the Kretschmann scalar also blows up on this state in that region. On the other hand, by following a minimal uncertainty quantization approach, not only the state remains well-defined, but also the expectation value of the Kretschmann scalar remains finite over the whole interior region, therefore signaling the resolution of the black hole singularity. Moreover, we find a new quantum number $m$ which plays a crucial role in determining the convergence and finiteness of the expectation value of the Kretschmann scalar. We also find an exact relation between the Barbero-Immirzi parameter and the minimal uncertainty scale as a result of demanding the convergence of the expectation value of the Kretschmann scalar.
FLATTEN: optical FLow-guided ATTENtion for consistent text-to-video editing

Text-to-video editing aims to edit the visual appearance of a source video conditional on textual prompts. A major challenge in this task is to ensure that all frames in the edited video are visually consistent. Most recent works apply advanced text-to-image diffusion models to this task by inflating 2D spatial attention in the U-Net into spatio-temporal attention. Although temporal context can be added through spatio-temporal attention, it may introduce some irrelevant information for each patch and therefore cause inconsistency in the edited video. In this paper, for the first time, we introduce optical flow into the attention module in the diffusion model's U-Net to address the inconsistency issue for text-to-video editing. Our method, FLATTEN, enforces the patches on the same flow path across different frames to attend to each other in the attention module, thus improving the visual consistency in the edited videos. Additionally, our method is training-free and can be seamlessly integrated into any diffusion-based text-to-video editing methods and improve their visual consistency. Experiment results on existing text-to-video editing benchmarks show that our proposed method achieves the new state-of-the-art performance. In particular, our method excels in maintaining the visual consistency in the edited videos.
Synaptic delay induced macroscopic dynamics of the large-scale network of Izhikevich neurons

We consider a large network of Izhikevich neurons. Each neuron has a quadratic integrate-and-fire type model with a recovery variable modelling spike frequency adaptation (SFA). We introduce a biologically motivated synaptic current expression and a delay in the synaptic transmission. Following the Ott-Antonsen theory, we reduce the network model to a mean-field system of delayed differential equations. Numerical bifurcation analysis allows us to locate higher-codimension bifurcations and to identify the regions in the parameter space where the network exhibits changes in the macroscopic dynamics, including transitions between states where the individual neurons exhibit asynchronous tonic firing and different types of synchronous bursting. We investigate the impact of the heterogeneity of the quenched input current, the SFA mechanism and the synaptic delay on macroscopic dynamics. In the limit that the heterogeneity goes to zero, our perturbation and bifurcation analysis shows that the behaviour of the mean-field model remains consistent, although this limit breaks an assumption of the model reduction. For a single population of neurons with SFA, the synaptic delay has little effect on the generation of COs for weak coupling, but favours their emergence beyond that, and even induces new macroscopic dynamics. In particular, Torus bifurcations may occur, and these are a crucial mechanism for the emergence of population bursting with two nested frequencies. We discuss how these solutions may relate to cross-frequency coupling which is potentially relevant for understanding healthy and pathological brain functions.
A new indeterminacy-based quantum theory

I propose a novel (interpretation of) quantum theory, which I will call Environmental Determinacy-based or EnD Quantum Theory (EnDQT). In contrast to the well-known interpretations of quantum theory, EnDQT has the benefit of not adding hidden variables, is not in tension with relativity, and provides a local causal explanation of quantum correlations without measurement outcomes varying according to perspectives or worlds. It is conservative, and so unlike collapse theories, in principle, arbitrary systems can be placed in a superposition for an arbitrary amount of time, and no modifications of the fundamental equations of quantum theory are required. Furthermore, it provides a series of novel empirical posits that may distinguish it from other interpretations of quantum theory. According to EnDQT, some systems acquire determinate values at some point, and the capacity to give rise to determinate values through interactions propagates to other systems in spacetime via local interactions. This process can be represented via certain networks. When a system is isolated from the systems that belong to these networks, it will non-relationally have indeterminate values.
Deep Concept Removal

We address the problem of concept removal in deep neural networks, aiming to learn representations that do not encode certain specified concepts (e.g., gender etc.) We propose a novel method based on adversarial linear classifiers trained on a concept dataset, which helps to remove the targeted attribute while maintaining model performance. Our approach Deep Concept Removal incorporates adversarial probing classifiers at various layers of the network, effectively addressing concept entanglement and improving out-of-distribution generalization. We also introduce an implicit gradient-based technique to tackle the challenges associated with adversarial training using linear classifiers. We evaluate the ability to remove a concept on a set of popular distributionally robust optimization (DRO) benchmarks with spurious correlations, as well as out-of-distribution (OOD) generalization tasks.
Promptspeaker: Speaker Generation Based on Text Descriptions

Recently, text-guided content generation has received extensive attention. In this work, we explore the possibility of text description-based speaker generation, i.e., using text prompts to control the speaker generation process. Specifically, we propose PromptSpeaker, a text-guided speaker generation system. PromptSpeaker consists of a prompt encoder, a zero-shot VITS, and a Glow model, where the prompt encoder predicts a prior distribution based on the text description and samples from this distribution to obtain a semantic representation. The Glow model subsequently converts the semantic representation into a speaker representation, and the zero-shot VITS finally synthesizes the speaker’s voice based on the speaker representation. We verify that PromptSpeaker can generate speakers new from the training set by objective metrics, and the synthetic speaker voice has reasonable subjective matching quality with the speaker prompt. Our audio samples are available on the demo website1.1Demo: https://promptspeaker.github. io/demo/
Implicit Variational Inference for High-Dimensional Posteriors

In variational inference, the benefits of Bayesian models rely on accurately capturing the true posterior distribution. We propose using neural samplers that specify implicit distributions, which are well-suited for approximating complex multimodal and correlated posteriors in high-dimensional spaces. Our approach introduces novel bounds for approximate inference using implicit distributions by locally linearising the neural sampler. This is distinct from existing methods that rely on additional discriminator networks and unstable adversarial objectives. Furthermore, we present a new sampler architecture that, for the first time, enables implicit distributions over tens of millions of latent variables, addressing computational concerns by using differentiable numerical approximations. We empirically show that our method is capable of recovering correlations across layers in large Bayesian neural networks, a property that is crucial for a network's performance but notoriously challenging to achieve. To the best of our knowledge, no other method has been shown to accomplish this task for such large models. Through experiments in downstream tasks, we demonstrate that our expressive posteriors outperform state-of-the-art uncertainty quantification methods, validating the effectiveness of our training algorithm and the quality of the learned implicit approximation.
Numerical Analysis of time-dependent Hamilton-Jacobi Equations on Networks

A new algorithm for time dependent Hamilton Jacobi equations on networks, based on semi Lagrangian scheme, is proposed. It is based on the definition of viscosity solution for this kind of problems recently given in. A thorough convergence analysis, not requiring weak semilimits, is provided. In particular, the check of the supersolution property at the vertices is performed through a dynamical technique which seems new. The scheme is efficient, explicit, allows long time steps, and is suitable to be implemented in a parallel algorithm. We present some numerical tests, showing the advantage in terms of computational cost over the one proposed in [7]
Boundary controllability of the Korteweg-de Vries equation: The Neumann case

This article gives a necessary first step to understanding the critical set phenomenon for the Korteweg-de Vries (KdV) equation posed on interval $[0,L]$ considering the Neumann boundary conditions with only one control input. We showed that the KdV equation is controllable in the critical case, i.e., when the spatial domain $L$ belongs to the set $\mathcal{R}_c$, where $c\neq-1$ and $$ \mathcal{R}_c:=\left\{\frac{2\pi}{\sqrt{3(c+1)}}\sqrt{m^2+ml+m^2};\ m,l\in \mathbb{N}^*\right\}\cup\left\{\frac{m\pi}{\sqrt{c+1}};\ m\in \mathbb{N}^*\right\}, $$ the KdV equation is exactly controllable in $L^2(0,L)$. The result is achieved using the return method together with a fixed point argument.
Albanese map for K\"ahler manifolds with nef anticanonical bundle

We study the structure of the Albanese map for K\"ahler manifolds with nef anticanonical bundle. First, we give a result for fourfolds whose Albanse torus is an elliptic curve. In the general case of any dimension, we look at two cases: The general fiber of the Albanese map is a Calabi-Yau manifold or a projective space. In the first case, we show that the manifold itself must be Calabi-Yau. In the second case, we give a more topological proof of a result by Cao and H\"oring which says that the manifold must be a projectivization of a numerically flat vector bundle.
PriViT: Vision Transformers for Fast Private Inference

The Vision Transformer (ViT) architecture has emerged as the backbone of choice for state-of-the-art deep models for computer vision applications. However, ViTs are ill-suited for private inference using secure multi-party computation (MPC) protocols, due to the large number of non-polynomial operations (self-attention, feed-forward rectifiers, layer normalization). We propose PriViT, a gradient based algorithm to selectively"Taylorize"nonlinearities in ViTs while maintaining their prediction accuracy. Our algorithm is conceptually simple, easy to implement, and achieves improved performance over existing approaches for designing MPC-friendly transformer architectures in terms of achieving the Pareto frontier in latency-accuracy. We confirm these improvements via experiments on several standard image classification tasks. Public code is available at https://github.com/NYU-DICE-Lab/privit.
Quantitative Propagation of Chaos for 2D Viscous Vortex Model on the Whole Space

We derive the quantitative estimates of propagation of chaos for the large interacting particle systems in terms of the relative entropy between the joint law of the particles and the tensorized law of the mean field PDE. We resolve this problem for the first time for the viscous vortex model that approximating 2D Navier-Stokes equation in the vorticity formulation on the whole space. We obtain as key tools the Li-Yau-type estimates and Hamilton-type heat kernel estimates for 2D Navier-Stokes in the whole space.
Outlier Weighed Layerwise Sparsity (OWL): A Missing Secret Sauce for Pruning LLMs to High Sparsity

Large Language Models (LLMs), renowned for their remarkable performance across diverse domains, present a challenge when it comes to practical deployment due to their colossal model size. In response to this challenge, efforts have been directed toward the application of traditional network pruning techniques to LLMs, uncovering a massive number of parameters that can be pruned in one-shot without hurting performance. Prevailing LLM pruning strategies have consistently adhered to the practice of uniformly pruning all layers at equivalent sparsity, resulting in robust performance. However, this observation stands in contrast to the prevailing trends observed in the field of vision models, where non-uniform layerwise sparsity typically yields stronger results. To understand the underlying reasons for this disparity, we conduct a comprehensive study and discover a strong correlation with the emergence of activation outliers in LLMs. Inspired by this finding, we introduce a novel LLM pruning methodology that incorporates a tailored set of non-uniform layerwise sparsity ratios, termed as Outlier Weighed Layerwise sparsity (OWL). The sparsity ratio of OWL is proportional to the outlier ratio observed within each layer, facilitating a more effective alignment between layerwise weight sparsity and outlier ratios. Our empirical evaluation, conducted across the LLaMA-V1 family and OPT, spanning various benchmarks, demonstrates the distinct advantages offered by OWL over previous methods. For instance, OWL exhibits a remarkable performance gain, surpassing the state-of-the-art Wanda and SparseGPT by 61.22 and 6.80 perplexity at a high sparsity level of 70%, respectively, while delivering 2x end-to-end inference speed-up in the DeepSparse inference engine. Codes are available at https://github.com/luuyin/OWL.
Floral biodiversity and geology of the Talcher Basin, Orissa, India during the Permian–Triassic interval

The review paper provides an updated account of the previous and recently published records concerning the palaeobiology and the geology of the Talcher Basin of Orissa State, India. We conclude that fossil floral species in this basin originated in the earliest Permian Talchir Formation and evolved and diversified through the Karharbari Fm. , Barakar Fm. , Barren Measures Fm. and the uppermost Kamthi Fm. (Late Permian–Triassic). The megaflora and the palynology of the different formations of the basin are also discussed briefly. The geological setting of the basin along with the status of different formations (especially the Kamthi Formation) has been redefined. The post‐Barakar Fm. rocks, earlier retained in the Raniganj/Kamthi, Panchet and Mahadeva formations in this basin, have been critically assessed and redefined as the Lower and Upper Kamthi formations of Late Permian and Triassic ages, respectively. Accordingly, the geological map of the basin has been modified. Permian deposits (particularly the Barakar and the lower Kamthi formations) not only have the best preserved flora but also possess the highest diversity, whereas the upper Kamthi Triassic sediments have a meagre number of taxa. The plant diversity of the basin has been discussed in detail to interpret the development of the flora, evolutionary trends and palaeoenvironments of the basin. The patchy Gangamopteris vegetation of the Talchir glacial phase has ultimately evolved and diversified through time (Karharbari Fm. to Lower Kamthi Fm.) and gave rise to the thick dense swampy forests consisting of large Glossopteris trees and other shade‐loving under‐storied pteridophytes. Several groups of plants including spores and pollen have disappeared in a ladder pattern during the Permian–Triassic interval (Lower Kamthi–Upper Kamthi Fm.) and, similarly, in steps, many new fore‐runners appeared in the Upper Kamthi Formation.
Context-aware Status Updating: Wireless Scheduling for Maximizing Situational Awareness in Safety-critical Systems

In this study, we investigate a context-aware status updating system consisting of multiple sensor-estimator pairs. A centralized monitor pulls status updates from multiple sensors that are monitoring several safety-critical situations (e.g., carbon monoxide density in forest fire detection, machine safety in industrial automation, and road safety). Based on the received sensor updates, multiple estimators determine the current safety-critical situations. Due to transmission errors and limited communication resources, the sensor updates may not be timely, resulting in the possibility of misunderstanding the current situation. In particular, if a dangerous situation is misinterpreted as safe, the safety risk is high. In this paper, we introduce a novel framework that quantifies the penalty due to the unawareness of a potentially dangerous situation. This situation-unaware penalty function depends on two key factors: the Age of Information (AoI) and the observed signal value. For optimal estimators, we provide an information-theoretic bound of the penalty function that evaluates the fundamental performance limit of the system. To minimize the penalty, we study a pull-based multi-sensor, multi-channel transmission scheduling problem. Our analysis reveals that for optimal estimators, it is always beneficial to keep the channels busy. Due to communication resource constraints, the scheduling problem can be modelled as a Restless Multi-armed Bandit (RMAB) problem. By utilizing relaxation and Lagrangian decomposition of the RMAB, we provide a low-complexity scheduling algorithm which is asymptotically optimal. Our results hold for both reliable and unreliable channels. Numerical evidence shows that our scheduling policy can achieve up to 100 times performance gain over periodic updating and up to 10 times over randomized policy.